[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User]
Read the long tool-call trace. Explain why returning a recoverable tool result is better than failing the server request, and identify the first line showing the model intended a tool call.

0514 22:16:10 ds4-server: chat ctx=13695..22840:9145 gen=5000 TOOLS DSML_START decoding chunk=16.06 t/s avg=17.00 t/s
0514 22:16:15 ds4-server: chat ctx=13695..22840:9145 gen=5025 TOOLS DSML_START decoding chunk=16.16 t/s avg=17.10 t/s
0514 22:16:20 ds4-server: chat ctx=13695..22840:9145 gen=5050 TOOLS DSML_START decoding chunk=16.26 t/s avg=17.20 t/s
0514 22:16:25 ds4-server: chat ctx=13695..22840:9145 gen=5075 TOOLS DSML_START decoding chunk=16.36 t/s avg=17.30 t/s
0514 22:16:30 ds4-server: chat ctx=13695..22840:9145 gen=5100 TOOLS DSML_START decoding chunk=16.46 t/s avg=17.40 t/s
0514 22:16:35 ds4-server: chat ctx=13695..22840:9145 gen=5125 TOOLS DSML_START decoding chunk=16.56 t/s avg=17.00 t/s
0514 22:16:40 ds4-server: chat ctx=13695..22840:9145 gen=5150 TOOLS DSML_START decoding chunk=16.66 t/s avg=17.10 t/s
0514 22:16:45 ds4-server: chat ctx=13695..22840:9145 gen=5175 TOOLS DSML_START decoding chunk=16.76 t/s avg=17.20 t/s
0514 22:16:50 ds4-server: chat ctx=13695..22840:9145 gen=5200 TOOLS DSML_START decoding chunk=16.86 t/s avg=17.30 t/s
0514 22:16:55 ds4-server: chat ctx=13695..22840:9145 gen=5225 TOOLS DSML_START decoding chunk=16.06 t/s avg=17.40 t/s
0514 22:17:10 ds4-server: chat ctx=13695..22840:9145 gen=5250 TOOLS DSML_START decoding chunk=16.16 t/s avg=17.00 t/s
0514 22:17:15 ds4-server: chat ctx=13695..22840:9145 gen=5275 TOOLS DSML_START decoding chunk=16.26 t/s avg=17.10 t/s
0514 22:17:20 ds4-server: chat ctx=13695..22840:9145 gen=5300 TOOLS DSML_START decoding chunk=16.36 t/s avg=17.20 t/s
0514 22:17:25 ds4-server: chat ctx=13695..22840:9145 gen=5325 TOOLS DSML_START decoding chunk=16.46 t/s avg=17.30 t/s
0514 22:17:30 ds4-server: chat ctx=13695..22840:9145 gen=5350 TOOLS DSML_START decoding chunk=16.56 t/s avg=17.40 t/s
0514 22:17:35 ds4-server: chat ctx=13695..22840:9145 gen=5375 TOOLS DSML_START decoding chunk=16.66 t/s avg=17.00 t/s
0514 22:17:40 ds4-server: chat ctx=13695..22840:9145 gen=5400 TOOLS DSML_START decoding chunk=16.76 t/s avg=17.10 t/s
0514 22:17:45 ds4-server: chat ctx=13695..22840:9145 gen=5425 TOOLS DSML_START decoding chunk=16.86 t/s avg=17.20 t/s
0514 22:17:50 ds4-server: chat ctx=13695..22840:9145 gen=5450 TOOLS DSML_START decoding chunk=16.06 t/s avg=17.30 t/s
0514 22:17:55 ds4-server: chat ctx=13695..22840:9145 gen=5475 TOOLS DSML_START decoding chunk=16.16 t/s avg=17.40 t/s
0514 22:18:10 ds4-server: chat ctx=13695..22840:9145 gen=5500 TOOLS DSML_START decoding chunk=16.26 t/s avg=17.00 t/s
0514 22:18:15 ds4-server: chat ctx=13695..22840:9145 gen=5525 TOOLS DSML_START decoding chunk=16.36 t/s avg=17.10 t/s
0514 22:18:20 ds4-server: chat ctx=13695..22840:9145 gen=5550 TOOLS DSML_START decoding chunk=16.46 t/s avg=17.20 t/s
0514 22:18:25 ds4-server: chat ctx=13695..22840:9145 gen=5575 TOOLS DSML_START decoding chunk=16.56 t/s avg=17.30 t/s
0514 22:18:30 ds4-server: chat ctx=13695..22840:9145 gen=5600 TOOLS DSML_START decoding chunk=16.66 t/s avg=17.40 t/s
0514 22:18:35 ds4-server: chat ctx=13695..22840:9145 gen=5625 TOOLS DSML_START decoding chunk=16.76 t/s avg=17.00 t/s
0514 22:18:40 ds4-server: chat ctx=13695..22840:9145 gen=5650 TOOLS DSML_START decoding chunk=16.86 t/s avg=17.10 t/s
0514 22:18:45 ds4-server: chat ctx=13695..22840:9145 gen=5675 TOOLS DSML_START decoding chunk=16.06 t/s avg=17.20 t/s
0514 22:18:50 ds4-server: chat ctx=13695..22840:9145 gen=5700 TOOLS DSML_START decoding chunk=16.16 t/s avg=17.30 t/s
0514 22:18:55 ds4-server: chat ctx=13695..22840:9145 gen=5725 TOOLS DSML_START decoding chunk=16.26 t/s avg=17.40 t/s
0514 22:19:10 ds4-server: chat ctx=13695..22840:9145 gen=5750 TOOLS DSML_START decoding chunk=16.36 t/s avg=17.00 t/s
0514 22:19:15 ds4-server: chat ctx=13695..22840:9145 gen=5775 TOOLS DSML_START decoding chunk=16.46 t/s avg=17.10 t/s
0514 22:19:20 ds4-server: chat ctx=13695..22840:9145 gen=5800 TOOLS DSML_START decoding chunk=16.56 t/s avg=17.20 t/s
0514 22:19:25 ds4-server: chat ctx=13695..22840:9145 gen=5825 TOOLS DSML_START decoding chunk=16.66 t/s avg=17.30 t/s
0514 22:19:30 ds4-server: chat ctx=13695..22840:9145 gen=5850 TOOLS DSML_START decoding chunk=16.76 t/s avg=17.40 t/s
0514 22:19:35 ds4-server: chat ctx=13695..22840:9145 gen=5875 TOOLS DSML_START decoding chunk=16.86 t/s avg=17.00 t/s
0514 22:17:06 ds4-server: chat ctx=13695..22840:9145 gen=6080 TOOLS DSML_START DSML_END finish=tool_calls
0514 22:17:06 ds4-server: tool parse recovered loose nested parameter name=write
0514 22:17:06 ds4-server: tool calls ctx=13695..22840:9145 n=1 names=[write]

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Read the long tool-call trace. Explain why returning a recoverable tool result is better than failing the server request, and identify the first line showing the model intended a tool call.

0514 22:16:10 ds4-server: chat ctx=13695..22840:9145 gen=5000 TOOLS DSML_START decoding chunk=16.06 t/s avg=17.00 t/s
0514 22:16:15 ds4-server: chat ctx=13695..22840:9145 gen=5025 TOOLS DSML_START decoding chunk=16.16 t/s avg=17.10 t/s
0514 22:16:20 ds4-server: chat ctx=13695..22840:9145 gen=5050 TOOLS DSML_START decoding chunk=16.26 t/s avg=17.20 t/s
0514 22:16:25 ds4-server: chat ctx=13695..22840:9145 gen=5075 TOOLS DSML_START decoding chunk=16.36 t/s avg=17.30 t/s
0514 22:16:30 ds4-server: chat ctx=13695..22840:9145 gen=5100 TOOLS DSML_START decoding chunk=16.46 t/s avg=17.40 t/s
0514 22:16:35 ds4-server: chat ctx=13695..22840:9145 gen=5125 TOOLS DSML_START decoding chunk=16.56 t/s avg=17.00 t/s
0514 22:16:40 ds4-server: chat ctx=13695..22840:9145 gen=5150 TOOLS DSML_START decoding chunk=16.66 t/s avg=17.10 t/s
0514 22:16:45 ds4-server: chat ctx=13695..22840:9145 gen=5175 TOOLS DSML_START decoding chunk=16.76 t/s avg=17.20 t/s
0514 22:16:50 ds4-server: chat ctx=13695..22840:9145 gen=5200 TOOLS DSML_START decoding chunk=16.86 t/s avg=17.30 t/s
0514 22:16:55 ds4-server: chat ctx=13695..22840:9145 gen=5225 TOOLS DSML_START decoding chunk=16.06 t/s avg=17.40 t/s
0514 22:17:10 ds4-server: chat ctx=13695..22840:9145 gen=5250 TOOLS DSML_START decoding chunk=16.16 t/s avg=17.00 t/s
0514 22:17:15 ds4-server: chat ctx=13695..22840:9145 gen=5275 TOOLS DSML_START decoding chunk=16.26 t/s avg=17.10 t/s
0514 22:17:20 ds4-server: chat ctx=13695..22840:9145 gen=5300 TOOLS DSML_START decoding chunk=16.36 t/s avg=17.20 t/s
0514 22:17:25 ds4-server: chat ctx=13695..22840:9145 gen=5325 TOOLS DSML_START decoding chunk=16.46 t/s avg=17.30 t/s
0514 22:17:30 ds4-server: chat ctx=13695..22840:9145 gen=5350 TOOLS DSML_START decoding chunk=16.56 t/s avg=17.40 t/s
0514 22:17:35 ds4-server: chat ctx=13695..22840:9145 gen=5375 TOOLS DSML_START decoding chunk=16.66 t/s avg=17.00 t/s
0514 22:17:40 ds4-server: chat ctx=13695..22840:9145 gen=5400 TOOLS DSML_START decoding chunk=16.76 t/s avg=17.10 t/s
0514 22:17:45 ds4-server: chat ctx=13695..22840:9145 gen=5425 TOOLS DSML_START decoding chunk=16.86 t/s avg=17.20 t/s
0514 22:17:50 ds4-server: chat ctx=13695..22840:9145 gen=5450 TOOLS DSML_START decoding chunk=16.06 t/s avg=17.30 t/s
0514 22:17:55 ds4-server: chat ctx=13695..22840:9145 gen=5475 TOOLS DSML_START decoding chunk=16.16 t/s avg=17.40 t/s
0514 22:18:10 ds4-server: chat ctx=13695..22840:9145 gen=5500 TOOLS DSML_START decoding chunk=16.26 t/s avg=17.00 t/s
0514 22:18:15 ds4-server: chat ctx=13695..22840:9145 gen=5525 TOOLS DSML_START decoding chunk=16.36 t/s avg=17.10 t/s
0514 22:18:20 ds4-server: chat ctx=13695..22840:9145 gen=5550 TOOLS DSML_START decoding chunk=16.46 t/s avg=17.20 t/s
0514 22:18:25 ds4-server: chat ctx=13695..22840:9145 gen=5575 TOOLS DSML_START decoding chunk=16.56 t/s avg=17.30 t/s
0514 22:18:30 ds4-server: chat ctx=13695..22840:9145 gen=5600 TOOLS DSML_START decoding chunk=16.66 t/s avg=17.40 t/s
0514 22:18:35 ds4-server: chat ctx=13695..22840:9145 gen=5625 TOOLS DSML_START decoding chunk=16.76 t/s avg=17.00 t/s
0514 22:18:40 ds4-server: chat ctx=13695..22840:9145 gen=5650 TOOLS DSML_START decoding chunk=16.86 t/s avg=17.10 t/s
0514 22:18:45 ds4-server: chat ctx=13695..22840:9145 gen=5675 TOOLS DSML_START decoding chunk=16.06 t/s avg=17.20 t/s
0514 22:18:50 ds4-server: chat ctx=13695..22840:9145 gen=5700 TOOLS DSML_START decoding chunk=16.16 t/s avg=17.30 t/s
0514 22:18:55 ds4-server: chat ctx=13695..22840:9145 gen=5725 TOOLS DSML_START decoding chunk=16.26 t/s avg=17.40 t/s
0514 22:19:10 ds4-server: chat ctx=13695..22840:9145 gen=5750 TOOLS DSML_START decoding chunk=16.36 t/s avg=17.00 t/s
0514 22:19:15 ds4-server: chat ctx=13695..22840:9145 gen=5775 TOOLS DSML_START decoding chunk=16.46 t/s avg=17.10 t/s
0514 22:19:20 ds4-server: chat ctx=13695..22840:9145 gen=5800 TOOLS DSML_START decoding chunk=16.56 t/s avg=17.20 t/s
0514 22:19:25 ds4-server: chat ctx=13695..22840:9145 gen=5825 TOOLS DSML_START decoding chunk=16.66 t/s avg=17.30 t/s
0514 22:19:30 ds4-server: chat ctx=13695..22840:9145 gen=5850 TOOLS DSML_START decoding chunk=16.76 t/s avg=17.40 t/s
0514 22:19:35 ds4-server: chat ctx=13695..22840:9145 gen=5875 TOOLS DSML_START decoding chunk=16.86 t/s avg=17.00 t/s
0514 22:17:06 ds4-server: chat ctx=13695..22840:9145 gen=6080 TOOLS DSML_START DSML_END finish=tool_calls
0514 22:17:06 ds4-server: tool parse recovered loose nested parameter name=write
0514 22:17:06 ds4-server: tool calls ctx=13695..22840:9145 n=1 names=[write]<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User]
Read the long tool-call trace. Explain why returning a recoverable tool result is better than failing the server request, and identify the first line showing the model intended a tool call.

0514 22:16:10 ds4-server: chat ctx=13695..22840:9145 gen=5000 TOOLS DSML_START decoding chunk=16.06 t/s avg=17.00 t/s
0514 22:16:15 ds4-server: chat ctx=13695..22840:9145 gen=5025 TOOLS DSML_START decoding chunk=16.16 t/s avg=17.10 t/s
0514 22:16:20 ds4-server: chat ctx=13695..22840:9145 gen=5050 TOOLS DSML_START decoding chunk=16.26 t/s avg=17.20 t/s
0514 22:16:25 ds4-server: chat ctx=13695..22840:9145 gen=5075 TOOLS DSML_START decoding chunk=16.36 t/s avg=17.30 t/s
0514 22:16:30 ds4-server: chat ctx=13695..22840:9145 gen=5100 TOOLS DSML_START decoding chunk=16.46 t/s avg=17.40 t/s
0514 22:16:35 ds4-server: chat ctx=13695..22840:9145 gen=5125 TOOLS DSML_START decoding chunk=16.56 t/s avg=17.00 t/s
0514 22:16:40 ds4-server: chat ctx=13695..22840:9145 gen=5150 TOOLS DSML_START decoding chunk=16.66 t/s avg=17.10 t/s
0514 22:16:45 ds4-server: chat ctx=13695..22840:9145 gen=5175 TOOLS DSML_START decoding chunk=16.76 t/s avg=17.20 t/s
0514 22:16:50 ds4-server: chat ctx=13695..22840:9145 gen=5200 TOOLS DSML_START decoding chunk=16.86 t/s avg=17.30 t/s
0514 22:16:55 ds4-server: chat ctx=13695..22840:9145 gen=5225 TOOLS DSML_START decoding chunk=16.06 t/s avg=17.40 t/s
0514 22:17:10 ds4-server: chat ctx=13695..22840:9145 gen=5250 TOOLS DSML_START decoding chunk=16.16 t/s avg=17.00 t/s
0514 22:17:15 ds4-server: chat ctx=13695..22840:9145 gen=5275 TOOLS DSML_START decoding chunk=16.26 t/s avg=17.10 t/s
0514 22:17:20 ds4-server: chat ctx=13695..22840:9145 gen=5300 TOOLS DSML_START decoding chunk=16.36 t/s avg=17.20 t/s
0514 22:17:25 ds4-server: chat ctx=13695..22840:9145 gen=5325 TOOLS DSML_START decoding chunk=16.46 t/s avg=17.30 t/s
0514 22:17:30 ds4-server: chat ctx=13695..22840:9145 gen=5350 TOOLS DSML_START decoding chunk=16.56 t/s avg=17.40 t/s
0514 22:17:35 ds4-server: chat ctx=13695..22840:9145 gen=5375 TOOLS DSML_START decoding chunk=16.66 t/s avg=17.00 t/s
0514 22:17:40 ds4-server: chat ctx=13695..22840:9145 gen=5400 TOOLS DSML_START decoding chunk=16.76 t/s avg=17.10 t/s
0514 22:17:45 ds4-server: chat ctx=13695..22840:9145 gen=5425 TOOLS DSML_START decoding chunk=16.86 t/s avg=17.20 t/s
0514 22:17:50 ds4-server: chat ctx=13695..22840:9145 gen=5450 TOOLS DSML_START decoding chunk=16.06 t/s avg=17.30 t/s
0514 22:17:55 ds4-server: chat ctx=13695..22840:9145 gen=5475 TOOLS DSML_START decoding chunk=16.16 t/s avg=17.40 t/s
0514 22:18:10 ds4-server: chat ctx=13695..22840:9145 gen=5500 TOOLS DSML_START decoding chunk=16.26 t/s avg=17.00 t/s
0514 22:18:15 ds4-server: chat ctx=13695..22840:9145 gen=5525 TOOLS DSML_START decoding chunk=16.36 t/s avg=17.10 t/s
0514 22:18:20 ds4-server: chat ctx=13695..22840:9145 gen=5550 TOOLS DSML_START decoding chunk=16.46 t/s avg=17.20 t/s
0514 22:18:25 ds4-server: chat ctx=13695..22840:9145 gen=5575 TOOLS DSML_START decoding chunk=16.56 t/s avg=17.30 t/s
0514 22:18:30 ds4-server: chat ctx=13695..22840:9145 gen=5600 TOOLS DSML_START decoding chunk=16.66 t/s avg=17.40 t/s
0514 22:18:35 ds4-server: chat ctx=13695..22840:9145 gen=5625 TOOLS DSML_START decoding chunk=16.76 t/s avg=17.00 t/s
0514 22:18:40 ds4-server: chat ctx=13695..22840:9145 gen=5650 TOOLS DSML_START decoding chunk=16.86 t/s avg=17.10 t/s
0514 22:18:45 ds4-server: chat ctx=13695..22840:9145 gen=5675 TOOLS DSML_START decoding chunk=16.06 t/s avg=17.20 t/s
0514 22:18:50 ds4-server: chat ctx=13695..22840:9145 gen=5700 TOOLS DSML_START decoding chunk=16.16 t/s avg=17.30 t/s
0514 22:18:55 ds4-server: chat ctx=13695..22840:9145 gen=5725 TOOLS DSML_START decoding chunk=16.26 t/s avg=17.40 t/s
0514 22:19:10 ds4-server: chat ctx=13695..22840:9145 gen=5750 TOOLS DSML_START decoding chunk=16.36 t/s avg=17.00 t/s
0514 22:19:15 ds4-server: chat ctx=13695..22840:9145 gen=5775 TOOLS DSML_START decoding chunk=16.46 t/s avg=17.10 t/s
0514 22:19:20 ds4-server: chat ctx=13695..22840:9145 gen=5800 TOOLS DSML_START decoding chunk=16.56 t/s avg=17.20 t/s
0514 22:19:25 ds4-server: chat ctx=13695..22840:9145 gen=5825 TOOLS DSML_START decoding chunk=16.66 t/s avg=17.30 t/s
0514 22:19:30 ds4-server: chat ctx=13695..22840:9145 gen=5850 TOOLS DSML_START decoding chunk=16.76 t/s avg=17.40 t/s
0514 22:19:35 ds4-server: chat ctx=13695..22840:9145 gen=5875 TOOLS DSML_START decoding chunk=16.86 t/s avg=17.00 t/s
0514 22:17:06 ds4-server: chat ctx=13695..22840:9145 gen=6080 TOOLS DSML_START DSML_END finish=tool_calls
0514 22:17:06 ds4-server: tool parse recovered loose nested parameter name=write
0514 22:17:06 ds4-server: tool calls ctx=13695..22840:9145 n=1 names=[write]

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Read the long tool-call trace. Explain why returning a recoverable tool result is better than failing the server request, and identify the first line showing the model intended a tool call.

0514 22:16:10 ds4-server: chat ctx=13695..22840:9145 gen=5000 TOOLS DSML_START decoding chunk=16.06 t/s avg=17.00 t/s
0514 22:16:15 ds4-server: chat ctx=13695..22840:9145 gen=5025 TOOLS DSML_START decoding chunk=16.16 t/s avg=17.10 t/s
0514 22:16:20 ds4-server: chat ctx=13695..22840:9145 gen=5050 TOOLS DSML_START decoding chunk=16.26 t/s avg=17.20 t/s
0514 22:16:25 ds4-server: chat ctx=13695..22840:9145 gen=5075 TOOLS DSML_START decoding chunk=16.36 t/s avg=17.30 t/s
0514 22:16:30 ds4-server: chat ctx=13695..22840:9145 gen=5100 TOOLS DSML_START decoding chunk=16.46 t/s avg=17.40 t/s
0514 22:16:35 ds4-server: chat ctx=13695..22840:9145 gen=5125 TOOLS DSML_START decoding chunk=16.56 t/s avg=17.00 t/s
0514 22:16:40 ds4-server: chat ctx=13695..22840:9145 gen=5150 TOOLS DSML_START decoding chunk=16.66 t/s avg=17.10 t/s
0514 22:16:45 ds4-server: chat ctx=13695..22840:9145 gen=5175 TOOLS DSML_START decoding chunk=16.76 t/s avg=17.20 t/s
0514 22:16:50 ds4-server: chat ctx=13695..22840:9145 gen=5200 TOOLS DSML_START decoding chunk=16.86 t/s avg=17.30 t/s
0514 22:16:55 ds4-server: chat ctx=13695..22840:9145 gen=5225 TOOLS DSML_START decoding chunk=16.06 t/s avg=17.40 t/s
0514 22:17:10 ds4-server: chat ctx=13695..22840:9145 gen=5250 TOOLS DSML_START decoding chunk=16.16 t/s avg=17.00 t/s
0514 22:17:15 ds4-server: chat ctx=13695..22840:9145 gen=5275 TOOLS DSML_START decoding chunk=16.26 t/s avg=17.10 t/s
0514 22:17:20 ds4-server: chat ctx=13695..22840:9145 gen=5300 TOOLS DSML_START decoding chunk=16.36 t/s avg=17.20 t/s
0514 22:17:25 ds4-server: chat ctx=13695..22840:9145 gen=5325 TOOLS DSML_START decoding chunk=16.46 t/s avg=17.30 t/s
0514 22:17:30 ds4-server: chat ctx=13695..22840:9145 gen=5350 TOOLS DSML_START decoding chunk=16.56 t/s avg=17.40 t/s
0514 22:17:35 ds4-server: chat ctx=13695..22840:9145 gen=5375 TOOLS DSML_START decoding chunk=16.66 t/s avg=17.00 t/s
0514 22:17:40 ds4-server: chat ctx=13695..22840:9145 gen=5400 TOOLS DSML_START decoding chunk=16.76 t/s avg=17.10 t/s
0514 22:17:45 ds4-server: chat ctx=13695..22840:9145 gen=5425 TOOLS DSML_START decoding chunk=16.86 t/s avg=17.20 t/s
0514 22:17:50 ds4-server: chat ctx=13695..22840:9145 gen=5450 TOOLS DSML_START decoding chunk=16.06 t/s avg=17.30 t/s
0514 22:17:55 ds4-server: chat ctx=13695..22840:9145 gen=5475 TOOLS DSML_START decoding chunk=16.16 t/s avg=17.40 t/s
0514 22:18:10 ds4-server: chat ctx=13695..22840:9145 gen=5500 TOOLS DSML_START decoding chunk=16.26 t/s avg=17.00 t/s
0514 22:18:15 ds4-server: chat ctx=13695..22840:9145 gen=5525 TOOLS DSML_START decoding chunk=16.36 t/s avg=17.10 t/s
0514 22:18:20 ds4-server: chat ctx=13695..22840:9145 gen=5550 TOOLS DSML_START decoding chunk=16.46 t/s avg=17.20 t/s
0514 22:18:25 ds4-server: chat ctx=13695..22840:9145 gen=5575 TOOLS DSML_START decoding chunk=16.56 t/s avg=17.30 t/s
0514 22:18:30 ds4-server: chat ctx=13695..22840:9145 gen=5600 TOOLS DSML_START decoding chunk=16.66 t/s avg=17.40 t/s
0514 22:18:35 ds4-server: chat ctx=13695..22840:9145 gen=5625 TOOLS DSML_START decoding chunk=16.76 t/s avg=17.00 t/s
0514 22:18:40 ds4-server: chat ctx=13695..22840:9145 gen=5650 TOOLS DSML_START decoding chunk=16.86 t/s avg=17.10 t/s
0514 22:18:45 ds4-server: chat ctx=13695..22840:9145 gen=5675 TOOLS DSML_START decoding chunk=16.06 t/s avg=17.20 t/s
0514 22:18:50 ds4-server: chat ctx=13695..22840:9145 gen=5700 TOOLS DSML_START decoding chunk=16.16 t/s avg=17.30 t/s
0514 22:18:55 ds4-server: chat ctx=13695..22840:9145 gen=5725 TOOLS DSML_START decoding chunk=16.26 t/s avg=17.40 t/s
0514 22:19:10 ds4-server: chat ctx=13695..22840:9145 gen=5750 TOOLS DSML_START decoding chunk=16.36 t/s avg=17.00 t/s
0514 22:19:15 ds4-server: chat ctx=13695..22840:9145 gen=5775 TOOLS DSML_START decoding chunk=16.46 t/s avg=17.10 t/s
0514 22:19:20 ds4-server: chat ctx=13695..22840:9145 gen=5800 TOOLS DSML_START decoding chunk=16.56 t/s avg=17.20 t/s
0514 22:19:25 ds4-server: chat ctx=13695..22840:9145 gen=5825 TOOLS DSML_START decoding chunk=16.66 t/s avg=17.30 t/s
0514 22:19:30 ds4-server: chat ctx=13695..22840:9145 gen=5850 TOOLS DSML_START decoding chunk=16.76 t/s avg=17.40 t/s
0514 22:19:35 ds4-server: chat ctx=13695..22840:9145 gen=5875 TOOLS DSML_START decoding chunk=16.86 t/s avg=17.00 t/s
0514 22:17:06 ds4-server: chat ctx=13695..22840:9145 gen=6080 TOOLS DSML_START DSML_END finish=tool_calls
0514 22:17:06 ds4-server: tool parse recovered loose nested parameter name=write
0514 22:17:06 ds4-server: tool calls ctx=13695..22840:9145 n=1 names=[write]<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Write a Bash script that resumes a curl download, validates SHA256, and updates a symlink atomically.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Write a Bash script that resumes a curl download, validates SHA256, and updates a symlink atomically.<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Write a Bash script that resumes a curl download, validates SHA256, and updates a symlink atomically.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Write a Bash script that resumes a curl download, validates SHA256, and updates a symlink atomically.<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Review this Bash snippet for quoting bugs: for f in $FILES; do cp $f $DEST; done

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Review this Bash snippet for quoting bugs: for f in $FILES; do cp $f $DEST; done<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Review this Bash snippet for quoting bugs: for f in $FILES; do cp $f $DEST; done

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Review this Bash snippet for quoting bugs: for f in $FILES; do cp $f $DEST; done<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User]
Find the lifetime bug in this C function and give a minimal patch:

char *join(const char *a,const char *b){char tmp[256];snprintf(tmp,sizeof tmp,"%s/%s",a,b);return tmp;}

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Find the lifetime bug in this C function and give a minimal patch:

char *join(const char *a,const char *b){char tmp[256];snprintf(tmp,sizeof tmp,"%s/%s",a,b);return tmp;}<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User]
Find the lifetime bug in this C function and give a minimal patch:

char *join(const char *a,const char *b){char tmp[256];snprintf(tmp,sizeof tmp,"%s/%s",a,b);return tmp;}

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Find the lifetime bug in this C function and give a minimal patch:

char *join(const char *a,const char *b){char tmp[256];snprintf(tmp,sizeof tmp,"%s/%s",a,b);return tmp;}<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Explain why this C ring buffer can overwrite unread data, then rewrite the push condition.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Explain why this C ring buffer can overwrite unread data, then rewrite the push condition.<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Explain why this C ring buffer can overwrite unread data, then rewrite the push condition.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Explain why this C ring buffer can overwrite unread data, then rewrite the push condition.<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Convert this C++ RAII wrapper to avoid double-close after move assignment.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Convert this C++ RAII wrapper to avoid double-close after move assignment.<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Convert this C++ RAII wrapper to avoid double-close after move assignment.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Convert this C++ RAII wrapper to avoid double-close after move assignment.<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Review a C++17 template that stores string_view keys and explain the dangling-reference risk.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Review a C++17 template that stores string_view keys and explain the dangling-reference risk.<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Review a C++17 template that stores string_view keys and explain the dangling-reference risk.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Review a C++17 template that stores string_view keys and explain the dangling-reference risk.<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Find the data race in this Go worker pool and show where the mutex or channel ownership belongs.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Find the data race in this Go worker pool and show where the mutex or channel ownership belongs.<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Find the data race in this Go worker pool and show where the mutex or channel ownership belongs.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Find the data race in this Go worker pool and show where the mutex or channel ownership belongs.<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Write a small Go HTTP handler that validates JSON input and returns structured errors.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Write a small Go HTTP handler that validates JSON input and returns structured errors.<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Write a small Go HTTP handler that validates JSON input and returns structured errors.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Write a small Go HTTP handler that validates JSON input and returns structured errors.<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Explain how to make this Java cache thread-safe without synchronizing the whole hot path.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Explain how to make this Java cache thread-safe without synchronizing the whole hot path.<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Explain how to make this Java cache thread-safe without synchronizing the whole hot path.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Explain how to make this Java cache thread-safe without synchronizing the whole hot path.<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Refactor a Java method that catches Exception and hides interrupted status.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Refactor a Java method that catches Exception and hides interrupted status.<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Refactor a Java method that catches Exception and hides interrupted status.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Refactor a Java method that catches Exception and hides interrupted status.<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Debug this JavaScript async loop that accidentally runs requests sequentially.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Debug this JavaScript async loop that accidentally runs requests sequentially.<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Debug this JavaScript async loop that accidentally runs requests sequentially.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Debug this JavaScript async loop that accidentally runs requests sequentially.<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Write a Lua function that memoizes pure function calls with string keys.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Write a Lua function that memoizes pure function calls with string keys.<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Write a Lua function that memoizes pure function calls with string keys.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Write a Lua function that memoizes pure function calls with string keys.<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Find the SQL injection risk in a PHP PDO snippet and rewrite it with bound parameters.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Find the SQL injection risk in a PHP PDO snippet and rewrite it with bound parameters.<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Find the SQL injection risk in a PHP PDO snippet and rewrite it with bound parameters.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Find the SQL injection risk in a PHP PDO snippet and rewrite it with bound parameters.<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Rewrite this Python log parser so it streams lines instead of loading the whole file.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Rewrite this Python log parser so it streams lines instead of loading the whole file.<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Rewrite this Python log parser so it streams lines instead of loading the whole file.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Rewrite this Python log parser so it streams lines instead of loading the whole file.<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Given a failing pytest fixture that mutates global state, explain the bug and propose a fix.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Given a failing pytest fixture that mutates global state, explain the bug and propose a fix.<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Given a failing pytest fixture that mutates global state, explain the bug and propose a fix.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Given a failing pytest fixture that mutates global state, explain the bug and propose a fix.<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Convert a Ruby script from slurping a file to enumerating lines lazily.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Convert a Ruby script from slurping a file to enumerating lines lazily.<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Convert a Ruby script from slurping a file to enumerating lines lazily.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Convert a Ruby script from slurping a file to enumerating lines lazily.<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Explain the borrow-checker error in a Rust iterator adapter that returns references into a temporary Vec.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Explain the borrow-checker error in a Rust iterator adapter that returns references into a temporary Vec.<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Explain the borrow-checker error in a Rust iterator adapter that returns references into a temporary Vec.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Explain the borrow-checker error in a Rust iterator adapter that returns references into a temporary Vec.<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Implement a Rust LRU cache skeleton using HashMap plus a linked list index arena.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Implement a Rust LRU cache skeleton using HashMap plus a linked list index arena.<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Implement a Rust LRU cache skeleton using HashMap plus a linked list index arena.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Implement a Rust LRU cache skeleton using HashMap plus a linked list index arena.<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Explain when POSIX sh is preferable to Bash, and rewrite [[ -f "$x" ]] for POSIX sh.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Explain when POSIX sh is preferable to Bash, and rewrite [[ -f "$x" ]] for POSIX sh.<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Explain when POSIX sh is preferable to Bash, and rewrite [[ -f "$x" ]] for POSIX sh.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Explain when POSIX sh is preferable to Bash, and rewrite [[ -f "$x" ]] for POSIX sh.<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Optimize this SQL query with a covering index and explain why the current plan scans too many rows.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Optimize this SQL query with a covering index and explain why the current plan scans too many rows.<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Optimize this SQL query with a covering index and explain why the current plan scans too many rows.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Optimize this SQL query with a covering index and explain why the current plan scans too many rows.<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Translate a denormalized event table query into a window-function query with row_number().

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Translate a denormalized event table query into a window-function query with row_number().<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Translate a denormalized event table query into a window-function query with row_number().

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Translate a denormalized event table query into a window-function query with row_number().<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Write a Swift function that parses command-line flags into a small struct with defaults.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Write a Swift function that parses command-line flags into a small struct with defaults.<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Write a Swift function that parses command-line flags into a small struct with defaults.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Write a Swift function that parses command-line flags into a small struct with defaults.<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Design TypeScript types for a tool-call JSON schema with discriminated union variants.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Design TypeScript types for a tool-call JSON schema with discriminated union variants.<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Design TypeScript types for a tool-call JSON schema with discriminated union variants.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Design TypeScript types for a tool-call JSON schema with discriminated union variants.<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Explain how Zig error unions differ from nullable pointers in a file-reading helper.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Explain how Zig error unions differ from nullable pointers in a file-reading helper.<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User] Explain how Zig error unions differ from nullable pointers in a file-reading helper.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Explain how Zig error unions differ from nullable pointers in a file-reading helper.<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User]
Review this excerpt for correctness risks, memory lifetime issues, and performance bottlenecks.

Document: AGENT.md

# Agent Notes

`ds4.c` is a DeepSeek V4 Flash specific inference engine. It is not a generic
GGUF runner. The goal is a small, readable, high-performance C codebase with
Objective-C only where Metal requires it and Metal kernels under `metal/`.

## Goals

- Keep the production path as whole-model Metal graph inference.
- Keep model loading mmap-backed; do not eagerly copy the full GGUF.
- Keep the CPU backend CPU-only and use it only as reference/debug code.
- Preserve correctness before speed. Do not keep a faster path with unexplained
  attention, KV cache, or logits drift.
- Make long local agent sessions practical through live KV reuse and disk KV
  checkpoints.

## Quality Rules

- Comment important inference code where the model mechanics, cache lifetime,
  memory policy, or API orchestration are not obvious from the local code.
- Prefer comments beside the implementation over separate design documents.
- Keep comments instructive and compact: explain why a shape, ordering, cache
  boundary, or memory choice exists.
- Keep public APIs narrow. CLI/server code should not know tensor internals.
- Do not add permanent semantic variants behind flags. Diagnostic switches are
  fine when they validate the one release path.
- Do not introduce C++.

## Safety

- Avoid large CPU inference runs on macOS; the CPU path has previously exposed
  kernel VM failures with very large mappings.
- Do not run multiple huge model processes concurrently. The instance lock is
  intentional.
- Prefer short Metal smoke tests for build verification.

## Layout

- `ds4.c`: model loading, tokenizer, CPU reference code, Metal graph scheduling,
  sessions, disk-cache payload serialization.
- `ds4_cli.c`: command line, linenoise REPL, interactive transcript handling.
- `ds4_server.c`: OpenAI/Anthropic compatible HTTP API, worker queue, streaming,
  tool-call mapping, disk KV cache policy.
- `ds4_metal.m`: Objective-C Metal runtime and kernel wrappers.
- `metal/*.metal`: compute kernels.
- `tests/`: unit and live integration tests.
- `misc/`: ignored notes, experiments, and old planning material.

## Testing

Use `make` for build validation. Use `make test` for unit/regression tests when a
model and Metal are available. Use live server tests only when intentionally
testing the API surface.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Review this excerpt for correctness risks, memory lifetime issues, and performance bottlenecks.

Document: AGENT.md

# Agent Notes

`ds4.c` is a DeepSeek V4 Flash specific inference engine. It is not a generic
GGUF runner. The goal is a small, readable, high-performance C codebase with
Objective-C only where Metal requires it and Metal kernels under `metal/`.

## Goals

- Keep the production path as whole-model Metal graph inference.
- Keep model loading mmap-backed; do not eagerly copy the full GGUF.
- Keep the CPU backend CPU-only and use it only as reference/debug code.
- Preserve correctness before speed. Do not keep a faster path with unexplained
  attention, KV cache, or logits drift.
- Make long local agent sessions practical through live KV reuse and disk KV
  checkpoints.

## Quality Rules

- Comment important inference code where the model mechanics, cache lifetime,
  memory policy, or API orchestration are not obvious from the local code.
- Prefer comments beside the implementation over separate design documents.
- Keep comments instructive and compact: explain why a shape, ordering, cache
  boundary, or memory choice exists.
- Keep public APIs narrow. CLI/server code should not know tensor internals.
- Do not add permanent semantic variants behind flags. Diagnostic switches are
  fine when they validate the one release path.
- Do not introduce C++.

## Safety

- Avoid large CPU inference runs on macOS; the CPU path has previously exposed
  kernel VM failures with very large mappings.
- Do not run multiple huge model processes concurrently. The instance lock is
  intentional.
- Prefer short Metal smoke tests for build verification.

## Layout

- `ds4.c`: model loading, tokenizer, CPU reference code, Metal graph scheduling,
  sessions, disk-cache payload serialization.
- `ds4_cli.c`: command line, linenoise REPL, interactive transcript handling.
- `ds4_server.c`: OpenAI/Anthropic compatible HTTP API, worker queue, streaming,
  tool-call mapping, disk KV cache policy.
- `ds4_metal.m`: Objective-C Metal runtime and kernel wrappers.
- `metal/*.metal`: compute kernels.
- `tests/`: unit and live integration tests.
- `misc/`: ignored notes, experiments, and old planning material.

## Testing

Use `make` for build validation. Use `make test` for unit/regression tests when a
model and Metal are available. Use live server tests only when intentionally
testing the API surface.<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User]
Review this excerpt for correctness risks, memory lifetime issues, and performance bottlenecks.

Document: AGENT.md

# Agent Notes

`ds4.c` is a DeepSeek V4 Flash specific inference engine. It is not a generic
GGUF runner. The goal is a small, readable, high-performance C codebase with
Objective-C only where Metal requires it and Metal kernels under `metal/`.

## Goals

- Keep the production path as whole-model Metal graph inference.
- Keep model loading mmap-backed; do not eagerly copy the full GGUF.
- Keep the CPU backend CPU-only and use it only as reference/debug code.
- Preserve correctness before speed. Do not keep a faster path with unexplained
  attention, KV cache, or logits drift.
- Make long local agent sessions practical through live KV reuse and disk KV
  checkpoints.

## Quality Rules

- Comment important inference code where the model mechanics, cache lifetime,
  memory policy, or API orchestration are not obvious from the local code.
- Prefer comments beside the implementation over separate design documents.
- Keep comments instructive and compact: explain why a shape, ordering, cache
  boundary, or memory choice exists.
- Keep public APIs narrow. CLI/server code should not know tensor internals.
- Do not add permanent semantic variants behind flags. Diagnostic switches are
  fine when they validate the one release path.
- Do not introduce C++.

## Safety

- Avoid large CPU inference runs on macOS; the CPU path has previously exposed
  kernel VM failures with very large mappings.
- Do not run multiple huge model processes concurrently. The instance lock is
  intentional.
- Prefer short Metal smoke tests for build verification.

## Layout

- `ds4.c`: model loading, tokenizer, CPU reference code, Metal graph scheduling,
  sessions, disk-cache payload serialization.
- `ds4_cli.c`: command line, linenoise REPL, interactive transcript handling.
- `ds4_server.c`: OpenAI/Anthropic compatible HTTP API, worker queue, streaming,
  tool-call mapping, disk KV cache policy.
- `ds4_metal.m`: Objective-C Metal runtime and kernel wrappers.
- `metal/*.metal`: compute kernels.
- `tests/`: unit and live integration tests.
- `misc/`: ignored notes, experiments, and old planning material.

## Testing

Use `make` for build validation. Use `make test` for unit/regression tests when a
model and Metal are available. Use live server tests only when intentionally
testing the API surface.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Review this excerpt for correctness risks, memory lifetime issues, and performance bottlenecks.

Document: AGENT.md

# Agent Notes

`ds4.c` is a DeepSeek V4 Flash specific inference engine. It is not a generic
GGUF runner. The goal is a small, readable, high-performance C codebase with
Objective-C only where Metal requires it and Metal kernels under `metal/`.

## Goals

- Keep the production path as whole-model Metal graph inference.
- Keep model loading mmap-backed; do not eagerly copy the full GGUF.
- Keep the CPU backend CPU-only and use it only as reference/debug code.
- Preserve correctness before speed. Do not keep a faster path with unexplained
  attention, KV cache, or logits drift.
- Make long local agent sessions practical through live KV reuse and disk KV
  checkpoints.

## Quality Rules

- Comment important inference code where the model mechanics, cache lifetime,
  memory policy, or API orchestration are not obvious from the local code.
- Prefer comments beside the implementation over separate design documents.
- Keep comments instructive and compact: explain why a shape, ordering, cache
  boundary, or memory choice exists.
- Keep public APIs narrow. CLI/server code should not know tensor internals.
- Do not add permanent semantic variants behind flags. Diagnostic switches are
  fine when they validate the one release path.
- Do not introduce C++.

## Safety

- Avoid large CPU inference runs on macOS; the CPU path has previously exposed
  kernel VM failures with very large mappings.
- Do not run multiple huge model processes concurrently. The instance lock is
  intentional.
- Prefer short Metal smoke tests for build verification.

## Layout

- `ds4.c`: model loading, tokenizer, CPU reference code, Metal graph scheduling,
  sessions, disk-cache payload serialization.
- `ds4_cli.c`: command line, linenoise REPL, interactive transcript handling.
- `ds4_server.c`: OpenAI/Anthropic compatible HTTP API, worker queue, streaming,
  tool-call mapping, disk KV cache policy.
- `ds4_metal.m`: Objective-C Metal runtime and kernel wrappers.
- `metal/*.metal`: compute kernels.
- `tests/`: unit and live integration tests.
- `misc/`: ignored notes, experiments, and old planning material.

## Testing

Use `make` for build validation. Use `make test` for unit/regression tests when a
model and Metal are available. Use live server tests only when intentionally
testing the API surface.<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User]
Explain what this code does and identify the inference stage it belongs to.

Document: README.md

# DwarfStar 4

DwarfStar 4 is a small native inference engine specific for **DeepSeek V4 Flash**. It is
intentionally narrow: not a generic GGUF runner, not a wrapper around another
runtime: it is completely self-contained. Other than running the model in a
correct and fast way, the project goal is to provide DS4 specific loading,
prompt rendering, tool calling, KV state handling (RAM and on-disk), and server
API, all ready to work with coding agents or with the provided CLI interface.
There are also tools for GGUF and imatrix generation, and for quality and
speed testing.

We support the following backends:
* **Metal** is our primary target. Starting from MacBooks with 96GB of RAM.
* **NVIDIA CUDA** with special care for the DGX Spark.
* **AMD ROCm** is only supported in the [rocm](https://github.com/antirez/ds4/tree/rocm) branch. It is kept separate from main since I (antirez) don't have direct hardware access, so the community rebases the branch as needed.

This project would not exist without **llama.cpp and GGML**, make sure to read
the acknowledgements section, a big thank you to Georgi Gerganov and all the
other contributors.

## Motivations

Now, back at this project. Why we believe DeepSeek v4 Flash to be a pretty special
model deserving a standalone engine? Because after comparing it with powerful smaller
dense models, we can report that:

1. DeepSeek v4 Flash is faster because of less active parameters.
2. In thinking mode, if you avoid *max thinking*, it produces a thinking section that is a lot shorter than other models, even 1/5 of other models in many cases, and crucially, the thinking section length is **proportional to the problem complexity**. This makes DeepSeek v4 Flash usable with thinking enabled when other models are practically impossible to use in the same conditions.
3. The model features a context window of **1 million tokens**.
4. Being so large, it knows more things if you go sampling at the edge of knowledge. For instance asking about Italian show or political questions soon uncovers that 284B parameters are a lot more than 27B or 35B parameters.
5. It writes much better English and Italian. It *feels* a quasi-frontier model.
6. The KV cache is incredibly compressed, allowing long context inference on local computers and **on disk KV cache persistence**.
7. It works well with 2-bit quantization, if quantized in a special way (read later). This allows to run it in MacBooks with 128GB of RAM (and many people reported it working with 96GB as well, even at 250k context window!).
8. We expect DeepSeek to release **updated versions of v4 Flash** in the future, even better than the current one.

That said, a few important things about this project:

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Explain what this code does and identify the inference stage it belongs to.

Document: README.md

# DwarfStar 4

DwarfStar 4 is a small native inference engine specific for **DeepSeek V4 Flash**. It is
intentionally narrow: not a generic GGUF runner, not a wrapper around another
runtime: it is completely self-contained. Other than running the model in a
correct and fast way, the project goal is to provide DS4 specific loading,
prompt rendering, tool calling, KV state handling (RAM and on-disk), and server
API, all ready to work with coding agents or with the provided CLI interface.
There are also tools for GGUF and imatrix generation, and for quality and
speed testing.

We support the following backends:
* **Metal** is our primary target. Starting from MacBooks with 96GB of RAM.
* **NVIDIA CUDA** with special care for the DGX Spark.
* **AMD ROCm** is only supported in the [rocm](https://github.com/antirez/ds4/tree/rocm) branch. It is kept separate from main since I (antirez) don't have direct hardware access, so the community rebases the branch as needed.

This project would not exist without **llama.cpp and GGML**, make sure to read
the acknowledgements section, a big thank you to Georgi Gerganov and all the
other contributors.

## Motivations

Now, back at this project. Why we believe DeepSeek v4 Flash to be a pretty special
model deserving a standalone engine? Because after comparing it with powerful smaller
dense models, we can report that:

1. DeepSeek v4 Flash is faster because of less active parameters.
2. In thinking mode, if you avoid *max thinking*, it produces a thinking section that is a lot shorter than other models, even 1/5 of other models in many cases, and crucially, the thinking section length is **proportional to the problem complexity**. This makes DeepSeek v4 Flash usable with thinking enabled when other models are practically impossible to use in the same conditions.
3. The model features a context window of **1 million tokens**.
4. Being so large, it knows more things if you go sampling at the edge of knowledge. For instance asking about Italian show or political questions soon uncovers that 284B parameters are a lot more than 27B or 35B parameters.
5. It writes much better English and Italian. It *feels* a quasi-frontier model.
6. The KV cache is incredibly compressed, allowing long context inference on local computers and **on disk KV cache persistence**.
7. It works well with 2-bit quantization, if quantized in a special way (read later). This allows to run it in MacBooks with 128GB of RAM (and many people reported it working with 96GB as well, even at 250k context window!).
8. We expect DeepSeek to release **updated versions of v4 Flash** in the future, even better than the current one.

That said, a few important things about this project:<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User]
Explain what this code does and identify the inference stage it belongs to.

Document: README.md

# DwarfStar 4

DwarfStar 4 is a small native inference engine specific for **DeepSeek V4 Flash**. It is
intentionally narrow: not a generic GGUF runner, not a wrapper around another
runtime: it is completely self-contained. Other than running the model in a
correct and fast way, the project goal is to provide DS4 specific loading,
prompt rendering, tool calling, KV state handling (RAM and on-disk), and server
API, all ready to work with coding agents or with the provided CLI interface.
There are also tools for GGUF and imatrix generation, and for quality and
speed testing.

We support the following backends:
* **Metal** is our primary target. Starting from MacBooks with 96GB of RAM.
* **NVIDIA CUDA** with special care for the DGX Spark.
* **AMD ROCm** is only supported in the [rocm](https://github.com/antirez/ds4/tree/rocm) branch. It is kept separate from main since I (antirez) don't have direct hardware access, so the community rebases the branch as needed.

This project would not exist without **llama.cpp and GGML**, make sure to read
the acknowledgements section, a big thank you to Georgi Gerganov and all the
other contributors.

## Motivations

Now, back at this project. Why we believe DeepSeek v4 Flash to be a pretty special
model deserving a standalone engine? Because after comparing it with powerful smaller
dense models, we can report that:

1. DeepSeek v4 Flash is faster because of less active parameters.
2. In thinking mode, if you avoid *max thinking*, it produces a thinking section that is a lot shorter than other models, even 1/5 of other models in many cases, and crucially, the thinking section length is **proportional to the problem complexity**. This makes DeepSeek v4 Flash usable with thinking enabled when other models are practically impossible to use in the same conditions.
3. The model features a context window of **1 million tokens**.
4. Being so large, it knows more things if you go sampling at the edge of knowledge. For instance asking about Italian show or political questions soon uncovers that 284B parameters are a lot more than 27B or 35B parameters.
5. It writes much better English and Italian. It *feels* a quasi-frontier model.
6. The KV cache is incredibly compressed, allowing long context inference on local computers and **on disk KV cache persistence**.
7. It works well with 2-bit quantization, if quantized in a special way (read later). This allows to run it in MacBooks with 128GB of RAM (and many people reported it working with 96GB as well, even at 250k context window!).
8. We expect DeepSeek to release **updated versions of v4 Flash** in the future, even better than the current one.

That said, a few important things about this project:

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Explain what this code does and identify the inference stage it belongs to.

Document: README.md

# DwarfStar 4

DwarfStar 4 is a small native inference engine specific for **DeepSeek V4 Flash**. It is
intentionally narrow: not a generic GGUF runner, not a wrapper around another
runtime: it is completely self-contained. Other than running the model in a
correct and fast way, the project goal is to provide DS4 specific loading,
prompt rendering, tool calling, KV state handling (RAM and on-disk), and server
API, all ready to work with coding agents or with the provided CLI interface.
There are also tools for GGUF and imatrix generation, and for quality and
speed testing.

We support the following backends:
* **Metal** is our primary target. Starting from MacBooks with 96GB of RAM.
* **NVIDIA CUDA** with special care for the DGX Spark.
* **AMD ROCm** is only supported in the [rocm](https://github.com/antirez/ds4/tree/rocm) branch. It is kept separate from main since I (antirez) don't have direct hardware access, so the community rebases the branch as needed.

This project would not exist without **llama.cpp and GGML**, make sure to read
the acknowledgements section, a big thank you to Georgi Gerganov and all the
other contributors.

## Motivations

Now, back at this project. Why we believe DeepSeek v4 Flash to be a pretty special
model deserving a standalone engine? Because after comparing it with powerful smaller
dense models, we can report that:

1. DeepSeek v4 Flash is faster because of less active parameters.
2. In thinking mode, if you avoid *max thinking*, it produces a thinking section that is a lot shorter than other models, even 1/5 of other models in many cases, and crucially, the thinking section length is **proportional to the problem complexity**. This makes DeepSeek v4 Flash usable with thinking enabled when other models are practically impossible to use in the same conditions.
3. The model features a context window of **1 million tokens**.
4. Being so large, it knows more things if you go sampling at the edge of knowledge. For instance asking about Italian show or political questions soon uncovers that 284B parameters are a lot more than 27B or 35B parameters.
5. It writes much better English and Italian. It *feels* a quasi-frontier model.
6. The KV cache is incredibly compressed, allowing long context inference on local computers and **on disk KV cache persistence**.
7. It works well with 2-bit quantization, if quantized in a special way (read later). This allows to run it in MacBooks with 128GB of RAM (and many people reported it working with 96GB as well, even at 250k context window!).
8. We expect DeepSeek to release **updated versions of v4 Flash** in the future, even better than the current one.

That said, a few important things about this project:<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User]
Suggest a minimal correctness-preserving optimization for this excerpt.

Document: README.md

window!).
8. We expect DeepSeek to release **updated versions of v4 Flash** in the future, even better than the current one.

That said, a few important things about this project:

* The local inference landscape contains many excellent projects, but new models are released continuously, and the attention immediately gets captured by the next model to implement. This project takes a deliberately narrow bet: one model at a time, official-vector validation (logits obtained with the official implementation), long-context tests, and enough agent integration to know if it really works. The exact model may change as the landscape evolves, but the constraint remains: local inference credible on high end personal machines or Mac Studios, starting from 96/128GB of memory.
* This software is developed with **strong assistance from GPT 5.5** and with humans leading the ideas, testing, and debugging. We say this openly because it shaped how the project was built. If you are not happy with AI-developed code, this software is not for you. The acknowledgement below is equally important: this would not exist without `llama.cpp` and GGML, largely written by hand.
* This implementation is based on the idea that compressed KV caches like the one of DeepSeek v4 and the fast SSD disks of modern MacBooks should change our idea that KV cache belongs to RAM. **The KV cache is actually a first-class disk citizen**.
* Our vision is that local inference should be a set of three things working well together, out of the box: A) inference engine with HTTP API + B) GGUF specially crafted to run well under a given engine and given assumptions + C) testing and validation with coding agents implementations. This inference engine only runs with the GGUF files provided. It gets tested against officially obtained logits at different context sizes. This project exists because we wanted to make one local model feel finished end to end, not just runnable. However this is just alpha quality code, so probably we are not still there.
* The optimized graph path targets **Metal on macOS** and **CUDA on Linux**. The CPU path is only for correctness checks and model/tokenizer diagnostics. For CPU-only Linux builds, use `make cpu`; it builds the normal `./ds4` and `./ds4-server` binaries without CUDA or Metal. On macOS, **warning: current macOS versions have a bug in the virtual memory implementation that will crash the kernel** if you try to run the CPU code. Remember? Software sucks. It was not possible to fix the CPU inference to avoid crashing, since each time you have to restart the computer, which is not funny. Help us, if you have the guts.

## Acknowledgements to llama.cpp and GGML

`ds4.c` does not link against GGML, but it **exists thanks to the path opened by the
llama.cpp project and the kernels, quantization formats, GGUF ecosystem, and hard-won
engineering knowledge developed there**.
We are thankful and indebted to [`llama.cpp`](https://github.com/ggml-org/llama.cpp)
and its contributors. Their implementation, kernels, tests, and design choices were
an essential reference while building this DeepSeek V4 Flash-specific inference path.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Suggest a minimal correctness-preserving optimization for this excerpt.

Document: README.md

window!).
8. We expect DeepSeek to release **updated versions of v4 Flash** in the future, even better than the current one.

That said, a few important things about this project:

* The local inference landscape contains many excellent projects, but new models are released continuously, and the attention immediately gets captured by the next model to implement. This project takes a deliberately narrow bet: one model at a time, official-vector validation (logits obtained with the official implementation), long-context tests, and enough agent integration to know if it really works. The exact model may change as the landscape evolves, but the constraint remains: local inference credible on high end personal machines or Mac Studios, starting from 96/128GB of memory.
* This software is developed with **strong assistance from GPT 5.5** and with humans leading the ideas, testing, and debugging. We say this openly because it shaped how the project was built. If you are not happy with AI-developed code, this software is not for you. The acknowledgement below is equally important: this would not exist without `llama.cpp` and GGML, largely written by hand.
* This implementation is based on the idea that compressed KV caches like the one of DeepSeek v4 and the fast SSD disks of modern MacBooks should change our idea that KV cache belongs to RAM. **The KV cache is actually a first-class disk citizen**.
* Our vision is that local inference should be a set of three things working well together, out of the box: A) inference engine with HTTP API + B) GGUF specially crafted to run well under a given engine and given assumptions + C) testing and validation with coding agents implementations. This inference engine only runs with the GGUF files provided. It gets tested against officially obtained logits at different context sizes. This project exists because we wanted to make one local model feel finished end to end, not just runnable. However this is just alpha quality code, so probably we are not still there.
* The optimized graph path targets **Metal on macOS** and **CUDA on Linux**. The CPU path is only for correctness checks and model/tokenizer diagnostics. For CPU-only Linux builds, use `make cpu`; it builds the normal `./ds4` and `./ds4-server` binaries without CUDA or Metal. On macOS, **warning: current macOS versions have a bug in the virtual memory implementation that will crash the kernel** if you try to run the CPU code. Remember? Software sucks. It was not possible to fix the CPU inference to avoid crashing, since each time you have to restart the computer, which is not funny. Help us, if you have the guts.

## Acknowledgements to llama.cpp and GGML

`ds4.c` does not link against GGML, but it **exists thanks to the path opened by the
llama.cpp project and the kernels, quantization formats, GGUF ecosystem, and hard-won
engineering knowledge developed there**.
We are thankful and indebted to [`llama.cpp`](https://github.com/ggml-org/llama.cpp)
and its contributors. Their implementation, kernels, tests, and design choices were
an essential reference while building this DeepSeek V4 Flash-specific inference path.<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User]
Suggest a minimal correctness-preserving optimization for this excerpt.

Document: README.md

window!).
8. We expect DeepSeek to release **updated versions of v4 Flash** in the future, even better than the current one.

That said, a few important things about this project:

* The local inference landscape contains many excellent projects, but new models are released continuously, and the attention immediately gets captured by the next model to implement. This project takes a deliberately narrow bet: one model at a time, official-vector validation (logits obtained with the official implementation), long-context tests, and enough agent integration to know if it really works. The exact model may change as the landscape evolves, but the constraint remains: local inference credible on high end personal machines or Mac Studios, starting from 96/128GB of memory.
* This software is developed with **strong assistance from GPT 5.5** and with humans leading the ideas, testing, and debugging. We say this openly because it shaped how the project was built. If you are not happy with AI-developed code, this software is not for you. The acknowledgement below is equally important: this would not exist without `llama.cpp` and GGML, largely written by hand.
* This implementation is based on the idea that compressed KV caches like the one of DeepSeek v4 and the fast SSD disks of modern MacBooks should change our idea that KV cache belongs to RAM. **The KV cache is actually a first-class disk citizen**.
* Our vision is that local inference should be a set of three things working well together, out of the box: A) inference engine with HTTP API + B) GGUF specially crafted to run well under a given engine and given assumptions + C) testing and validation with coding agents implementations. This inference engine only runs with the GGUF files provided. It gets tested against officially obtained logits at different context sizes. This project exists because we wanted to make one local model feel finished end to end, not just runnable. However this is just alpha quality code, so probably we are not still there.
* The optimized graph path targets **Metal on macOS** and **CUDA on Linux**. The CPU path is only for correctness checks and model/tokenizer diagnostics. For CPU-only Linux builds, use `make cpu`; it builds the normal `./ds4` and `./ds4-server` binaries without CUDA or Metal. On macOS, **warning: current macOS versions have a bug in the virtual memory implementation that will crash the kernel** if you try to run the CPU code. Remember? Software sucks. It was not possible to fix the CPU inference to avoid crashing, since each time you have to restart the computer, which is not funny. Help us, if you have the guts.

## Acknowledgements to llama.cpp and GGML

`ds4.c` does not link against GGML, but it **exists thanks to the path opened by the
llama.cpp project and the kernels, quantization formats, GGUF ecosystem, and hard-won
engineering knowledge developed there**.
We are thankful and indebted to [`llama.cpp`](https://github.com/ggml-org/llama.cpp)
and its contributors. Their implementation, kernels, tests, and design choices were
an essential reference while building this DeepSeek V4 Flash-specific inference path.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Suggest a minimal correctness-preserving optimization for this excerpt.

Document: README.md

window!).
8. We expect DeepSeek to release **updated versions of v4 Flash** in the future, even better than the current one.

That said, a few important things about this project:

* The local inference landscape contains many excellent projects, but new models are released continuously, and the attention immediately gets captured by the next model to implement. This project takes a deliberately narrow bet: one model at a time, official-vector validation (logits obtained with the official implementation), long-context tests, and enough agent integration to know if it really works. The exact model may change as the landscape evolves, but the constraint remains: local inference credible on high end personal machines or Mac Studios, starting from 96/128GB of memory.
* This software is developed with **strong assistance from GPT 5.5** and with humans leading the ideas, testing, and debugging. We say this openly because it shaped how the project was built. If you are not happy with AI-developed code, this software is not for you. The acknowledgement below is equally important: this would not exist without `llama.cpp` and GGML, largely written by hand.
* This implementation is based on the idea that compressed KV caches like the one of DeepSeek v4 and the fast SSD disks of modern MacBooks should change our idea that KV cache belongs to RAM. **The KV cache is actually a first-class disk citizen**.
* Our vision is that local inference should be a set of three things working well together, out of the box: A) inference engine with HTTP API + B) GGUF specially crafted to run well under a given engine and given assumptions + C) testing and validation with coding agents implementations. This inference engine only runs with the GGUF files provided. It gets tested against officially obtained logits at different context sizes. This project exists because we wanted to make one local model feel finished end to end, not just runnable. However this is just alpha quality code, so probably we are not still there.
* The optimized graph path targets **Metal on macOS** and **CUDA on Linux**. The CPU path is only for correctness checks and model/tokenizer diagnostics. For CPU-only Linux builds, use `make cpu`; it builds the normal `./ds4` and `./ds4-server` binaries without CUDA or Metal. On macOS, **warning: current macOS versions have a bug in the virtual memory implementation that will crash the kernel** if you try to run the CPU code. Remember? Software sucks. It was not possible to fix the CPU inference to avoid crashing, since each time you have to restart the computer, which is not funny. Help us, if you have the guts.

## Acknowledgements to llama.cpp and GGML

`ds4.c` does not link against GGML, but it **exists thanks to the path opened by the
llama.cpp project and the kernels, quantization formats, GGUF ecosystem, and hard-won
engineering knowledge developed there**.
We are thankful and indebted to [`llama.cpp`](https://github.com/ggml-org/llama.cpp)
and its contributors. Their implementation, kernels, tests, and design choices were
an essential reference while building this DeepSeek V4 Flash-specific inference path.<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User]
Trova bug sottili e spiega quali invarianti dell'inferenza possono rompersi.

Document: README.md

incoming rendered prompt. After load, the exact checkpoint tokens from the DS4
payload remain authoritative, and only the incoming text suffix after the cached
bytes is tokenized.

The optional tool-id map is present only when header extension bit 0 is set.
Appended sections use fixed bit order, so future extension bits can add fields
without ambiguity. The map stores unguessable API tool call IDs back to the
exact DSML block the model sampled. Only mappings whose DSML block is present
in the rendered cached text are stored. This lets restarted servers render
later client history byte-for-byte like the original model output, even if the
client reorders JSON arguments.

The current tool-id map section is:

```text
0   u8[3]  magic = "KTM"
3   u8     version = 1
4   u32    entry count

For each entry:
0   u32    tool id byte length
4   u32    sampled DSML byte length
8   bytes  tool id
... bytes  exact sampled DSML block
```

The section is auxiliary replay memory, not model state. A cache hit restores
the session payload first, then loads the map if present. Before rendering a
request, the server can also scan cache files for the tool IDs present in the
client history and load just those mappings, so an exact DSML replay can survive
server restarts even when the matching KV snapshot is not the one ultimately
used for the rendered-prefix hit.

The DS4 session payload starts with thirteen little-endian `u32` fields:

```text
0   magic = "DSV4"
1   payload version = 1
2   saved context size
3   prefill chunk size
4   raw KV ring capacity
5   raw sliding-window length
6   compressed KV capacity
7   checkpoint token count
8   layer count
9   raw/head KV dimension
10  indexer head dimension
11  vocabulary size
12  live raw rows serialized below
```

Then it stores:

- `u32[token_count]` checkpoint token IDs.
- `float32[vocab_size]` logits for the next token after that checkpoint.
- `u32[layer_count]` compressed attention row counts.
- `u32[layer_count]` ratio-4 indexer row counts.
- For every layer: the live raw sliding-window KV rows, written in logical
  position order rather than physical ring order.
- For compressed layers: live compressed KV rows and compressor frontier
  tensors.
- For ratio-4 compressed layers: live indexer compressed rows and indexer
  frontier tensors.

The logits are raw IEEE-754 `float32` values from the host `ds4_session`
buffer. They are saved immediately after the checkpoint tokens so a loaded
snapshot can sample or continue from the exact next-token distribution without
running one extra decode step. MTP draft logits/state are not persisted; after
loading a disk checkpoint the draft state is invalidated and rebuilt by normal
generation.

The tensor payload is DS4-specific KV/session state, not a generic inference
graph dump. It is expected to be portable only across compatible `ds4.c`
builds for this model layout.

The cache stores checkpoints at four moments:

- `cold`: after a long first prompt reaches a stable prefix, before generation.
- `continued`: when prefill or generation reaches the next absolute aligned frontier.
- `evict`: before an unrelated request replaces the live in-memory session.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Trova bug sottili e spiega quali invarianti dell'inferenza possono rompersi.

Document: README.md

incoming rendered prompt. After load, the exact checkpoint tokens from the DS4
payload remain authoritative, and only the incoming text suffix after the cached
bytes is tokenized.

The optional tool-id map is present only when header extension bit 0 is set.
Appended sections use fixed bit order, so future extension bits can add fields
without ambiguity. The map stores unguessable API tool call IDs back to the
exact DSML block the model sampled. Only mappings whose DSML block is present
in the rendered cached text are stored. This lets restarted servers render
later client history byte-for-byte like the original model output, even if the
client reorders JSON arguments.

The current tool-id map section is:

```text
0   u8[3]  magic = "KTM"
3   u8     version = 1
4   u32    entry count

For each entry:
0   u32    tool id byte length
4   u32    sampled DSML byte length
8   bytes  tool id
... bytes  exact sampled DSML block
```

The section is auxiliary replay memory, not model state. A cache hit restores
the session payload first, then loads the map if present. Before rendering a
request, the server can also scan cache files for the tool IDs present in the
client history and load just those mappings, so an exact DSML replay can survive
server restarts even when the matching KV snapshot is not the one ultimately
used for the rendered-prefix hit.

The DS4 session payload starts with thirteen little-endian `u32` fields:

```text
0   magic = "DSV4"
1   payload version = 1
2   saved context size
3   prefill chunk size
4   raw KV ring capacity
5   raw sliding-window length
6   compressed KV capacity
7   checkpoint token count
8   layer count
9   raw/head KV dimension
10  indexer head dimension
11  vocabulary size
12  live raw rows serialized below
```

Then it stores:

- `u32[token_count]` checkpoint token IDs.
- `float32[vocab_size]` logits for the next token after that checkpoint.
- `u32[layer_count]` compressed attention row counts.
- `u32[layer_count]` ratio-4 indexer row counts.
- For every layer: the live raw sliding-window KV rows, written in logical
  position order rather than physical ring order.
- For compressed layers: live compressed KV rows and compressor frontier
  tensors.
- For ratio-4 compressed layers: live indexer compressed rows and indexer
  frontier tensors.

The logits are raw IEEE-754 `float32` values from the host `ds4_session`
buffer. They are saved immediately after the checkpoint tokens so a loaded
snapshot can sample or continue from the exact next-token distribution without
running one extra decode step. MTP draft logits/state are not persisted; after
loading a disk checkpoint the draft state is invalidated and rebuilt by normal
generation.

The tensor payload is DS4-specific KV/session state, not a generic inference
graph dump. It is expected to be portable only across compatible `ds4.c`
builds for this model layout.

The cache stores checkpoints at four moments:

- `cold`: after a long first prompt reaches a stable prefix, before generation.
- `continued`: when prefill or generation reaches the next absolute aligned frontier.
- `evict`: before an unrelated request replaces the live in-memory session.<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User]
Trova bug sottili e spiega quali invarianti dell'inferenza possono rompersi.

Document: README.md

incoming rendered prompt. After load, the exact checkpoint tokens from the DS4
payload remain authoritative, and only the incoming text suffix after the cached
bytes is tokenized.

The optional tool-id map is present only when header extension bit 0 is set.
Appended sections use fixed bit order, so future extension bits can add fields
without ambiguity. The map stores unguessable API tool call IDs back to the
exact DSML block the model sampled. Only mappings whose DSML block is present
in the rendered cached text are stored. This lets restarted servers render
later client history byte-for-byte like the original model output, even if the
client reorders JSON arguments.

The current tool-id map section is:

```text
0   u8[3]  magic = "KTM"
3   u8     version = 1
4   u32    entry count

For each entry:
0   u32    tool id byte length
4   u32    sampled DSML byte length
8   bytes  tool id
... bytes  exact sampled DSML block
```

The section is auxiliary replay memory, not model state. A cache hit restores
the session payload first, then loads the map if present. Before rendering a
request, the server can also scan cache files for the tool IDs present in the
client history and load just those mappings, so an exact DSML replay can survive
server restarts even when the matching KV snapshot is not the one ultimately
used for the rendered-prefix hit.

The DS4 session payload starts with thirteen little-endian `u32` fields:

```text
0   magic = "DSV4"
1   payload version = 1
2   saved context size
3   prefill chunk size
4   raw KV ring capacity
5   raw sliding-window length
6   compressed KV capacity
7   checkpoint token count
8   layer count
9   raw/head KV dimension
10  indexer head dimension
11  vocabulary size
12  live raw rows serialized below
```

Then it stores:

- `u32[token_count]` checkpoint token IDs.
- `float32[vocab_size]` logits for the next token after that checkpoint.
- `u32[layer_count]` compressed attention row counts.
- `u32[layer_count]` ratio-4 indexer row counts.
- For every layer: the live raw sliding-window KV rows, written in logical
  position order rather than physical ring order.
- For compressed layers: live compressed KV rows and compressor frontier
  tensors.
- For ratio-4 compressed layers: live indexer compressed rows and indexer
  frontier tensors.

The logits are raw IEEE-754 `float32` values from the host `ds4_session`
buffer. They are saved immediately after the checkpoint tokens so a loaded
snapshot can sample or continue from the exact next-token distribution without
running one extra decode step. MTP draft logits/state are not persisted; after
loading a disk checkpoint the draft state is invalidated and rebuilt by normal
generation.

The tensor payload is DS4-specific KV/session state, not a generic inference
graph dump. It is expected to be portable only across compatible `ds4.c`
builds for this model layout.

The cache stores checkpoints at four moments:

- `cold`: after a long first prompt reaches a stable prefix, before generation.
- `continued`: when prefill or generation reaches the next absolute aligned frontier.
- `evict`: before an unrelated request replaces the live in-memory session.

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Trova bug sottili e spiega quali invarianti dell'inferenza possono rompersi.

Document: README.md

incoming rendered prompt. After load, the exact checkpoint tokens from the DS4
payload remain authoritative, and only the incoming text suffix after the cached
bytes is tokenized.

The optional tool-id map is present only when header extension bit 0 is set.
Appended sections use fixed bit order, so future extension bits can add fields
without ambiguity. The map stores unguessable API tool call IDs back to the
exact DSML block the model sampled. Only mappings whose DSML block is present
in the rendered cached text are stored. This lets restarted servers render
later client history byte-for-byte like the original model output, even if the
client reorders JSON arguments.

The current tool-id map section is:

```text
0   u8[3]  magic = "KTM"
3   u8     version = 1
4   u32    entry count

For each entry:
0   u32    tool id byte length
4   u32    sampled DSML byte length
8   bytes  tool id
... bytes  exact sampled DSML block
```

The section is auxiliary replay memory, not model state. A cache hit restores
the session payload first, then loads the map if present. Before rendering a
request, the server can also scan cache files for the tool IDs present in the
client history and load just those mappings, so an exact DSML replay can survive
server restarts even when the matching KV snapshot is not the one ultimately
used for the rendered-prefix hit.

The DS4 session payload starts with thirteen little-endian `u32` fields:

```text
0   magic = "DSV4"
1   payload version = 1
2   saved context size
3   prefill chunk size
4   raw KV ring capacity
5   raw sliding-window length
6   compressed KV capacity
7   checkpoint token count
8   layer count
9   raw/head KV dimension
10  indexer head dimension
11  vocabulary size
12  live raw rows serialized below
```

Then it stores:

- `u32[token_count]` checkpoint token IDs.
- `float32[vocab_size]` logits for the next token after that checkpoint.
- `u32[layer_count]` compressed attention row counts.
- `u32[layer_count]` ratio-4 indexer row counts.
- For every layer: the live raw sliding-window KV rows, written in logical
  position order rather than physical ring order.
- For compressed layers: live compressed KV rows and compressor frontier
  tensors.
- For ratio-4 compressed layers: live indexer compressed rows and indexer
  frontier tensors.

The logits are raw IEEE-754 `float32` values from the host `ds4_session`
buffer. They are saved immediately after the checkpoint tokens so a loaded
snapshot can sample or continue from the exact next-token distribution without
running one extra decode step. MTP draft logits/state are not persisted; after
loading a disk checkpoint the draft state is invalidated and rebuilt by normal
generation.

The tensor payload is DS4-specific KV/session state, not a generic inference
graph dump. It is expected to be portable only across compatible `ds4.c`
builds for this model layout.

The cache stores checkpoints at four moments:

- `cold`: after a long first prompt reaches a stable prefix, before generation.
- `continued`: when prefill or generation reaches the next absolute aligned frontier.
- `evict`: before an unrelated request replaces the live in-memory session.<｜Assistant｜><think>