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Model Details
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sojufx
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Jackrong/Qwopus3.8-27B-Flash
Input Modalities
Output Modalities
Supported Functionality
lm_headThe tested single-GPU path is a current Blackwell-capable SGLang build with FlashInfer, ModelOpt FP4 support, and a matching Qwen3.8 27B DFlash2 NVFP4 draft model. The draft model is required for the DFlash configuration below; do not use an arbitrary Qwen drafter.
bash
python3 -m sglang.launch_server \--trust-remote-code \--model-path /models/Qwopus3.8-27B-Flash-NVFP4 \--served-model-name qwopus3.8-27b-flash-nvfp4 \--host 127.0.0.1 \--port 8000 \--mem-fraction-static 0.80 \--attention-backend flashinfer \--chunked-prefill-size 2048 \--disable-prefill-cuda-graph \--kv-cache-dtype fp8_e4m3 \--max-running-requests 4 \--context-length 262144 \--mamba-full-memory-ratio 11.93 \--mamba-radix-cache-strategy extra_buffer \--mamba-ssm-dtype bfloat16 \--default-chat-template-kwargs '{"enable_thinking":false}' \--chat-template /models/Qwopus3.8-27B-Flash-NVFP4/chat_template.jinja \--mm-feature-transport cpu \--tool-call-parser qwen \--sampling-defaults model \--enable-metrics \--enable-cache-report \--speculative-algorithm DFLASH \--speculative-draft-model-path /models/Qwen3.8-27B-DFlash2-NVFP4 \--speculative-draft-model-quantization modelopt_fp4 \--speculative-draft-attention-backend flashinfer \--speculative-num-draft-tokens 10 \--speculative-attention-mode prefill
This profile is intended for a Blackwell-class GPU with enough memory for the
target model, draft model, FP8 KV cache, and runtime overhead. Start at a
smaller context or lower --mem-fraction-static on less capable hardware.
For multimodal use, keep the bundled chat_template.jinja so image tokens are
rendered correctly. For function calling, use SGLang's Qwen tool parser. The
default profile disables thinking for predictable interactive tool use; enable
it per request only when your application expects and budgets for reasoning.
The bundled chat template produces valid native tool calls; do not replace it
to solve an agent that stops after a sentence such as "I will inspect the
file." Some OpenCode-derived clients send tool_choice: "auto" for every
turn, including a fresh user request that clearly requires a tool. Under
auto, the model is allowed to answer with a plan and end the agent loop.
For a reliable unattended coding-agent setup, enforce tool_choice: "required"
at the client or a small OpenAI-compatible gateway only when all three are
true:
tool_choice is absent or "auto";user message.Leave turns whose last message is a tool result on auto. That lets the model
either make the next necessary call or provide its final answer, without
forcing an unnecessary tool call after every result.
Example request:
bash
curl http://127.0.0.1:8000/v1/chat/completions \-H 'Content-Type: application/json' \-d '{"model": "qwopus3.8-27b-flash-nvfp4","messages": [{"role": "user", "content": "Reply exactly: OK"}],"temperature": 0.6,"top_p": 0.95,"max_tokens": 2048,"chat_template_kwargs": {"enable_thinking": false}}'
Base model: Jackrong/Qwopus3.8-27B-Flash
Thanks to Kyle Hessling for creating Qwopus3.8-27B-Flash and making the original model available to the community.
44d24e8cb20ceb3cdf4fe200b5a0afd970ee748asafetensorsThis repository contains converted model weights. Please follow the base model's license and usage terms.