Text Generation
Transformers
Safetensors
qwen3_5
image-text-to-text
qwen3.6
qwopus
gptq
gptq-pro
marlin
vllm
int4
quantized
mmlu-pro
24-may-update
conversational
4-bit precision
Instructions to use XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1") model = AutoModelForMultimodalLM.from_pretrained("XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1
- SGLang
How to use XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1 with Docker Model Runner:
docker model run hf.co/XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1
Xavier Rey-Robert commited on
Commit ·
6e0077f
1
Parent(s): 3b1639f
Clarify Terminal-Bench protocol settings
Browse files
README.md
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@@ -327,7 +327,26 @@ Scope note: single-pass unrestricted generation exposed one pathological runaway
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</div>
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<div style="background: #fffbeb; border: 1px solid #fde68a; border-radius: 8px; padding: 12px 14px; font-size: 13px; color: #78350f; line-height: 1.6;">
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<b>Protocol caveat:</b> This is a recovery-corrected operational agent benchmark, not an official leaderboard submission and not a single uninterrupted pass@1 run. The final score keeps one valid result per task across the main run and recovery runs. Infrastructure-affected attempts, including a vLLM <code>404 page not found</code> outage and a verifier bind-mount/SELinux no-output issue, were excluded and replaced only when a later run produced normal verifier artifacts (<code>reward.txt</code> and <code>test-stdout.txt</code>).
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</div>
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<div style="overflow-x: auto;">
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</thead>
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<tbody>
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<tr style="background: rgba(16, 185, 129, 0.06);"><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 700; color: #047857;">Qwopus3.6-27B-v2-GPTQ-Pro-v1</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); text-align: right; font-weight: 800; color: #047857;">44.94%</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">40 / 89, recovery-corrected local operational run</td></tr>
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<tr><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 600;">Qwen/Qwen3.6-27B</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); text-align: right; font-weight: 700;">59.3%</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">Published on the <a href="https://huggingface.co/Qwen/Qwen3.6-27B">Qwen model card</a>; Qwen protocol uses Harbor/Terminus-2, 3h timeout, 32 CPU/48 GB RAM, max_tokens 80K, 256K context, average of 5 runs.</td></tr>
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<tr><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 600;">Qwen/Qwen3.6-35B-A3B</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); text-align: right; font-weight: 700;">51.5%</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">Published on the <a href="https://huggingface.co/Qwen/Qwen3.6-27B">Qwen model card</a> with the same Terminal-Bench 2.0 protocol as the Qwen3.6-27B row.</td></tr>
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</tbody>
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</div>
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<div style="background: #fffbeb; border: 1px solid #fde68a; border-radius: 8px; padding: 12px 14px; font-size: 13px; color: #78350f; line-height: 1.6;">
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<b>Protocol caveat:</b> This is a recovery-corrected operational agent benchmark, not an official leaderboard submission and not a single uninterrupted pass@1 run. The final score keeps one valid result per task across the main run and recovery runs. Infrastructure-affected attempts, including a vLLM <code>404 page not found</code> outage and a verifier bind-mount/SELinux no-output issue, were excluded and replaced only when a later run produced normal verifier artifacts (<code>reward.txt</code> and <code>test-stdout.txt</code>). The local protocol also differs from Qwen's published 3h / 80K max-token / 256K-context Terminal-Bench 2.0 setup: our runs used a 131,072-token vLLM context, explicit thinking budgets, and mixed timeout/output settings across the initial and recovery attempts.
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</div>
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<div style="overflow-x: auto;">
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<table style="width: 100%; border-collapse: collapse; font-size: 13px; min-width: 720px;">
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<thead>
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<tr style="background: rgba(124, 58, 237, 0.05);">
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<th style="padding: 8px 10px; border-bottom: 2px solid #7c3aed; text-align: left; color: #7c3aed; font-weight: bold;">Protocol</th>
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<th style="padding: 8px 10px; border-bottom: 2px solid #7c3aed; text-align: right; color: #7c3aed; font-weight: bold;">Timeout</th>
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<th style="padding: 8px 10px; border-bottom: 2px solid #7c3aed; text-align: right; color: #7c3aed; font-weight: bold;">Max output</th>
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<th style="padding: 8px 10px; border-bottom: 2px solid #7c3aed; text-align: right; color: #7c3aed; font-weight: bold;">Thinking budget</th>
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<th style="padding: 8px 10px; border-bottom: 2px solid #7c3aed; text-align: right; color: #7c3aed; font-weight: bold;">Serving context</th>
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</tr>
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</thead>
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<tbody>
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<tr><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 600;">Qwen published TB2.0 protocol</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); text-align: right;">3h</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); text-align: right;">80K</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); text-align: right;">Not separately reported</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); text-align: right;">256K</td></tr>
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<tr><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 600;">Qwopus initial/resume attempts</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); text-align: right;">~30 min</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); text-align: right;">40K</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); text-align: right;">32K</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); text-align: right;">131,072</td></tr>
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<tr><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 600;">Qwopus timeout-recovery attempts</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); text-align: right;">90 min</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); text-align: right;">24K</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); text-align: right;">16K</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); text-align: right;">131,072</td></tr>
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</tbody>
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</table>
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<tr style="background: rgba(16, 185, 129, 0.06);"><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 700; color: #047857;">Qwopus3.6-27B-v2-GPTQ-Pro-v1</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); text-align: right; font-weight: 800; color: #047857;">44.94%</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">40 / 89, recovery-corrected local operational run</td></tr>
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<tr><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 600;">Qwen/Qwen3.6-27B</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); text-align: right; font-weight: 700;">59.3%</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">Published on the <a href="https://huggingface.co/Qwen/Qwen3.6-27B">Qwen model card</a>; Qwen protocol uses Harbor/Terminus-2, 3h timeout, 32 CPU/48 GB RAM, max_tokens 80K, 256K context, average of 5 runs, with no separate thinking-token budget reported.</td></tr>
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<tr><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 600;">Qwen/Qwen3.6-35B-A3B</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); text-align: right; font-weight: 700;">51.5%</td><td style="padding: 8px 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">Published on the <a href="https://huggingface.co/Qwen/Qwen3.6-27B">Qwen model card</a> with the same Terminal-Bench 2.0 protocol as the Qwen3.6-27B row.</td></tr>
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</table>
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benchmarks/terminal-bench-2.0/tb20_qwopus3p6_27b_gptqpro_v1_recovery_corrected_20260616.md
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- Missing tasks: 0
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- Selection policy: valid per-task recovery-corrected result; invalid outage/no-output attempts excluded.
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## Failure Categories
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- agent_timeout: 21
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- Missing tasks: 0
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- Selection policy: valid per-task recovery-corrected result; invalid outage/no-output attempts excluded.
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## Protocol Notes
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- This is a recovery-corrected local operational run, not an official Terminal-Bench leaderboard submission and not a single uninterrupted pass@1 run.
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- Qwen's published Terminal-Bench 2.0 protocol for `Qwen/Qwen3.6-27B` reports Harbor/Terminus-2, 3h timeout, 32 CPU / 48 GB RAM, `max_tokens=80K`, 256K context, and average of 5 runs; it does not report a separate thinking-token budget.
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- The Qwopus runs here used Terminus-2, 32 CPU / 48 GB RAM task sandboxes, a vLLM serving context of 131,072 tokens, and `model_info.max_input_tokens=90000`.
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- Initial/resume attempts used approximately the default 30 minute task timeout, `max_tokens/max_output_tokens=40000`, and `thinking_token_budget=32768`.
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- Timeout-recovery attempts used a 90 minute timeout, `max_tokens/max_output_tokens=24000`, and `thinking_token_budget=16000`.
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## Failure Categories
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- agent_timeout: 21
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