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
Clean model card for public release
Browse files
README.md
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---
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base_model: Jackrong/Qwopus3.6-27B-v2
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library_name: transformers
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tags:
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- qwen3.6
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- qwopus
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- vllm
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- int4
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- quantized
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license:
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---
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# Qwopus3.6-27B-v2 GPTQ-Pro v1
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This is
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The goal is simple: preserve as much of the original model's character and capability as possible while making it efficient enough for
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This is not a new fine-tune. It is a quantized derivative of the original Qwopus3.6-27B-v2 model.
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| FOEM beta | `0.2` |
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| Batch size | `1` |
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Preserved modules include vision, `lm_head`, embeddings,
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Post-save compatibility patch:
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## Intended serving setup
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This checkpoint is intended for
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Recommended vLLM options:
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## Reasoning / thinking mode
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This model preserves Qwen3-style reasoning behavior. The
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## MTP / speculative decoding status
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## RTX 3090 validation status
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This checkpoint was validated
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Observed
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| Metric | Observed value | Notes |
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|---|---:|---|
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| Requests observed | `15` |
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| vLLM request success count | `15/15` | No vLLM errors observed during the sample |
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| Average prompt size | `33,172` tokens | Real multi-turn
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| Average output size | `322` tokens | Real
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| Average time to first token | `5.70s` | Prometheus TTFT summary |
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| Average end-to-end request latency | `13.07s` | Includes prefill, decode, and serving overhead |
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| Average time per output token | `0.0230s/token` | vLLM TPOT summary |
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| Live 60s generation throughput | about `19.1 generated tok/s` | Aggregate over full window, including prefill and idle mix |
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| Live 60s prefix-cache hit ratio | `78.9%` | Delta over the observed window |
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These are practical
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## Compatibility notes
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- Experimental quantization.
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- MTP/speculative decoding is not supported by this published artifact because `mtp.*` tensors are missing.
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- Quality has not yet been benchmarked against the BF16 source model.
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- RTX 3090 metrics above are observed
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- Use at your own risk, especially for long-context or tool-calling workflows.
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## References
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- GPTQ-Pro tooling: [groxaxo/GPTQ-Pro](https://github.com/groxaxo/GPTQ-Pro)
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- Reference GPTQ-Pro recipe: [groxaxo/Qwen3.6-27B-GPTQ-Pro-4bit](https://huggingface.co/groxaxo/Qwen3.6-27B-GPTQ-Pro-4bit)
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##
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This repository is
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---
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base_model: Jackrong/Qwopus3.6-27B-v2
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- qwen3.6
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- qwopus
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- vllm
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- int4
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- quantized
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license: apache-2.0
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---
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# Qwopus3.6-27B-v2 GPTQ-Pro v1
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This is a GPTQ-Pro 4-bit quantization of `Jackrong/Qwopus3.6-27B-v2`, built to make this excellent Qwopus/Qwen3.6 model practical to run in vLLM with GPTQ-Marlin kernels and long-context inference.
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The goal is simple: preserve as much of the original model's character and capability as possible while making it efficient enough for single-GPU RTX 3090-class vLLM deployments.
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This is not a new fine-tune. It is a quantized derivative of the original Qwopus3.6-27B-v2 model.
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| FOEM beta | `0.2` |
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| Batch size | `1` |
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Preserved modules include vision, `lm_head`, embeddings, and norms.
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Validation showed that this artifact preserves MTP-related configuration metadata, but does **not** include actual `mtp.*` tensors in `model.safetensors.index.json`, so this release should be treated as non-MTP for vLLM speculative decoding.
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Post-save compatibility patch:
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## Intended serving setup
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This checkpoint is intended for text-only vLLM serving on RTX 3090-class hardware.
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Recommended vLLM options:
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## Reasoning / thinking mode
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This model preserves Qwen3-style reasoning behavior. The validation workload below was run with thinking enabled.
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## MTP / speculative decoding status
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## RTX 3090 validation status
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This checkpoint was validated on an RTX 3090 24GB with vLLM, `max_model_len=131072`, `kv_cache_dtype=fp8_e5m2`, prefix caching enabled, and thinking enabled.
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Observed vLLM multi-turn agent workload metrics:
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| Metric | Observed value | Notes |
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|---|---:|---|
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| Requests observed | `15` | Multi-turn agent session calls |
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| vLLM request success count | `15/15` | No vLLM errors observed during the sample |
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| Average prompt size | `33,172` tokens | Real multi-turn workload |
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| Average output size | `322` tokens | Real generated responses |
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| Average time to first token | `5.70s` | Prometheus TTFT summary |
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| Average end-to-end request latency | `13.07s` | Includes prefill, decode, and serving overhead |
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| Average time per output token | `0.0230s/token` | vLLM TPOT summary |
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| Live 60s generation throughput | about `19.1 generated tok/s` | Aggregate over full window, including prefill and idle mix |
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| Live 60s prefix-cache hit ratio | `78.9%` | Delta over the observed window |
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These are practical multi-turn serving metrics, not a synthetic benchmark. They are useful for RTX 3090-class long-context serving expectations, especially multi-turn usage with prefix caching.
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## Compatibility notes
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- Experimental quantization.
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- MTP/speculative decoding is not supported by this published artifact because `mtp.*` tensors are missing.
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- Quality has not yet been benchmarked against the BF16 source model.
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- RTX 3090 metrics above are observed workload numbers, not a controlled benchmark suite.
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- Use at your own risk, especially for long-context or tool-calling workflows.
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## References
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- GPTQ-Pro tooling: [groxaxo/GPTQ-Pro](https://github.com/groxaxo/GPTQ-Pro)
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- Reference GPTQ-Pro recipe: [groxaxo/Qwen3.6-27B-GPTQ-Pro-4bit](https://huggingface.co/groxaxo/Qwen3.6-27B-GPTQ-Pro-4bit)
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## Individual project notice
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This repository is an individual research project. It is not affiliated with, sponsored by, or endorsed by any employer or organization.
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