Image-Text-to-Text
Transformers
Safetensors
English
qwen3_5
verus
coding
reasoning
r1
conversational
Instructions to use 8F-ai/Verus-R1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 8F-ai/Verus-R1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="8F-ai/Verus-R1") 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, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("8F-ai/Verus-R1") model = AutoModelForImageTextToText.from_pretrained("8F-ai/Verus-R1") 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
- vLLM
How to use 8F-ai/Verus-R1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "8F-ai/Verus-R1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "8F-ai/Verus-R1", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/8F-ai/Verus-R1
- SGLang
How to use 8F-ai/Verus-R1 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 "8F-ai/Verus-R1" \ --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": "8F-ai/Verus-R1", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "8F-ai/Verus-R1" \ --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": "8F-ai/Verus-R1", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use 8F-ai/Verus-R1 with Docker Model Runner:
docker model run hf.co/8F-ai/Verus-R1
Update README.md
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README.md
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license: apache-2.0
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---
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library_name: transformers
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license: apache-2.0
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license_link: LICENSE
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pipeline_tag: text-generation
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base_model:
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- Qwen/Qwen3.5-2B
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tags:
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- verus
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- coding
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- reasoning
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- r1
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language:
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- en
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---
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# Verus-r1
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[](LICENSE)
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[]()
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[]()
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[](https://github.com/huggingface/transformers)
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> [!Note]
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> This repository contains model weights and configuration files for **Verus-r1** in the Hugging Face Transformers format.
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>
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> Compatible with Hugging Face Transformers, vLLM, SGLang, and other major inference frameworks.
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>
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> Built for **coding**, **reasoning**, **debugging**, and concise general assistance.
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## Verus-r1 Highlights
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- **Coding-Focused**: Writes, fixes, explains, and reviews code.
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- **Reasoning-Oriented**: Works through multi-step problems clearly.
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- **Long Context**: Can handle large prompts, files, and long conversations.
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- **Instruction Following**: Responds in the format and style requested.
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- **Efficient**: A compact 2B model for local or hosted inference.
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## Model Overview
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| Property | Value |
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|---|---|
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| Parameters | ~2B |
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| Context Length | **262,144 tokens** |
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| Architecture | Qwen3.5 |
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| Chat Format | ChatML (`<\|im_start\|>` / `<\|im_end\|>`) |
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| Dtype | bfloat16 |
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| License | Apache 2.0 |
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## Quickstart
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### Installation
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```bash
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pip install "transformers>=4.52.0" accelerate torch
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```
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### Code Generation
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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MODEL_ID = "8F-ai/Verus-r1"
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_ID,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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model.eval()
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messages = [
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{
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"role": "system",
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"content": "You are Verus-r1, a reasoning coding assistant made by 8F-ai. You think through problems carefully before responding."
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},
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{
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"role": "user",
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"content": "Write a Python async context manager that manages a PostgreSQL connection pool using asyncpg."
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}
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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with torch.inference_mode():
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generated_ids = model.generate(**inputs, max_new_tokens=2048, temperature=0.6, top_p=0.95)
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output = tokenizer.decode(generated_ids[0][len(inputs.input_ids[0]):], skip_special_tokens=True)
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print(output)
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```
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### Quantized Inference (4-bit NF4, ~2 GB VRAM)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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import torch
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quantization_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_compute_dtype=torch.bfloat16,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type="nf4",
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)
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tokenizer = AutoTokenizer.from_pretrained("8F-ai/Verus-r1")
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model = AutoModelForCausalLM.from_pretrained(
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"8F-ai/Verus-r1",
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quantization_config=quantization_config,
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device_map="auto",
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)
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```
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## Intended Use Cases
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| Use Case | Example |
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|---|---|
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| **Code Generation** | Write functions, classes, and scripts |
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| **Debugging** | Fix bugs from code or error messages |
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| **Code Review** | Explain code and suggest improvements |
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| **Reasoning** | Break down multi-step problems |
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| **Long Context** | Work with long prompts and files |
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| **General Q&A** | Answer clearly and concisely |
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## Limitations
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- **English-Primary**: Fine-tuning was conducted predominantly on English-language code and documentation.
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## Citation
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```bibtex
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@misc{verusr12026,
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title = {Verus-r1: A Reasoning-Focused Coding Language Model with 262K Context},
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author = {8F-ai},
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year = {2026},
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howpublished = {\url{https://huggingface.co/8F-ai/Verus-r1}},
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note = {Apache 2.0 License}
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}
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```
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## License
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Verus-r1 is released under the **Apache License 2.0**. See [LICENSE](LICENSE) for full terms.
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Derived from [Qwen/Qwen3.5-2B](https://huggingface.co/Qwen/Qwen3.5-2B) (Apache 2.0).
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---
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<div align="center">
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<sub>Built by the 8F-ai Team</sub>
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</div>
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