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
| library_name: transformers | |
| license: apache-2.0 | |
| license_link: LICENSE | |
| pipeline_tag: image-text-to-text | |
| base_model: | |
| - Qwen/Qwen3.5-2B | |
| tags: | |
| - verus | |
| - coding | |
| - reasoning | |
| - r1 | |
| language: | |
| - en | |
| # Verus-r1 | |
| [](LICENSE) | |
| []() | |
| []() | |
| [](https://github.com/huggingface/transformers) | |
| > [!Note] | |
| > This repository contains model weights and configuration files for **Verus-r1** in the Hugging Face Transformers format. | |
| > | |
| > Compatible with Hugging Face Transformers, vLLM, SGLang, and other major inference frameworks. | |
| > | |
| > Built for **coding**, **reasoning**, **debugging**, and concise general assistance. | |
| ## Verus-r1 Highlights | |
| - **Coding-Focused**: Writes, fixes, explains, and reviews code. | |
| - **Reasoning-Oriented**: Works through multi-step problems clearly. | |
| - **Long Context**: Can handle large prompts, files, and long conversations. | |
| - **Instruction Following**: Responds in the format and style requested. | |
| - **Efficient**: A compact 2B model for local or hosted inference. | |
| ## Model Overview | |
| | Property | Value | | |
| |---|---| | |
| | Parameters | ~2B | | |
| | Context Length | **262,144 tokens** | | |
| | Architecture | Qwen3.5 | | |
| | Chat Format | ChatML (`<\|im_start\|>` / `<\|im_end\|>`) | | |
| | Dtype | bfloat16 | | |
| | License | Apache 2.0 | | |
| ## Quickstart | |
| ### Installation | |
| ```bash | |
| pip install "transformers>=4.52.0" accelerate torch | |
| ``` | |
| ### Code Generation | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import torch | |
| MODEL_ID = "8F-ai/Verus-r1" | |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| MODEL_ID, | |
| torch_dtype=torch.bfloat16, | |
| device_map="auto", | |
| ) | |
| model.eval() | |
| messages = [ | |
| { | |
| "role": "system", | |
| "content": "You are Verus-r1, a reasoning coding assistant made by 8F-ai. You think through problems carefully before responding." | |
| }, | |
| { | |
| "role": "user", | |
| "content": "Write a Python async context manager that manages a PostgreSQL connection pool using asyncpg." | |
| } | |
| ] | |
| text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) | |
| inputs = tokenizer(text, return_tensors="pt").to(model.device) | |
| with torch.inference_mode(): | |
| generated_ids = model.generate(**inputs, max_new_tokens=2048, temperature=0.6, top_p=0.95) | |
| output = tokenizer.decode(generated_ids[0][len(inputs.input_ids[0]):], skip_special_tokens=True) | |
| print(output) | |
| ``` | |
| ### Quantized Inference (4-bit NF4, ~2 GB VRAM) | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig | |
| import torch | |
| quantization_config = BitsAndBytesConfig( | |
| load_in_4bit=True, | |
| bnb_4bit_compute_dtype=torch.bfloat16, | |
| bnb_4bit_use_double_quant=True, | |
| bnb_4bit_quant_type="nf4", | |
| ) | |
| tokenizer = AutoTokenizer.from_pretrained("8F-ai/Verus-r1") | |
| model = AutoModelForCausalLM.from_pretrained( | |
| "8F-ai/Verus-r1", | |
| quantization_config=quantization_config, | |
| device_map="auto", | |
| ) | |
| ``` | |
| ## Intended Use Cases | |
| | Use Case | Example | | |
| |---|---| | |
| | **Code Generation** | Write functions, classes, and scripts | | |
| | **Debugging** | Fix bugs from code or error messages | | |
| | **Code Review** | Explain code and suggest improvements | | |
| | **Reasoning** | Break down multi-step problems | | |
| | **Long Context** | Work with long prompts and files | | |
| | **General Q&A** | Answer clearly and concisely | | |
| ## Limitations | |
| - **English-Primary**: Fine-tuning was conducted predominantly on English-language code and documentation. | |
| ## Citation | |
| ```bibtex | |
| @misc{verusr12026, | |
| title = {Verus-r1: A Reasoning-Focused Coding Language Model with 262K Context}, | |
| author = {8F-ai}, | |
| year = {2026}, | |
| howpublished = {\url{https://huggingface.co/8F-ai/Verus-r1}}, | |
| note = {Apache 2.0 License} | |
| } | |
| ``` | |
| ## License | |
| Verus-r1 is released under the **Apache License 2.0**. See [LICENSE](LICENSE) for full terms. | |
| Derived from [Qwen/Qwen3.5-2B](https://huggingface.co/Qwen/Qwen3.5-2B) (Apache 2.0). | |
| --- | |
| <div align="center"> | |
| <sub>Built by the 8F-ai Team</sub> | |
| </div> |