---
library_name: transformers
license: apache-2.0
license_link: LICENSE
pipeline_tag: image-text-to-text
base_model:
- Qwen/Qwen3.5-4B
tags:
  - verus
  - coding
  - multimodal
  - vision
  - 262k-context
language:
  - en
---

# Verus-4B

[![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)
[![Model Size](https://img.shields.io/badge/Parameters-4B-brightgreen)]()
[![Context](https://img.shields.io/badge/Context-262K%20tokens-orange)]()
[![HF Transformers](https://img.shields.io/badge/Transformers-%E2%89%A54.52-red)](https://github.com/huggingface/transformers)

> [!Note]
> This repository contains model weights and configuration files for **Verus-4B** in the Hugging Face Transformers format.
>
> Compatible with Hugging Face Transformers, vLLM, SGLang, and other major inference frameworks.
>
> Primary intended use cases are **code generation**, **code review**, **debugging**, and **general coding assistance**.

## Verus-4B Highlights

- **Coding-First**: Fine-tuned specifically on high-quality coding datasets — handles everything from simple scripts to complex multi-file implementations cleanly.
- **Image + Text Input**: Accepts both images and text, allowing you to describe UIs, diagrams, or screenshots alongside code questions.
- **262K Token Context Window**: Process entire codebases, long specifications, or lengthy conversations in a single pass.
- **Strong Instruction Following**: Stays focused, responds clearly, and redirects to the task at hand.
- **Efficient**: At 4B parameters in bfloat16, runs comfortably on a single consumer GPU with 8GB+ VRAM.

## Model Overview

| Property | Value |
|---|---|
| Parameters | ~4B |
| 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-4B"

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, a coding assistant made by 8F-ai. You help with coding tasks and keep responses focused and clean."
    },
    {
        "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=0.1, top_p=0.95)

output = tokenizer.decode(generated_ids[0][len(inputs.input_ids[0]):], skip_special_tokens=True)
print(output)
```

### Image + Text Input

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

MODEL_ID = "8F-ai/Verus-4B"

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
model.eval()

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "path/to/screenshot.png"},
            {"type": "text", "text": "Convert this UI screenshot into a React component using Tailwind CSS."}
        ]
    }
]

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=0.1, 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, ~4 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-4B")
model = AutoModelForCausalLM.from_pretrained(
    "8F-ai/Verus-4B",
    quantization_config=quantization_config,
    device_map="auto",
)
```

## Intended Use Cases

| Use Case | Example |
|---|---|
| **Code Generation** | Write functions, classes, scripts in any language |
| **Debugging** | Identify and fix bugs from error messages or code |
| **Code Review** | Suggest improvements, catch issues, explain code |
| **UI to Code** | Convert screenshots or diagrams into working code |
| **Long Context Codebase** | Reason over entire repos up to ~200K tokens |
| **General Q&A** | Answer programming questions clearly and concisely |

## Limitations

- **English-Primary**: Fine-tuning was conducted predominantly on English-language code and documentation.
- **Not for Math/Science**: Not optimized for mathematical proofs or scientific computation.

## Citation

```bibtex
@misc{verus4b2026,
  title        = {Verus-4B: A Coding-Focused Multimodal Language Model with 262K Context},
  author       = {8F-ai},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/8F-ai/Verus-4B}},
  note         = {Apache 2.0 License}
}
```

## License

Verus-4B is released under the **Apache License 2.0**. See [LICENSE](LICENSE) for full terms.

Derived from [Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B) (Apache 2.0).

---

<div align="center">
  <sub>Built with ❤️ by the 8F-ai Team</sub>
</div>