Wolf-Rayet-2B-Prime3/README.md

---
library_name: transformers
tags:
- text-generation-inference
- code
- reinforcement-learning
- math
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen3-1.7B
pipeline_tag: text-generation
---

![78.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/l1J-T76goSfuIfoKX_uL5.png)

# **Wolf-Rayet-2B-Prime3**

> **Wolf-Rayet-2B-Prime3** is a compact, coding-optimized language model built on the **Qwen3 1.7B architecture**, fine-tuned for high-accuracy **code generation**, **debugging**, and **technical reasoning**. With approximately **2 billion effective parameters**, it offers a strong balance between performance and deployability—ideal for developers, educators, and engineers operating in resource-constrained or latency-sensitive environments.

> \[!note]
> GGUF: [https://huggingface.co/prithivMLmods/Wolf-Rayet-2B-Prime3-GGUF](https://huggingface.co/prithivMLmods/Wolf-Rayet-2B-Prime3-GGUF)

---

## **Key Features**

1. **Qwen3 Architecture Core**
   Based on the modern and efficient **Qwen3 1.7B** transformer backbone, offering improved context handling and token efficiency for both single-turn and multi-turn programming tasks.

2. **Code-First Fine-Tuning**
   Trained extensively on diverse code datasets including Python, JavaScript, C++, and Bash, with auxiliary tuning on software documentation, APIs, and debugging dialogues.

3. **Multi-Step Technical Reasoning**
   Demonstrates the ability to deconstruct complex programming problems, explain logic, refactor code, and correct errors—particularly useful for students, engineers, and coding educators.

4. **Structured Output Proficiency**
   Supports accurate generation of structured formats like JSON, YAML, Markdown, and code blocks—ready to plug into developer tools, notebooks, and documentation pipelines.

5. **Compact Yet Capable**
   With a \~2B parameter scale, it delivers competitive performance without the high resource requirements of larger models, and is easily deployable on modern GPUs or high-end CPUs.

6. **Multilingual Coding Support**
   Capable of generating and understanding code in 10+ programming languages, with a focus on real-world use cases, automation scripts, and algorithmic solutions.

---

## **Quickstart with Transformers**

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Wolf-Rayet-2B-Prime3"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Write a Python function to check if a number is prime."

messages = [
    {"role": "system", "content": "You are a helpful coding assistant."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
```

---

## **Intended Use**

* Code generation, refactoring, and cross-language translation
* Programming education and tutoring
* Technical documentation and boilerplate generation
* Debugging assistance and bug-fix suggestions
* Lightweight integration into IDEs, developer tools, and offline environments

---

## **Limitations**

* Context length is shorter than that of larger models (>7B)
* May require prompt engineering for complex or deeply nested code
* Limited general natural language conversation capabilities
* Not intended for creative writing or non-technical tasks

---

## **References**

1. [Qwen3 (1.7B) Model Overview](https://huggingface.co/Qwen/Qwen1.5-1.8B)
2. [YaRN: Efficient Context Window Extension of Large Language Models](https://arxiv.org/pdf/2309.00071)