prithivMLmods
/

Wolf-Rayet-2B-Prime3

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 ---
 library_name: transformers
-tags: []
 ---
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 ---
 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)