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Model: prithivMLmods/Wolf-Rayet-2B-Prime3
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2026-08-28 09:06:13 +08:00
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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)

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- endif %}
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0].role == 'system' %}
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
{%- for message in messages[::-1] %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endfor %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set content = message.content %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in message.content %}
{%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
{%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and reasoning_content) %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
{%- endif %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- if enable_thinking is defined and enable_thinking is false %}
{{- '<think>\n\n</think>\n\n' }}
{%- endif %}
{%- endif %}

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{
"architectures": [
"Qwen3ForCausalLM"
],
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"transformers_version": "4.53.0.dev0",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151936
}

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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

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