---
license: apache-2.0
language:
- en
base_model:
- prithivMLmods/Qwen3-4B-ft-bf16
pipeline_tag: text-generation
library_name: transformers
tags:
- RL
- text-generation-inference
- blitzar
- coder
- trl
- code
datasets:
- livecodebench/code_generation_lite
- PrimeIntellect/verifiable-coding-problems
- likaixin/TACO-verified
- open-r1/codeforces-cots
---
 
![C.png](/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F65bb837dbfb878f46c77de4c%2FmAaFPD5SwVwx7hoOLWvO2.png)

# **Blitzar-Coder-4B-F.1**

> **Blitzar-Coder-4B-F.1** is a high-efficiency, multi-language coding model fine-tuned on **Qwen3-4B** using **larger coding traces datasets** spanning **10+ programming languages** including Python, Java, C#, C++, C, Go, JavaScript, TypeScript, Rust, and more. This model delivers exceptional code generation, debugging, and reasoning capabilities—making it an ideal tool for developers seeking advanced programming assistance under constrained compute.

> \[!note]
> GGUF: [https://huggingface.co/prithivMLmods/Blitzar-Coder-4B-F.1-GGUF](https://huggingface.co/prithivMLmods/Blitzar-Coder-4B-F.1-GGUF)

---

## **Key Features**

1. **Multi-Language Code Mastery**
   Fine-tuned on **extensive coding traces datasets** covering **10+ programming languages** (Python, Java, C#, C++, C, Go, JavaScript, TypeScript, Rust, Swift, Kotlin, and more), enabling cross-language development and translation.

2. **Advanced Code Generation & Reasoning**
   Supports complex algorithm synthesis, code optimization, debugging workflows, and architectural design patterns across multiple paradigms—from systems programming to web development.

3. **Cross-Language Development Support**
   Seamlessly handles polyglot codebases, API integrations, and framework-specific implementations while maintaining language-specific best practices and idioms.

4. **Intelligent Code Analysis**
   Performs code reviews, identifies performance bottlenecks, suggests refactoring opportunities, and provides detailed explanations for complex programming concepts.

5. **Structured Output for Development**
   Generates clean code documentation, API specifications, configuration files, and technical documentation in various formats including **Markdown**, **JSON**, **YAML**, and inline comments.

6. **Optimized 4B Footprint for Developer Workflows**
   Balanced for performance and efficiency, deployable on **developer workstations**, **CI/CD pipelines**, and **edge development environments** without compromising code quality.

---

## **Quickstart with Transformers**

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Blitzar-Coder-4B-F.1"

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

prompt = "Create a REST API endpoint in Python using FastAPI that handles file uploads with validation and returns processing status."

messages = [
    {"role": "system", "content": "You are an expert programming assistant skilled in multiple languages and development practices."},
    {"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**

* Multi-language code generation and debugging assistance
* Cross-platform development and code translation between languages
* Code review, optimization, and refactoring suggestions
* Technical documentation and API specification generation
* Developer productivity tools and IDE integrations
* Educational coding tutorials and programming concept explanations

---

## **Limitations**

* Optimized for coding tasks—may underperform on general conversation
* Context limitations may affect analysis of very large codebases
* Focused on programming domains—creative writing capabilities are limited
* Best suited for technical development workflows rather than casual chat

---

## **References**

1. [Qwen2.5 Technical Report (2024)](https://arxiv.org/pdf/2412.15115)
2. [YaRN: Efficient Context Window Extension of Large Language Models](https://arxiv.org/pdf/2309.00071)