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

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

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)
2. YaRN: Efficient Context Window Extension of Large Language Models