4.4 KiB
license, language, base_model, pipeline_tag, library_name, tags, datasets
| license | language | base_model | pipeline_tag | library_name | tags | datasets | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| apache-2.0 |
|
|
text-generation | transformers |
|
|
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
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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.
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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.
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Cross-Language Development Support Seamlessly handles polyglot codebases, API integrations, and framework-specific implementations while maintaining language-specific best practices and idioms.
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Intelligent Code Analysis Performs code reviews, identifies performance bottlenecks, suggests refactoring opportunities, and provides detailed explanations for complex programming concepts.
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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.
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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