File size: 4,535 Bytes
a97c8bc 55914d9 18c191a 6c1a11e 833462a 3de8590 32a36a0 3de8590 a955d84 3de8590 a955d84 3de8590 a955d84 3de8590 a955d84 3de8590 a955d84 3de8590 a955d84 3de8590 a955d84 3de8590 a955d84 3de8590 a955d84 3de8590 a955d84 3de8590 a955d84 3de8590 a955d84 3de8590 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 |
---
license: apache-2.0
language:
- en
base_model:
- prithivMLmods/Qwen3-4B-ft-bf16
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- code
- RL
- moe
datasets:
- livecodebench/code_generation_lite
- PrimeIntellect/verifiable-coding-problems
- likaixin/TACO-verified
- open-r1/codeforces-cots
---
# **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) |