Blitzar-Coder-4B-F.1 Open Source Coding Model - Supports code generation, debugging, and inference in over 10 languages

Blitzar Coder 4B F.1 GGUF

Developed by prithivMLmods

Blitzar-Coder-4B-F.1 is an efficient multilingual coding model fine-tuned based on Qwen3-4B, supporting more than 10 programming languages and having excellent code generation, debugging, and reasoning capabilities.

Large Language Model

Transformers

Open Source License:Apache-2.0 #Multilingual code generation #Efficient quantitative reasoning #Programming assistance tools

Downloads 267

Release Time : 6/5/2025

Model Overview

This model is a multilingual coding model focusing on code generation and programming assistance, suitable for developers to use in scenarios with limited computing resources.

Model Features

Multilingual support

Supports code generation and debugging for more than 10 mainstream programming languages

Efficient reasoning

Optimized for environments with limited computing resources, providing efficient reasoning performance

Multiple quantization options

Provides multiple quantization versions from 2-bit to 32-bit to meet different hardware requirements

High code quality

Fine-tuned based on a large coding trajectory dataset, generating code of excellent quality

Model Capabilities

Code generation

Code debugging

Code reasoning

Multilingual programming assistance

Use Cases

Development assistance

Code auto-completion

Provides intelligent code completion suggestions in the IDE

Improves development efficiency and reduces coding errors

Code debugging assistance

Helps developers analyze and fix problems in the code

Shortens debugging time and improves code quality

Cross-language code conversion

Converts code from one programming language to another

Simplifies the development of multilingual projects

Education

Programming learning assistance

Provides sample code and explanations for programming learners

Accelerates the learning process and deepens understanding

🚀 Blitzar-Coder-4B-F.1-GGUF

Blitzar-Coder-4B-F.1 is a high - efficiency, multi - language coding model. It's fine - tuned on Qwen3 - 4B with larger coding traces datasets covering over 10 programming languages. It offers excellent code generation, debugging, and reasoning capabilities, making it a great tool for developers under compute constraints.

🚀 Quick Start

This section provides an overview of the Blitzar - Coder - 4B - F.1 - GGUF model and its related information.

✨ Features

Blitzar - Coder - 4B - F.1 is fine - tuned on Qwen3 - 4B using larger coding traces datasets across 10+ programming languages such as Python, Java, C#, C++, C, Go, JavaScript, TypeScript, Rust, etc. It has outstanding code generation, debugging, and reasoning capabilities, which is suitable for developers in need of advanced programming assistance under limited computing resources.

📚 Documentation

Model Files

Property	Details
Model Type	Blitzar - Coder - 4B - F.1 - GGUF
Base Model	prithivMLmods/Blitzar - Coder - 4B - F.1
Pipeline Tag	text - generation
Library Name	transformers
Tags	text - generation - inference, code, coder

Filename	Size	Format	Description
Blitzar - Coder - 4B - F.1.BF16.gguf	8.05 GB	BF16	Brain Float 16 - bit quantization
Blitzar - Coder - 4B - F.1.F16.gguf	8.05 GB	F16	Half precision (16 - bit) floating point
Blitzar - Coder - 4B - F.1.F32.gguf	16.1 GB	F32	Full precision (32 - bit) floating point
Blitzar - Coder - 4B - F.1.Q2_K.gguf	1.67 GB	Q2_K	2 - bit quantization with K - quant
Blitzar - Coder - 4B - F.1.Q3_K_L.gguf	2.24 GB	Q3_K_L	3 - bit quantization (Large) with K - quant
Blitzar - Coder - 4B - F.1.Q3_K_M.gguf	2.08 GB	Q3_K_M	3 - bit quantization (Medium) with K - quant
Blitzar - Coder - 4B - F.1.Q3_K_S.gguf	1.89 GB	Q3_K_S	3 - bit quantization (Small) with K - quant
Blitzar - Coder - 4B - F.1.Q4_K_M.gguf	2.5 GB	Q4_K_M	4 - bit quantization (Medium) with K - quant
Blitzar - Coder - 4B - F.1.Q4_K_S.gguf	2.38 GB	Q4_K_S	4 - bit quantization (Small) with K - quant
Blitzar - Coder - 4B - F.1.Q5_K_M.gguf	2.89 GB	Q5_K_M	5 - bit quantization (Medium) with K - quant
Blitzar - Coder - 4B - F.1.Q5_K_S.gguf	2.82 GB	Q5_K_S	5 - bit quantization (Small) with K - quant
Blitzar - Coder - 4B - F.1.Q6_K.gguf	3.31 GB	Q6_K	6 - bit quantization with K - quant
Blitzar - Coder - 4B - F.1.Q8_0.gguf	4.28 GB	Q8_0	8 - bit quantization

Recommended Usage

Q4_K_M or Q5_K_M: Offer the best balance of quality and performance for most users.
Q6_K or Q8_0: Provide higher quality but come with larger file sizes.
Q2_K or Q3_K_S: Enable the fastest inference but with lower quality.
F16 or BF16: Deliver high quality but require more VRAM.
F32: Provide the highest quality but demand significant VRAM.

Quants Usage

(sorted by size, not necessarily quality. IQ - quants are often preferable over similar sized non - IQ quants)

Here is a handy graph by ikawrakow comparing some lower - quality quant types (lower is better):

📄 License

This project is licensed under the Apache - 2.0 license.

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