uaytug/uCoder-8b-base-mlx-4bit

uCoder-8b-base-mlx

This is an MLX format conversion of uaytug/uCoder-8b-base for efficient inference on Apple Silicon devices.

Available Quantizations

This repository contains multiple quantization options:

Folder	Bits	Description
4bit/	4-bit	Smallest size, fastest inference

Quick Start

Installation

pip install mlx-lm

Usage

from mlx_lm import load, generate

# Load 4-bit quantized model (fastest, smallest)
model, tokenizer = load("uaytug/uCoder-8b-base-mlx", adapter_path="4bit")

# Or load 8-bit for higher quality
# model, tokenizer = load("uaytug/uCoder-8b-base-mlx", adapter_path="8bit")

# Generate text
prompt = "def fibonacci(n):"
response = generate(model, tokenizer, prompt=prompt, max_tokens=256)
print(response)

Command Line

# Generate with 4-bit model
mlx_lm.generate --model uaytug/uCoder-8b-base-mlx --adapter-path 4bit --prompt "def hello_world():"

# Chat mode
mlx_lm.chat --model uaytug/uCoder-8b-base-mlx --adapter-path 4bit

Performance

MLX provides optimized inference on Apple Silicon (M1/M2/M3/M4) with:

• Unified memory architecture utilization
• Metal GPU acceleration
• Efficient memory management

Memory Requirements (Approximate)

Quantization	Memory Usage
4-bit	~4 GB

Model Details

• Base Model: uaytug/uCoder-8b-base
• Architecture: Qwen3
• Parameters: 8B
• Framework: MLX

Original Model Information

uCoder-8b-base

uCoder-8b-base is a coding-specialized 8B parameter model created by TIES-merging five high-quality distilled models based on Qwen3-8B. This merge is designed to combine advanced reasoning capabilities with state-of-the-art coding performance, making it an ideal base for further instruction tuning or direct code generation tasks.

🚀 Model Description

This model leverages the TIES (Trimming, Electing, and Signs) merging method to effectively combine the weights of multiple expert models without losing the specific competencies of each. By normalizing the weights and focusing on high-reasoning distillations from top-tier frontier models (GPT-5.x, Claude 4.5, etc.), uCoder-8b-base achieves a robust balance between logic and syntax accuracy.

Key Features

• High Reasoning: Inherits logic handling from Claude and GPT-based distills.
• Polyglot Coding: Proficient in Python, JavaScript, C++, Rust, and other major languages.
• Base Model: Built on the powerful Qwen3-8B architecture.
• Efficient: 8B size allows for local inference on consumer hardware (12GB+ VRAM recommended for FP16, less for quantized).

🧩 Merged Models

The following models were merged using equal weights to create uCoder-8b-base:

Model Name	Primary Contribution
Qwen3 8B GPT 5.2 High Reasoning Distill	Advanced logic & multi-step reasoning
Qwen3 8B Claude 4.5 Opus High Reasoning Distill	Safe code generation & detailed explanations
Qwen3 8B Gemini 3 Pro Preview Distill	Long-context handling & creative solutions
Qwen3 8B DeepSeek v3.2 Speciale Distill	Mathematical problem solving & optimization
Qwen3 8B GPT 5 Codex Distill	Syntax accuracy & API implementation

Limitations

• Base Model Nature: This is a base model (merge), not fully instruction-tuned for chat. While it can handle chat formats, it performs best when fine-tuned or given specific few-shot examples.
• Coding Focus: While capable of general reasoning, its domain expertise is heavily skewed towards programming and technical tasks.

License

This model is released under the Apache 2.0 license.