Instructions to use uaytug/uCoder-8b-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use uaytug/uCoder-8b-base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="uaytug/uCoder-8b-base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("uaytug/uCoder-8b-base")
model = AutoModelForCausalLM.from_pretrained("uaytug/uCoder-8b-base", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use uaytug/uCoder-8b-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "uaytug/uCoder-8b-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "uaytug/uCoder-8b-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/uaytug/uCoder-8b-base

SGLang

How to use uaytug/uCoder-8b-base with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "uaytug/uCoder-8b-base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "uaytug/uCoder-8b-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "uaytug/uCoder-8b-base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "uaytug/uCoder-8b-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use uaytug/uCoder-8b-base with Docker Model Runner:
```
docker model run hf.co/uaytug/uCoder-8b-base
```

uCoder-8b-base / README.md

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	---
	language:
	- en
	- code
	license: apache-2.0
	tags:
	- merge
	- mergekit
	- ties
	- text-generation
	- programming
	library_name: transformers
	pipeline_tag: text-generation
	datasets:
	- uaytug/ucoder-reasoning-ds
	---

	# uCoder-8b-base

	![Model Architecture](https://img.shields.io/badge/Model-Qwen3--8B-blue) ![Task](https://img.shields.io/badge/Task-Coding-green) ![License](https://img.shields.io/badge/License-Apache_2.0-red) ![Method](https://img.shields.io/badge/Method-TIES_Merge-orange)

	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.