Instructions to use micymike/codemate-qwen3.5-2b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use micymike/codemate-qwen3.5-2b-gguf with Transformers:

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

pipe = pipeline("text-generation", model="micymike/codemate-qwen3.5-2b-gguf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("micymike/codemate-qwen3.5-2b-gguf", device_map="auto")

llama-cpp-python

How to use micymike/codemate-qwen3.5-2b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="micymike/codemate-qwen3.5-2b-gguf",
	filename="codemate-qwen3.5-2b-BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use micymike/codemate-qwen3.5-2b-gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf micymike/codemate-qwen3.5-2b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf micymike/codemate-qwen3.5-2b-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf micymike/codemate-qwen3.5-2b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf micymike/codemate-qwen3.5-2b-gguf:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf micymike/codemate-qwen3.5-2b-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf micymike/codemate-qwen3.5-2b-gguf:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf micymike/codemate-qwen3.5-2b-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf micymike/codemate-qwen3.5-2b-gguf:Q4_K_M

Use Docker

docker model run hf.co/micymike/codemate-qwen3.5-2b-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use micymike/codemate-qwen3.5-2b-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "micymike/codemate-qwen3.5-2b-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "micymike/codemate-qwen3.5-2b-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/micymike/codemate-qwen3.5-2b-gguf:Q4_K_M

SGLang

How to use micymike/codemate-qwen3.5-2b-gguf 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 "micymike/codemate-qwen3.5-2b-gguf" \
    --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": "micymike/codemate-qwen3.5-2b-gguf",
		"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 "micymike/codemate-qwen3.5-2b-gguf" \
        --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": "micymike/codemate-qwen3.5-2b-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use micymike/codemate-qwen3.5-2b-gguf with Ollama:
```
ollama run hf.co/micymike/codemate-qwen3.5-2b-gguf:Q4_K_M
```

Unsloth Studio

How to use micymike/codemate-qwen3.5-2b-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for micymike/codemate-qwen3.5-2b-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for micymike/codemate-qwen3.5-2b-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for micymike/codemate-qwen3.5-2b-gguf to start chatting

How to use micymike/codemate-qwen3.5-2b-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf micymike/codemate-qwen3.5-2b-gguf:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "micymike/codemate-qwen3.5-2b-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use micymike/codemate-qwen3.5-2b-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf micymike/codemate-qwen3.5-2b-gguf:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default micymike/codemate-qwen3.5-2b-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use micymike/codemate-qwen3.5-2b-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf micymike/codemate-qwen3.5-2b-gguf:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "micymike/codemate-qwen3.5-2b-gguf:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use micymike/codemate-qwen3.5-2b-gguf with Docker Model Runner:
```
docker model run hf.co/micymike/codemate-qwen3.5-2b-gguf:Q4_K_M
```

Lemonade

How to use micymike/codemate-qwen3.5-2b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull micymike/codemate-qwen3.5-2b-gguf:Q4_K_M

Run and chat with the model

lemonade run user.codemate-qwen3.5-2b-gguf-Q4_K_M

List all available models

lemonade list

🚀 CodeMate-Qwen3.5-2B

A lightweight coding assistant specialized for debugging, code generation, code explanation, and software engineering workflows.

Overview

CodeMate-Qwen3.5-2B is a LoRA fine-tuned version of Qwen3.5-2B focused on helping developers write, understand and debug code.

Unlike general-purpose assistants, CodeMate has been optimized for practical programming tasks including:

Python debugging
JavaScript & TypeScript
React
Next.js
API development
Backend engineering
Error diagnosis
Code explanation
Refactoring
Best practices

The objective of this project is to create a fast and efficient coding model that runs comfortably on consumer hardware while maintaining strong software engineering capabilities.

Base Model

Qwen/Qwen3.5-2B

Highlights of the base model include:

2 Billion Parameters
Native 262K context length
Apache 2.0 License
Hybrid Delta Attention Architecture
Strong multilingual support
Optimized for instruction following and coding tasks :contentReference[oaicite:0]{index=0}

Fine-tuning Objectives

The model was optimized to improve performance on:

Bug fixing
Stack trace interpretation
Code reasoning
Production debugging
Code review
Refactoring
Software engineering conversations
Practical programming assistance

Training

Base Model:

Qwen/Qwen3.5-2B

Method:

PEFT
LoRA

Frameworks:

Transformers
PEFT
Accelerate
PyTorch

Output:

Merged HuggingFace model

GGUF quantizations generated using:

llama.cpp

Quantizations

File	Recommended
BF16	Research / Highest Quality
Q8_0	⭐⭐⭐⭐⭐
Q6_K	⭐⭐⭐⭐☆
Q5_K_M	⭐⭐⭐⭐☆
Q4_K_M	⭐⭐⭐⭐⭐ Recommended
Q3_K_M	Low-memory
Q2_K	Smallest

Example

def reverse(text):
    return text[::-1]

Prompt:

Optimize this function and explain its time complexity.

Intended Use

✅ Code Generation

✅ Debugging

✅ Learning Programming

✅ Code Review

✅ Refactoring

✅ API Development

✅ Backend Development

Evaluation

Formal benchmark evaluations are currently in progress.

Planned evaluations include:

HumanEval
HumanEval+
MBPP
MultiPL-E
LiveCodeBench
SWE-Bench Lite
Aider Bench

Benchmark results will be published in future releases.

Roadmap

Improved reasoning
Better long-context coding
Larger instruction dataset
Agentic coding support
Better tool use
Higher benchmark performance
Production evaluation suite

Acknowledgements

Alibaba Qwen Team
Hugging Face
llama.cpp
PEFT
Transformers

License

Apache 2.0 (inherits from the base model license.)

Made with ❤️ by Michael Moses (Micymike)

Downloads last month: 627

GGUF

Model size

2B params

Architecture

qwen35

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for micymike/codemate-qwen3.5-2b-gguf

Base model

Qwen/Qwen3.5-2B-Base

Finetuned

Qwen/Qwen3.5-2B

Adapter

(111)

this model