Instructions to use LoneStriker/CodeMate-v0.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LoneStriker/CodeMate-v0.1-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="LoneStriker/CodeMate-v0.1-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("LoneStriker/CodeMate-v0.1-GGUF", dtype="auto")

llama-cpp-python

How to use LoneStriker/CodeMate-v0.1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="LoneStriker/CodeMate-v0.1-GGUF",
	filename="CodeMate-v0.1-Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use LoneStriker/CodeMate-v0.1-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LoneStriker/CodeMate-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf LoneStriker/CodeMate-v0.1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LoneStriker/CodeMate-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf LoneStriker/CodeMate-v0.1-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 LoneStriker/CodeMate-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf LoneStriker/CodeMate-v0.1-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 LoneStriker/CodeMate-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LoneStriker/CodeMate-v0.1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/LoneStriker/CodeMate-v0.1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use LoneStriker/CodeMate-v0.1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LoneStriker/CodeMate-v0.1-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LoneStriker/CodeMate-v0.1-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LoneStriker/CodeMate-v0.1-GGUF:Q4_K_M

SGLang

How to use LoneStriker/CodeMate-v0.1-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 "LoneStriker/CodeMate-v0.1-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LoneStriker/CodeMate-v0.1-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "LoneStriker/CodeMate-v0.1-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LoneStriker/CodeMate-v0.1-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use LoneStriker/CodeMate-v0.1-GGUF with Ollama:
```
ollama run hf.co/LoneStriker/CodeMate-v0.1-GGUF:Q4_K_M
```

Unsloth Studio

How to use LoneStriker/CodeMate-v0.1-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 LoneStriker/CodeMate-v0.1-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 LoneStriker/CodeMate-v0.1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for LoneStriker/CodeMate-v0.1-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use LoneStriker/CodeMate-v0.1-GGUF with Docker Model Runner:
```
docker model run hf.co/LoneStriker/CodeMate-v0.1-GGUF:Q4_K_M
```

Lemonade

How to use LoneStriker/CodeMate-v0.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LoneStriker/CodeMate-v0.1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.CodeMate-v0.1-GGUF-Q4_K_M

List all available models

lemonade list

CodeMate-v0.1

CodeMate-v0.1 is an intelligent programming assistant developed by CodeMate. This model aims to assist users in generating high-quality code solutions for programming problems. Please note that this model is currently in version 0.1.

Model Details

Training Data: Exclusively fine-tuned on a proprietary dataset of 1.8 billion tokens of high-quality programming problems and solutions.
The dataset was generated manually and is internal to CodeMate.
Training Techniques: The model was fine-tuned using Flash Attention 2, trained over 15 hours on 40 A100-80GB GPUs.
A sequence length of 8096 tokens was used during training.
Multilingual Support: CodeMate-v0.1 is proficient in multiple programming languages, including Python, C/C++, TypeScript, Java, and more.

How to Get Started with the Model

Make sure to install Transformers from the main git branch:

pip install git+https://github.com/huggingface/transformers.git

How to Prompt the Model

This model accepts prompts in the Alpaca/Vicuna instruction format. For example:

### System Prompt
You are an intelligent programming assistant.

### User Message
Implement a linked list in C++

### Assistant
...

Load the Model:

To load the model, utilize the following Python script:

from transformers import AutoTokenizer, AutoModelForCausalLM

# Initialize the model
model_path = "codemateai/CodeMate-v0.1"
model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_path)

# ... generate response ...

Bias, Risks, and Limitations

This model has undergone very limited testing. CodeMate recommends additional safety testing before any real-world deployments.

For more information and updates, visit the CodeMate website.

Downloads last month: 57

GGUF

Model size

34B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit