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

Libraries

How to use codemateai/cac-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="codemateai/cac-v0.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("codemateai/cac-v0.1")
model = AutoModelForCausalLM.from_pretrained("codemateai/cac-v0.1")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use codemateai/cac-v0.1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/codemateai/cac-v0.1

SGLang

How to use codemateai/cac-v0.1 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 "codemateai/cac-v0.1" \
    --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": "codemateai/cac-v0.1",
		"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 "codemateai/cac-v0.1" \
        --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": "codemateai/cac-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use codemateai/cac-v0.1 with Docker Model Runner:
```
docker model run hf.co/codemateai/cac-v0.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

CAC-v0.1

CAC is a large language model with 6.7B parameters specifically finetuned for code completions. CAC, although trained for code autocompletion, can also be used for other code related tasks such as:

Generation
Summarization
Translation
Question Answering
Optimization
Debugging
Code Review and more.

This is the very first version of CAC (0.1) and is still under development. For this version, we chose to go ahead with DeepSeek-Coder-6.7B as the base model.

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.
A sequence length of 8096 tokens was used during training.
Multilingual Support: CAC-v0.1 is proficient in multiple programming languages, including Python, C/C++, TypeScript, Java, and more.

Load the model with Transformers:

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
...

You can also use the Mistral chat template for conversations:

<s>[INST] .... [/INST] ... </s>

Load the model:

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 ...

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: 3

Safetensors

Model size

7B params

Tensor type

F16

Model tree for codemateai/cac-v0.1

Base model

deepseek-ai/deepseek-coder-6.7b-base

Finetuned

(8)

this model

Quantizations

2 models