Instructions to use SebastianBodza/DeepMagiCoder-6.7B-Magicoder-Base-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SebastianBodza/DeepMagiCoder-6.7B-Magicoder-Base-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="SebastianBodza/DeepMagiCoder-6.7B-Magicoder-Base-AWQ")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SebastianBodza/DeepMagiCoder-6.7B-Magicoder-Base-AWQ")
model = AutoModelForCausalLM.from_pretrained("SebastianBodza/DeepMagiCoder-6.7B-Magicoder-Base-AWQ")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SebastianBodza/DeepMagiCoder-6.7B-Magicoder-Base-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SebastianBodza/DeepMagiCoder-6.7B-Magicoder-Base-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SebastianBodza/DeepMagiCoder-6.7B-Magicoder-Base-AWQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SebastianBodza/DeepMagiCoder-6.7B-Magicoder-Base-AWQ

SGLang

How to use SebastianBodza/DeepMagiCoder-6.7B-Magicoder-Base-AWQ 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 "SebastianBodza/DeepMagiCoder-6.7B-Magicoder-Base-AWQ" \
    --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": "SebastianBodza/DeepMagiCoder-6.7B-Magicoder-Base-AWQ",
		"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 "SebastianBodza/DeepMagiCoder-6.7B-Magicoder-Base-AWQ" \
        --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": "SebastianBodza/DeepMagiCoder-6.7B-Magicoder-Base-AWQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SebastianBodza/DeepMagiCoder-6.7B-Magicoder-Base-AWQ with Docker Model Runner:
```
docker model run hf.co/SebastianBodza/DeepMagiCoder-6.7B-Magicoder-Base-AWQ
```

DeepMagiCoder-6.7B-Magicoder-Base-AWQ / README.md

SebastianBodza

Create README.md

947db39 verified over 2 years ago

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Quantized version of: https://huggingface.co/SebastianBodza/DeepMagiCoder-6.7B-Magicoder-Base

Used the Magicoder Template and the Evol-Instruct Code dataset for quantization:

You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.

@@ Instruction
{prompt}

@@ Response
{response}