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

Libraries

How to use SebastianBodza/DeepMagiCoder-6.7B-DS-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-DS-Base-AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

vLLM

How to use SebastianBodza/DeepMagiCoder-6.7B-DS-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-DS-Base-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SebastianBodza/DeepMagiCoder-6.7B-DS-Base-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use SebastianBodza/DeepMagiCoder-6.7B-DS-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-DS-Base-AWQ" \
    --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": "SebastianBodza/DeepMagiCoder-6.7B-DS-Base-AWQ",
		"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 "SebastianBodza/DeepMagiCoder-6.7B-DS-Base-AWQ" \
        --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": "SebastianBodza/DeepMagiCoder-6.7B-DS-Base-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

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Check out the documentation for more information.

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

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

You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer.
### Instruction:
{prompt}
### Response:
{response}

Humaneval: {'pass@1': 0.7195121951219512}

Downloads last month: 5

Safetensors

Model size

7B params

Tensor type

I32

F16