Instructions to use LoneStriker/DeepMagic-Coder-7b-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LoneStriker/DeepMagic-Coder-7b-GPTQ with Transformers:

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

pipe = pipeline("text-generation", model="LoneStriker/DeepMagic-Coder-7b-GPTQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LoneStriker/DeepMagic-Coder-7b-GPTQ")
model = AutoModelForCausalLM.from_pretrained("LoneStriker/DeepMagic-Coder-7b-GPTQ")
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 LoneStriker/DeepMagic-Coder-7b-GPTQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LoneStriker/DeepMagic-Coder-7b-GPTQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LoneStriker/DeepMagic-Coder-7b-GPTQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LoneStriker/DeepMagic-Coder-7b-GPTQ

SGLang

How to use LoneStriker/DeepMagic-Coder-7b-GPTQ 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/DeepMagic-Coder-7b-GPTQ" \
    --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": "LoneStriker/DeepMagic-Coder-7b-GPTQ",
		"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 "LoneStriker/DeepMagic-Coder-7b-GPTQ" \
        --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": "LoneStriker/DeepMagic-Coder-7b-GPTQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LoneStriker/DeepMagic-Coder-7b-GPTQ with Docker Model Runner:
```
docker model run hf.co/LoneStriker/DeepMagic-Coder-7b-GPTQ
```

DeepMagic-Coder-7b

Alternate version:

https://huggingface.co/rombodawg/DeepMagic-Coder-7b-Alt

This is an extremely successful merge of the deepseek-coder-6.7b-instruct and Magicoder-S-DS-6.7B models, bringing an uplift in overall coding performance without any compromise to the models integrity (at least with limited testing).

This is the first of my models to use the merge-kits task_arithmetic merging method. The method is detailed bellow, and its clearly very usefull for merging ai models that were fine-tuned from a common base:

Task Arithmetic:

Computes "task vectors" for each model by subtracting a base model. 
Merges the task vectors linearly and adds back the base. 
Works great for models that were fine tuned from a common ancestor. 
Also a super useful mental framework for several of the more involved 
merge methods.

The original models used in this merge can be found here:

The Merge was created using Mergekit and the paremeters can be found bellow:

models:
  - model: deepseek-ai_deepseek-coder-6.7b-instruct
    parameters:
      weight: 1
  - model: ise-uiuc_Magicoder-S-DS-6.7B
    parameters:
      weight: 1
merge_method: task_arithmetic
base_model: ise-uiuc_Magicoder-S-DS-6.7B
parameters:
  normalize: true
  int8_mask: true
dtype: float16

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