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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

rombodawg commited on Feb 6, 2024

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@@ -11,7 +11,10 @@ This is an extremely successful merge of the deepseek-coder-6.7b-instruct and Ma
 This is the first of my models to use the merge-kits *task_arithmetic* merging method. The method is detailed bellow, and I clearly very usefull for merging ai models that were fine-tuned from a common base:
 ```
-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 Merge was created using Mergekit and the paremeters can be found bellow:

 This is the first of my models to use the merge-kits *task_arithmetic* merging method. The method is detailed bellow, and I clearly very usefull for merging ai models that were fine-tuned from a common base:
 ```
+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 Merge was created using Mergekit and the paremeters can be found bellow: