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
```

DeepMagic-Coder-7b / tokenizer_config.json

rombodawg

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	{
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	"add_eos_token": false,
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	"legacy": true,
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	"sp_model_kwargs": {},
	"tokenizer_class": "LlamaTokenizer",
	"unk_token": null,
	"use_default_system_prompt": false,
	"chat_template": "{%- set ns = namespace(found=false) -%}\n{%- for message in messages -%}\n {%- if message['role'] == 'system' -%}\n {%- set ns.found = true -%}\n {%- endif -%}\n{%- endfor -%}\n{{bos_token}}{%- if not ns.found -%}\n{%- endif %}\n{%- for message in messages %}\n {%- if message['role'] == 'system' %}\n{{ message['content'] + '\\n\\n' }}\n {%- else %}\n {%- if message['role'] == 'user' %}\n{{'@@ Instruction\\n' + message['content'] + '\\n\\n'}}\n {%- else %}\n{{'@@ Response\\n' + message['content'] + '\\n' + eos_token + '\\n'}}\n {%- endif %}\n {%- endif %}\n{%- endfor %}{% if add_generation_prompt %}{{ '@@ Response\n' }}{% endif %}"
	}