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

Libraries

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

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

pipe = pipeline("text-generation", model="rombodawg/DeepMagic-Coder-7b-Alt")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rombodawg/DeepMagic-Coder-7b-Alt 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-Alt"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rombodawg/DeepMagic-Coder-7b-Alt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use rombodawg/DeepMagic-Coder-7b-Alt 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-Alt" \
    --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": "rombodawg/DeepMagic-Coder-7b-Alt",
		"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 "rombodawg/DeepMagic-Coder-7b-Alt" \
        --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": "rombodawg/DeepMagic-Coder-7b-Alt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rombodawg/DeepMagic-Coder-7b-Alt with Docker Model Runner:
```
docker model run hf.co/rombodawg/DeepMagic-Coder-7b-Alt
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

(Note: From short testing, this Alt version generated much better code)

Alternate version of DeepMagic-Coder-7b which can be found bellow.

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

This version uses a diffrent config setup, with the actual base model of the two merges as the "base_model". Test both for yourself and see which is better at coding. Benchmarks coming soon.

Config 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: deepseek-ai_deepseek-coder-6.7b-base
parameters:
  normalize: true
  int8_mask: true
dtype: float16

Downloads last month: 9

Safetensors

Model size

7B params

Tensor type

F16

Model tree for rombodawg/DeepMagic-Coder-7b-Alt

Quantizations

3 models