Instructions to use LoneStriker/DeepMagic-Coder-7b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use LoneStriker/DeepMagic-Coder-7b-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf LoneStriker/DeepMagic-Coder-7b-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf LoneStriker/DeepMagic-Coder-7b-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf LoneStriker/DeepMagic-Coder-7b-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf LoneStriker/DeepMagic-Coder-7b-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf LoneStriker/DeepMagic-Coder-7b-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf LoneStriker/DeepMagic-Coder-7b-GGUF:Q4_K_M
Use Docker
docker model run hf.co/LoneStriker/DeepMagic-Coder-7b-GGUF:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use LoneStriker/DeepMagic-Coder-7b-GGUF with Ollama:
ollama run hf.co/LoneStriker/DeepMagic-Coder-7b-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use LoneStriker/DeepMagic-Coder-7b-GGUF with Docker Model Runner:
docker model run hf.co/LoneStriker/DeepMagic-Coder-7b-GGUF:Q4_K_M
- Lemonade
How to use LoneStriker/DeepMagic-Coder-7b-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull LoneStriker/DeepMagic-Coder-7b-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.DeepMagic-Coder-7b-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
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Download README.md from LoneStriker/DeepMagic-Coder-7b-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 1.67 kB
-
https://huggingface.co/LoneStriker/DeepMagic-Coder-7b-GGUF/resolve/main/README.md
- Command line
-
hf download hf://LoneStriker/DeepMagic-Coder-7b-GGUF/README.md
-
curl -L -o README.md https://huggingface.co/LoneStriker/DeepMagic-Coder-7b-GGUF/resolve/main/README.md
1.67 kB
| license: other | |
| license_name: deepseek | |
| license_link: https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL | |
| 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: | |
| - https://huggingface.co/ise-uiuc/Magicoder-S-DS-6.7B | |
| - https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct | |
| The Merge was created using Mergekit and the paremeters can be found bellow: | |
| ```yaml | |
| 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 | |
| ``` |