Instructions to use DevShubham/Codellama-7B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DevShubham/Codellama-7B-Instruct-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DevShubham/Codellama-7B-Instruct-GGUF")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DevShubham/Codellama-7B-Instruct-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use DevShubham/Codellama-7B-Instruct-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 DevShubham/Codellama-7B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf DevShubham/Codellama-7B-Instruct-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 DevShubham/Codellama-7B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf DevShubham/Codellama-7B-Instruct-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf DevShubham/Codellama-7B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf DevShubham/Codellama-7B-Instruct-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf DevShubham/Codellama-7B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf DevShubham/Codellama-7B-Instruct-GGUF:Q4_K_M
Use Docker
docker model run hf.co/DevShubham/Codellama-7B-Instruct-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use DevShubham/Codellama-7B-Instruct-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DevShubham/Codellama-7B-Instruct-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DevShubham/Codellama-7B-Instruct-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/DevShubham/Codellama-7B-Instruct-GGUF:Q4_K_M
- SGLang
How to use DevShubham/Codellama-7B-Instruct-GGUF 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 "DevShubham/Codellama-7B-Instruct-GGUF" \ --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": "DevShubham/Codellama-7B-Instruct-GGUF", "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 "DevShubham/Codellama-7B-Instruct-GGUF" \ --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": "DevShubham/Codellama-7B-Instruct-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use DevShubham/Codellama-7B-Instruct-GGUF with Ollama:
ollama run hf.co/DevShubham/Codellama-7B-Instruct-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use DevShubham/Codellama-7B-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/DevShubham/Codellama-7B-Instruct-GGUF:Q4_K_M
- Lemonade
How to use DevShubham/Codellama-7B-Instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull DevShubham/Codellama-7B-Instruct-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Codellama-7B-Instruct-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download codellama-7b-instruct.Q6_K-013.gguf from DevShubham/Codellama-7B-Instruct-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 5.53 GB
-
https://huggingface.co/DevShubham/Codellama-7B-Instruct-GGUF/resolve/main/codellama-7b-instruct.Q6_K-013.gguf
- Command line
-
hf download hf://DevShubham/Codellama-7B-Instruct-GGUF/codellama-7b-instruct.Q6_K-013.gguf
-
curl -L -o codellama-7b-instruct.Q6_K-013.gguf https://huggingface.co/DevShubham/Codellama-7B-Instruct-GGUF/resolve/main/codellama-7b-instruct.Q6_K-013.gguf
5.53 GB
- Xet hash:
- 826a5d7b7ccf21f364091e0fc6ff3cd722263e8d4fd4f9fe7de0a4dae2cbe154
- Size of remote file:
- 5.53 GB
- SHA256:
- 2f516cd9c16181832ffceaf94b13e8600d88c9bc8d7f75717d25d8c9cf9aa973
·
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