Text Generation
Transformers
GGUF
code
granite
llama-cpp
gguf-my-repo
Eval Results (legacy)
conversational
Instructions to use pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use pankaj217/granite-8b-code-instruct-Q5_K_M-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 pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF:Q5_K_M # Run inference directly in the terminal: llama cli -hf pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF:Q5_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF:Q5_K_M # Run inference directly in the terminal: llama cli -hf pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF:Q5_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 pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF:Q5_K_M # Run inference directly in the terminal: ./llama-cli -hf pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF:Q5_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 pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF:Q5_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF:Q5_K_M
Use Docker
docker model run hf.co/pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF:Q5_K_M
- LM Studio
- Jan
- vLLM
How to use pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF:Q5_K_M
- SGLang
How to use pankaj217/granite-8b-code-instruct-Q5_K_M-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 "pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF" \ --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": "pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF", "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 "pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF" \ --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": "pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF with Ollama:
ollama run hf.co/pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF:Q5_K_M
- Unsloth Desktop
- Docker Model Runner
How to use pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF with Docker Model Runner:
docker model run hf.co/pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF:Q5_K_M
- Lemonade
How to use pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF:Q5_K_M
Run and chat with the model
lemonade run user.granite-8b-code-instruct-Q5_K_M-GGUF-Q5_K_M
List all available models
lemonade list
- Atomic Chat
Download granite-8b-code-instruct-q5_k_m.gguf from pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 5.72 GB
-
https://huggingface.co/pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF/resolve/main/granite-8b-code-instruct-q5_k_m.gguf
- Command line
-
hf download hf://pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF/granite-8b-code-instruct-q5_k_m.gguf
-
curl -L -o granite-8b-code-instruct-q5_k_m.gguf https://huggingface.co/pankaj217/granite-8b-code-instruct-Q5_K_M-GGUF/resolve/main/granite-8b-code-instruct-q5_k_m.gguf
5.72 GB
- Xet hash:
- f0eb4ecdb2007afbcf8dd906470f38b1692025ebe3f714603542863b4025309f
- Size of remote file:
- 5.72 GB
- SHA256:
- b5fc6932efed30323bb62d2f7bdf7009033c61e885d829a18995e1471c305fbf
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