Instructions to use afrideva/mpt-3b-8k-instruct-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 afrideva/mpt-3b-8k-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 afrideva/mpt-3b-8k-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf afrideva/mpt-3b-8k-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 afrideva/mpt-3b-8k-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf afrideva/mpt-3b-8k-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 afrideva/mpt-3b-8k-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf afrideva/mpt-3b-8k-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 afrideva/mpt-3b-8k-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf afrideva/mpt-3b-8k-instruct-GGUF:Q4_K_M
Use Docker
docker model run hf.co/afrideva/mpt-3b-8k-instruct-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use afrideva/mpt-3b-8k-instruct-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "afrideva/mpt-3b-8k-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": "afrideva/mpt-3b-8k-instruct-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/afrideva/mpt-3b-8k-instruct-GGUF:Q4_K_M
- Ollama
How to use afrideva/mpt-3b-8k-instruct-GGUF with Ollama:
ollama run hf.co/afrideva/mpt-3b-8k-instruct-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use afrideva/mpt-3b-8k-instruct-GGUF with Docker Model Runner:
docker model run hf.co/afrideva/mpt-3b-8k-instruct-GGUF:Q4_K_M
- Lemonade
How to use afrideva/mpt-3b-8k-instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull afrideva/mpt-3b-8k-instruct-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.mpt-3b-8k-instruct-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
niallturbitt/mpt-3b-8k-instruct-GGUF
Quantized GGUF model files for mpt-3b-8k-instruct from niallturbitt
| Name | Quant method | Size |
|---|---|---|
| mpt-3b-8k-instruct.q2_k.gguf | q2_k | 1.54 GB |
| mpt-3b-8k-instruct.q3_k_m.gguf | q3_k_m | 1.95 GB |
| mpt-3b-8k-instruct.q4_k_m.gguf | q4_k_m | 2.34 GB |
| mpt-3b-8k-instruct.q5_k_m.gguf | q5_k_m | 2.71 GB |
| mpt-3b-8k-instruct.q6_k.gguf | q6_k | 2.98 GB |
| mpt-3b-8k-instruct.q8_0.gguf | q8_0 | 3.86 GB |
Original Model Card:
- Downloads last month
- 974
Hardware compatibility
Log In to add your hardware
2-bit
3-bit
4-bit
5-bit
6-bit
8-bit
Model tree for afrideva/mpt-3b-8k-instruct-GGUF
Base model
niallturbitt/mpt-3b-8k-instruct