Instructions to use MuntasirHossain/Meta-Llama-3-8B-OpenOrca-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 MuntasirHossain/Meta-Llama-3-8B-OpenOrca-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 MuntasirHossain/Meta-Llama-3-8B-OpenOrca-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf MuntasirHossain/Meta-Llama-3-8B-OpenOrca-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 MuntasirHossain/Meta-Llama-3-8B-OpenOrca-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf MuntasirHossain/Meta-Llama-3-8B-OpenOrca-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 MuntasirHossain/Meta-Llama-3-8B-OpenOrca-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf MuntasirHossain/Meta-Llama-3-8B-OpenOrca-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 MuntasirHossain/Meta-Llama-3-8B-OpenOrca-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf MuntasirHossain/Meta-Llama-3-8B-OpenOrca-GGUF:Q4_K_M
Use Docker
docker model run hf.co/MuntasirHossain/Meta-Llama-3-8B-OpenOrca-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use MuntasirHossain/Meta-Llama-3-8B-OpenOrca-GGUF with Ollama:
ollama run hf.co/MuntasirHossain/Meta-Llama-3-8B-OpenOrca-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use MuntasirHossain/Meta-Llama-3-8B-OpenOrca-GGUF with Docker Model Runner:
docker model run hf.co/MuntasirHossain/Meta-Llama-3-8B-OpenOrca-GGUF:Q4_K_M
- Lemonade
How to use MuntasirHossain/Meta-Llama-3-8B-OpenOrca-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull MuntasirHossain/Meta-Llama-3-8B-OpenOrca-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Meta-Llama-3-8B-OpenOrca-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Model description
This is a GGUF version of the Meta-Llama-3-8B-OpenOrca model which itself is a fine-tuned version of the meta-llama/Meta-Llama-3-8B on 1.5k subsamples of the OpenOrca dataset.
This LLM follows the popular follows the ChatML template!
How to use
# Download the Q4_K_M.gguf or Q6_K.gguf version of the MuntasirHossain/Meta-Llama-3-8B-OpenOrca-GGUF model
!huggingface-cli download MuntasirHossain/Meta-Llama-3-8B-OpenOrca-GGUF Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
from llama_cpp import Llama
llm = Llama(
model_path="./content/Q4_K_M.gguf",
n_ctx=0, # input text context length, 0 = from model
verbose = False
)
# Define a function for inference
def llm_response(input_text = '', max_tokens=256):
system_prompt = "You are a helpful AI assistant."
prompt = f"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{input_text}<|im_end|>\n<|im_start|>assistant"
output = llm(
prompt,
max_tokens=max_tokens,
stop=["<|im_end|>"],
)
return output
# generate model response
input_text = "Explain artificial general intelligence (AGI) in a few lines."
result = llm_response(input_text)
result['choices'][0]['text']
- Downloads last month
- 30
Hardware compatibility
Log In to add your hardware
4-bit
6-bit
Inference Providers NEW
This model isn't deployed by any Inference Provider. ๐ Ask for provider support