Instructions to use cortexso/llama3.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use cortexso/llama3.1 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="cortexso/llama3.1", filename="llama-3.1-8b-q2_k.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use cortexso/llama3.1 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf cortexso/llama3.1:Q4_K_M # Run inference directly in the terminal: llama-cli -hf cortexso/llama3.1:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf cortexso/llama3.1:Q4_K_M # Run inference directly in the terminal: llama-cli -hf cortexso/llama3.1: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 cortexso/llama3.1:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf cortexso/llama3.1: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 cortexso/llama3.1:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf cortexso/llama3.1:Q4_K_M
Use Docker
docker model run hf.co/cortexso/llama3.1:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use cortexso/llama3.1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cortexso/llama3.1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cortexso/llama3.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/cortexso/llama3.1:Q4_K_M
- Ollama
How to use cortexso/llama3.1 with Ollama:
ollama run hf.co/cortexso/llama3.1:Q4_K_M
- Unsloth Studio new
How to use cortexso/llama3.1 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for cortexso/llama3.1 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for cortexso/llama3.1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for cortexso/llama3.1 to start chatting
- Docker Model Runner
How to use cortexso/llama3.1 with Docker Model Runner:
docker model run hf.co/cortexso/llama3.1:Q4_K_M
- Lemonade
How to use cortexso/llama3.1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cortexso/llama3.1:Q4_K_M
Run and chat with the model
lemonade run user.llama3.1-Q4_K_M
List all available models
lemonade list
File size: 887 Bytes
3f5e406 deb2cba a7ae556 deb2cba 18bf9de deb2cba | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | name: Llama 3.1
model: llama3.1:8B
version: 1
# Results Preferences
stop:
- <|end_of_text|>
- <|eot_id|>
- <|eom_id|>
top_p: 0.9
temperature: 0.6
frequency_penalty: 0
presence_penalty: 0
max_tokens: 8192 # Infer from base config.json -> max_position_embeddings
stream: true # true | false
# Engine / Model Settings
ngl: 33 # Infer from base config.json -> num_attention_heads
ctx_len: 8192 # Infer from base config.json -> max_position_embeddings
engine: llama-cpp
prompt_template: "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{system_message}<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
# Prompt template: Can only be retrieved from instruct model
# - https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/blob/main/tokenizer_config.json#L2053
# - Requires jinja format parser |