Instructions to use smcleod/llama-3-1-8b-smcleod-golang-coder-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use smcleod/llama-3-1-8b-smcleod-golang-coder-v3 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="smcleod/llama-3-1-8b-smcleod-golang-coder-v3", filename="llama-3-1-8b-smcleod-golang-coder-v3.etf16-Q8_0.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use smcleod/llama-3-1-8b-smcleod-golang-coder-v3 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf smcleod/llama-3-1-8b-smcleod-golang-coder-v3:Q8_0 # Run inference directly in the terminal: llama-cli -hf smcleod/llama-3-1-8b-smcleod-golang-coder-v3:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf smcleod/llama-3-1-8b-smcleod-golang-coder-v3:Q8_0 # Run inference directly in the terminal: llama-cli -hf smcleod/llama-3-1-8b-smcleod-golang-coder-v3:Q8_0
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 smcleod/llama-3-1-8b-smcleod-golang-coder-v3:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf smcleod/llama-3-1-8b-smcleod-golang-coder-v3:Q8_0
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 smcleod/llama-3-1-8b-smcleod-golang-coder-v3:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf smcleod/llama-3-1-8b-smcleod-golang-coder-v3:Q8_0
Use Docker
docker model run hf.co/smcleod/llama-3-1-8b-smcleod-golang-coder-v3:Q8_0
- LM Studio
- Jan
- Ollama
How to use smcleod/llama-3-1-8b-smcleod-golang-coder-v3 with Ollama:
ollama run hf.co/smcleod/llama-3-1-8b-smcleod-golang-coder-v3:Q8_0
- Unsloth Studio new
How to use smcleod/llama-3-1-8b-smcleod-golang-coder-v3 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 smcleod/llama-3-1-8b-smcleod-golang-coder-v3 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 smcleod/llama-3-1-8b-smcleod-golang-coder-v3 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for smcleod/llama-3-1-8b-smcleod-golang-coder-v3 to start chatting
- Pi new
How to use smcleod/llama-3-1-8b-smcleod-golang-coder-v3 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf smcleod/llama-3-1-8b-smcleod-golang-coder-v3:Q8_0
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "smcleod/llama-3-1-8b-smcleod-golang-coder-v3:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use smcleod/llama-3-1-8b-smcleod-golang-coder-v3 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf smcleod/llama-3-1-8b-smcleod-golang-coder-v3:Q8_0
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default smcleod/llama-3-1-8b-smcleod-golang-coder-v3:Q8_0
Run Hermes
hermes
- Docker Model Runner
How to use smcleod/llama-3-1-8b-smcleod-golang-coder-v3 with Docker Model Runner:
docker model run hf.co/smcleod/llama-3-1-8b-smcleod-golang-coder-v3:Q8_0
- Lemonade
How to use smcleod/llama-3-1-8b-smcleod-golang-coder-v3 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull smcleod/llama-3-1-8b-smcleod-golang-coder-v3:Q8_0
Run and chat with the model
lemonade run user.llama-3-1-8b-smcleod-golang-coder-v3-Q8_0
List all available models
lemonade list
Llama 3.1 8b Golang Coder v3
This model has been trained on Golang style guides, best practices and code examples. This should (hopefully) make it quite capable with Golang coding tasks.
LoRA
GGUF
- Q8_0 (with f16 embeddings): https://huggingface.co/smcleod/llama-3-1-8b-smcleod-golang-coder-v3/blob/main/llama-3-1-8b-smcleod-golang-coder-v2.etf16-Q8_0.gguf
Ollama
Training
I trained this model (based on Llama 3.1 8b) on a merged dataset I created consisting of 50,627 rows, 13.3M input tokens and 2.2M output tokens. The total training consisted of 1,020,719 input tokens and 445,810 output tokens from 45,565 items in the dataset.
The dataset I created for this consists of multiple golang/programming focused datasets cleaned and merged and my own synthetically generated dataset based on several open source golang coding guides.
- Downloads last month
- 83
8-bit
