Instructions to use kshabana/GOAT-coder-llama3.1-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kshabana/GOAT-coder-llama3.1-8b with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("kshabana/GOAT-coder-llama3.1-8b", dtype="auto")

llama-cpp-python

How to use kshabana/GOAT-coder-llama3.1-8b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="kshabana/GOAT-coder-llama3.1-8b",
	filename="GOAT-coder-llama3.1-8b-F16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use kshabana/GOAT-coder-llama3.1-8b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf kshabana/GOAT-coder-llama3.1-8b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf kshabana/GOAT-coder-llama3.1-8b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf kshabana/GOAT-coder-llama3.1-8b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf kshabana/GOAT-coder-llama3.1-8b: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 kshabana/GOAT-coder-llama3.1-8b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf kshabana/GOAT-coder-llama3.1-8b: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 kshabana/GOAT-coder-llama3.1-8b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kshabana/GOAT-coder-llama3.1-8b:Q4_K_M

Use Docker

docker model run hf.co/kshabana/GOAT-coder-llama3.1-8b:Q4_K_M

LM Studio
Jan
Ollama
How to use kshabana/GOAT-coder-llama3.1-8b with Ollama:
```
ollama run hf.co/kshabana/GOAT-coder-llama3.1-8b:Q4_K_M
```

Unsloth Studio

How to use kshabana/GOAT-coder-llama3.1-8b 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 kshabana/GOAT-coder-llama3.1-8b 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 kshabana/GOAT-coder-llama3.1-8b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kshabana/GOAT-coder-llama3.1-8b to start chatting

How to use kshabana/GOAT-coder-llama3.1-8b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf kshabana/GOAT-coder-llama3.1-8b:Q4_K_M

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": "kshabana/GOAT-coder-llama3.1-8b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use kshabana/GOAT-coder-llama3.1-8b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf kshabana/GOAT-coder-llama3.1-8b:Q4_K_M

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 kshabana/GOAT-coder-llama3.1-8b:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use kshabana/GOAT-coder-llama3.1-8b with Docker Model Runner:
```
docker model run hf.co/kshabana/GOAT-coder-llama3.1-8b:Q4_K_M
```

Lemonade

How to use kshabana/GOAT-coder-llama3.1-8b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kshabana/GOAT-coder-llama3.1-8b:Q4_K_M

Run and chat with the model

lemonade run user.GOAT-coder-llama3.1-8b-Q4_K_M

List all available models

lemonade list

INTRO

we are happy to announce our frist coding model

Model Card for Model ID

this is a finetune model of llama3.1 that can perform well in coding

**Developed by: kshabana4ai
**Funded by no one
**Shared by kshabana
**Model type: safetensors and gguf
**Language(s) English
License: Apache2.0
**Finetuned from model llama3.1-instruct

installation

download ollama

ollama run hf.co/kshabana/GOAT-coder-llama3.1-8b:Q4_K_M

Model Sources

https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct

Dataset Repository:

https://huggingface.co/datasets/Replete-AI/code_bagel

Uses

This model is finetuned specifically for coding and it has a 131072 context lingth.

Bias, Risks, and Limitations

it can some times produce a wrong answers.

Recommendations

NOTE: you shold have lm studio or ollama to use this model

IN LM-STUDIO: it is recommended to use this model with the defult lm-studio configration.

IN OLLAMA: we will be pushing the model soon to ollama.

Training Details

model trained with unsloth.

Training Data

the training dataset that i used: https://huggingface.co/datasets/Replete-AI/code_bagel

IMPORTANT LINKS

OLLAMA: https://ollama.com

LM-STUDIO: https://lmstudio.ai

llama3.1: instruct: https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct

dataset: https://huggingface.co/datasets/Replete-AI/code_bagel

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

kshabana4ai

width="200"/>](https://huggingface.co/kshabana-ai)

Downloads last month: 173

Safetensors

Model size

8B params

Tensor type

F16

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for kshabana/GOAT-coder-llama3.1-8b

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Quantized

unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit

Quantized

(361)

this model

Finetunes

1 model

Quantizations