Instructions to use sourabhdattawad/meta-llama-3-8b-instruct-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sourabhdattawad/meta-llama-3-8b-instruct-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sourabhdattawad/meta-llama-3-8b-instruct-gguf",
	filename="meta-llama-3-8b-instruct.Q8_0.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use sourabhdattawad/meta-llama-3-8b-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 sourabhdattawad/meta-llama-3-8b-instruct-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf sourabhdattawad/meta-llama-3-8b-instruct-gguf:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sourabhdattawad/meta-llama-3-8b-instruct-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf sourabhdattawad/meta-llama-3-8b-instruct-gguf: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 sourabhdattawad/meta-llama-3-8b-instruct-gguf:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf sourabhdattawad/meta-llama-3-8b-instruct-gguf: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 sourabhdattawad/meta-llama-3-8b-instruct-gguf:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sourabhdattawad/meta-llama-3-8b-instruct-gguf:Q8_0

Use Docker

docker model run hf.co/sourabhdattawad/meta-llama-3-8b-instruct-gguf:Q8_0

LM Studio
Jan

vLLM

How to use sourabhdattawad/meta-llama-3-8b-instruct-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sourabhdattawad/meta-llama-3-8b-instruct-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sourabhdattawad/meta-llama-3-8b-instruct-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sourabhdattawad/meta-llama-3-8b-instruct-gguf:Q8_0

Ollama
How to use sourabhdattawad/meta-llama-3-8b-instruct-gguf with Ollama:
```
ollama run hf.co/sourabhdattawad/meta-llama-3-8b-instruct-gguf:Q8_0
```

Unsloth Studio

How to use sourabhdattawad/meta-llama-3-8b-instruct-gguf 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 sourabhdattawad/meta-llama-3-8b-instruct-gguf 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 sourabhdattawad/meta-llama-3-8b-instruct-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sourabhdattawad/meta-llama-3-8b-instruct-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use sourabhdattawad/meta-llama-3-8b-instruct-gguf with Docker Model Runner:
```
docker model run hf.co/sourabhdattawad/meta-llama-3-8b-instruct-gguf:Q8_0
```

Lemonade

How to use sourabhdattawad/meta-llama-3-8b-instruct-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sourabhdattawad/meta-llama-3-8b-instruct-gguf:Q8_0

Run and chat with the model

lemonade run user.meta-llama-3-8b-instruct-gguf-Q8_0

List all available models

lemonade list

Usage

Package installation

pip install llama-cpp-python "huggingface_hub[cli]"

Download the model:

huggingface-cli download sourabhdattawad/meta-llama-3-8b-instruct-gguf meta-llama-3-8b-instruct.Q8_0.gguf --local-dir . --local-dir-use-symlinks False

from llama_cpp import Llama
llm = Llama(
      model_path="meta-llama-3-8b-instruct.Q8_0.gguf",
      # n_gpu_layers=-1, # Uncomment to use GPU acceleration
      # seed=1337, # Uncomment to set a specific seed
      # n_ctx=2048, # Uncomment to increase the context window
)
output = llm(
      "Q: Name the planets in the solar system? A: ", # Prompt
      max_tokens=50, # Generate up to 50 tokens, set to None to generate up to the end of the context window
      stop=["Q:", "\n"], # Stop generating just before the model would generate a new question
      echo=True # Echo the prompt back in the output
)
output

Llama.generate: prefix-match hit

llama_print_timings:        load time =    7770.49 ms
llama_print_timings:      sample time =     100.16 ms /    40 runs   (    2.50 ms per token,   399.35 tokens per second)
llama_print_timings: prompt eval time =       0.00 ms /     1 tokens (    0.00 ms per token,      inf tokens per second)
llama_print_timings:        eval time =   35214.73 ms /    40 runs   (  880.37 ms per token,     1.14 tokens per second)
llama_print_timings:       total time =   35895.91 ms /    41 tokens
{'id': 'cmpl-01e2feb3-c0ff-4a6e-8ca4-b8bf2172da01',
 'object': 'text_completion',
 'created': 1713912080,
 'model': 'meta-llama-3-8b-instruct.Q8_0.gguf',
 'choices': [{'text': 'Q: Name the planets in the solar system? A: 1. Mercury, 2. Venus, 3. Earth, 4. Mars, 5. Jupiter, 6. Saturn, 7. Uranus, 8. Neptune.',
   'index': 0,
   'logprobs': None,
   'finish_reason': 'stop'}],
 'usage': {'prompt_tokens': 13, 'completion_tokens': 40, 'total_tokens': 53}}

Google Colab

https://colab.research.google.com/drive/1vhrCKGzY7KP5mScHNUl7hjmbPsUyj_sj?usp=sharing)

Downloads last month: 11

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

8-bit