Instructions to use second-state/Meta-Llama-3.1-8B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use second-state/Meta-Llama-3.1-8B-Instruct-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="second-state/Meta-Llama-3.1-8B-Instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("second-state/Meta-Llama-3.1-8B-Instruct-GGUF")
model = AutoModelForCausalLM.from_pretrained("second-state/Meta-Llama-3.1-8B-Instruct-GGUF")

llama-cpp-python

How to use second-state/Meta-Llama-3.1-8B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="second-state/Meta-Llama-3.1-8B-Instruct-GGUF",
	filename="Llama-3.1-8B-Instruct-Q2_K.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 second-state/Meta-Llama-3.1-8B-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 second-state/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf second-state/Meta-Llama-3.1-8B-Instruct-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 second-state/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf second-state/Meta-Llama-3.1-8B-Instruct-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 second-state/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf second-state/Meta-Llama-3.1-8B-Instruct-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 second-state/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf second-state/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/second-state/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use second-state/Meta-Llama-3.1-8B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "second-state/Meta-Llama-3.1-8B-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": "second-state/Meta-Llama-3.1-8B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/second-state/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M

SGLang

How to use second-state/Meta-Llama-3.1-8B-Instruct-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "second-state/Meta-Llama-3.1-8B-Instruct-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "second-state/Meta-Llama-3.1-8B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "second-state/Meta-Llama-3.1-8B-Instruct-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "second-state/Meta-Llama-3.1-8B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use second-state/Meta-Llama-3.1-8B-Instruct-GGUF with Ollama:
```
ollama run hf.co/second-state/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use second-state/Meta-Llama-3.1-8B-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 second-state/Meta-Llama-3.1-8B-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 second-state/Meta-Llama-3.1-8B-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 second-state/Meta-Llama-3.1-8B-Instruct-GGUF to start chatting

How to use second-state/Meta-Llama-3.1-8B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf second-state/Meta-Llama-3.1-8B-Instruct-GGUF: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": "second-state/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use second-state/Meta-Llama-3.1-8B-Instruct-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf second-state/Meta-Llama-3.1-8B-Instruct-GGUF: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 second-state/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use second-state/Meta-Llama-3.1-8B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/second-state/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use second-state/Meta-Llama-3.1-8B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull second-state/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Meta-Llama-3.1-8B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Meta-Llama-3.1-8B-Instruct-GGUF / README.md

apepkuss79

Update README.md

6d6118a verified over 1 year ago

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	---
	license: llama3.1
	model_name: Meta-Llama-3.1-8B-Instruct-GGUF
	arxiv: 2307.09288
	base_model: meta-llama/Meta-Llama-3.1-8B-Instruct-GGUF
	inference: false
	model_creator: Meta Llama3
	model_type: llama
	pipeline_tag: text-generation
	quantized_by: Second State Inc.
	language:
	- en
	- de
	- fr
	- it
	- pt
	- hi
	- es
	- th
	tags:
	- facebook
	- meta
	- pytorch
	- llama
	- llama-3
	---

	<!-- header start -->
	<!-- 200823 -->
	<div style="width: auto; margin-left: auto; margin-right: auto">
	<img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;">
	</div>
	<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
	<!-- header end -->

	# Meta-Llama-3.1-8B-Instruct-GGUF

	## Original Model

	[meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)

	## Run with LlamaEdge

	- LlamaEdge version: [v0.16.5](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.16.5) and above

	- Prompt template

	- Prompt type for chat: `llama-3-chat`

	- Prompt string

	```text
	<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|>

	{{ system_prompt }}<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>

	{{ user_message_1 }}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>

	{{ model_answer_1 }}<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>

	{{ user_message_2 }}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>
	```

	- Prompt type for tool use: `llama-3-tool`

	- Prompt string

	```text
	<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|>

	{system_message}<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>

	Given the following functions, please respond with a JSON for a function call with its proper arguments that best answers the given prompt.

	Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}. Do not use variables.

	[{"type":"function","function":{"name":"get_current_weather","description":"Get the current weather in a given location","parameters":{"type":"object","properties":{"location":{"type":"string","description":"The city and state, e.g. San Francisco, CA"},"unit":{"type":"string","description":"The temperature unit to use. Infer this from the users location.","enum":["celsius","fahrenheit"]}},"required":["location","unit"]}}}]

	Question: {user_message}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>
	```

	- Context size: `128000`

	- Run as LlamaEdge service

	- Chat

	```bash
	wasmedge --dir .:. --nn-preload default:GGML:AUTO:Llama-3.1-8B-Instruct-Q5_K_M.gguf \
	llama-api-server.wasm \
	--prompt-template llama-3-chat \
	--ctx-size 128000 \
	--model-name Llama-3.1-8b
	```

	- Tool use

	```bash
	wasmedge --dir .:. --nn-preload default:GGML:AUTO:Llama-3.1-8B-Instruct-Q5_K_M.gguf \
	llama-api-server.wasm \
	--prompt-template llama-3-tool \
	--ctx-size 128000 \
	--model-name Llama-3.1-8b
	```

	- Run as LlamaEdge command app

	```bash
	wasmedge --dir .:. --nn-preload default:GGML:AUTO:Llama-3.1-8B-Instruct-Q5_K_M.gguf \
	llama-chat.wasm \
	--prompt-template llama-3-chat \
	--ctx-size 128000
	```

	## Quantized GGUF Models

	\| Name \| Quant method \| Bits \| Size \| Use case \|
	\| ---- \| ---- \| ---- \| ---- \| ----- \|
	\| [Llama-3.1-8B-Instruct-Q2_K.gguf](https://huggingface.co/second-state/Meta-Llama-3.1-8B-Instruct-GGUF/blob/main/Llama-3.1-8B-Instruct-Q2_K.gguf) \| Q2_K \| 2 \| 3.18 GB\| smallest, significant quality loss - not recommended for most purposes \|
	\| [Llama-3.1-8B-Instruct-Q3_K_L.gguf](https://huggingface.co/second-state/Meta-Llama-3.1-8B-Instruct-GGUF/blob/main/Llama-3.1-8B-Instruct-Q3_K_L.gguf) \| Q3_K_L \| 3 \| 4.32 GB\| small, substantial quality loss \|
	\| [Llama-3.1-8B-Instruct-Q3_K_M.gguf](https://huggingface.co/second-state/Meta-Llama-3.1-8B-Instruct-GGUF/blob/main/Llama-3.1-8B-Instruct-Q3_K_M.gguf) \| Q3_K_M \| 3 \| 4.02 GB\| very small, high quality loss \|
	\| [Llama-3.1-8B-Instruct-Q3_K_S.gguf](https://huggingface.co/second-state/Meta-Llama-3.1-8B-Instruct-GGUF/blob/main/Llama-3.1-8B-Instruct-Q3_K_S.gguf) \| Q3_K_S \| 3 \| 3.66 GB\| very small, high quality loss \|
	\| [Llama-3.1-8B-Instruct-Q4_0.gguf](https://huggingface.co/second-state/Meta-Llama-3.1-8B-Instruct-GGUF/blob/main/Llama-3.1-8B-Instruct-Q4_0.gguf) \| Q4_0 \| 4 \| 4.66 GB\| legacy; small, very high quality loss - prefer using Q3_K_M \|
	\| [Llama-3.1-8B-Instruct-Q4_K_M.gguf](https://huggingface.co/second-state/Meta-Llama-3.1-8B-Instruct-GGUF/blob/main/Llama-3.1-8B-Instruct-Q4_K_M.gguf) \| Q4_K_M \| 4 \| 4.92 GB\| medium, balanced quality - recommended \|
	\| [Llama-3.1-8B-Instruct-Q4_K_S.gguf](https://huggingface.co/second-state/Meta-Llama-3.1-8B-Instruct-GGUF/blob/main/Llama-3.1-8B-Instruct-Q4_K_S.gguf) \| Q4_K_S \| 4 \| 4.69 GB\| small, greater quality loss \|
	\| [Llama-3.1-8B-Instruct-Q5_0.gguf](https://huggingface.co/second-state/Meta-Llama-3.1-8B-Instruct-GGUF/blob/main/Llama-3.1-8B-Instruct-Q5_0.gguf) \| Q5_0 \| 5 \| 5.6 GB\| legacy; medium, balanced quality - prefer using Q4_K_M \|
	\| [Llama-3.1-8B-Instruct-Q5_K_M.gguf](https://huggingface.co/second-state/Meta-Llama-3.1-8B-Instruct-GGUF/blob/main/Llama-3.1-8B-Instruct-Q5_K_M.gguf) \| Q5_K_M \| 5 \| 5.73 GB\| large, very low quality loss - recommended \|
	\| [Llama-3.1-8B-Instruct-Q5_K_S.gguf](https://huggingface.co/second-state/Meta-Llama-3.1-8B-Instruct-GGUF/blob/main/Llama-3.1-8B-Instruct-Q5_K_S.gguf) \| Q5_K_S \| 5 \| 5.6 GB\| large, low quality loss - recommended \|
	\| [Llama-3.1-8B-Instruct-Q6_K.gguf](https://huggingface.co/second-state/Meta-Llama-3.1-8B-Instruct-GGUF/blob/main/Llama-3.1-8B-Instruct-Q6_K.gguf) \| Q6_K \| 6 \| 6.6 GB\| very large, extremely low quality loss \|
	\| [Llama-3.1-8B-Instruct-Q8_0.gguf](https://huggingface.co/second-state/Meta-Llama-3.1-8B-Instruct-GGUF/blob/main/Llama-3.1-8B-Instruct-Q8_0.gguf) \| Q8_0 \| 8 \| 8.54 GB\| very large, extremely low quality loss - not recommended \|
	\| [Llama-3.1-8B-Instruct-f16.gguf](https://huggingface.co/second-state/Meta-Llama-3.1-8B-Instruct-GGUF/blob/main/Llama-3.1-8B-Instruct-f16.gguf) \| f16 \| 16 \| 16.1 GB\| \|

	Quantized with llama.cpp b4466.