Llama 3.1 8B Instruct GGUF

🚀 Llama-3.1-8B-Instruct GGUF Models

This project offers a series of Llama-3.1-8B-Instruct GGUF models with ultra-low-bit quantization techniques. These models are designed to balance memory efficiency and accuracy, making them suitable for various deployment scenarios, including memory-constrained environments and edge devices.

✨ Features

• Ultra-Low-Bit Quantization: Our latest quantization method introduces precision-adaptive quantization for ultra-low-bit models (1 - 2 bit), with benchmark-proven improvements on Llama-3-8B.
• Dynamic Precision Allocation: The method uses layer-specific strategies to preserve accuracy while maintaining extreme memory efficiency.
• Critical Component Protection: Embeddings and output layers use Q5_K to reduce error propagation by 38% compared to standard 1 - 2 bit quantization.
• Multiple Model Formats: Available in various formats, including BF16, F16, and quantized models (Q4_K, Q6_K, Q8_0, etc.), to meet different hardware and memory requirements.

📦 Installation

The README does not provide specific installation steps. If you need to install these models, please refer to the official documentation or relevant repositories for detailed instructions.

💻 Usage Examples

Basic Usage

import transformers
import torch

model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
    {"role": "user", "content": "Who are you?"},
]

outputs = pipeline(
    messages,
    max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])

Advanced Usage

# First, define a tool
def get_current_temperature(location: str) -> float:
    """
    Get the current temperature at a location.
    
    Args:
        location: The location to get the temperature for, in the format "City, Country"
    Returns:
        The current temperature at the specified location in the specified units, as a float.
    """
    return 22.  # A real function should probably actually get the temperature!

# Next, create a chat and apply the chat template
messages = [
  {"role": "system", "content": "You are a bot that responds to weather queries."},
  {"role": "user", "content": "Hey, what's the temperature in Paris right now?"}
]

inputs = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True)

tool_call = {"name": "get_current_temperature", "arguments": {"location": "Paris, France"}}
messages.append({"role": "assistant", "tool_calls": [{"type": "function", "function": tool_call}]})

messages.append({"role": "tool", "name": "get_current_temperature", "content": "22.0"})

# After that, you can generate() again to let the model use the tool result in the chat.

📚 Documentation

Model Information

The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B, and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.

Intended Use

• Intended Use Cases: Llama 3.1 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. The Llama 3.1 model collection also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 3.1 Community License allows for these use cases.
• Out-of-scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.1 Community License. Use in languages beyond those explicitly referenced as supported in this model card.

How to Use

This repository contains two versions of Meta-Llama-3.1-8B-Instruct, for use with transformers and with the original llama codebase.

Use with transformers

Starting with transformers >= 4.43.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.

Make sure to update your transformers installation via pip install --upgrade transformers.

Use with llama

Please follow the instructions in the repository.

To download Original checkpoints, see the example command below leveraging huggingface-cli:

huggingface-cli download meta-llama/Meta-Llama-3.1-8B-Instruct --include "original/*" --local-dir Meta-Llama-3.1-8B-Instruct

Hardware and Software

• Training Factors: We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure.
• Training utilized a cumulative of 39.3M GPU hours of computation on H100 - 80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency.

The methodology used to determine training energy use and greenhouse gas emissions can be found here. Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others.

Training Data

• Overview: Llama 3.1 was pretrained on ~15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 25M synthetically generated examples.
• Data Freshness: The pretraining data has a cutoff of December 2023.

Benchmark scores

In this section, we report the results for Llama 3.1 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library.

Base pretrained models

Instruction tuned models

Multilingual benchmarks

🔧 Technical Details

Ultra-Low-Bit Quantization

Our latest quantization method introduces precision-adaptive quantization for ultra-low-bit models (1 - 2 bit), with benchmark-proven improvements on Llama-3-8B. This approach uses layer-specific strategies to preserve accuracy while maintaining extreme memory efficiency.

Dynamic Precision Allocation

• First/Last 25% of layers → IQ4_XS (selected layers)
• Middle 50% → IQ2_XXS/IQ3_S (increase efficiency)

Critical Component Protection

• Embeddings/output layers use Q5_K
• Reduces error propagation by 38% vs standard 1 - 2 bit

Quantization Performance Comparison (Llama-3-8B)

Key Improvements

• 🔥 IQ1_M shows a massive 43.9% perplexity reduction (27.46 → 15.41)
• 🚀 IQ2_S cuts perplexity by 36.9% while adding only 0.2GB
• ⚡ IQ1_S maintains 39.7% better accuracy despite 1-bit quantization

Tradeoffs

• All variants have modest size increases (0.1 - 0.3GB)
• Inference speeds remain comparable (<5% difference)

When to Use These Models

• 📌 Fitting models into GPU VRAM
• ✔ Memory-constrained deployments
• ✔ CPU and Edge Devices where 1 - 2 bit errors can be tolerated
• ✔ Research into ultra-low-bit quantization

Choosing the Right Model Format

Selecting the correct model format depends on your hardware capabilities and memory constraints.

📄 License

A custom commercial license, the Llama 3.1 Community License, is available at: https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE