Llama 3.1 70B Instruct GGUF

🚀 Llama-3.1-70B-Instruct GGUF Models

Our project focuses on the Llama-3.1-70B-Instruct GGUF models. It introduces an ultra - low - bit quantization method with IQ - DynamicGate (1 - 2 bit), which significantly improves the performance of ultra - low - bit models while maintaining high memory efficiency.

🚀 Quick Start

This README provides a detailed introduction to the Llama-3.1-70B-Instruct GGUF models, including the quantization method, performance comparison, and guidance on choosing the right model format based on your hardware capabilities and memory constraints.

✨ Features

Ultra - Low - Bit Quantization with IQ - DynamicGate (1 - 2 bit)

• Precision - Adaptive 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. It uses layer - specific strategies to preserve accuracy while maintaining extreme memory efficiency.
• Dynamic Precision Allocation: The first/last 25% of layers use IQ4_XS (selected layers), and the middle 50% uses IQ2_XXS/IQ3_S to increase efficiency.
• Critical Component Protection: Embeddings/output layers use Q5_K, which reduces error propagation by 38% compared to standard 1 - 2 bit.

Benchmark Context

All tests were conducted on Llama - 3 - 8B - Instruct using a standard perplexity evaluation pipeline, a 2048 - token context window, and the same prompt set across all quantizations.

Quantization Performance Comparison (Llama - 3 - 8B)

Key:

• PPL = Perplexity (lower is better)
• Δ PPL = Percentage change from standard to DynamicGate
• Speed = Inference time (CPU avx2, 2048 token context)
• Size differences reflect mixed quantization overhead

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.

Trade - offs:

• 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 - 2bit errors can be tolerated
• ✔ Research into ultra - low - bit quantization

📚 Documentation

Choosing the Right Model Format

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

BF16 (Brain Float 16) – Use if BF16 acceleration is available

• A 16 - bit floating - point format designed for faster computation while retaining good precision.
• Provides similar dynamic range as FP32 but with lower memory usage.
• Recommended if your hardware supports BF16 acceleration (check your device's specs).
• Ideal for high - performance inference with reduced memory footprint compared to FP32.

⚠️ Important Note

Use BF16 if your hardware has native BF16 support (e.g., newer GPUs, TPUs), you want higher precision while saving memory, or you plan to requantize the model into another format. Avoid it if your hardware does not support BF16 (it may fall back to FP32 and run slower) or if you need compatibility with older devices that lack BF16 optimization.

F16 (Float 16) – More widely supported than BF16

• A 16 - bit floating - point format with high precision but a smaller range of values than BF16.
• Works on most devices with FP16 acceleration support (including many GPUs and some CPUs).
• Slightly lower numerical precision than BF16 but generally sufficient for inference.

⚠️ Important Note

Use F16 if your hardware supports FP16 but not BF16, you need a balance between speed, memory usage, and accuracy, or you are running on a GPU or another device optimized for FP16 computations. Avoid it if your device lacks native FP16 support (it may run slower than expected) or if you have memory limitations.

Quantized Models (Q4_K, Q6_K, Q8, etc.) – For CPU & Low - VRAM Inference

Quantization reduces model size and memory usage while maintaining as much accuracy as possible.

• Lower - bit models (Q4_K) → Best for minimal memory usage, may have lower precision.
• Higher - bit models (Q6_K, Q8_0) → Better accuracy, requires more memory.

⚠️ Important Note

Use Quantized Models if you are running inference on a CPU and need an optimized model, your device has low VRAM and cannot load full - precision models, or you want to reduce memory footprint while keeping reasonable accuracy. Avoid them if you need maximum accuracy (full - precision models are better for this) or if your hardware has enough VRAM for higher - precision formats (BF16/F16).

Very Low - Bit Quantization (IQ3_XS, IQ3_S, IQ3_M, Q4_K, Q4_0)

These models are optimized for extreme memory efficiency, making them ideal for low - power devices or large - scale deployments where memory is a critical constraint.

• IQ3_XS: Ultra - low - bit quantization (3 - bit) with extreme memory efficiency. Use case: Best for ultra - low - memory devices where even Q4_K is too large. Trade - off: Lower accuracy compared to higher - bit quantizations.
• IQ3_S: Small block size for maximum memory efficiency. Use case: Best for low - memory devices where IQ3_XS is too aggressive.
• IQ3_M: Medium block size for better accuracy than IQ3_S. Use case: Suitable for low - memory devices where IQ3_S is too limiting.
• Q4_K: 4 - bit quantization with block - wise optimization for better accuracy. Use case: Best for low - memory devices where Q6_K is too large.

📄 License

The Llama 3.1 models are licensed under the Llama 3.1 Community License. The detailed license agreement is as follows:

LLAMA 3.1 COMMUNITY LICENSE AGREEMENT

Llama 3.1 Version Release Date: July 23, 2024 "Agreement" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. "Documentation" means the specifications, manuals and documentation accompanying Llama 3.1 distributed by Meta at https://llama.meta.com/doc/overview. "Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. "Llama 3.1" means the foundational large language models and software and algorithms, including machine - learning model code, trained model weights, inference - enabling code, training - enabling code, fine - tuning enabling code and other elements of the foregoing distributed by Meta at https://llama.meta.com/llama - downloads. "Llama Materials" means, collectively, Meta’s proprietary Llama 3.1 and Documentation (and any portion thereof) made available under this Agreement. "Meta" or "we" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland).

1. License Rights and Redistribution. a. Grant of Rights. You are granted a non - exclusive, worldwide, non - transferable and royalty - free limited license under Meta’s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials. b. Redistribution and Use. i. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service (including another AI model) that contains any of them, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with Llama” on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials or any outputs or results of the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include “Llama” at the beginning of any such AI model name. ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you. iii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a “Notice” text file distributed as a part of such copies: “Llama 3.1 is licensed under the Llama 3.1 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.” iv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://llama.meta.com/llama3_1/use - policy), which is hereby incorporated by reference into this Agreement.
2. Additional Commercial Terms. If, on the Llama 3.1 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights.
3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON - INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.
4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.
5. Intellectual Property. a. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to use “Llama” (the “Mark”) solely as required to comply with the last sentence of Section 1.b.i. You will comply with Meta’s brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company - brand/). All goodwill arising out of your use of the Mark will inure to the benefit of Meta. b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications. c. If you institute litigation or other proceedings against Meta or any entity (including a cross - claim or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 3.1 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials.
6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement.
7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement.

Llama 3.1 Acceptable Use Policy

Meta is committed to promoting safe and fair use of its tools and features, including Llama 3.1. If you access or use Llama 3.1, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [https://llama.meta.com/llama3_1/use - policy](https://llama.meta.com/llama3_1/use - policy)

Prohibited Uses

We want everyone to use Llama 3.1 safely and responsibly. You agree you will not use, or allow others to use, Llama 3.1 to:

1. Violate the law or others’ rights, including to:
   1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
      1. Violence or terrorism
      2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
      3. Human trafficking, exploitation, and sexual violence
      4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age - gating in connection with such information or materials.
      5. Sexual solicitation
      6. Any other criminal activity
   2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
   3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
   4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
   5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
   6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third - party rights, including the outputs or results of any products or services using the Llama Materials
   7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 3.1 related to the following:
   1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
   2. Guns and illegal weapons (including weapon development)
   3. Illegal drugs and regulated/controlled substances
   4. Operation of critical infrastructure, transportation technologies, or heavy machinery
   5. Self - harm or harm to others, including suicide, cutting, and eating disorders
   6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
3. Intentionally deceive or mislead others, including use of Llama 3.1 related to the following:
   1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
   2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
   3. Generating, promoting, or further distributing spam
   4. Impersonating another individual without consent, authorization, or legal right
   5. Representing that the use of Llama 3.1 or outputs are human - generated
   6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
4. Fail to appropriately disclose to end users any known dangers of your AI system

Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means:

• Reporting issues with the model: [https://github.com/meta - llama/llama - models/issues](https://github.com/meta - llama/llama - models/issues)
• Reporting risky content generated by the model: developers.facebook.com/llama_output_feedback
• Reporting bugs and security concerns: facebook.com/whitehat/info
• Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: LlamaUseReport@meta.com