README.md

---
language:
- en
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
license: llama3
extra_gated_prompt: >-
  ### META LLAMA 3 COMMUNITY LICENSE AGREEMENT

  Meta Llama 3 Version Release Date: April 18, 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 Meta Llama 3
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  "Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into
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  "Meta Llama 3" means the foundational large language models and software and algorithms, including
  machine-learning model code, trained model weights, inference-enabling code, training-enabling code,
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  v. You will not use the Llama Materials or any output or results of the Llama Materials to
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  ### Meta Llama 3 Acceptable Use Policy

  Meta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you
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  this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy)

  #### Prohibited Uses

  We want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow
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  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/llama3](https://github.com/meta-llama/llama3)
      * 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
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---

## Model Details (GGUF 4bit)

Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety. 

**Model developers** Meta

**Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants.

**Input** Models input text only.

**Output** Models generate text and code only.

**Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.


<table>
  <tr>
   <td>
   </td>
   <td><strong>Training Data</strong>
   </td>
   <td><strong>Params</strong>
   </td>
   <td><strong>Context length</strong>
   </td>
   <td><strong>GQA</strong>
   </td>
   <td><strong>Token count</strong>
   </td>
   <td><strong>Knowledge cutoff</strong>
   </td>
  </tr>
  <tr>
   <td rowspan="2" >Llama 3
   </td>
   <td rowspan="2" >A new mix of publicly available online data.
   </td>
   <td>8B
   </td>
   <td>8k
   </td>
   <td>Yes
   </td>
   <td rowspan="2" >15T+
   </td>
   <td>March, 2023
   </td>
  </tr>
  <tr>
   <td>70B
   </td>
   <td>8k
   </td>
   <td>Yes
   </td>
   <td>December, 2023
   </td>
  </tr>
</table>


**Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability.

**Model Release Date** April 18, 2024.

**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.

**License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)

Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes). 


## Intended Use

**Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.

**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 Community License. Use in languages other than English**.

**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.

#### First install the package

Run one of the following commands, according to your system:

```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python

# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```

#### Download the model into current directory

```
from huggingface_hub import hf_hub_download

REPO_ID = "SalmanFaroz/Meta-Llama-3-8B-Instruct-GGUF"
FILENAME = "Q4_K_M.gguf"

hf_hub_download(repo_id=REPO_ID, filename=FILENAME,local_dir="./")
```

#### Simple llama-cpp-python example code

```python
from llama_cpp import Llama

# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
  model_path="./Q4_K_M.gguf",  # Download the model file first
  n_ctx=4096,  # The max sequence length to use - note that longer sequence lengths require much more resources
  n_threads=8,            # The number of CPU threads to use, tailor to your system and the resulting performance
  n_gpu_layers=35         # The number of layers to offload to GPU, if you have GPU acceleration available
)

prompt = "Tell me about AI"

output = llm(
  f'''[INST] <<SYS>>
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
<</SYS>>
{prompt}[/INST]

''', # Prompt
  max_tokens=100,  # Generate up to 512 tokens
  stop=["</s>"],   # Example stop token - not necessarily correct for this specific model! Please check before using.
  echo=True ,       # Whether to echo the prompt
  temperature=0.001
)

```