---
base_model: meta-llama/Meta-Llama-3.1-70B-Instruct
language:
  - en
  - de
  - fr
  - it
  - pt
  - hi
  - es
  - th
library_name: transformers
license: llama3.1
pipeline_tag: text-generation
tags:
  - facebook
  - meta
  - pytorch
  - llama
  - llama-3
---

## Model Information
The Llama 3.1 instruction tuned text only 70B model is optimized for multilingual dialogue use cases
and outperform many of the available open source and closed chat models on common industry benchmarks.

This repository stores a experimental IQ_1S quantized GGUF Llama 3.1 instruction tuned 70B model.

**Model developer**: Meta

**Model Architecture**: Llama 3.1 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. 

|                     |Training Data                               |Params|Input modalities |Output modalities         |Context length|GQA|Token count|Knowledge cutoff|
|---------------------|--------------------------------------------|------|-----------------|--------------------------|--------------|---|-----------|----------------|
|Llama 3.1 (text only)|A new mix of publicly available online data.|70B    |Multilingual Text|Multilingual Text and code|128k          |Yes|15T+       |December 2023   |

**Supported languages**: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.

# Quantization Information
|Weight Quantization| PPL                |
|-------------------|--------------------|
| FP16              | 4.1892 +/- 0.01430 |
| IQ_1S             | 8.5005 +/- 0.03298 |

Dataset used for re-calibration: Mix of [standard_cal_data](https://github.com/turboderp/exllamav2/tree/master/exllamav2/conversion/standard_cal_data)

The generated `imatrix` can be downloaded from [imatrix.dat](https://huggingface.co/npc0/Meta-Llama-3.1-70B-Instruct-IQ_1S/resolve/main/imatrix.dat)

**Usage**: with `llama-cpp-python`
```python
from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="npc0/Meta-Llama-3.1-70B-Instruct-IQ_1S",
	filename="GGUF_FILE",
)

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