---
license: apache-2.0
base_model: meta-llama/Meta-Llama-3-70B-Instruct
library_name: transformers
language:
  - en
tags:
  - quantllm
---

<div align="center">

# 📦 Meta-Llama-3-70B-Instruct-4bit-gguf

**meta-llama/Meta-Llama-3-70B-Instruct** converted to **GUFF** format

[![QuantLLM](https://img.shields.io/badge/🚀_Made_with-QuantLLM-orange?style=for-the-badge)](https://github.com/codewithdark-git/QuantLLM)
[![Format](https://img.shields.io/badge/Format-GUFF-blue?style=for-the-badge)]()


<a href="https://github.com/codewithdark-git/QuantLLM">⭐ Star QuantLLM on GitHub</a>

</div>

---


## 📖 About This Model

This model is **[meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct)** converted to GUFF format.

| Property | Value |
|----------|-------|
| **Base Model** | [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) |
| **Format** | GUFF |
| **Quantization** | None (Full Precision) |
| **License** | apache-2.0 |
| **Created With** | [QuantLLM](https://github.com/codewithdark-git/QuantLLM) |


## 🚀 Quick Start

### With Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("QuantLLM/Meta-Llama-3-70B-Instruct-4bit-gguf")
tokenizer = AutoTokenizer.from_pretrained("QuantLLM/Meta-Llama-3-70B-Instruct-4bit-gguf")

# Generate text
inputs = tokenizer("Once upon a time", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### With QuantLLM

```python
from quantllm import TurboModel

# Load with automatic optimization
model = TurboModel.from_pretrained("QuantLLM/Meta-Llama-3-70B-Instruct-4bit-gguf")

# Generate
response = model.generate("Write a poem about coding")
print(response)
```

### Requirements

```bash
pip install transformers torch
```


## 📊 Model Details

| Property | Value |
|----------|-------|
| **Original Model** | [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) |
| **Format** | GUFF |
| **Quantization** | Full Precision |
| **License** | `apache-2.0` |
| **Export Date** | 2026-04-24 |
| **Exported By** | [QuantLLM v2.0](https://github.com/codewithdark-git/QuantLLM) |



---

## 🚀 Created with QuantLLM

<div align="center">

[![QuantLLM](https://img.shields.io/badge/🚀_QuantLLM-Ultra--fast_LLM_Quantization-orange?style=for-the-badge)](https://github.com/codewithdark-git/QuantLLM)

**Convert any model to GGUF, ONNX, or MLX in one line!**

```python
from quantllm import turbo

# Load any HuggingFace model
model = turbo("meta-llama/Meta-Llama-3-70B-Instruct")

# Export to any format
model.export("guff", quantization="Q4_K_M")

# Push to HuggingFace
model.push("your-repo", format="guff")
```

<a href="https://github.com/codewithdark-git/QuantLLM">
  <img src="https://img.shields.io/github/stars/codewithdark-git/QuantLLM?style=social" alt="GitHub Stars">
</a>

**[📚 Documentation](https://github.com/codewithdark-git/QuantLLM#readme)** · 
**[🐛 Report Issue](https://github.com/codewithdark-git/QuantLLM/issues)** · 
**[💡 Request Feature](https://github.com/codewithdark-git/QuantLLM/issues)**

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