---
base_model:
- meta-llama/Llama-3.1-405B-Instruct
library_name: transformers
license: llama3.1
---

# This model has been xMADified!

This repository contains [`meta-llama/Meta-Llama-3.1-405B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct) quantized, using xMAD.ai proprietary technology, from 16-bit floats to 4-bit integers.

# How to Run Model

Loading the model checkpoint of this xMADified model requires < 200 GiB of VRAM.

Hence it can be efficiently run on **1 node** of **8 x V100-32GB** GPUs, or **3 x A100-80GB** GPUs.

**Fine-tuning**:  Our 405B model is fine-tunable over the same reduced (< 200 GB) hardware in mere 3-clicks. Watch our product demo [here](https://www.youtube.com/watch?v=S0wX32kT90s&list=TLGGL9fvmJ-d4xsxODEwMjAyNA)


**Package prerequisites**: Run the following commands to install the required packages.
```bash
pip install -q --upgrade transformers accelerate optimum
pip install -q --no-build-isolation auto-gptq
```

**Sample Inference Code**

```python
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM

model_id = "xmadai/Llama-3.1-405B-Instruct-xMADai-4bit"
prompt = [
  {"role": "system", "content": "You are a helpful assistant, that responds as a pirate."},
  {"role": "user", "content": "What's Deep Learning?"},
]

tokenizer = AutoTokenizer.from_pretrained(model_id)

inputs = tokenizer.apply_chat_template(
  prompt,
  tokenize=True,
  add_generation_prompt=True,
  return_tensors="pt",
  return_dict=True,
).to("cuda")

model = AutoGPTQForCausalLM.from_quantized(
    model_id,
    device_map='auto',
    trust_remote_code=True,
)

outputs = model.generate(**inputs, do_sample=True, max_new_tokens=256)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
```

# Model Quality

We report the zero-shot accuracy of this xMADified model on popular benchmarks below. The results are obtained using [`lm-evaluation-harness`](https://github.com/EleutherAI/lm-evaluation-harness).

| Model | Arc Challenge | Arc Easy | LAMBADA OpenAI | LAMBADA Standard | MMLU Humanities | MMLU STEM | WinoGrande |
|---|---|---|---|---|---|---|---|
| xMADified Llama-3.1-405B-Instruct | 64.76 | 88.26 | 77.08 | 73.32 | 82.74 | 82.18 | 81.22 |

Other xMADified models and their GPU memory requirements are listed below.

For additional xMADified models, access to fine-tuning, and general questions, please contact us at support@xmad.ai and join our waiting list.