xmadai
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Llama-3.1-Nemotron-70B-Instruct-xMADai-INT4

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 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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-## Model Details
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
 library_name: transformers
+license: llama3.1
+base_model:
+- nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
 ---
+# This model has been xMADified!
+This repository contains [`nvidia/Llama-3.1-Nemotron-70B-Instruct-HF`](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF) quantized from 16-bit floats to 4-bit integers, using xMAD.ai proprietary technology.
+# Why should I use this model?
+1. **Accuracy:** This xMADified model is the **best quantized** version of the [`nvidia/Llama-3.1-Nemotron-70B-Instruct-HF`](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF) model (40 GB only). See _Table 1_ below for model quality benchmarks.
+2. **Memory-efficiency:** The full-precision model is around 140 GB, while this xMADified model is under 40 GB, making it feasible to run on a single 48 GB GPU.
+3. **Fine-tuning**:  These models are fine-tunable over the same reduced (a single 48 GB GPU) hardware in mere 3-clicks. Watch our product demo [here](https://www.youtube.com/watch?v=S0wX32kT90s&list=TLGGL9fvmJ-d4xsxODEwMjAyNA)
+## Table 1: xMAD vs. Unsloth
+| Model | Arc Challenge | Arc Easy | LAMBADA Standard | LAMBADA OpenAI | MMLU STEM | MMLU Humanities | MMLU Social Sciences | MMLU Other |
+|---|---|---|---|---|---|---|---|---|
+| [xmadai/Llama-3.1-Nemotron-70B-Instruct-xMADai-INT4](https://huggingface.co/xmadai/Llama-3.1-Nemotron-70B-Instruct-xMADai-INT4) (this model) | **63.05** | **86.36** | **71.96** | **75.82** | **75.55** | **80.62** | **87.42** | **83.71** |
+| [unsloth/Llama-3.1-Nemotron-70B-Instruct-bnb-4bit](https://huggingface.co/unsloth/Llama-3.1-Nemotron-70B-Instruct-bnb-4bit) | 60.32 | 85.35 | 71.01 | 74.64 | 75.1 | 80.06 | 87.33 | 83.71 |
+# How to Run Model
+Loading the model checkpoint of this xMADified model requires around 40 GB of VRAM. Hence it can be efficiently run on a single 48 GB GPU.
+**Package prerequisites**:
+1. Run the following *commands to install the required packages.
+```bash
+pip install torch==2.4.0  # Run following if you have CUDA version 11.8: pip install torch==2.4.0 --index-url https://download.pytorch.org/whl/cu118
+pip install transformers accelerate optimum
+pip install -vvv --no-build-isolation "git+https://github.com/PanQiWei/[email protected]"
+```
+**Sample Inference Code**
+```python
+from transformers import AutoTokenizer
+from auto_gptq import AutoGPTQForCausalLM
+model_id = "xmadai/Llama-3.1-Nemotron-70B-Instruct-xMADai-INT4"
+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, use_fast=False)
+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=1024)
+print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
+```
+# Contact Us
+For additional xMADified models, access to fine-tuning, and general questions, please contact us at [email protected] and join our waiting list.