xmadai
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gemma-2-9b-it-xMADai-INT4

@@ -1,199 +1,79 @@
 ---
 library_name: transformers
-tags: []
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-# Model Card for Model ID
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-## Model Details
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 ---
 library_name: transformers
+license: gemma
+base_model:
+- google/gemma-2-9b-it
 ---
+# This model has been xMADified!
+This repository contains [`google/gemma-2-9b-it`](https://huggingface.co/google/gemma-2-9b-it) 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 [`google/gemma-2-9b-it`](https://huggingface.co/google/gemma-2-9b-it) model (8 GB only). See _Table 1_ below for model quality benchmarks.
+2. **Memory-efficiency:** The full-precision model is around 18.5 GB, while this xMADified model is only around 8 GB, making it feasible to run on a 12 GB GPU.
+3. **Fine-tuning**:  These models are fine-tunable over the same reduced (12 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. Hugging Quants
+| Model | MMLU | Arc Challenge | Arc Easy | LAMBADA Standard | LAMBADA OpenAI | PIQA | WinoGrande |
+|---|---|---|---|---|---|---|---|
+| [xmadai/gemma-2-9b-it-xMADai-INT4](https://huggingface.co/xmadai/gemma-2-9b-it-xMADai-INT4) (this model) | **71.17** | **62.37** | **85.61** | **70.60** | **72.15** | **81.50** | **75.06** |
+| [hugging-quants/gemma-2-9b-it-AWQ-INT4](https://huggingface.co/hugging-quants/gemma-2-9b-it-AWQ-INT4) | 71.04 | 61.77 | 85.14 | 69.16 | 70.68 | 80.41 | 75.06 |
+# How to Run Model
+Loading the model checkpoint of this xMADified model requires around 8 GB of VRAM. Hence it can be efficiently run on a 12 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/AutoGPTQ.git@v0.7.1"
+```
+**Sample Inference Code**
+```python
+from transformers import AutoTokenizer
+from auto_gptq import AutoGPTQForCausalLM
+model_id = "xmadai/gemma-2-9b-it-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))
+```
+# Citation
+If you found this model useful, please cite our research paper.
+```
+@article{zhang2024leanquant,
+  title={Leanquant: Accurate large language model quantization with loss-error-aware grid},
+  author={Zhang, Tianyi and Shrivastava, Anshumali},
+  journal={arXiv preprint arXiv:2407.10032},
+  year={2024}
+}
+```
+# Contact Us
+For additional xMADified models, access to fine-tuning, and general questions, please contact us at support@xmad.ai and join our waiting list.