Create README.md

README.md

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+---
+license: cc-by-sa-3.0
+tags:
+- MosaicML
+- AWQ
+inference: false
+---
+
+# MPT-30B-Instruct (4-bit 128g AWQ Quantized)
+[MPT-30B-Instruct](https://huggingface.co/mosaicml/mpt-30b-instruct) is a model for short-form instruction following. 
+
+This model is a 4-bit 128 group size AWQ quantized model. For more information about AWQ quantization, please click [here](https://github.com/mit-han-lab/llm-awq).
+
+## Model Date
+
+July 5, 2023
+
+## Model License
+
+Please refer to original MPT model license ([link](https://huggingface.co/mosaicml/mpt-30b-instruct)).
+
+Please refer to the AWQ quantization license ([link](https://github.com/llm-awq/blob/main/LICENSE)).
+
+## CUDA Version
+
+This model was successfully tested on CUDA driver v530.30.02 and runtime v11.7 with Python v3.10.11. Please note that AWQ requires NVIDIA GPUs with compute capability of 80 or higher.
+
+For Docker users, the `nvcr.io/nvidia/pytorch:23.06-py3` image is runtime v12.1 but otherwise the same as the configuration above and has also been verified to work.
+
+## How to Use
+
+```bash
+git clone https://github.com/mit-han-lab/llm-awq \
+&& cd llm-awq \
+&& git checkout 71d8e68df78de6c0c817b029a568c064bf22132d \
+&& pip install -e . \
+&& cd awq/kernels \\
+&& python setup.py install
+```
+
+```python
+import torch
+from awq.quantize.quantizer import real_quantize_model_weight
+from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer
+from accelerate import init_empty_weights, load_checkpoint_and_dispatch
+from huggingface_hub import snapshot_download
+
+model_name = "mosaicml/mpt-30b-instruct"
+
+# Config
+config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
+
+# Tokenizer
+tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_name)
+
+# Model
+w_bit = 4
+q_config = {
+    "zero_point": True,
+    "q_group_size": 128,
+}
+
+load_quant = snapshot_download('abhinavkulkarni/mpt-30b-instruct-w4-g128-awq')
+
+with init_empty_weights():
+    model = AutoModelForCausalLM.from_config(config=config, 
+                                                 torch_dtype=torch.float16, trust_remote_code=True)
+
+real_quantize_model_weight(model, w_bit=w_bit, q_config=q_config, init_only=True)
+
+model = load_checkpoint_and_dispatch(model, load_quant, device_map="balanced")
+
+# Inference
+prompt = f'''What is the difference between nuclear fusion and fission?
+###Response:'''
+
+input_ids = tokenizer(prompt, return_tensors='pt').input_ids.cuda()
+output = model.generate(
+    inputs=input_ids, 
+    temperature=0.7,
+    max_new_tokens=512,
+    top_p=0.15,
+    top_k=0,
+    repetition_penalty=1.1,
+    eos_token_id=tokenizer.eos_token_id
+)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+
+## Evaluation
+
+This evaluation was done using [LM-Eval](https://github.com/EleutherAI/lm-evaluation-harness).
+
+[MPT-30B-Instruct](https://huggingface.co/mosaicml/mpt-30b-instruct)
+
+|  Task  |Version|    Metric     | Value |   |Stderr|
+|--------|------:|---------------|------:|---|------|
+|wikitext|      1|word_perplexity|11.3275|   |      |
+|        |       |byte_perplexity| 1.5744|   |      |
+|        |       |bits_per_byte  | 0.6548|   |      |
+
+[MPT-30B-Instruct (4-bit 128-group AWQ)](https://huggingface.co/abhinavkulkarni/mosaicml-mpt-30b-instruct-w4-g128-awq)
+
+|  Task  |Version|    Metric     | Value |   |Stderr|
+|--------|------:|---------------|------:|---|------|
+|wikitext|      1|word_perplexity|11.6058|   |      |
+|        |       |byte_perplexity| 1.5816|   |      |
+|        |       |bits_per_byte  | 0.6614|   |      |
+
+## Acknowledgements
+
+The MPT model was originally finetuned by Sam Havens and the MosaicML NLP team. Please cite this model using the following format:
+
+```
+@online{MosaicML2023Introducing,
+    author    = {MosaicML NLP Team},
+    title     = {Introducing MPT-30B: A New Standard for Open-Source, Commercially Usable LLMs},
+    year      = {2023},
+    url       = {www.mosaicml.com/blog/mpt-30b},
+    note      = {Accessed: 2023-03-28}, % change this date
+    urldate   = {2023-03-28} % change this date
+}
+```
+
+
+The model was quantized with AWQ technique. If you find AWQ useful or relevant to your research, please kindly cite the paper:
+
+```
+@article{lin2023awq,
+  title={AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration},
+  author={Lin, Ji and Tang, Jiaming and Tang, Haotian and Yang, Shang and Dang, Xingyu and Han, Song},
+  journal={arXiv},
+  year={2023}
+}
+```
+