Create README.md

README.md

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+---
+license: cc-by-nc-sa-4.0
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+datasets:
+- psmathur/orca_minis_uncensored_dataset
+---
+
+# orca_mini_v2_7b
+An **Uncensored** LLaMA-7b model in collaboration with [Eric Hartford](https://huggingface.co/ehartford), trained on explain tuned datasets, created using Instructions and Input from WizardLM, Alpaca & Dolly-V2 datasets and applying Orca Research Paper dataset construction approaches.
+
+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 8, 2023
+
+## Model License
+
+Please refer to original Orca Mini v2 model license ([link](https://huggingface.co/psmathur/orca_mini_v2_7b)).
+
+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.
+
+## 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 hf_hub_download
+
+model_name = "psmathur/orca_mini_v2_7b"
+
+# 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 = hf_hub_download('abhinavkulkarni/psmathur-orca_mini_v2_7b-w4-g128-awq', 'pytorch_model.bin')
+
+with init_empty_weights():
+    model = AutoModelForCausalLM.from_pretrained(model_name, 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).
+
+[orca_mini_v2_7b](https://huggingface.co/psmathur/orca_mini_v2_7b)
+
+|  Task  |Version|    Metric     | Value |   |Stderr|
+|--------|------:|---------------|------:|---|------|
+|wikitext|      1|word_perplexity|13.7024|   |      |
+|        |       |byte_perplexity| 1.6315|   |      |
+|        |       |bits_per_byte  | 0.7062|   |      |
+
+[orca_mini_v2_7b (4-bit 128-group AWQ)](https://huggingface.co/abhinavkulkarni/psmathur-orca_mini_v2_7b-w4-g128-awq)
+
+|  Task  |Version|    Metric     | Value |   |Stderr|
+|--------|------:|---------------|------:|---|------|
+|wikitext|      1|word_perplexity|14.1097|   |      |
+|        |       |byte_perplexity| 1.6405|   |      |
+|        |       |bits_per_byte  | 0.7141|   |      |
+
+## Acknowledgements
+
+If you found `orca_mini_v2_7b` useful in your research or applications, please kindly cite using the following BibTeX:
+
+```
+@misc{orca_mini_v2_7b,
+  author = {Pankaj Mathur},
+  title = {orca_mini_v2_7b: An explain tuned LLaMA-7b model on uncensored wizardlm, alpaca, & dolly datasets},
+  year = {2023},
+  publisher = {GitHub, HuggingFace},
+  journal = {GitHub repository, HuggingFace repository},
+  howpublished = {\url{https://https://huggingface.co/psmathur/orca_mini_v2_7b},
+}
+```
+```
+@software{touvron2023llama,
+  title={LLaMA: Open and Efficient Foundation Language Models},
+  author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
+  journal={arXiv preprint arXiv:2302.13971},
+  year={2023}
+}
+```
+```
+@misc{openalpaca,
+  author = {Yixuan Su and Tian Lan and Deng Cai},
+  title = {OpenAlpaca: A Fully Open-Source Instruction-Following Model Based On OpenLLaMA},
+  year = {2023},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/yxuansu/OpenAlpaca}},
+}
+```
+```
+@misc{alpaca,
+  author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
+  title = {Stanford Alpaca: An Instruction-following LLaMA model},
+  year = {2023},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}},
+}
+```
+```
+@online{DatabricksBlog2023DollyV2,
+    author    = {Mike Conover and Matt Hayes and Ankit Mathur and Jianwei Xie and Jun Wan and Sam Shah and Ali Ghodsi and Patrick Wendell and Matei Zaharia and Reynold Xin},
+    title     = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM},
+    year      = {2023},
+    url       = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm},
+    urldate   = {2023-06-30}
+}
+```
+```
+@misc{xu2023wizardlm,
+      title={WizardLM: Empowering Large Language Models to Follow Complex Instructions}, 
+      author={Can Xu and Qingfeng Sun and Kai Zheng and Xiubo Geng and Pu Zhao and Jiazhan Feng and Chongyang Tao and Daxin Jiang},
+      year={2023},
+      eprint={2304.12244},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+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}
+}
+```