Create README.md

README.md

@@ -0,0 +1,162 @@
+---
+datasets:
+- NeelNanda/pile-10k
+base_model:
+- deepseek-ai/DeepSeek-R1-Distill-Llama-70B
+
+
+
+---
+
+## Model Details
+
+This model is an int4 model with group_size 128 and symmetric quantization of [deepseek-ai/DeepSeek-R1-Distill-Llama-70B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B) generated by [intel/auto-round](https://github.com/intel/auto-round) algorithm. 
+
+Please follow the license of the original model.
+
+## How To Use
+
+**INT4 Inference on CUDA**
+
+~~~python
+import transformers
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+quantized_model_dir = "OPEA/DeepSeek-R1-Distill-Llama-70B-int4-gptq-sym-inc"
+
+device_map="auto"
+model = AutoModelForCausalLM.from_pretrained(
+    quantized_model_dir,
+    torch_dtype=torch.float16,
+    trust_remote_code=True,
+    device_map=device_map,
+)
+
+tokenizer = AutoTokenizer.from_pretrained(quantized_model_dir, trust_remote_code=True)
+prompts = [
+    "9.11和9.8哪个数字大",
+    "如果你是人，你最想做什么",
+    "How many e in word deepseek",
+    "There are ten birds in a tree. A hunter shoots one. How many are left in the tree?",
+]
+
+texts = []
+for prompt in prompts:
+    messages = [
+        {"role": "user", "content": prompt}
+    ]
+    text = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+    texts.append(text)
+
+inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True)
+
+outputs = model.generate(
+    input_ids=inputs["input_ids"].to(model.device),
+    attention_mask=inputs["attention_mask"].to(model.device),
+    max_length=512,  ##change this to align with the official usage
+    num_return_sequences=1,
+    do_sample=False  ##change this to align with the official usage
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs["input_ids"], outputs)
+]
+
+decoded_outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+
+for i, prompt in enumerate(prompts):
+    input_id = inputs
+    print(f"Prompt: {prompt}")
+    print(f"Generated: {decoded_outputs[i]}")
+    print("-" * 50)
+
+
+"""
+Prompt: 9.11和9.8哪个数字大
+
+--------------------------------------------------
+Prompt: 如果你是人类，你最想做什么
+
+--------------------------------------------------
+Prompt: How many e in word deepseek
+
+--------------------------------------------------
+Prompt: There are ten birds in a tree. A hunter shoots one. How many are left in the tree?
+
+~~~
+
+### Evaluate the model
+
+will update later
+<!-- pip3 install lm-eval==0.4.7
+we found lm-eval is very unstable for this model. Please set `add_bos_token=True `to align with the origin model. **Please use autogptq format**
+
+```bash
+lm-eval --model hf --model_args pretrained=OPEA/DeepSeek-R1-Distill-Llama-70B-int4-gptq-sym-inc,add_bos_token=True   --tasks leaderboard_mmlu_pro,leaderboard_ifeval,lambada_openai,hellaswag,piqa,winogrande,truthfulqa_mc1,openbookqa,boolq,arc_easy,arc_challenge,mmlu,gsm8k --batch_size 16
+```
+|           Metric           |   BF16   |      INT4      |
+| :------------------------ | :---------------------- | :--------------- |
+| avg                  | 0.6647 |  0.6639|
+| leaderboard_mmlu_pro | -      | -      |
+| mmlu                 | 0.7964 | 0.7928 |
+| lambada_openai       | 0.6649 | 0.6718 |
+| hellaswag            | 0.6292 | 0.6223 |
+| winogrande           | 0.7482 | 0.7482 |
+| piqa                 | 0.8058 | 0.7982 |
+| truthfulqa_mc1       | 0.3831 | 0.3905 |
+| openbookqa           | 0.3520 | 0.3520 |
+| boolq                | 0.8963 | 0.8972 |
+| arc_easy             | 0.8207 | 0.8194 |
+| arc_challenge        | 0.5503 | 0.5469 |
+| leaderboard_ifeval   | -      | -      |
+| gsm8k                | -      | -      | -->
+
+
+
+### Generate the model
+Here is the sample command to generate the model.
+
+
+```bash
+auto-round  \
+--model deepseek-ai/DeepSeek-R1-Distill-Llama-70B \
+--device 0 \
+--bits 4 \
+--iter 200 \
+--disable_eval \
+--format 'auto_gptq,auto_round,auto_awq' \
+--output_dir "./tmp_autoround" 
+
+```
+
+
+
+
+
+## Ethical Considerations and Limitations
+
+The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
+
+Therefore, before deploying any applications of the model, developers should perform safety testing.
+
+## Caveats and Recommendations
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
+
+Here are a couple of useful links to learn more about Intel's AI software:
+
+- Intel Neural Compressor [link](https://github.com/intel/neural-compressor)
+
+## Disclaimer
+
+The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes.
+
+## Cite
+
+@article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi}, journal={arXiv preprint arXiv:2309.05516}, year={2023} }
+
+[arxiv](https://arxiv.org/abs/2309.05516) [github](https://github.com/intel/auto-round)