Update README.md

README.md

@@ -4,14 +4,9 @@ datasets:
 - NeelNanda/pile-10k
 ---
 
----
-license: apache-2.0
-datasets:
-- NeelNanda/pile-10k
-
 ## Model Details
 
-This model is an int4 model with group_size 128 with quantized lm-head of [Qwen/Qwen2-7B](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) generated by [intel/auto-round](https://github.com/intel/auto-round), auto-round is needed to run this model
+This model is an int4 model with group_size 128 with quantized lm-head of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) generated by [intel/auto-round](https://github.com/intel/auto-round), auto-round is needed to run this model
 
 ## How To Use
 
@@ -22,21 +17,64 @@ This model is an int4 model with group_size 128 with quantized lm-head of [Qwen/
 ```python
 ##git clone https://github.com/intel/auto-round.git
 ##cd auto-round && pip install -vvv --no-build-isolation -e .
-from auto_round.auto_quantizer import AutoHfQuantizer
+from auto_round import AutoHfQuantizer ##must import
+import torch
 from transformers import AutoModelForCausalLM,AutoTokenizer
 quantized_model_dir = "Intel/Qwen2.5-1.5B-Instruct-int4-inc"
 tokenizer = AutoTokenizer.from_pretrained(quantized_model_dir)
-model = AutoModelForCausalLM.from_pretrained(quantized_model_dir, device_map="auto")
-text = "下面我来介绍一下阿里巴巴公司，"
-text = "88+99等于多少？"
-text = "Once upon a time,"
-text = "There is a girl who likes adventure,"
-inputs = tokenizer(text, return_tensors="pt").to(model.device)
-print(tokenizer.decode(model.generate(**inputs, max_new_tokens=50, do_sample=False)[0]))
-##下面我来介绍一下阿里巴巴公司，阿里巴巴集团是一家全球领先的电子商务和数字技术公司。它成立于1999年，总部位于中国杭州，并在全球拥有超过20个办公室和超过3万名员工。阿里巴巴集团的业务包括在线零售、云计算、金融服务、物流
-##88+99等于多少？!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
-##Once upon a time, there was a young girl named Lily who loved to explore the world around her. She would often wander through fields and forests, looking for new places to visit and things to discover.One day, while she was wandering in a nearby forest, she stumbled
-##There is a girl who likes adventure, and she has been exploring the world for many years. She has visited every country in Europe, climbed mountains all over the world, and even sailed across oceans on her own. However, she still feels that there are more places to explore.One day
+
+model = AutoModelForCausalLM.from_pretrained(
+    quantized_model_dir,
+    torch_dtype='auto',
+    device_map="auto",
+)
+
+##import habana_frameworks.torch.core as htcore ## uncommnet it for HPU
+##import habana_frameworks.torch.hpu as hthpu ## uncommnet it for HPU
+##model = model.to(torch.bfloat16).to("hpu") ## uncommnet it for HPU
+
+prompt = "There is a girl who likes adventure,"
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": prompt}
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    model_inputs.input_ids,
+    max_new_tokens=50,  ##change this to align with the official usage
+    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(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+
+##prompt = "There is a girl who likes adventure,"
+##That's great! What kind of adventure does she like?
+
+##prompt = "Which one is bigger, 9.11 or 9.8"
+##To determine which number is larger between 9.11 and 9.8, you can compare them directly:
+##1. Start with the numbers: 9.11 and 9.8.
+##2. Compare their digits from left to
+
+
+##prompt = "Once upon a time,"
+##once upon a time, there was a young girl named Lily who lived in a small village nestled among the rolling hills of England. She had always been fascinated by nature and the beauty of the world around her.One day, while exploring the woods near\
+
+##prompt = "请介绍一下阿里巴巴公司"
+##阿里巴巴集团是一家全球领先的电子商务和科技企业，成立于1999年。阿里巴巴集团总部位于中国杭州，并在全球范围内拥有超过20个运营中心。
+##阿里巴巴集团的业务范围包括：
+##1. 电子商务：阿里巴巴集团是全球
+
 ```
 
 ### Evaluate the model 
@@ -45,27 +83,26 @@ pip3 install lm-eval==0.4.2
 
 ```bash
 git clone https://github.com/intel/auto-round
-cd auto-round/examples/language-modeling
-python3 eval_042/evluation.py --model_name "Intel/Qwen2.5-1.5B-Instruct-int4-inc" --eval_bs 16  --tasks lambada_openai,hellaswag,piqa,winogrande,truthfulqa_mc1,truthfulqa_mc2,openbookqa,boolq,arc_easy,arc_challenge,mmlu,gsm8k,cmmlu,ceval-valid
+cd auto-round
+python -m auto_round --model "Intel/Qwen2.5-1.5B-Instruct-int4-inc" --eval --eval_bs 16  --tasks lambada_openai,hellaswag,piqa,winogrande,truthfulqa_mc1,openbookqa,boolq,arc_easy,arc_challenge,mmlu,gsm8k,cmmlu,ceval-valid
 ```
 
-| Metric         | BF16   | INT4   | 
-| -------------- | ------ | ------ |
-| Avg            | 0.5575 | 0.5618 |
-| mmlu           | 0.6010 | 0.5924 |
-| cmmlu          | 0.6497 | 0.6259 |
-| ceval-valid    | 0.6597 | 0.6404 |
-| lambada_openai | 0.6095 | 0.5924 |
-| hellaswag      | 0.5082 | 0.5012 |
-| winogrande     | 0.6298 | 0.6409 | 
-| piqa           | 0.7633 | 0.7650 |
-| truthfulqa_mc1 | 0.3109 | 0.3060 |
-| truthfulqa_mc2 | 0.4663 | 0.4563 |
-| openbookqa     | 0.3160 | 0.3020 |
-| boolq          | 0.7789 | 0.7709 |
-| arc_easy       | 0.7677 | 0.7681 |
-| arc_challenge  | 0.4343 | 0.4360 |
-| gsm8k 5 shots  | 0.3101 | 0.4519 |
+| Metric         | BF16   | INT4(group_size 128)   | INT4(group_size 32)   |
+|:--------------:| :----: | :----: |:------:|
+| Avg            | 0.5646 | 0.5668 | 0.5699 |
+| mmlu           | 0.6010 | 0.5876 | 0.5924 |
+| cmmlu          | 0.6497 | 0.6146 | 0.6259 |
+| ceval-valid    | 0.6597 | 0.6382 | 0.6404 |
+| lambada_openai | 0.6095 | 0.5886 | 0.6082 |
+| hellaswag      | 0.5082 | 0.4985 | 0.5012 |
+| winogrande     | 0.6298 | 0.6204 | 0.6409 |
+| piqa           | 0.7633 | 0.7519 | 0.7650 |
+| truthfulqa_mc1 | 0.3109 | 0.3158 | 0.3060 |
+| openbookqa     | 0.3160 | 0.2940 | 0.3020 |
+| boolq          | 0.7789 | 0.7703 | 0.7681 |
+| arc_easy       | 0.7677 | 0.7660 | 0.7681 |
+| arc_challenge  | 0.4343 | 0.4454 | 0.4360 |
+| gsm8k 5 shots  | 0.3101 | 0.4776 | 0.4519 |
 
 
 
@@ -77,12 +114,11 @@ Here is the sample command to reproduce the model. We observed a larger accuracy
 
 ```bash
 git clone https://github.com/intel/auto-round
-cd auto-round/examples/language-modeling
-pip install -r requirements.txt
-python3 main.py \
+cd auto-round
+python -m auto_round \
 --model_name  Qwen/Qwen2.5-1.5B-Instruct \
 --device 0 \
---group_size 32 \
+--group_size 128 \
 --nsamples 512 \
 --bits 4 \
 --iter 1000 \