Update README.md

README.md

@@ -7,61 +7,36 @@ basemodel: google/gemma-7b-it
 ## Model Card for Firefly-Gemma
 
 [gemma-7B-it-firefly](https://huggingface.co/yys/gemma-7B-it-firefly) is trained based on [gemma-7b-it](https://huggingface.co/google/gemma-7b-it) to act as a helpful and harmless AI assistant. 
-We use [Firefly](https://github.com/yangjianxin1/Firefly) to train the model with LoRA.
+we trained the model on [firefly-train-1.1M](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M) dataset with LoRA.
 
 
 <img src="gemma-7B-it-firefly.jpg" width="250">
 
-We advise you to install transformers>=4.38.2.
 
 ## Performance
-We evaluate our models on [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), they achieve good performance.
-
+we evaluate the model on [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
 
 ## Usage
-The chat template of our chat models is similar as Official gemma-7b-it:
+The chat template of our chat models is same as Official gemma-7b-it:
 ```text
 <bos><start_of_turn>user
-hello, who are you?<end_of_turn>
+Write a hello world program<end_of_turn>
 <start_of_turn>model
-I am a AI program developed by Firefly<eos>
 ```
 
 You can also use the following code:
 ```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
 model_name_or_path = "yys/gemma-7B-it-firefly"
-model = AutoModelForCausalLM.from_pretrained(
-    model_name_or_path,
-    trust_remote_code=True,
-    low_cpu_mem_usage=True,
-    torch_dtype=torch.float16,
-    device_map='auto',
-)
 tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
+model = AutoModelForCausalLM.from_pretrained(model_name_or_path)
 
-prompt = "Compose an engaging travel blog post about a recent trip to Hawaii, highlighting cultural experiences and must-see attractions. "
-text = f"""
-<bos><start_of_turn>user
-{prompt}<end_of_turn>
-<start_of_turn>model
-""".strip()
-model_inputs = tokenizer([text], return_tensors="pt").to('cuda')
+input_text = "给我写一首关于机器学习的诗歌。"
+input_ids = tokenizer(input_text, return_tensors="pt")
 
-generated_ids = model.generate(
-    model_inputs.input_ids,
-    max_new_tokens=1500,
-    top_p = 0.9,
-    temperature = 0.35,
-    repetition_penalty = 1.0,
-    eos_token_id=tokenizer.encode('<eos>', add_special_tokens=False)
-)
-generated_ids = [
-    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
-]
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
 
-response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
-print(response)
 ```