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README.md

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 ---
+library_name: transformers
 license: apache-2.0
+basemodel: google/gemma-7b
 ---
+
+## 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.
+
+<img src="open_llm_leaderboard.png" width="800">
+
+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.
+
+
+## Usage
+The chat template of our chat models is similar as Official gemma-7b-it:
+```text
+<bos><start_of_turn>user
+hello, who are you?<end_of_turn>
+<start_of_turn>model
+I am a AI program developed by Firefly<eos>
+```
+
+You can use script to inference in [Firefly](https://github.com/yangjianxin1/Firefly/blob/master/script/chat/chat.py).
+
+You can also use the following code:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+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)
+
+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')
+
+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)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```

configuration.json

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+{"framework":"Pytorch","task":"text-generation"}

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