YeungNLP
/

firefly-qwen1.5-en-14b-alpha

@@ -4,197 +4,128 @@ license: apache-2.0
 basemodel: Qwen/Qwen1.5-14B
 ---
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 basemodel: Qwen/Qwen1.5-14B
 ---
+## Model Card for Firefly-Qwen1.5-14B-En-Alpha
+[firefly-qwen1.5-en-14b-alpha](https://huggingface.co/YeungNLP/firefly-qwen1.5-en-14b-alpha) is a preview version model of our new model.
+It outperforms [Qwen1.5-14B-Chat](https://huggingface.co/Qwen/Qwen1.5-14B-Chat) on [AlpacaEval 2.0](https://github.com/tatsu-lab/alpaca_eval) and [MT-Bench](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge)' single-turn task.
+**Note: More importantly, it is not trained with neither SFT nor RLHF, maybe we will share our method later.**
+What's exciting is that our experimental method can achieve good performance, even though it's still in a very preliminary stage.
+Although our model is trained with English data, you can also try to chat with models in Chinese because Qwen1.5 is also good at Chinese. But we have not evaluated
+the performance in Chinese yet.
+We advise you to install transformers>=4.37.0.
+Because this is a validation experiment and our training resources are limited, we use QLoRA to train this model with the max length of 1024, it may limit the performance of this model.
+## Performance
+We automatically evaluate models on [AlpacaEval 2.0](https://github.com/tatsu-lab/alpaca_eval) and [MT-Bench](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge) with **gpt-4o**.
+We evaluate models on [AlpacaEval 2.0](https://github.com/tatsu-lab/alpaca_eval) with 805 questions, our model outperforms [Qwen1.5-14B-Chat](https://huggingface.co/Qwen/Qwen1.5-14B-Chat).
+The win rate is **52.17% : 47.83%**.
+| Task          | Ours wins | Qwen1.5-14B-Chat wins |
+|---------------|-----------|-----------------------|
+| helpful_base  | **67**    | 62                    |
+| koala         | **80**    | 76                    |
+| oasst         | **100**   | 88                    |
+| selfinstruct  | **127**   | 125                   |
+| vicuna        | **46**    | 34                    |
+| total         | **420**   | 385                   |
+We also evaluate models on [MT-Bench](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge). Though the overall performance of our model is not as good as [Qwen1.5-14B-Chat](https://huggingface.co/Qwen/Qwen1.5-14B-Chat),
+we find that our model outperforms [Qwen1.5-14B-Chat](https://huggingface.co/Qwen/Qwen1.5-14B-Chat) in almost all single-turn tasks. Our model is worse than [Qwen1.5-14B-Chat](https://huggingface.co/Qwen/Qwen1.5-14B-Chat) in almost all multi-turn tasks.
+We conjecture that it may be caused by the training length, and we will dive into this phenomenon later.
+Overall Performances on MT-Bench:
+| Task              | Ours     | Qwen1.5-14B-Chat  |
+|-------------------|----------|-------------------|
+| Avg Score         | 7.03     | **7.21**          |
+| Single-turn Score | **8.01** | 7.66              |
+| Multi-turn Score  | 6.05     | **6.75**          |
+Performances on MT-Bench' single-turn tasks:
+| Task          | Ours     | Qwen1.5-14B-Chat |
+|---------------|----------|------------------|
+| writing	      | **9.1**	 | 8.9              |
+| roleplay	     | **8.5**  | 	8.3             |
+| extraction	   | **8.6**	 | 8.2              |
+| stem	         | **8.8**  | 	8.5             |
+| humanities	   | **9**    | 	8.8             |
+| reasoning     | 	**6.8** | 	5.3             |
+| math	         | **7.5**  | 	7.1             |
+| coding	       | 5.8	     | **6.2**              |
+Performances on MT-Bench' multi-turn tasks:
+| Task           | Ours     | Qwen1.5-14B-Chat   |
+|----------------|----------|--------------------|
+| writing        | 	6.5     | 	**7.7**           |
+| roleplay       | 	7.7     | 	**8.3**           |
+| extraction     | 	5.1     | 	**6.7**           |
+| stem           | 	6.3     | 	**6.9**           |
+| humanities     | 	8.3     | 	**8.8**           |
+| reasoning      | 	4.7     | 	**5.7**           |
+| math           | 	4.9     | 	**5.5 **          |
+| coding         | 	**4.9** | 	4.4               |
+## Usage
+The chat templates of our chat models are the same as Official Qwen1.5-14B-Chat:
+```text
+<|im_start|>system
+You are a helpful assistant.<|im_end|>
+<|im_start|>user
+hello, who are you?<|im_end|>
+<|im_start|>assistant
+I am a AI program developed by Firefly<|im_end|>
+```
+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 = "YeungNLP/firefly-qwen1.5-en-14b-alpha"
+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. "
+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('cuda')
+generated_ids = model.generate(
+    model_inputs.input_ids,
+    max_new_tokens=1500,
+    top_p = 0.8,
+    temperature = 0.6,
+    repetition_penalty = 1.0,
+    eos_token_id=tokenizer.encode('<|im_end|>', 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)
+```