YeungNLP
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firefly-qwen1.5-en-7b

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 library_name: transformers
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 ---
 library_name: transformers
 license: apache-2.0
+basemodel: Qwen/Qwen1.5-7B
 ---
+## Model Card for Firefly-Qwen1.5
+[firefly-qwen1.5-en-7b](https://huggingface.co/YeungNLP/firefly-qwen1.5-en-7b) and [firefly-qwen1.5-en-7b-dpo-v0.1](https://huggingface.co/YeungNLP/firefly-qwen1.5-en-7b-dpo-v0.1) are trained based on [Qwen1.5-7B](https://huggingface.co/Qwen/Qwen1.5-7B) to act as a helpful and harmless AI assistant.
+We use [Firefly](https://github.com/yangjianxin1/Firefly) to train our models on **a single V100 GPU** with QLoRA.
+firefly-qwen1.5-en-7b is fine-tuned based on Qwen1.5-7B with English instruction data, and firefly-qwen1.5-en-7b-dpo-v0.1 is trained with [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290) based on firefly-qwen1.5-en-7b.
+Our models outperform official [Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat), [Gemma-7B-it](https://huggingface.co/google/gemma-7b-it), [Zephyr-7B-Beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
+<img src="pics/open_llm.png" width="800">
+Although our models are 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.
+## Performance
+We evaluate our models on [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), they achieve good performance.
+| Model                             | Average | ARC    | HellaSwag | MMLU   | TruthfulQA | Winogrande | GSM8K  |
+|-----------------------------------|--------|--------|-----------|--------|------------|------------|--------|
+| firefly-gemma-7b                  | 62.93  | 	62.12 | 79.77     | 61.57  | 49.41      | 75.45      | 49.28  |
+| **firefly-qwen1.5-en-7b-dpo-v0.1** | 62.36  | 54.35  | 76.04     | 61.21  | 56.4       | 72.06      | 54.13  |
+| zephyr-7b-beta                    | 61.95  | 62.03  | 84.36     | 61.07  | 	57.45     | 77.74      | 	29.04 |
+| **firefly-qwen1.5-en-7b**         | 61.44  | 53.41  | 	75.51          | 61.67       |51.96          |70.72         | 55.34       |
+| vicuna-13b-v1.5                   | 55.41  | 57.08  | 	81.24    | 56.67  | 51.51      | 	74.66     | 11.3   |
+| Xwin-LM-13B-V0.1                  | 55.29  | 	62.54 | 82.8      | 56.53  | 45.96      | 74.27      | 9.63   |
+| Qwen1.5-7B-Chat                   | 55.15  | 	55.89 | 78.56     | 61.65  | 53.54      | 	67.72     | 13.57  |
+| gemma-7b-it                       | 53.56  | 51.45  | 71.96     | 53.52  | 47.29      | 	67.96     | 	29.19 |
+## Usage
+The chat templates of our chat models are the same as Official Qwen1.5-7B-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-7b"
+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 = 0.9,
+    temperature = 0.35,
+    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)
+```
 ## Training Details
+Both in SFT and DPO stages, **We only use a single V100 GPU** with QLoRA, and we use [Firefly](https://github.com/yangjianxin1/Firefly) to train our models.
+### Training Setting
+The following hyperparameters are used during SFT:
+- num_epochs: 1
+- learning_rate: 2e-4
+- total_train_batch_size: 32
+- max_seq_length: 2048
+- optimizer: paged_adamw_32bit
+- lr_scheduler_type: constant_with_warmup
+- warmup_steps: 700
+- lora_rank: 64
+- lora_alpha: 16
+- lora_dropout: 0.05
+- gradient_checkpointing: true
+- fp16: true
+The following hyperparameters were used during DPO:
+- num_epochs: 1
+- learning_rate: 2e-4
+- total_train_batch_size: 32
+- max_seq_length: 1600
+- max_prompt_length: 500
+- optimizer: paged_adamw_32bit
+- lr_scheduler_type: constant_with_warmup
+- warmup_steps: 200
+- lora_rank: 64
+- lora_alpha: 16
+- lora_dropout: 0.05
+- gradient_checkpointing: true
+- fp16: true
+### Training metrics
+Training Rewards/margins in DPO:
+<img src="pics/margins.png" width="600">
+Training Rewards/accuracies in DPO:
+<img src="pics/accuracies.png" width="500">
+Training loss in DPO:
+<img src="pics/loss.png" width="500">
+The table below shows the full set of DPO training metrics:
+| Epoch | Step | Loss    | Rewards/accuracies | Rewards/margins   | Rewards/chosen | Rewards/rejected | Logits/chosen| Logits/rejected |  Logps/chosen| Logps/rejected|
+|---|---|---|---|---|---|---|---|---|---|---|
+|0.05|100|0.6231|0.6587|0.3179|0.0404|-0.2774|1.1694|1.2377|-284.5586|-255.4863|
+|0.1|200|0.5945|0.6894|0.5988|-0.1704|-0.7693|1.012|1.0283|-284.3049|-268.1887|
+|0.16|300|0.5754|0.6981|0.8314|-0.282|-1.1133|0.8912|0.8956|-283.6926|-270.3117|
+|0.21|400|0.5702|0.7194|0.9369|-0.1944|-1.1313|0.7255|0.7557|-291.2833|-273.9706|
+|0.26|500|0.5913|0.695|0.8784|-0.4524|-1.3309|0.5491|0.5535|-289.5705|-271.754|
+|0.31|600|0.5743|0.6994|1.0192|-0.4505|-1.4698|0.6446|0.6399|-296.5292|-277.824|
+|0.37|700|0.5876|0.7219|1.0471|-0.6998|-1.747|0.4955|0.4329|-303.7684|-289.0117|
+|0.42|800|0.5831|0.715|1.0485|-0.8185|-1.8671|0.5589|0.4804|-295.6313|-288.0656|
+|0.47|900|0.5674|0.7119|1.1854|-1.2085|-2.3939|0.3467|0.2249|-302.3643|-286.2816|
+|0.52|1000|0.5794|0.7138|1.1458|-0.8423|-1.9881|0.5116|0.4248|-299.3136|-287.3934|
+|0.58|1100|0.5718|0.7194|1.2897|-1.4944|-2.7841|0.6392|0.5739|-316.6829|-294.1148|
+|0.63|1200|0.5718|0.7275|1.2459|-1.7543|-3.0002|0.4999|0.4065|-316.7873|-297.8514|
+|0.68|1300|0.5789|0.72|1.3379|-1.8485|-3.1864|0.4289|0.3172|-314.8326|-296.8319|
+|0.73|1400|0.5462|0.7425|1.4074|-1.9865|-3.3939|0.3645|0.2333|-309.4503|-294.3931|
+|0.79|1500|0.5829|0.7156|1.2582|-2.1183|-3.3766|0.4193|0.2796|-307.5281|-292.0817|
+|0.84|1600|0.5575|0.7375|1.471|-2.1429|-3.6139|0.6547|0.5152|-310.9912|-298.899|
+|0.89|1700|0.5638|0.745|1.5433|-2.991|-4.5343|0.7336|0.6782|-328.2657|-307.5182|
+|0.94|1800|0.5559|0.7181|1.4484|-2.8818|-4.3302|0.7997|0.8327|-316.2716|-295.1836|
+|0.99|1900|0.5627|0.7387|1.5378|-2.7941|-4.332|0.8573|0.858|-324.9405|-310.1192|