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--- |
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library_name: transformers |
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license: apache-2.0 |
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basemodel: Qwen/Qwen1.5-7B |
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--- |
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## Model Card for Firefly-Qwen1.5 |
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[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. |
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We use [Firefly](https://github.com/yangjianxin1/Firefly) to train our models on **a single V100 GPU** with QLoRA. |
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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. |
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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). |
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<img src="pics/open_llm.png" width="800"> |
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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 |
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the performance in Chinese yet. |
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We advise you to install transformers>=4.37.0. |
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## Performance |
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We evaluate our models on [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), they achieve good performance. |
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| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K | |
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|-----------------------------------|--------|--------|-----------|--------|------------|------------|--------| |
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| firefly-gemma-7b | 62.93 | 62.12 | 79.77 | 61.57 | 49.41 | 75.45 | 49.28 | |
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| **firefly-qwen1.5-en-7b-dpo-v0.1** | 62.36 | 54.35 | 76.04 | 61.21 | 56.4 | 72.06 | 54.13 | |
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| zephyr-7b-beta | 61.95 | 62.03 | 84.36 | 61.07 | 57.45 | 77.74 | 29.04 | |
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| **firefly-qwen1.5-en-7b** | 61.44 | 53.41 | 75.51 | 61.67 |51.96 |70.72 | 55.34 | |
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| vicuna-13b-v1.5 | 55.41 | 57.08 | 81.24 | 56.67 | 51.51 | 74.66 | 11.3 | |
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| Xwin-LM-13B-V0.1 | 55.29 | 62.54 | 82.8 | 56.53 | 45.96 | 74.27 | 9.63 | |
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| Qwen1.5-7B-Chat | 55.15 | 55.89 | 78.56 | 61.65 | 53.54 | 67.72 | 13.57 | |
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| gemma-7b-it | 53.56 | 51.45 | 71.96 | 53.52 | 47.29 | 67.96 | 29.19 | |
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## Usage |
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The chat templates of our chat models are the same as Official Qwen1.5-7B-Chat: |
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```text |
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<|im_start|>system |
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You are a helpful assistant.<|im_end|> |
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<|im_start|>user |
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hello, who are you?<|im_end|> |
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<|im_start|>assistant |
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I am a AI program developed by Firefly<|im_end|> |
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``` |
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You can use script to inference in [Firefly](https://github.com/yangjianxin1/Firefly/blob/master/script/chat/chat.py). |
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You can also use the following code: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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model_name_or_path = "YeungNLP/firefly-qwen1.5-en-7b-dpo-v0.1" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name_or_path, |
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trust_remote_code=True, |
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low_cpu_mem_usage=True, |
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torch_dtype=torch.float16, |
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device_map='auto', |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) |
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prompt = "Compose an engaging travel blog post about a recent trip to Hawaii, highlighting cultural experiences and must-see attractions. " |
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messages = [ |
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{"role": "system", "content": "You are a helpful assistant."}, |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to('cuda') |
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generated_ids = model.generate( |
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model_inputs.input_ids, |
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max_new_tokens=1500, |
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top_p = 0.9, |
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temperature = 0.35, |
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repetition_penalty = 1.0, |
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eos_token_id=tokenizer.encode('<|im_end|>', add_special_tokens=False) |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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print(response) |
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``` |
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## Training Details |
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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. |
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### Training Setting |
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The following hyperparameters are used during SFT: |
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- num_epochs: 1 |
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- learning_rate: 2e-4 |
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- total_train_batch_size: 32 |
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- max_seq_length: 2048 |
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- optimizer: paged_adamw_32bit |
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- lr_scheduler_type: constant_with_warmup |
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- warmup_steps: 700 |
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- lora_rank: 64 |
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- lora_alpha: 16 |
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- lora_dropout: 0.05 |
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- gradient_checkpointing: true |
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- fp16: true |
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The following hyperparameters were used during DPO: |
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- num_epochs: 1 |
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- learning_rate: 2e-4 |
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- total_train_batch_size: 32 |
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- max_seq_length: 1600 |
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- max_prompt_length: 500 |
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- optimizer: paged_adamw_32bit |
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- lr_scheduler_type: constant_with_warmup |
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- warmup_steps: 200 |
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- lora_rank: 64 |
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- lora_alpha: 16 |
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- lora_dropout: 0.05 |
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- gradient_checkpointing: true |
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- fp16: true |
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### Training metrics |
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Training Rewards/margins in DPO: |
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<img src="pics/margins.png" width="600"> |
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Training Rewards/accuracies in DPO: |
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<img src="pics/accuracies.png" width="500"> |
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Training loss in DPO: |
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<img src="pics/loss.png" width="500"> |
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The table below shows the full set of DPO training metrics: |
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| Epoch | Step | Loss | Rewards/accuracies | Rewards/margins | Rewards/chosen | Rewards/rejected | Logits/chosen| Logits/rejected | Logps/chosen| Logps/rejected| |
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|---|---|---|---|---|---|---|---|---|---|---| |
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|0.05|100|0.6231|0.6587|0.3179|0.0404|-0.2774|1.1694|1.2377|-284.5586|-255.4863| |
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|0.1|200|0.5945|0.6894|0.5988|-0.1704|-0.7693|1.012|1.0283|-284.3049|-268.1887| |
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|0.16|300|0.5754|0.6981|0.8314|-0.282|-1.1133|0.8912|0.8956|-283.6926|-270.3117| |
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|0.21|400|0.5702|0.7194|0.9369|-0.1944|-1.1313|0.7255|0.7557|-291.2833|-273.9706| |
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|0.26|500|0.5913|0.695|0.8784|-0.4524|-1.3309|0.5491|0.5535|-289.5705|-271.754| |
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|0.31|600|0.5743|0.6994|1.0192|-0.4505|-1.4698|0.6446|0.6399|-296.5292|-277.824| |
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|0.37|700|0.5876|0.7219|1.0471|-0.6998|-1.747|0.4955|0.4329|-303.7684|-289.0117| |
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|0.42|800|0.5831|0.715|1.0485|-0.8185|-1.8671|0.5589|0.4804|-295.6313|-288.0656| |
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|0.47|900|0.5674|0.7119|1.1854|-1.2085|-2.3939|0.3467|0.2249|-302.3643|-286.2816| |
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|0.52|1000|0.5794|0.7138|1.1458|-0.8423|-1.9881|0.5116|0.4248|-299.3136|-287.3934| |
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|0.58|1100|0.5718|0.7194|1.2897|-1.4944|-2.7841|0.6392|0.5739|-316.6829|-294.1148| |
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|0.63|1200|0.5718|0.7275|1.2459|-1.7543|-3.0002|0.4999|0.4065|-316.7873|-297.8514| |
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|0.68|1300|0.5789|0.72|1.3379|-1.8485|-3.1864|0.4289|0.3172|-314.8326|-296.8319| |
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|0.73|1400|0.5462|0.7425|1.4074|-1.9865|-3.3939|0.3645|0.2333|-309.4503|-294.3931| |
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|0.79|1500|0.5829|0.7156|1.2582|-2.1183|-3.3766|0.4193|0.2796|-307.5281|-292.0817| |
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|0.84|1600|0.5575|0.7375|1.471|-2.1429|-3.6139|0.6547|0.5152|-310.9912|-298.899| |
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|0.89|1700|0.5638|0.745|1.5433|-2.991|-4.5343|0.7336|0.6782|-328.2657|-307.5182| |
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|0.94|1800|0.5559|0.7181|1.4484|-2.8818|-4.3302|0.7997|0.8327|-316.2716|-295.1836| |
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|0.99|1900|0.5627|0.7387|1.5378|-2.7941|-4.332|0.8573|0.858|-324.9405|-310.1192| |
