YeungNLP
/

firefly-qwen1.5-en-7b-dpo-v0.1-unsloth

@@ -4,197 +4,178 @@ license: apache-2.0
 basemodel: Qwen/Qwen1.5-7B
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 basemodel: Qwen/Qwen1.5-7B
 ---
+## Unsloth x Qwen2
+[Unsloth](https://github.com/unslothai/unsloth) can speed up training LLM and reduce memory usage, but currently it only supports Llama3, Mistral, Gemma, ORPR, Phi-3 and TinyLlama.
+We can't train Qwen2 with Unsloth, even though Qwen2 is popular in community.
+It's exciting that we succeed to make Unsloth support Qwen2, it can speed up training and reduce much memory usage.
+If you want to train Qwen2 with Unsloth, you can use [our repo](https://github.com/yangjianxin1/unsloth) rather than the official one. And we will commit our code to the [official repo](https://github.com/unslothai/unsloth).
+Install our Unsloth:
+```bash
+pip install git+https://github.com/yangjianxin1/unsloth.git
+```
+[Firefly](https://github.com/yangjianxin1/Firefly) already supports training Qwen2 with Unsloth, and the subsequent models are trained with Firefly, you can try it.
+## Model Card for Firefly-Qwen1.5-Unsloth
+[firefly-qwen1.5-en-7b-unsloth](https://huggingface.co/YeungNLP/firefly-qwen1.5-en-7b-unsloth) and [firefly-qwen1.5-en-7b-dpo-v0.1-unloth](https://huggingface.co/YeungNLP/firefly-qwen1.5-en-7b-dpo-v0.1-unsloth) 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 and [Unsloth](https://github.com/yangjianxin1/unsloth).
+firefly-qwen1.5-en-7b-unsloth is fine-tuned based on Qwen1.5-7B with English instruction data, and firefly-qwen1.5-en-7b-dpo-v0.1-unsloth is trained with [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290) based on firefly-qwen1.5-en-7b-unsloth.
+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).
+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 have evaluated the training gain of Qwen1.5-7B, we use QLoRA and Unsloth to train model for 20 steps on a single V100. The result can be listed as follows.
+**Unsloth can reduce GPU memory by 39.13% and training time by 32.12%, and the training speed can increase by 47.32%.**
+| max_seq_length | per_device_train_batch_size | gradient_accumulation_steps | use_unsloth | rank | GPU                     | Time              |
+|----------------|----------------------------|-----------------------------|-------------|------|-------------------------|-------------------|
+| 1024           | 1                          | 16                          | false       | 8    | 13.72GB                 | 448s              |
+| 1024           | 1                          | 16                          | true        | 8    | **8.43GB**(**-38.56%**) | 308s(**-31.25%**) |
+| 1024           | 1                          | 16                          | false       | 64   | 16.01GB                 | 452s              |
+| 1024           | 1                          | 16                          | true        | 64   | 11.07GB(**-30.86%**)    | 311s(**-31.19%**) |
+| 2048           | 1                          | 16                          | false       | 64   | 18.55GB                 | 840s              |
+| 2048           | 1                          | 16                          | true        | 64   | 12.99GB(**-29.97%**)    | 596s(**-29.05%**) |
+| 1024           | 4                          | 4                           | false       | 64   | 24.70GB                 | 357s              |
+| 1024           | 4                          | 4                           | true        | 64   | 14.36GB(**-41.86%**)    | 253s(**-29.13%**) |
+| 2048           | 4                          | 4                           | false       | 64   | 32.51GB                 | 741s              |
+| 2048           | 4                          | 4                           | true        | 64   | 19.79GB(**-39.13%**)    | 503s(**-32.12%**) |
+We evaluate our sft and dpo 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-unsloth** | 62.65   | 56.14  | 75.5      | 60.87 | 58.09      | 70.72      | 54.59  |
+| zephyr-7b-beta                             | 61.95   | 62.03  | 84.36     | 61.07 | 	57.45     | 77.74      | 	29.04 |
+| **firefly-qwen1.5-en-7b-unsloth**                 | 61.81   | 54.27  | 	76.22    | 61.55 | 50.62      | 70.48      | 57.7   |
+| 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-unsloth"
+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 Unsloth, 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: 600
+- 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: 2048
+- max_prompt_length: 500
+- optimizer: paged_adamw_32bit
+- lr_scheduler_type: constant_with_warmup
+- warmup_steps: 100
+- lora_rank: 64
+- lora_alpha: 16
+- lora_dropout: 0.05
+- gradient_checkpointing: true
+- fp16: true
+### Training metrics
+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  | 0.6128 | 0.6572             | 0.3914          | -0.0622        | -0.4537          | 1.107         | 1.1104          | -283.7632    | -264.5925      |
+| 0.1   | 200  | 0.6066 | 0.6913             | 0.662           | -0.3589        | -1.0209          | 0.9433        | 0.9431          | -279.0002    | -268.6432      |
+| 0.16  | 300  | 0.5803 | 0.7069             | 0.876           | -0.3849        | -1.2609          | 0.8411        | 0.8537          | -289.9482    | -274.3425      |
+| 0.21  | 400  | 0.5624 | 0.7169             | 0.9575          | -0.2447        | -1.2022          | 0.7615        | 0.7497          | -293.8072    | -274.4167      |
+| 0.26  | 500  | 0.5863 | 0.7                | 0.8908          | -0.5283        | -1.4191          | 0.537         | 0.5085          | -284.3388    | -267.9294      |
+| 0.31  | 600  | 0.5612 | 0.7166             | 1.0791          | -0.592         | -1.6711          | 0.7121        | 0.7219          | -293.2425    | -278.5992      |
+| 0.37  | 700  | 0.5741 | 0.7234             | 1.0742          | -0.8469        | -1.9211          | 0.6002        | 0.5769          | -300.8099    | -285.9137      |
+| 0.42  | 800  | 0.582  | 0.7141             | 1.0414          | -1.1658        | -2.2072          | 0.7191        | 0.5934          | -300.458     | -286.1         |
+| 0.47  | 900  | 0.5694 | 0.7178             | 1.2055          | -1.7372        | -2.9426          | 0.4226        | 0.316           | -305.5303    | -290.7548      |
+| 0.52  | 1000 | 0.5827 | 0.7134             | 1.1063          | -1.354         | -2.4603          | 0.535         | 0.4022          | -302.7598    | -286.636       |
+| 0.58  | 1100 | 0.5553 | 0.7306             | 1.3631          | -1.5861        | -2.9492          | 0.7636        | 0.6559          | -312.9375    | -290.3474      |
+| 0.63  | 1200 | 0.5633 | 0.7341             | 1.2689          | -1.7187        | -2.9876          | 0.6555        | 0.5894          | -315.0179    | -298.2406      |
+| 0.68  | 1300 | 0.5705 | 0.7284             | 1.3501          | -1.7762        | -3.1263          | 0.7419        | 0.6874          | -310.9056    | -294.2934      |
+| 0.73  | 1400 | 0.5458 | 0.7347             | 1.4555          | -2.2377        | -3.6932          | 0.7279        | 0.6564          | -309.141     | -299.1613      |
+| 0.79  | 1500 | 0.5797 | 0.7222             | 1.2937          | -2.4483        | -3.742           | 0.8444        | 0.771           | -321.578     | -298.111       |
+| 0.84  | 1600 | 0.5572 | 0.7319             | 1.4824          | -2.9344        | -4.4168          | 0.9202        | 0.8605          | -323.4034    | -307.0114      |
+| 0.89  | 1700 | 0.5518 | 0.7281             | 1.4263          | -2.7301        | -4.1564          | 0.9257        | 0.8785          | -313.694     | -298.1267      |
+| 0.94  | 1800 | 0.5572 | 0.7272             | 1.5121          | -2.9505        | -4.4627          | 0.7899        | 0.7503          | -314.1552    | -305.9873      |
+| 0.99  | 1900 | 0.5763 | 0.7241             | 1.4982          | -2.7064        | -4.2047          | 0.7841        | 0.7023          | -310.6677    | -299.5064      |