YeungNLP
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firefly-qwen1.5-en-7b-unsloth

@@ -4,198 +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|