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--- |
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base_model: meta-llama/Llama-2-13b-chat-hf |
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tags: |
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- generated_from_trainer |
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- trl |
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metrics: |
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- accuracy |
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model-index: |
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- name: llama-2-13b-reward-oasst1 |
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results: [] |
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datasets: |
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- tasksource/oasst1_pairwise_rlhf_reward |
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library_name: peft |
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pipeline_tag: text-classification |
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--- |
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# llama-2-13b-reward-oasst1 |
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This model is a fine-tuned version of [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) on the [tasksource/oasst1_pairwise_rlhf_reward](https://huggingface.co/datasets/tasksource/oasst1_pairwise_rlhf_reward) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4810 |
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- Accuracy: 0.7869 |
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See also [vincentmin/llama-2-7b-reward-oasst1](https://huggingface.co/vincentmin/llama-2-7b-reward-oasst1) for a 7b version of this model. |
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## Model description |
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This is a reward model trained with QLoRA in 4bit precision. The base model is [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) for which you need to have accepted the license in order to be able use it. Once you've been given permission, you can load the reward model as follows: |
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``` |
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import torch |
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from peft import PeftModel, PeftConfig |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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peft_model_id = "vincentmin/llama-2-13b-reward-oasst1" |
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config = PeftConfig.from_pretrained(peft_model_id) |
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model = AutoModelForSequenceClassification.from_pretrained( |
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config.base_model_name_or_path, |
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num_labels=1, |
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load_in_4bit=True, |
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torch_dtype=torch.float16, |
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) |
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model = PeftModel.from_pretrained(model, peft_model_id) |
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tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path, use_auth_token=True) |
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model.eval() |
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with torch.no_grad(): |
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reward = model(**tokenizer("prompter: hello world. assistant: foo bar", return_tensors='pt')).logits |
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reward |
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``` |
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For best results, one should use the prompt format used during training: |
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``` |
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prompt = "prompter: <prompt_1> assistant: <response_1> prompter: <prompt_2> ..." |
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``` |
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Please use a version of peft where [#755](https://github.com/huggingface/peft/pull/755) has been merged to make sure the model is loaded correctly. You can install `peft` with `pip install git+https://github.com/huggingface/peft.git` to make sure this is the case. |
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## Intended uses & limitations |
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Since the model was trained on oasst1 data, the reward will reflect any biases present in the oasst1 data. |
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## Training and evaluation data |
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The model was trained using QLoRA and the `trl` library's `RewardTrainer` on the [tasksource/oasst1_pairwise_rlhf_reward](https://huggingface.co/datasets/tasksource/oasst1_pairwise_rlhf_reward) dataset where examples with more than 512 tokens were filtered out from both the training and eval data. |
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## Training procedure |
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### Training hyperparameters |
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The following `bitsandbytes` quantization config was used during training: |
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- load_in_8bit: False |
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- load_in_4bit: True |
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- llm_int8_threshold: 6.0 |
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- llm_int8_skip_modules: None |
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- llm_int8_enable_fp32_cpu_offload: False |
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- llm_int8_has_fp16_weight: False |
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- bnb_4bit_quant_type: nf4 |
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- bnb_4bit_use_double_quant: False |
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- bnb_4bit_compute_dtype: float16 |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 4 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 1 |
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- max_seq_length: 512 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.5602 | 0.08 | 250 | 0.5436 | 0.7388 | |
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| 0.6166 | 0.17 | 500 | 0.5340 | 0.7468 | |
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| 0.6545 | 0.25 | 750 | 0.4899 | 0.7644 | |
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| 0.5635 | 0.33 | 1000 | 0.4877 | 0.7532 | |
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| 0.5933 | 0.42 | 1250 | 0.4930 | 0.7660 | |
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| 0.5758 | 0.5 | 1500 | 0.4851 | 0.7740 | |
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| 0.5212 | 0.58 | 1750 | 0.5021 | 0.7788 | |
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| 0.5251 | 0.67 | 2000 | 0.4893 | 0.7804 | |
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| 0.5145 | 0.75 | 2250 | 0.4924 | 0.7853 | |
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| 0.5085 | 0.83 | 2500 | 0.4934 | 0.7853 | |
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| 0.617 | 0.92 | 2750 | 0.4803 | 0.7821 | |
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| 0.5525 | 1.0 | 3000 | 0.4810 | 0.7869 | |
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### Framework versions |
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- PEFT 0.5.0.dev0 (with https://github.com/huggingface/peft/pull/755) |
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- Transformers 4.32.0.dev0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.0 |
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- Tokenizers 0.13.3 |