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--- |
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language: |
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- en |
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license: apache-2.0 |
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library_name: peft |
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tags: |
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- mistral |
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- generated_from_trainer |
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- Transformers |
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- text-generation-inference |
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datasets: |
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- robinsmits/ChatAlpaca-20K |
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inference: false |
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base_model: mistralai/Mistral-7B-Instruct-v0.2 |
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model-index: |
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- name: Mistral-Instruct-7B-v0.2-ChatAlpaca |
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results: [] |
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pipeline_tag: text-generation |
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--- |
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# Mistral-Instruct-7B-v0.2-ChatAlpaca |
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## Model description |
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This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the English [robinsmits/ChatAlpaca-20K](https://www.huggingface.co/datasets/robinsmits/ChatAlpaca-20K) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8584 |
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## Model usage |
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A basic example of how to use the finetuned model. Note this example is a modified version from the base model. |
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``` |
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import torch |
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from peft import AutoPeftModelForCausalLM |
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from transformers import AutoTokenizer |
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device = "cuda" |
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model = AutoPeftModelForCausalLM.from_pretrained("robinsmits/Mistral-Instruct-7B-v0.2-ChatAlpaca", |
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device_map = "auto", |
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load_in_4bit = True, |
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torch_dtype = torch.bfloat16) |
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tokenizer = AutoTokenizer.from_pretrained("robinsmits/Mistral-Instruct-7B-v0.2-ChatAlpaca") |
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messages = [ |
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{"role": "user", "content": "What is your favourite condiment?"}, |
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{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, |
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{"role": "user", "content": "Do you have mayonnaise recipes?"} |
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] |
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encodeds = tokenizer.apply_chat_template(messages, return_tensors = "pt") |
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generated_ids = model.generate(input_ids = encodeds.to(device), max_new_tokens = 512, do_sample = True) |
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decoded = tokenizer.batch_decode(generated_ids) |
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print(decoded[0]) |
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``` |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 4e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 32 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.05 |
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- num_epochs: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.99 | 0.2 | 120 | 0.9355 | |
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| 0.8793 | 0.39 | 240 | 0.8848 | |
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| 0.8671 | 0.59 | 360 | 0.8737 | |
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| 0.8662 | 0.78 | 480 | 0.8679 | |
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| 0.8627 | 0.98 | 600 | 0.8639 | |
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| 0.8426 | 1.18 | 720 | 0.8615 | |
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| 0.8574 | 1.37 | 840 | 0.8598 | |
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| 0.8473 | 1.57 | 960 | 0.8589 | |
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| 0.8528 | 1.76 | 1080 | 0.8585 | |
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| 0.852 | 1.96 | 1200 | 0.8584 | |
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### Framework versions |
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- PEFT 0.7.1 |
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- Transformers 4.36.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.16.0 |
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- Tokenizers 0.15.0 |
