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--- |
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library_name: peft |
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base_model: mistralai/Mistral-7B-v0.1 |
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datasets: |
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- amazon_us_reviews |
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--- |
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# Model Card for Model ID |
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Trained with [Ludwig.ai](https://ludwig.ai) and [Predibase](https://predibase.com)! |
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Given the text of a review, predict the score from the user from 1 to 5. |
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Try it in [LoRAX](https://github.com/predibase/lorax): |
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```python |
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from lorax import Client |
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client = Client("http://<your_endpoint>") |
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review = "<your product review>" |
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prompt = f""" |
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Below is the text from a review from an Amazon user for a product they |
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purchased. Please predict how many stars they gave the product in their |
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review. |
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Review: {review} |
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Number of stars: |
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""" |
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adapter_id = "tgaddair/mistral-7b-amazon-reviews-lora-r8" |
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resp = client.generate(prompt, max_new_tokens=64, adapter_id=adapter_id) |
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print(resp.generated_text) |
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``` |
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## Model Details |
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### Model Description |
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Ludwig config (v0.9.3): |
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```yaml |
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model_type: llm |
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input_features: |
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- name: prompt |
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type: text |
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preprocessing: |
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max_sequence_length: null |
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column: prompt |
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output_features: |
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- name: stars |
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type: text |
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preprocessing: |
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max_sequence_length: null |
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column: stars |
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prompt: |
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template: >- |
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Below is the text from a review from an Amazon user for a product they |
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purchased. Please predict how many stars they gave the product in their |
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review. |
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Review: {text} |
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Number of stars: |
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preprocessing: |
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split: |
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type: random |
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probabilities: |
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- 0.95 |
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- 0 |
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- 0.05 |
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global_max_sequence_length: 2048 |
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adapter: |
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type: lora |
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generation: |
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max_new_tokens: 64 |
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trainer: |
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type: finetune |
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epochs: 3 |
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optimizer: |
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type: paged_adam |
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batch_size: 1 |
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eval_steps: 100 |
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learning_rate: 0.0002 |
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eval_batch_size: 2 |
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steps_per_checkpoint: 1000 |
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learning_rate_scheduler: |
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decay: cosine |
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warmup_fraction: 0.03 |
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gradient_accumulation_steps: 16 |
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enable_gradient_checkpointing: true |
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base_model: mistralai/Mistral-7B-v0.1 |
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quantization: |
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bits: 4 |
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``` |
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