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mistral-7b-amazon-reviews-lora-r8

PEFT
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mistral-7b-amazon-reviews-lora-r8
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---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
datasets:
- amazon_us_reviews
---

# Model Card for Model ID

Trained with [Ludwig.ai](https://ludwig.ai) and [Predibase](https://predibase.com)!

Given the text of a review, predict the score from the user from 1 to 5.

Try it in [LoRAX](https://github.com/predibase/lorax):

```python
from lorax import Client

client = Client("http://<your_endpoint>")

review = "<your product review>"

prompt = f"""
Below is the text from a review from an Amazon user for a product they
purchased. Please predict how many stars they gave the product in their
review.

Review: {review}

Number of stars:
"""

adapter_id = "tgaddair/mistral-7b-amazon-reviews-lora-r8"
resp = client.generate(prompt, max_new_tokens=64, adapter_id=adapter_id)
print(resp.generated_text)
```



## Model Details

### Model Description

Ludwig config (v0.9.3):

```yaml
model_type: llm
input_features:
  - name: prompt
    type: text
    preprocessing:
      max_sequence_length: null
    column: prompt
output_features:
  - name: stars
    type: text
    preprocessing:
      max_sequence_length: null
    column: stars
prompt:
  template: >-
    Below is the text from a review from an Amazon user for a product they
    purchased. Please predict how many stars they gave the product in their
    review.


    Review: {text}


    Number of stars: 
preprocessing:
  split:
    type: random
    probabilities:
      - 0.95
      - 0
      - 0.05
  global_max_sequence_length: 2048
adapter:
  type: lora
generation:
  max_new_tokens: 64
trainer:
  type: finetune
  epochs: 3
  optimizer:
    type: paged_adam
  batch_size: 1
  eval_steps: 100
  learning_rate: 0.0002
  eval_batch_size: 2
  steps_per_checkpoint: 1000
  learning_rate_scheduler:
    decay: cosine
    warmup_fraction: 0.03
  gradient_accumulation_steps: 16
  enable_gradient_checkpointing: true
base_model: mistralai/Mistral-7B-v0.1
quantization:
  bits: 4
```