You can deploy this model and start earning money today!

fionazhang/
mistral-environment-adapter

Name: mistral-environment-adapter
Availability: InStock

mistralai/Mistral-7B-v0.1

finetune

apache-2.0

autotrain_compatible

text-generation-inference

endpoints_compatible

Views

No views yet

Model Description

The model is a fine-tuned (quantized) Mistral7b model on a self-organised dataset about environmental knowledge. This model is currently still under development.

Developed by: Fiona Zhang
Funded: CSIRO, Pawsey Supercomputing Research Centre
Finetuned from model: Mistral7b

Uses

This repository includes the weights learned during the training process. It should be loaded witht the pre-trained Mistral 7b and tokenizer.

python

from transformers import AutoModelForSequenceClassification, AutoTokenizer

# Load the tokenizer, adjust configuration if needed
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Load the fine-tuned model with its trained weights
fine_tuned_model = AutoModelForSequenceClassification.from_pretrained(
    'fionazhang/mistral_7b_environment',
)

# Now you can use `fine_tuned_model` for inference or further training
input_text = "The impact of climate change on"
output_text = fine_tuned_model.generate(tokenizer.encode(input_text, return_tensors="pt"))

print(tokenizer.decode(output_text[0], skip_special_tokens=True))

Bias, Risks, and Limitations

There are no modifications applied to the model. The model may return undesired or offensive response. Filters are encouraged to apply.

Training Data

The fine-tuning data are parsed from these public Wikipedia websites:

The text corpus are preprocessed for better format.

Training Procedure

The fine-tuning is self-supervised.

Training Hyperparameters