mistral-environment-all

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mistral-environment-all

Model Description

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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

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

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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

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

# Text generation
def generate_text_sequences(pipe, prompt):
    sequences = pipe(
        f"prompt",
        do_sample=True,
        max_new_tokens=100,
        temperature=0.8,
        top_k=50,
        top_p=0.95,
        num_return_sequences=1,
    )
    return sequences[0]['generated_text']

# Now you can use the model for inference
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    pad_token_id=2
)
print(generate_text_sequences(pipe, "your prompt"))

Training Data

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

• Environmental Issues
• Natural Environment
• Biophysical Environment
• Ecology
• Environment (Systems)
• Built Environment
• Climate Change
• Human Impact on the Environment
• Environment of Australia
• Environmental Protection
• Environmental Issues in Australia

The text corpus are preprocessed for better format.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0002
• train_batch_size: 32
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.03
• num_epochs: 1

Training results

Framework versions

• Transformers 4.36.2
• Pytorch 2.1.0a0+git7bcf7da
• Datasets 2.16.1
• Tokenizers 0.15.0

Environmental Impact

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• Hardware Type: Setonix (Pawsey Supercomputing Research Centre)
• Hours used: <1
• Cloud Provider: Google Cloud
• Compute Region: [More Information Needed]
• Carbon Emitted: [More Information Needed]