README.md

---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: mistral-environment-all
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# mistral-environment-all

## Model Description

<!-- Provide a longer summary of what this model is. -->
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](https://huggingface.co/mistralai/Mistral-7B-v0.1)



## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
This repository includes the weights learned during the training process. It should be loaded witht the pre-trained Mistral 7b and tokenizer.

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
```python
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

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
The fine-tuning data are parsed from these public Wikipedia websites:
- [Environmental Issues](https://en.wikipedia.org/wiki/Environmental_issues)
- [Natural Environment](https://en.wikipedia.org/wiki/Natural_environment)
- [Biophysical Environment](https://en.wikipedia.org/wiki/Biophysical_environment)
- [Ecology](https://en.wikipedia.org/wiki/Ecology)
- [Environment (Systems)](https://en.wikipedia.org/wiki/Environment_(systems))
- [Built Environment](https://en.wikipedia.org/wiki/Built_environment)
- [Climate Change](https://en.wikipedia.org/wiki/Climate_change)
- [Human Impact on the Environment](https://en.wikipedia.org/wiki/Human_impact_on_the_environment)
- [Environment of Australia](https://en.wikipedia.org/wiki/Environment_of_Australia)
- [Environmental Protection](https://en.wikipedia.org/wiki/Environmental_protection)
- [Environmental Issues in Australia](https://en.wikipedia.org/wiki/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
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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