Instructions to use fionazhang/mistral-environment-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fionazhang/mistral-environment-adapter with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="fionazhang/mistral-environment-adapter")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fionazhang/mistral-environment-adapter")
model = AutoModelForCausalLM.from_pretrained("fionazhang/mistral-environment-adapter")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use fionazhang/mistral-environment-adapter with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fionazhang/mistral-environment-adapter"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fionazhang/mistral-environment-adapter",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/fionazhang/mistral-environment-adapter

SGLang

How to use fionazhang/mistral-environment-adapter with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "fionazhang/mistral-environment-adapter" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fionazhang/mistral-environment-adapter",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "fionazhang/mistral-environment-adapter" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fionazhang/mistral-environment-adapter",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use fionazhang/mistral-environment-adapter with Docker Model Runner:
```
docker model run hf.co/fionazhang/mistral-environment-adapter
```

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.

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

training_arguments = TrainingArguments(
    output_dir="",
    num_train_epochs=1,
    per_device_train_batch_size=4,
    gradient_accumulation_steps=1,
    optim="paged_adamw_32bit",
    save_steps=25,
    logging_steps=25,
    learning_rate=2e-4,
    weight_decay=0.001,
    fp16=False,
    bf16=False,
    max_grad_norm=0.3,
    max_steps=-1,
    warmup_ratio=0.03,
    group_by_length=True,
    lr_scheduler_type="constant",
    report_to="wandb"
)

Evaluation

Not yet evaluated. Still working

Environmental Impact

Hardware Type: T4 GPU
Hours used: <1
Cloud Provider: Google Cloud
Compute Region: [More Information Needed]
Carbon Emitted: [More Information Needed]

Downloads last month: 4

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for fionazhang/mistral-environment-adapter

Base model

mistralai/Mistral-7B-v0.1

Finetuned

(1033)

this model