Instructions to use neovalle/H4rmoniousCaramel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use neovalle/H4rmoniousCaramel with Transformers:

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

pipe = pipeline("text-generation", model="neovalle/H4rmoniousCaramel")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("neovalle/H4rmoniousCaramel")
model = AutoModelForSeq2SeqLM.from_pretrained("neovalle/H4rmoniousCaramel")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use neovalle/H4rmoniousCaramel with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "neovalle/H4rmoniousCaramel"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "neovalle/H4rmoniousCaramel",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/neovalle/H4rmoniousCaramel

SGLang

How to use neovalle/H4rmoniousCaramel 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 "neovalle/H4rmoniousCaramel" \
    --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": "neovalle/H4rmoniousCaramel",
		"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 "neovalle/H4rmoniousCaramel" \
        --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": "neovalle/H4rmoniousCaramel",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use neovalle/H4rmoniousCaramel with Docker Model Runner:
```
docker model run hf.co/neovalle/H4rmoniousCaramel
```

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Model Card for Model neovalle/H4rmoniousCaramel

Model Details

Model Description

This is model is a fine-tuned version of google/flan-t5-large finetuned with the H4rmony dataset which aims to better align the model with ecological values through the use of ecolinguistics principles.

Developed by: Jorge Vallego
Funded by : Neovalle Ltd.
Shared by : airesearch@neovalle.co.uk
Model type: t5 Language Model
Language(s) (NLP): Primarily English
License: MIT
Finetuned from model: google/flan-t5-large

Uses

Intended as PoC to show the effect of H4rmony dataset.

Direct Use

For testing purposes to gain insight in order to help with the continous improvement of the H4rmony dataset.

Downstream Use

Its direct use in applications is not recommended as this model is under testing for a specific task only

Out-of-Scope Use

Not meant to be used other than testing and evaluation of the H4rmony dataset.

Bias, Risks, and Limitations

This model might produce biased completions already existing in the base model and unintentionally introduced during fine-tuning.

How to Get Started with the Model

It can be loaded and run in a free Colab instance.

Code to load base and finetuned models to compare outputs:

https://github.com/Neovalle/H4rmony/blob/main/H4rmoniousCaramel.ipynb

Training Details

Supervised Fine Tuning

Training Data

H4rmony Dataset - https://huggingface.co/datasets/neovalle/H4rmony

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