Instructions to use neovalle/H4rmoniousAnthea with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use neovalle/H4rmoniousAnthea with Transformers:

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

pipe = pipeline("text-generation", model="neovalle/H4rmoniousAnthea")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("neovalle/H4rmoniousAnthea")
model = AutoModelForCausalLM.from_pretrained("neovalle/H4rmoniousAnthea", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use neovalle/H4rmoniousAnthea with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "neovalle/H4rmoniousAnthea"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "neovalle/H4rmoniousAnthea",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/neovalle/H4rmoniousAnthea

SGLang

How to use neovalle/H4rmoniousAnthea 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/H4rmoniousAnthea" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "neovalle/H4rmoniousAnthea",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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/H4rmoniousAnthea" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "neovalle/H4rmoniousAnthea",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use neovalle/H4rmoniousAnthea with Docker Model Runner:
```
docker model run hf.co/neovalle/H4rmoniousAnthea
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Details

Model Description

This model is based on teknium/OpenHermes-2.5-Mistral-7B, DPO fine-tuned with the H4rmony_dpo dataset. Its completions should be more ecologically aware than the base model.

Developed by: Jorge Vallego
Funded by : Neovalle Ltd.
Shared by : airesearch@neovalle.co.uk
Model type: mistral
Language(s) (NLP): Primarily English
License: MIT
Finetuned from model: teknium/OpenHermes-2.5-Mistral-7B
Methodology: DPO

Uses

Intended as PoC to show the effects of H4rmony_dpo dataset with DPO fine-tuning.

Direct Use

For testing purposes to gain insight in order to help with the continous improvement of the H4rmony_dpo dataset.

Downstream Use

Its direct use in applications is not recommended as this model is under testing for a specific task only (Ecological Alignment) Out-of-Scope Use

Not meant to be used other than testing and evaluation of the H4rmony_dpo dataset and ecological alignment. Bias, Risks, and Limitations

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

How to Get Started with the Model

It can be loaded and run in a Colab instance with High RAM.

Training Details

Trained using DPO

Training Data

H4rmony Dataset - https://huggingface.co/datasets/neovalle/H4rmony_dpo

Downloads last month: 53

Safetensors

Model size

7B params

Tensor type

F16

Model tree for neovalle/H4rmoniousAnthea

Quantizations

1 model

neovalle
/

H4rmoniousAnthea