Instructions to use ArthurZ/d3-lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ArthurZ/d3-lite with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="ArthurZ/d3-lite")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ArthurZ/d3-lite", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from ArthurZ/d3-lite: direct link, hf CLI and curl.
- Browser
- Download file 2.69 kB
-
https://huggingface.co/ArthurZ/d3-lite/resolve/main/README.md
- Command line
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hf download hf://ArthurZ/d3-lite/README.md
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curl -L -o README.md https://huggingface.co/ArthurZ/d3-lite/resolve/main/README.md
2.69 kB
| license: apache-2.0 | |
| base_model: vllm-sr/d3-lite | |
| pipeline_tag: zero-shot-classification | |
| library_name: transformers | |
| tags: [decision-model, system-one] | |
| # d3-lite for 🤗 Transformers | |
| The 255-way answer-code readout of [vllm-sr/d3-lite](https://huggingface.co/vllm-sr/d3-lite) as a JEV decision head, for the `decision` | |
| pipeline. This repo holds the readout, the tokenizer and the prompt; the backbone loads from the original repo, | |
| with bidirectional full attention as it was trained. | |
| ## Questions | |
| Each question is answered with a probability for every option: `noul` (yes/no), `choice` and `score`. | |
| ```python | |
| from transformers import pipeline | |
| decide = pipeline("decision", model="ArthurZ/d3-lite") | |
| answers = decide( | |
| { | |
| "state": {"customer": "Lena", "message": "I was charged twice for my order and I want my money back today."}, | |
| "questions": { | |
| "refund": {"type": "noul", "instructions": "Does the customer ask for a refund?"}, | |
| "tone": {"type": "choice", "instructions": "What is the tone?", "criteria": {"calm": "Polite", "angry": None}}, | |
| "urgency": {"type": "score", "instructions": "How urgent is it?", "criteria": ["Can wait", "This week", "Today", "Now"]}, | |
| }, | |
| } | |
| ) | |
| answers["refund"]["noul"] # probability of true | |
| answers["tone"]["choice"], answers["tone"]["probabilities"] | |
| answers["urgency"]["score"] # expected level, 0 to 3 | |
| ``` | |
| ## Same questions, new states | |
| A bare state is asked the questions of the last request, or the ones given up front: | |
| ```python | |
| decide = pipeline( | |
| "decision", | |
| model="ArthurZ/d3-lite", | |
| questions={"spam": {"type": "noul", "instructions": "Is this spam?"}}, | |
| ) | |
| decide("WIN A FREE IPHONE!!! click here to claim your prize") | |
| decide("Hi! The parcel arrived, thanks a lot.") | |
| ``` | |
| ## Images | |
| Images are `assets`, as in the System One API: a placeholder in the state is replaced with its image. An asset is a | |
| PIL image, a path, a URL or `{"type": "image", "format": "png", "data_base64": ...}`. | |
| ```python | |
| decide( | |
| { | |
| "state": "The customer sent photo-1 and asks if the product is damaged.", | |
| "assets": {"photo-1": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png"}, | |
| "questions": {"damaged": {"type": "noul", "instructions": "Is the product in the photo damaged?"}}, | |
| } | |
| ) | |
| ``` | |
| Converted with | |
| [`convert_decider_to_hf.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/models/jev/convert_decider_to_hf.py). | |
| Its prompts are token-for-token those of the original code (`decision_test.json`), except that control tokens written | |
| in a request are escaped instead of read as such. | |