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--- |
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language: |
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- en |
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tags: |
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- text-generation-inference |
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--- |
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# Model Card for OPT Spell Generation |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This model is a fine-tuned **opt-2.7b** model for the generation of *D&D 5th edition spells* |
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- **Language(s) (NLP):** English |
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- **Finetuned from model:** [opt-2.7b](https://huggingface.co/facebook/opt-2.7b) |
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- **Dataset used for fine-tuning:** [m-elio/spell_generation](https://huggingface.co/datasets/m-elio/spell_generation) |
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## Prompt Format |
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This prompt format based on the Alpaca model was used for fine-tuning: |
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```python |
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"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n" \ |
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f"### Instruction:\n{instruction}\n\n### Response:\n{response}" |
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``` |
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It is recommended to use the same prompt in inference to obtain the best results! |
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## Output Format |
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The output format for a generated spell should be the following: |
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``` |
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Name: |
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Level: |
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School: |
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Classes: |
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Casting time: |
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Range: |
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Duration: |
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Components: [If no components are required, then this field has a None value] |
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Material cost: [If there is no "M" character in the Components field, then this field is skipped] |
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Description: |
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``` |
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Example: |
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``` |
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Name: The Shadow |
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Level: 1 |
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School: Evocation |
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Classes: Bard, Cleric, Druid, Ranger, Sorcerer, Warlock, Wizard |
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Casting time: 1 Action |
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Range: Self |
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Duration: Concentration, Up To 1 Minute |
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Components: V, S, M |
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Material cost: a small piece of cloth |
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Description: You touch a creature within range. The target must make a Dexterity saving throw. On a failed save, the target takes 2d6 psychic damage and is charmed by you. On a successful save, the target takes half as much damage. |
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At Higher Levels. When you cast this spell using a spell slot of 4th level or higher, the damage increases by 1d6 for each slot level above 1st. |
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``` |
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## Example use |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_id = "m-elio/spell_generation_opt-2.7b" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained(model_id) |
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instruction = "Write a spell for the 5th edition of the Dungeons & Dragons game." |
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prompt = "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n" \ |
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f"### Instruction:\n{instruction}\n\n### Response:\n" |
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tokenized_input = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate(**tokenized_input, max_length=512) |
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print(tokenizer.batch_decode(outputs.detach().cpu().numpy()[:, tokenized_input.input_ids.shape[1]:], skip_special_tokens=True)[0]) |
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``` |
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