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--- |
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datasets: |
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- namelessai/helply |
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base_model: trillionlabs/Trillion-7B-preview |
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library_name: transformers |
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tags: |
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- pysch |
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- medical |
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- chat |
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- instruction |
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license: mit |
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language: |
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- en |
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- ko |
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--- |
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# Model Card for TrillionHelp |
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**TrillionHelp** uses `trillionlabs/Trillion-7B-preview` as the backbone. |
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## Model Details |
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This model is fine-tuned on the `namelessai/helply` dataset designed to enhance mental health reasoning capabilities. |
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### Model Description |
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This model was fine-tuned for assisting pyschologists in assiting patients. |
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- **Developed by:** Alex Scott |
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- **Model type:** Language Model, Adapter Model (available in a folder in the model repo) |
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- **Finetuned from model:** trillionlabs/Trillion-7B-preview |
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## Usage (Adapter Only, full model snippet coming soon) |
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Use the code snippet below to load the base model and apply the adapter for inference: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from peft import PeftModel |
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# Load the base model |
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base_model_name = "trillionlabs/Trillion-7B-preview" |
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adapter_path = "/path/to/adapter" # Replace with actual adapter path |
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tokenizer = AutoTokenizer.from_pretrained(base_model_name) |
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base_model = AutoModelForCausalLM.from_pretrained(base_model_name) |
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# Apply the adapter |
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model = PeftModel.from_pretrained(base_model, adapter_path) |
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model = model.merge_and_unload() |
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# Run inference |
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input_text = "Your input text here" |
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inputs = tokenizer(input_text, return_tensors="pt") |
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outputs = model.generate(**inputs) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
