Instructions to use HMC83/request_writer_sbp_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use HMC83/request_writer_sbp_2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("HMC83/skunkbadger-phoenix") model = PeftModel.from_pretrained(base_model, "HMC83/request_writer_sbp_2") - Transformers
How to use HMC83/request_writer_sbp_2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("HMC83/request_writer_sbp_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from HMC83/request_writer_sbp_2: direct link, hf CLI and curl.
- Browser
- Download file 890 Bytes
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https://huggingface.co/HMC83/request_writer_sbp_2/resolve/main/README.md
- Command line
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hf download hf://HMC83/request_writer_sbp_2/README.md
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curl -L -H "Authorization: Bearer $HF_TOKEN" -o README.md https://huggingface.co/HMC83/request_writer_sbp_2/resolve/main/README.md
890 Bytes
metadata
base_model: HMC83/skunkbadger-phoenix
library_name: peft
tags:
- base_model:adapter:HMC83/skunkbadger-phoenix
- lora
- sft
- transformers
- trl
datasets:
- HMC83/synthetic_foi_requests
Tiny 22M param model based on GPT2
Recommended Prompt Template
This model expects a prompt following the Alpaca instruction format. The user's query should be placed after ### Instruction:.
You are an expert at writing formal Freedom of Information requests to UK public authorities. Write clear, specific, professional requests that comply with FOI requirements and use accessible language. ### Instruction:
Generate a formal Freedom of Information request to {authority_name} using these keywords: {keywords}
### Response:
- Replace
{authority_name}with the name of the public body. - Replace
{keywords}with a comma-separated list of relevant keywords.