Instructions to use saaketh-j/business-model-trial-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use saaketh-j/business-model-trial-3 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("tiiuae/falcon-7b-instruct") model = PeftModel.from_pretrained(base_model, "saaketh-j/business-model-trial-3") - Notebooks
- Google Colab
- Kaggle
Training procedure
The following bitsandbytes quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
Framework versions
- PEFT 0.4.0
prompt: prompt = f""" You are going to determine whether [{data_point["Description"]}] includes the business model. Don't use any prior knowledge, only base your answer off of what's given. It might not be explicitly stated but infer whether the class is B2C, B2B, B2G, or No business model. Respond in sentence form with the class and reasoning -> : {data_point['Answer']} """
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