Instructions to use mkly/crypto-sales with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mkly/crypto-sales with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf") model = PeftModel.from_pretrained(base_model, "mkly/crypto-sales") - Notebooks
- Google Colab
- Kaggle
| library_name: peft | |
| license: mit | |
| datasets: | |
| - mkly/crypto-sales-question-answers | |
| language: | |
| - en | |
| # Adapter `mkly/crypto_sales` for `meta-llama/Llama-2-7b-chat-hf` | |
| An adapter for the [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) model that was trained on the [mkly/crypto-sales-question-answers](https://huggingface.co/datasets/mkly/crypto-sales-question-answers/) dataset. | |
| ## Training procedure | |
| The following `bitsandbytes` quantization config was used during training: | |
| - quant_method: bitsandbytes | |
| - 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: False | |
| - bnb_4bit_compute_dtype: bfloat16 | |
| ### Framework versions | |
| - PEFT 0.5.0 | |
| ## Prompt | |
| ``` | |
| ### INSTRUCTION | |
| Be clever and persuasive, while keeping things to one paragrah. Answer the following question while also upselling the following cryptocurrency. | |
| ### CRYPTOCURRENCY | |
| TRON is a blockchain-based operating system that eliminates the middleman, reducing costs for consumers and improving collection for content producers. | |
| ### QUESTION | |
| who founded the roanoke settlement? | |
| ### ANSWER | |
| ``` | |
| ## Usage | |
| ```python | |
| base_model_name = "meta-llama/Llama-2-7b-chat-hf" | |
| bnb_config = BitsAndBytesConfig( | |
| load_in_4bit=True, | |
| bnb_4bit_quant_type="nf4", | |
| bnb_4bit_compute_dtype=torch.bfloat16, | |
| ) | |
| base_model = AutoModelForCausalLM.from_pretrained( | |
| base_model_name, | |
| quantization_config=bnb_config, | |
| device_map="auto", | |
| trust_remote_code=True, | |
| ) | |
| model = PeftModel.from_pretrained(base_model, "mkly/crypto-sales") | |
| ``` | |