| --- |
| library_name: peft |
| --- |
| |
| For demonstration, please refer to demo.jpynb in the files. |
|
|
| To use the checkpoint: |
| ``` |
| from peft import PeftModel, PeftConfig |
| from transformers import AutoModelForCausalLM |
| |
| config = PeftConfig.from_pretrained("TorpilleAlpha/scanpy-llama") |
| model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf")# or use your local llama-2-7B-chat shards |
| model = PeftModel.from_pretrained(model, "TorpilleAlpha/scanpy-llama") |
| ``` |
|
|
| ## Training procedure |
|
|
|
|
| The following `bitsandbytes` quantization config was used during training: |
| - quant_method: bitsandbytes |
| - load_in_8bit: True |
| - load_in_4bit: False |
| - 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: fp4 |
| - bnb_4bit_use_double_quant: False |
| - bnb_4bit_compute_dtype: float32 |
| ### Framework versions |
| |
| |
| - PEFT 0.5.0 |
