TorpilleAlpha/scanpy-llama

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
• —loadin8bit: True
• —loadin4bit: False
• —llmint8threshold: 6.0
• —llmint8skip_modules: None
• —llmint8enablefp32cpu_offload: False
• —llmint8hasfp16weight: False
• —bnb4bitquant_type: fp4
• —bnb4bitusedoublequant: False
• —bnb4bitcompute_dtype: float32

Framework versions

• —PEFT 0.5.0