metadata
license: llama2
Fine-tuned Llama 2 on sheperd
from datasets import load_dataset
from random import randrange
import torch
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
output_dir = "philschmid/shepherd-2-hf-int4"
# load base LLM model and tokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
output_dir,
low_cpu_mem_usage=True,
torch_dtype=torch.float16,
load_in_4bit=True,
)
tokenizer = AutoTokenizer.from_pretrained(output_dir)
# Load dataset from the hub and get a sample
dataset = load_dataset("philschmid/meta-shepherd-human-data", split="train")
sample = dataset[randrange(len(dataset))]
prompt = f"""### Question: {sample['question']}
### Answer:
{sample['answer']}
### Feedback:
"""
input_ids = tokenizer(prompt, return_tensors="pt", truncation=True).input_ids.cuda()
# with torch.inference_mode():
outputs = model.generate(input_ids=input_ids, max_new_tokens=100, do_sample=True, top_p=0.9,temperature=0.9)
print(prompt[:-14])
print("---"*35)
print(f"### Generated Feedback:\n{tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0][len(prompt):]}")
print(f"### Ground truth Feedback:\n{sample['feedback']}")
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