metadata
library_name: transformers
license: creativeml-openrail-m
language:
- en
metrics:
- accuracy
tags:
- qlora
- peft
- prompts
datasets:
- knkarthick/dialogsum
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: False
- bnb_4bit_compute_dtype: float16
Framework versions
- PEFT 0.4.0
# adding back the LoRA adopters to the base Llama-2 model
lora_config = LoraConfig.from_pretrained('Andyrasika/qlora-dialogue-summary')
model = get_peft_model(model, lora_config)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(input_ids=inputs['input_ids'], attention_mask=inputs['attention_mask'], max_new_tokens=100 ,repetition_penalty=1.2)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))