Modelpublic

Andyrasika/qlora-dialogue-summary

sourceHugging Facecreativeml-openrail-mupdated 3y agoView on Hugging Face

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Model Card

Training procedure

The following bitsandbytes quantization config was used during training:

—loadin8bit: False
—loadin4bit: True
—llmint8threshold: 6.0
—llmint8skip_modules: None
—llmint8enablefp32cpu_offload: False
—llmint8hasfp16weight: False
—bnb4bitquant_type: nf4
—bnb4bitusedoublequant: False
—bnb4bitcompute_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))