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--- |
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language: |
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- en |
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license: creativeml-openrail-m |
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library_name: transformers |
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tags: |
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- qlora |
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- peft |
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- prompts |
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datasets: |
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- knkarthick/dialogsum |
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metrics: |
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- accuracy |
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base_model: TinyPixel/Llama-2-7B-bf16-sharded |
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--- |
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## Training procedure |
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The following `bitsandbytes` quantization config was used during training: |
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- load_in_8bit: False |
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- load_in_4bit: True |
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- llm_int8_threshold: 6.0 |
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- llm_int8_skip_modules: None |
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- llm_int8_enable_fp32_cpu_offload: False |
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- llm_int8_has_fp16_weight: False |
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- bnb_4bit_quant_type: nf4 |
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- bnb_4bit_use_double_quant: False |
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- bnb_4bit_compute_dtype: float16 |
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### Framework versions |
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- PEFT 0.4.0 |
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``` |
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# adding back the LoRA adopters to the base Llama-2 model |
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lora_config = LoraConfig.from_pretrained('Andyrasika/qlora-dialogue-summary') |
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model = get_peft_model(model, lora_config) |
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inputs = tokenizer(text, return_tensors="pt") |
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outputs = model.generate(input_ids=inputs['input_ids'], attention_mask=inputs['attention_mask'], max_new_tokens=100 ,repetition_penalty=1.2) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
