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--- |
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library_name: peft |
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license: mit |
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datasets: |
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- aloobun/mini-math23k-v1 |
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tags: |
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- math |
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- phi |
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--- |
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### WIP |
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## DONT USE THIS, ITS SHIT |
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## Usage: |
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``` |
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import torch |
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from peft import PeftModel |
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig |
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model_name = "microsoft/phi-1_5" |
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adapters_name = 'aloobun/phi_mini_math23k_v1' |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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load_in_4bit=True, |
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torch_dtype=torch.bfloat16, |
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device_map="auto", |
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quantization_config=BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_compute_dtype=torch.bfloat16, |
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bnb_4bit_use_double_quant=True, |
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bnb_4bit_quant_type='nf4' |
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), |
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) |
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model = PeftModel.from_pretrained(model, adapters_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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``` |
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``` |
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prompt = "What is the largest two-digit integer whose digits are distinct and form a geometric sequence?" |
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formatted_prompt = ( |
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f"### Instruction: {prompt} ### Response:" |
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) |
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inputs = tokenizer(formatted_prompt, return_tensors="pt").to("cuda:0") |
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outputs = model.generate(inputs=inputs.input_ids, max_new_tokens=1048) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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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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- quant_method: bitsandbytes |
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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: True |
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- bnb_4bit_compute_dtype: float16 |
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### Framework versions |
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- PEFT 0.5.0 |
