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--- |
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license: creativeml-openrail-m |
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datasets: |
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- GEM/viggo |
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metrics: |
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- accuracy |
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library_name: transformers |
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tags: |
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- 'transformers ' |
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- peft |
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- qlora |
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--- |
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``` |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig |
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|
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base_model_id = "mistralai/Mistral-7B-v0.1" |
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bnb_config = BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_use_double_quant=True, |
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bnb_4bit_quant_type="nf4", |
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bnb_4bit_compute_dtype=torch.bfloat16 |
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) |
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base_model = AutoModelForCausalLM.from_pretrained( |
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base_model_id, # Mistral, same as before |
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quantization_config=bnb_config, # Same quantization config as before |
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device_map="auto", |
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trust_remote_code=True, |
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use_auth_token=True |
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) |
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eval_tokenizer = AutoTokenizer.from_pretrained( |
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base_model_id, |
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add_bos_token=True, |
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trust_remote_code=True, |
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) |
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``` |
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Now load the QLoRA adapter from the appropriate checkpoint directory |
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``` |
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from peft import PeftModel |
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|
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ft_model = PeftModel.from_pretrained(base_model, "mistral-viggo-finetune/checkpoint-950") |
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``` |
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Let's try the same eval_prompt and thus model_input as above, and see if the new finetuned model performs better. |
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``` |
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eval_prompt = """Given a target sentence construct the underlying meaning representation of the input sentence as a single function with attributes and attribute values. |
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This function should describe the target string accurately and the function must be one of the following ['inform', 'request', 'give_opinion', 'confirm', 'verify_attribute', 'suggest', 'request_explanation', 'recommend', 'request_attribute']. |
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The attributes must be one of the following: ['name', 'exp_release_date', 'release_year', 'developer', 'esrb', 'rating', 'genres', 'player_perspective', 'has_multiplayer', 'platforms', 'available_on_steam', 'has_linux_release', 'has_mac_release', 'specifier'] |
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|
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### Target sentence: |
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Earlier, you stated that you didn't have strong feelings about PlayStation's Little Big Adventure. Is your opinion true for all games which don't have multiplayer? |
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|
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### Meaning representation: |
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""" |
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|
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model_input = tokenizer(eval_prompt, return_tensors="pt").to("cuda") |
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|
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ft_model.eval() |
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with torch.no_grad(): |
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print(eval_tokenizer.decode(ft_model.generate(**model_input, max_new_tokens=100)[0], skip_special_tokens=True)) |
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``` |
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