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--- |
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library_name: transformers |
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license: apache-2.0 |
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--- |
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This is an experimental model. |
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The idea is : |
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- Calculate the difference in weights between a donor model(meta-math/MetaMath-Mistral-7B) and the base model(mistralai/Mistral-7B-v0.1). This difference represents how much each parameter needs to be adjusted to go from the base state to the donor state. |
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- Vector retrieved from the result of step one, is added to third model(lex-hue/Delexa-7b). This should transfer **math** *skills* to our third model. |
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``` |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import torch |
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model_name = "aloobun/CosmicNoodle-7B" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto") |
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prompt = "For the natural number A, the quotient of A divided by 9 is 6 and the remainder is 5. What is the value of A?\n" |
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input_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt") |
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tokens = model.generate(input_ids.to(device=model.device), max_new_tokens=128, temperature=0.99, top_p=0.95, do_sample=True) |
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out = tokenizer.decode(tokens[0], skip_special_tokens=True) |
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print(out) |
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``` |
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