86 lines
2.8 KiB
Markdown
86 lines
2.8 KiB
Markdown
---
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license_name: tongyi-qianwen-research
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license_link: https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat/raw/main/LICENSE
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library_name: transformers
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license: other
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tags:
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- finetune
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- synthetic data
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- custom_code
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- qwen2
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- COT
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datasets:
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- kaist-ai/CoT-Collection
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---
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- Finetuned [Qwen/Qwen1.5-4B](https://huggingface.co/Qwen/Qwen1.5-4B), on variety of CoT tasks including Reasoning, Closed Book Question Answering, Ethics, and more.
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- Datasets : Curated from - [kaist-ai/CoT-Collection](https://huggingface.co/datasets/kaist-ai/CoT-Collection), [euclaise/TinyCoT](https://huggingface.co/datasets/euclaise/TinyCoT) and a very small subset from [teknium/OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5).
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- This marks the fourth model in this series. This experiment aims to improve Chain of Thought (CoT) capabilities on smaller language models.
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- I may rerun the finetuning experiment(with a more balanced dataset), using an iterative rationale-bootstrapping procedure inspired by euclaise/Memphis-CoT-3B.
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- Hyperparameter: adamw with eps of 1e-8, cosine decay with 20% warmup, lr=2e-5
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## Benchamrks:
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WIP
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## Example:
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```
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer, StoppingCriteria
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import torch
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class MyStoppingCriteria(StoppingCriteria):
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def __init__(self, target_sequence, prompt):
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self.target_sequence = target_sequence
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self.prompt=prompt
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def __call__(self, input_ids, scores, **kwargs):
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generated_text = tokenizer.decode(input_ids[0])
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generated_text = generated_text.replace(self.prompt,'')
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if self.target_sequence in generated_text:
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return True
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return False
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def __len__(self):
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return 1
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def __iter__(self):
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yield self
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modelpath="aloobun/Reyna-CoT-4B-v0.1"
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model = AutoModelForCausalLM.from_pretrained(
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modelpath,
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torch_dtype=torch.bfloat16,
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device_map="cuda",
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trust_remote_code=True,
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)
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tokenizer = AutoTokenizer.from_pretrained(
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modelpath,
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trust_remote_code=True,
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use_fast=False,
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)
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prompt = "Avery opens a flower shop. She ties 8 bunches of flowers with 9 flowers in each bunch. How many bunches would she have if she put 12 flowers in each bunch instead?\n"
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encoded_input = tokenizer(prompt, return_tensors='pt')
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input_ids=encoded_input['input_ids'].cuda()
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streamer = TextStreamer(tokenizer=tokenizer, skip_prompt=True)
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op = model.generate(
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input_ids,
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streamer=streamer,
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pad_token_id=tokenizer.eos_token_id,
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do_sample=True,
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temperature=0.6,
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top_p=0.8,
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max_new_tokens=512,
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stopping_criteria=MyStoppingCriteria("<|endoftext|>", prompt)
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)
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```
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## Output:
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>She would have 8 x 9 = 72 flowers in total.
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>She would have 72 / 12 = 6 bunches of flowers with 12 flowers in each bunch.
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>Therefore, the answer is 6.<|endoftext|> |