初始化项目，由ModelHub XC社区提供模型

Model: aloobun/Reyna-CoT-4B-v0.1
Source: Original Platform

README.md

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+---
+license_name: tongyi-qianwen-research
+license_link: https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat/raw/main/LICENSE
+library_name: transformers
+license: other
+tags:
+- finetune
+- synthetic data
+- custom_code
+- qwen2
+- COT
+datasets:
+- kaist-ai/CoT-Collection
+---
+
+![Reyna aloobun qwen4B](https://i.imgur.com/QfbOY6c.jpeg)
+- 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.
+- 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).
+- This marks the fourth model in this series. This experiment aims to improve Chain of Thought (CoT) capabilities on smaller language models.
+- I may rerun the finetuning experiment(with a more balanced dataset), using an iterative rationale-bootstrapping procedure inspired by euclaise/Memphis-CoT-3B.
+- Hyperparameter: adamw with eps of 1e-8, cosine decay with 20% warmup, lr=2e-5
+
+## Benchamrks:
+
+WIP
+
+## Example:
+```
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer, StoppingCriteria
+import torch
+
+class MyStoppingCriteria(StoppingCriteria):
+  def __init__(self, target_sequence, prompt):
+    self.target_sequence = target_sequence
+    self.prompt=prompt
+
+  def __call__(self, input_ids, scores, **kwargs):
+    generated_text = tokenizer.decode(input_ids[0])
+    generated_text = generated_text.replace(self.prompt,'')
+    if self.target_sequence in generated_text:
+        return True 
+    return False 
+
+  def __len__(self):
+    return 1
+
+  def __iter__(self):
+    yield self
+
+modelpath="aloobun/Reyna-CoT-4B-v0.1"
+
+model = AutoModelForCausalLM.from_pretrained(
+    modelpath,
+    torch_dtype=torch.bfloat16,
+    device_map="cuda",
+    trust_remote_code=True,       
+)
+
+tokenizer = AutoTokenizer.from_pretrained(
+    modelpath,
+    trust_remote_code=True,      
+    use_fast=False,
+)
+
+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"
+
+encoded_input = tokenizer(prompt, return_tensors='pt')
+input_ids=encoded_input['input_ids'].cuda()
+streamer = TextStreamer(tokenizer=tokenizer, skip_prompt=True)
+op = model.generate(
+    input_ids,
+    streamer=streamer,
+    pad_token_id=tokenizer.eos_token_id,
+    do_sample=True,
+    temperature=0.6,
+    top_p=0.8,
+    max_new_tokens=512,
+    stopping_criteria=MyStoppingCriteria("<|endoftext|>", prompt)
+)
+
+```
+
+## Output:
+>She would have 8 x 9 = 72 flowers in total.
+>She would have 72 / 12 = 6 bunches of flowers with 12 flowers in each bunch.
+>Therefore, the answer is 6.<|endoftext|>