README.md

---
license: other
library_name: transformers
tags:
- finetune
- synthetic data
- custom_code
- qwen2
- COT
datasets:
- kaist-ai/CoT-Collection
license_name: tongyi-qianwen-research
license_link: https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat/raw/main/LICENSE
model-index:
- name: Reyna-CoT-4B-v0.1
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 44.71
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aloobun/Reyna-CoT-4B-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 71.12
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aloobun/Reyna-CoT-4B-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 55.9
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aloobun/Reyna-CoT-4B-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 43.09
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aloobun/Reyna-CoT-4B-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 67.72
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aloobun/Reyna-CoT-4B-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 16.91
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aloobun/Reyna-CoT-4B-v0.1
      name: Open LLM Leaderboard
---

![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|>
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_aloobun__Reyna-CoT-4B-v0.1)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |49.91|
|AI2 Reasoning Challenge (25-Shot)|44.71|
|HellaSwag (10-Shot)              |71.12|
|MMLU (5-Shot)                    |55.90|
|TruthfulQA (0-shot)              |43.09|
|Winogrande (5-shot)              |67.72|
|GSM8k (5-shot)                   |16.91|