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---
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
---
- 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|
|