初始化项目，由ModelHub XC社区提供模型
Model: aloobun/Reyna-Mini-1.8B-v0.2 Source: Original Platform
This commit is contained in:
ModelHub XC
212
README.md
Normal file
212
README.md
Normal file
@@ -0,0 +1,212 @@
|
||||
---
|
||||
license: other
|
||||
library_name: transformers
|
||||
tags:
|
||||
- chatml
|
||||
- finetune
|
||||
- gpt4
|
||||
- synthetic data
|
||||
- custom_code
|
||||
- qwen2
|
||||
datasets:
|
||||
- Locutusque/Hercules-v3.0
|
||||
license_name: tongyi-qianwen-research
|
||||
license_link: https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat/raw/main/LICENSE
|
||||
model-index:
|
||||
- name: Reyna-Mini-1.8B-v0.2
|
||||
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: 36.6
|
||||
name: normalized accuracy
|
||||
source:
|
||||
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aloobun/Reyna-Mini-1.8B-v0.2
|
||||
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: 60.19
|
||||
name: normalized accuracy
|
||||
source:
|
||||
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aloobun/Reyna-Mini-1.8B-v0.2
|
||||
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: 44.75
|
||||
name: accuracy
|
||||
source:
|
||||
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aloobun/Reyna-Mini-1.8B-v0.2
|
||||
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: 41.24
|
||||
source:
|
||||
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aloobun/Reyna-Mini-1.8B-v0.2
|
||||
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: 61.56
|
||||
name: accuracy
|
||||
source:
|
||||
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aloobun/Reyna-Mini-1.8B-v0.2
|
||||
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: 31.31
|
||||
name: accuracy
|
||||
source:
|
||||
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aloobun/Reyna-Mini-1.8B-v0.2
|
||||
name: Open LLM Leaderboard
|
||||
---
|
||||
|
||||
|
||||
- Finetuned [Qwen/Qwen1.5-1.8B-Chat](https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat), with SFT on Hercules v3 dataset.
|
||||
- This marks the third model in this series.
|
||||
- Format: ChatML -
|
||||
```
|
||||
<|im_start|>system
|
||||
{system}<|im_end|>
|
||||
<|im_start|>user
|
||||
{prompt}<|im_end|>
|
||||
<|im_start|>assistant
|
||||
```
|
||||
- Next step would be to do a DPO train on top.
|
||||
|
||||
|
||||
## Benchamrks:
|
||||
|
||||
|Avg. | Arc | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
|
||||
|---|---|---|---|---|---|---|
|
||||
|45.94 | 36.6 |60.19 | 44.75 | 41.24 | 61.56 | 31.31 |
|
||||
|
||||
## 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-Mini-1.8B-v0.2"
|
||||
|
||||
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 = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nIs there inherent order in nature or is it all chaos and chance?<|im_end|>\n<|im_start|>assistant\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("<|im_end|>", prompt)
|
||||
)
|
||||
```
|
||||
|
||||
## Output:
|
||||
>Nature appears to be inherently organized, with patterns and structures that can be observed across different levels of organization. However, the exact mechanisms by which these patterns emerge and evolve remain largely unknown.
|
||||
>The universe seems to be governed by a series of laws and principles known as "laws of physics," such as Newton's laws of motion, electromagnetism, and thermodynamics. These laws govern how matter and energy interact with each other and how they behave over time.
|
||||
>Despite our understanding of these laws, we still struggle to comprehend the underlying mechanisms that allow for the emergence of complex patterns and structures. This is because the universe operates on a scale that is too small for us to observe directly, and therefore we cannot fully understand its internal workings.
|
||||
>In summary, while there may be some level of order and structure within the universe, the precise mechanisms governing this order remain largely unknown.<|im_end|>
|
||||
# [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-Mini-1.8B-v0.2)
|
||||
|
||||
| Metric |Value|
|
||||
|---------------------------------|----:|
|
||||
|Avg. |45.94|
|
||||
|AI2 Reasoning Challenge (25-Shot)|36.60|
|
||||
|HellaSwag (10-Shot) |60.19|
|
||||
|MMLU (5-Shot) |44.75|
|
||||
|TruthfulQA (0-shot) |41.24|
|
||||
|Winogrande (5-shot) |61.56|
|
||||
|GSM8k (5-shot) |31.31|
|
||||
|
||||
Reference in New Issue
Block a user