- Finetuned 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
Detailed results can be found here
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 |
- Downloads last month
- 523
Model tree for aloobun/Reyna-Mini-1.8B-v0.2
Dataset used to train aloobun/Reyna-Mini-1.8B-v0.2
Collection including aloobun/Reyna-Mini-1.8B-v0.2
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard36.600
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard60.190
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard44.750
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard41.240
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard61.560
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard31.310