apricot-wildflower-20
This model is the Mistral-7b model finetuned for 1k steps with a combined lm loss and distillation loss on Openwebtext2 with a >=20 reddit score filter with training logits from Mixtral. I'm not going to pretend it was a big project I did it in a dream and woke up and replicated the code without any actual reason, idk how well it fares in benchmarks.
(update: not very good)
| model | avg | arc | hellaswag | mmlu | truthfulqa | winogrande | gsm8k |
|---|---|---|---|---|---|---|---|
| apricot-wildflower-20 | 59.74 | 59.64 | 81.76 | 63.38 | 41.76 | 77.9 | 33.97 |
| mistralai/Mistral-7B-v0.1 | 60.97 | 59.98 | 83.31 | 64.16 | 42.15 | 78.37 | 37.83 |
use
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "crumb/apricot-wildflower-20"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, device_map="auto", load_in_8bit=True)
text = "Hello my name is"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
# Hello my name is Katie and I am a 20 year old student from the UK. I am currently studying for a degree in English Literature and Creative Writing at the University of Leeds. I am a huge fan of the Harry Potter series and have been since I was 10 years old. I have read the books countless times and have seen the films many times too. I am a huge fan of the Harry Potter fandom and have been a member of the Harry Potter forums for a few years now. I am also a member of the Harry Potter fan club and have been for a few years now. I
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 59.74 |
| AI2 Reasoning Challenge (25-Shot) | 59.64 |
| HellaSwag (10-Shot) | 81.76 |
| MMLU (5-Shot) | 63.38 |
| TruthfulQA (0-shot) | 41.76 |
| Winogrande (5-shot) | 77.90 |
| GSM8k (5-shot) | 33.97 |