crumb/apricot-wildflower-20

认证创作者 crumb 已认证
🤗 Hugging Face 来源text-generationapache-2.07.2B 参数14 GBsafetensors✓ 4 个校验和今天更新
一条命令提交

在你的模型文件夹旁边运行它。它会制作种子、将文件与 Hugging Face 比对,然后提交。你只需开始做种,并粘贴你账户中的密钥。它只读取你的文件,绝不修改。如果愿意,可以先阅读脚本。

curl -fsSL https://pirateface.co/package.sh | bash -s -- --repo crumb/apricot-wildflower-20 ./model-folder
需要做种者 →

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