README.md

metadata

license: bigscience-bloom-rail-1.0

https://github.com/zejunwang1/bloom_tuning

可以通过如下代码调用 bloom-820m-chat 模型来生成对话：

from transformers import BloomTokenizerFast, BloomForCausalLM

model_name_or_path = "WangZeJun/bloom-820m-chat"

tokenizer = BloomTokenizerFast.from_pretrained(model_name_or_path)
model = BloomForCausalLM.from_pretrained(model_name_or_path).cuda()
model = model.eval()

input_pattern = "{}</s>"
text = "你好"
input_ids = tokenizer(input_pattern.format(text), return_tensors="pt").input_ids
input_ids = input_ids.cuda()

outputs = model.generate(input_ids, do_sample=True, max_new_tokens=1024, top_p=0.85,
    temperature=0.3, repetition_penalty=1.2, eos_token_id=tokenizer.eos_token_id)

input_ids_len = input_ids.size(1)
response_ids = outputs[0][input_ids_len:]
response = tokenizer.decode(response_ids)
print(response)

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	26.55
ARC (25-shot)	23.38
HellaSwag (10-shot)	34.16
MMLU (5-shot)	25.98
TruthfulQA (0-shot)	40.32
Winogrande (5-shot)	53.2
GSM8K (5-shot)	0.0
DROP (3-shot)	8.85