README.md

---
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：

```python
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](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_WangZeJun__bloom-820m-chat)

| 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         |