File size: 5,429 Bytes
82adb0f 2e709f7 82adb0f 34b19b3 94d07d7 34b19b3 94d07d7 82adb0f 34b19b3 82adb0f a630c0e 82adb0f a630c0e 3a4a3e6 34b19b3 82adb0f 2e709f7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 |
---
license: apache-2.0
model-index:
- name: tigerbot-7b-sft
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 41.64
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TigerResearch/tigerbot-7b-sft
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 60.56
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TigerResearch/tigerbot-7b-sft
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 29.89
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TigerResearch/tigerbot-7b-sft
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 58.18
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TigerResearch/tigerbot-7b-sft
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 63.54
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TigerResearch/tigerbot-7b-sft
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 6.29
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TigerResearch/tigerbot-7b-sft
name: Open LLM Leaderboard
---
<div style="width: 100%;">
<img src="http://x-pai.algolet.com/bot/img/logo_core.png" alt="TigerBot" style="width: 20%; display: block; margin: auto;">
</div>
<p align="center">
<font face="黑体" size=5"> A cutting-edge foundation for your very own LLM. </font>
</p>
<p align="center">
🌐 <a href="https://tigerbot.com/" target="_blank">TigerBot</a> • 🤗 <a href="https://huggingface.co/TigerResearch" target="_blank">Hugging Face</a>
</p>
## Github
https://github.com/TigerResearch/TigerBot
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from accelerate import infer_auto_device_map, dispatch_model
from accelerate.utils import get_balanced_memory
tokenizer = AutoTokenizer.from_pretrained("TigerResearch/tigerbot-7b-sft-v1")
model = AutoModelForCausalLM.from_pretrained("TigerResearch/tigerbot-7b-sft-v1")
max_memory = get_balanced_memory(model)
device_map = infer_auto_device_map(model, max_memory=max_memory, no_split_module_classes=["BloomBlock"])
model = dispatch_model(model, device_map=device_map, offload_buffers=True)
device = torch.cuda.current_device()
tok_ins = "\n\n### Instruction:\n"
tok_res = "\n\n### Response:\n"
prompt_input = tok_ins + "{instruction}" + tok_res
input_text = "What is the next number after this list: [1, 2, 3, 5, 8, 13, 21]"
input_text = prompt_input.format_map({'instruction': input_text})
max_input_length = 512
max_generate_length = 1024
generation_kwargs = {
"top_p": 0.95,
"temperature": 0.8,
"max_length": max_generate_length,
"eos_token_id": tokenizer.eos_token_id,
"pad_token_id": tokenizer.pad_token_id,
"early_stopping": True,
"no_repeat_ngram_size": 4,
}
inputs = tokenizer(input_text, return_tensors='pt', truncation=True, max_length=max_input_length)
inputs = {k: v.to(device) for k, v in inputs.items()}
output = model.generate(**inputs, **generation_kwargs)
answer = ''
for tok_id in output[0][inputs['input_ids'].shape[1]:]:
if tok_id != tokenizer.eos_token_id:
answer += tokenizer.decode(tok_id)
print(answer)
```
# [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_TigerResearch__tigerbot-7b-sft)
| Metric |Value|
|---------------------------------|----:|
|Avg. |43.35|
|AI2 Reasoning Challenge (25-Shot)|41.64|
|HellaSwag (10-Shot) |60.56|
|MMLU (5-Shot) |29.89|
|TruthfulQA (0-shot) |58.18|
|Winogrande (5-shot) |63.54|
|GSM8k (5-shot) | 6.29|
|