Hugging Face's logo

matsuo-lab
/
weblab-10b-instruction-sft

Text Generation
Transformers
PyTorch
gpt_neox
text-generation-inference
Inference Endpoints
Model card Files Community
4
weblab-10b-instruction-sft
File size: 3,310 Bytes 
200a3a9
 
 
1c3d608
f60ee9c
1c3d608
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f8f840d
 
 
1c3d608
 
 
 
 
e9d3e3a
 
1c3d608
 
 
 
 
 
 
 
 
 
 
1aeb8d7
 
 
 
1c3d608
 
 
1aeb8d7
 
1c3d608
 
 
 
 
 
 
 
 
e9d3e3a
 
1c3d608
 
 
 
 
 
 
 
 
 
 
 
 
1abfc61
865ea56
1c3d608
 
 
 
 
 
 
 
 
 
 
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
 
---
license: cc-by-nc-4.0
---

# weblab-10b-instruction-sft

# Overview
This repository provides a Japanese-centric multilingual GPT-NeoX model of 10 billion parameters.

* **Library**
    
    The model was trained using code based on [EleutherAI/gpt-neox](https://github.com/EleutherAI/gpt-neox).

* **Model architecture**

    A 36-layer, 4864-hidden-size transformer-based language model.

* **Pre-training**

    The model was trained on around **600B** tokens from a mixture of the following corpora

    - [Japanese C4](https://huggingface.co./datasets/mc4)
    - [The Pile](https://huggingface.co./datasets/EleutherAI/pile)

* **Instruction-supervised-finetuning**

    The model was finetuned on a subset records from a mixture of the following dataset

    - [Alpaca (English)](https://github.com/gururise/AlpacaDataCleaned/blob/main/alpaca_data_cleaned.json)
    - [Alpaca (Japanese translation)](https://github.com/shi3z/alpaca_ja/blob/main/alpaca_cleaned_ja.json)
    - [Flan 2021 (English)](https://huggingface.co./datasets/conceptofmind/flan2021_submix_original)
    - [Flan CoT (English)](https://huggingface.co./datasets/conceptofmind/cot_submix_original)
    - [Flan Dialog (English)](https://huggingface.co./datasets/conceptofmind/dialog_submix_original)

* **Model Series**

    | Variant | Link |
    | :-- | :--|
    | weblab-10b-instruction-sft | https://huggingface.co./matsuo-lab/weblab-10b-instruction-sft |
    | weblab-10b | https://huggingface.co./matsuo-lab/weblab-10b |

* **Authors**
    
    Takeshi Kojima

---

# Benchmarking

* **Japanese benchmark**

    - *We used [Stability-AI/lm-evaluation-harness](https://github.com/Stability-AI/lm-evaluation-harness/tree/2f1583c0735eacdfdfa5b7d656074b69577b6774) library for evaluation.*
    - *The 4-task average accuracy is based on results of JCommonsenseQA-1.1, JNLI-1.1, MARC-ja-1.1, and JSQuAD-1.1.*
    - *model loading is performed with float16, and evaluation is performed with template version 0.3 using the few-shot in-context learning.*
    - *The number of few-shots is 3,3,3,2.*
   
    | Model | Average | JCommonsenseQA | JNLI | MARC-ja | JSQuAD |
    | :-- | :-- | :-- | :-- | :-- | :-- |
    | weblab-10b-instruction-sft | 78.78 | 74.35 | 65.65 | 96.06 | 79.04 |
    | weblab-10b | 66.38 | 65.86 | 54.19 | 84.49 | 60.98 |

---

# How to use the model

~~~~python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("matsuo-lab/weblab-10b-instruction-sft")
model = AutoModelForCausalLM.from_pretrained("matsuo-lab/weblab-10b-instruction-sft")

if torch.cuda.is_available():
    model = model.to("cuda")

text = "倧規樑言θͺžγƒ’デルに぀いてθͺ¬ζ˜Žγ—てください。"
text = f'δ»₯下は、タスクをθͺ¬ζ˜Žγ™γ‚‹ζŒ‡η€Ίγ§γ™γ€‚θ¦ζ±‚γ‚’ι©εˆ‡γ«ζΊ€γŸγ™εΏœη­”γ‚’ζ›Έγγͺさい。\n\n### ζŒ‡η€Ί:\n{text}\n\n### εΏœη­”:'
token_ids = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt")

with torch.no_grad():
    output_ids = model.generate(
        token_ids.to(model.device),
        max_new_tokens=100,
        do_sample=True,
        temperature=0.7,
        top_p=0.95
    )

output = tokenizer.decode(output_ids.tolist()[0])
print(output)

~~~~
---

# Licenese
[cc-by-nc-4.0](https://creativecommons.org/licenses/by-nc/4.0/)