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fdgvjhb/pennyheartattack | fdgvjhb | 2023-11-29T00:42:20Z | 0 | 0 | null | [
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ARDICAI/stable-diffusion-2-1-finetuned | ARDICAI | 2023-11-29T16:01:48Z | 86,093 | 7 | null | [
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license: creativeml-openrail-m
tags:
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---
### stable-diffusion-2-1-finetuned Dreambooth model trained by ARDIC AI team
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0.3633604347705841,
0.0844186469912529,
0.18473292887210846,
0.730056881904602,
-0.31939566135406494,
-0.28460413217544556,
-0.5048521161079407,
-0.44389936327934265,
... |
deepseek-ai/deepseek-coder-6.7b-instruct | deepseek-ai | 2023-11-29T06:00:29Z | 40,546 | 83 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:other",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2023-11-29T06:00:29Z | 2023-10-29T11:01:36.000Z | null | null | ---
license: other
license_name: deepseek
license_link: LICENSE
---
<p align="center">
<img width="1000px" alt="DeepSeek Coder" src="https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/pictures/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://coder.deepseek.com/">[🤖 Chat with DeepSeek Coder]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/guoday/assert/blob/main/QR.png?raw=true">[Wechat(微信)]</a> </p>
<hr>
### 1. Introduction of Deepseek Coder
Deepseek Coder is composed of a series of code language models, each trained from scratch on 2T tokens, with a composition of 87% code and 13% natural language in both English and Chinese. We provide various sizes of the code model, ranging from 1B to 33B versions. Each model is pre-trained on project-level code corpus by employing a window size of 16K and a extra fill-in-the-blank task, to support project-level code completion and infilling. For coding capabilities, Deepseek Coder achieves state-of-the-art performance among open-source code models on multiple programming languages and various benchmarks.
- **Massive Training Data**: Trained from scratch fon 2T tokens, including 87% code and 13% linguistic data in both English and Chinese languages.
- **Highly Flexible & Scalable**: Offered in model sizes of 1.3B, 5.7B, 6.7B, and 33B, enabling users to choose the setup most suitable for their requirements.
- **Superior Model Performance**: State-of-the-art performance among publicly available code models on HumanEval, MultiPL-E, MBPP, DS-1000, and APPS benchmarks.
- **Advanced Code Completion Capabilities**: A window size of 16K and a fill-in-the-blank task, supporting project-level code completion and infilling tasks.
### 2. Model Summary
deepseek-coder-6.7b-instruct is a 6.7B parameter model initialized from deepseek-coder-6.7b-base and fine-tuned on 2B tokens of instruction data.
- **Home Page:** [DeepSeek](https://deepseek.com/)
- **Repository:** [deepseek-ai/deepseek-coder](https://github.com/deepseek-ai/deepseek-coder)
- **Chat With DeepSeek Coder:** [DeepSeek-Coder](https://coder.deepseek.com/)
### 3. How to Use
Here give some examples of how to use our model.
#### Chat Model Inference
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-instruct", trust_remote_code=True).cuda()
messages=[
{ 'role': 'user', 'content': "write a quick sort algorithm in python."}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
# 32021 is the id of <|EOT|> token
outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=32021)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
```
### 4. License
This code repository is licensed under the MIT License. The use of DeepSeek Coder models is subject to the Model License. DeepSeek Coder supports commercial use.
See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-coder/blob/main/LICENSE-MODEL) for more details.
### 5. Contact
If you have any questions, please raise an issue or contact us at [agi_code@deepseek.com](mailto:agi_code@deepseek.com).
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | deepseek-ai/deepseek-coder-6.7b-instruct | [
-0.3025534152984619,
-0.6263840794563293,
0.17590683698654175,
0.3432497978210449,
-0.28495511412620544,
0.12760072946548462,
-0.21790799498558044,
-0.5951175689697266,
-0.038023971021175385,
0.14517329633235931,
-0.4744638502597809,
-0.5632725358009338,
-0.652996301651001,
-0.210024252533... |
thenlper/gte-large-zh | thenlper | 2023-11-29T14:19:08Z | 25,633 | 12 | null | [
"sentence-transformers",
"pytorch",
"safetensors",
"bert",
"mteb",
"sentence-similarity",
"Sentence Transformers",
"en",
"arxiv:2308.03281",
"license:mit",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | 2023-11-29T14:19:08Z | 2023-11-07T07:51:20.000Z | null | null | ---
tags:
- mteb
- sentence-similarity
- sentence-transformers
- Sentence Transformers
model-index:
- name: gte-large-zh
results:
- task:
type: STS
dataset:
type: C-MTEB/AFQMC
name: MTEB AFQMC
config: default
split: validation
revision: None
metrics:
- type: cos_sim_pearson
value: 48.94131905219026
- type: cos_sim_spearman
value: 54.58261199731436
- type: euclidean_pearson
value: 52.73929210805982
- type: euclidean_spearman
value: 54.582632097533676
- type: manhattan_pearson
value: 52.73123295724949
- type: manhattan_spearman
value: 54.572941830465794
- task:
type: STS
dataset:
type: C-MTEB/ATEC
name: MTEB ATEC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 47.292931669579005
- type: cos_sim_spearman
value: 54.601019783506466
- type: euclidean_pearson
value: 54.61393532658173
- type: euclidean_spearman
value: 54.60101865708542
- type: manhattan_pearson
value: 54.59369555606305
- type: manhattan_spearman
value: 54.601098593646036
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 47.233999999999995
- type: f1
value: 45.68998446563349
- task:
type: STS
dataset:
type: C-MTEB/BQ
name: MTEB BQ
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 62.55033151404683
- type: cos_sim_spearman
value: 64.40573802644984
- type: euclidean_pearson
value: 62.93453281081951
- type: euclidean_spearman
value: 64.40574149035828
- type: manhattan_pearson
value: 62.839969210895816
- type: manhattan_spearman
value: 64.30837945045283
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringP2P
name: MTEB CLSClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 42.098169316685045
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringS2S
name: MTEB CLSClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 38.90716707051822
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv1-reranking
name: MTEB CMedQAv1
config: default
split: test
revision: None
metrics:
- type: map
value: 86.09191911031553
- type: mrr
value: 88.6747619047619
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv2-reranking
name: MTEB CMedQAv2
config: default
split: test
revision: None
metrics:
- type: map
value: 86.45781885502122
- type: mrr
value: 89.01591269841269
- task:
type: Retrieval
dataset:
type: C-MTEB/CmedqaRetrieval
name: MTEB CmedqaRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 24.215
- type: map_at_10
value: 36.498000000000005
- type: map_at_100
value: 38.409
- type: map_at_1000
value: 38.524
- type: map_at_3
value: 32.428000000000004
- type: map_at_5
value: 34.664
- type: mrr_at_1
value: 36.834
- type: mrr_at_10
value: 45.196
- type: mrr_at_100
value: 46.214
- type: mrr_at_1000
value: 46.259
- type: mrr_at_3
value: 42.631
- type: mrr_at_5
value: 44.044
- type: ndcg_at_1
value: 36.834
- type: ndcg_at_10
value: 43.146
- type: ndcg_at_100
value: 50.632999999999996
- type: ndcg_at_1000
value: 52.608999999999995
- type: ndcg_at_3
value: 37.851
- type: ndcg_at_5
value: 40.005
- type: precision_at_1
value: 36.834
- type: precision_at_10
value: 9.647
- type: precision_at_100
value: 1.574
- type: precision_at_1000
value: 0.183
- type: precision_at_3
value: 21.48
- type: precision_at_5
value: 15.649
- type: recall_at_1
value: 24.215
- type: recall_at_10
value: 54.079
- type: recall_at_100
value: 84.943
- type: recall_at_1000
value: 98.098
- type: recall_at_3
value: 38.117000000000004
- type: recall_at_5
value: 44.775999999999996
- task:
type: PairClassification
dataset:
type: C-MTEB/CMNLI
name: MTEB Cmnli
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy
value: 82.51352976548407
- type: cos_sim_ap
value: 89.49905141462749
- type: cos_sim_f1
value: 83.89334489486234
- type: cos_sim_precision
value: 78.19761567993534
- type: cos_sim_recall
value: 90.48398410100538
- type: dot_accuracy
value: 82.51352976548407
- type: dot_ap
value: 89.49108293121158
- type: dot_f1
value: 83.89334489486234
- type: dot_precision
value: 78.19761567993534
- type: dot_recall
value: 90.48398410100538
- type: euclidean_accuracy
value: 82.51352976548407
- type: euclidean_ap
value: 89.49904709975154
- type: euclidean_f1
value: 83.89334489486234
- type: euclidean_precision
value: 78.19761567993534
- type: euclidean_recall
value: 90.48398410100538
- type: manhattan_accuracy
value: 82.48947684906794
- type: manhattan_ap
value: 89.49231995962901
- type: manhattan_f1
value: 83.84681215233205
- type: manhattan_precision
value: 77.28258726089528
- type: manhattan_recall
value: 91.62964694879588
- type: max_accuracy
value: 82.51352976548407
- type: max_ap
value: 89.49905141462749
- type: max_f1
value: 83.89334489486234
- task:
type: Retrieval
dataset:
type: C-MTEB/CovidRetrieval
name: MTEB CovidRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 78.583
- type: map_at_10
value: 85.613
- type: map_at_100
value: 85.777
- type: map_at_1000
value: 85.77900000000001
- type: map_at_3
value: 84.58
- type: map_at_5
value: 85.22800000000001
- type: mrr_at_1
value: 78.925
- type: mrr_at_10
value: 85.667
- type: mrr_at_100
value: 85.822
- type: mrr_at_1000
value: 85.824
- type: mrr_at_3
value: 84.651
- type: mrr_at_5
value: 85.299
- type: ndcg_at_1
value: 78.925
- type: ndcg_at_10
value: 88.405
- type: ndcg_at_100
value: 89.02799999999999
- type: ndcg_at_1000
value: 89.093
- type: ndcg_at_3
value: 86.393
- type: ndcg_at_5
value: 87.5
- type: precision_at_1
value: 78.925
- type: precision_at_10
value: 9.789
- type: precision_at_100
value: 1.005
- type: precision_at_1000
value: 0.101
- type: precision_at_3
value: 30.769000000000002
- type: precision_at_5
value: 19.031000000000002
- type: recall_at_1
value: 78.583
- type: recall_at_10
value: 96.891
- type: recall_at_100
value: 99.473
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 91.438
- type: recall_at_5
value: 94.152
- task:
type: Retrieval
dataset:
type: C-MTEB/DuRetrieval
name: MTEB DuRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 25.604
- type: map_at_10
value: 77.171
- type: map_at_100
value: 80.033
- type: map_at_1000
value: 80.099
- type: map_at_3
value: 54.364000000000004
- type: map_at_5
value: 68.024
- type: mrr_at_1
value: 89.85
- type: mrr_at_10
value: 93.009
- type: mrr_at_100
value: 93.065
- type: mrr_at_1000
value: 93.068
- type: mrr_at_3
value: 92.72500000000001
- type: mrr_at_5
value: 92.915
- type: ndcg_at_1
value: 89.85
- type: ndcg_at_10
value: 85.038
- type: ndcg_at_100
value: 88.247
- type: ndcg_at_1000
value: 88.837
- type: ndcg_at_3
value: 85.20299999999999
- type: ndcg_at_5
value: 83.47
- type: precision_at_1
value: 89.85
- type: precision_at_10
value: 40.275
- type: precision_at_100
value: 4.709
- type: precision_at_1000
value: 0.486
- type: precision_at_3
value: 76.36699999999999
- type: precision_at_5
value: 63.75999999999999
- type: recall_at_1
value: 25.604
- type: recall_at_10
value: 85.423
- type: recall_at_100
value: 95.695
- type: recall_at_1000
value: 98.669
- type: recall_at_3
value: 56.737
- type: recall_at_5
value: 72.646
- task:
type: Retrieval
dataset:
type: C-MTEB/EcomRetrieval
name: MTEB EcomRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 51.800000000000004
- type: map_at_10
value: 62.17
- type: map_at_100
value: 62.649
- type: map_at_1000
value: 62.663000000000004
- type: map_at_3
value: 59.699999999999996
- type: map_at_5
value: 61.23499999999999
- type: mrr_at_1
value: 51.800000000000004
- type: mrr_at_10
value: 62.17
- type: mrr_at_100
value: 62.649
- type: mrr_at_1000
value: 62.663000000000004
- type: mrr_at_3
value: 59.699999999999996
- type: mrr_at_5
value: 61.23499999999999
- type: ndcg_at_1
value: 51.800000000000004
- type: ndcg_at_10
value: 67.246
- type: ndcg_at_100
value: 69.58
- type: ndcg_at_1000
value: 69.925
- type: ndcg_at_3
value: 62.197
- type: ndcg_at_5
value: 64.981
- type: precision_at_1
value: 51.800000000000004
- type: precision_at_10
value: 8.32
- type: precision_at_100
value: 0.941
- type: precision_at_1000
value: 0.097
- type: precision_at_3
value: 23.133
- type: precision_at_5
value: 15.24
- type: recall_at_1
value: 51.800000000000004
- type: recall_at_10
value: 83.2
- type: recall_at_100
value: 94.1
- type: recall_at_1000
value: 96.8
- type: recall_at_3
value: 69.39999999999999
- type: recall_at_5
value: 76.2
- task:
type: Classification
dataset:
type: C-MTEB/IFlyTek-classification
name: MTEB IFlyTek
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 49.60369372835706
- type: f1
value: 38.24016248875209
- task:
type: Classification
dataset:
type: C-MTEB/JDReview-classification
name: MTEB JDReview
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 86.71669793621012
- type: ap
value: 55.75807094995178
- type: f1
value: 81.59033162805417
- task:
type: STS
dataset:
type: C-MTEB/LCQMC
name: MTEB LCQMC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 69.50947272908907
- type: cos_sim_spearman
value: 74.40054474949213
- type: euclidean_pearson
value: 73.53007373987617
- type: euclidean_spearman
value: 74.40054474732082
- type: manhattan_pearson
value: 73.51396571849736
- type: manhattan_spearman
value: 74.38395696630835
- task:
type: Reranking
dataset:
type: C-MTEB/Mmarco-reranking
name: MTEB MMarcoReranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 31.188333827724108
- type: mrr
value: 29.84801587301587
- task:
type: Retrieval
dataset:
type: C-MTEB/MMarcoRetrieval
name: MTEB MMarcoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 64.685
- type: map_at_10
value: 73.803
- type: map_at_100
value: 74.153
- type: map_at_1000
value: 74.167
- type: map_at_3
value: 71.98
- type: map_at_5
value: 73.21600000000001
- type: mrr_at_1
value: 66.891
- type: mrr_at_10
value: 74.48700000000001
- type: mrr_at_100
value: 74.788
- type: mrr_at_1000
value: 74.801
- type: mrr_at_3
value: 72.918
- type: mrr_at_5
value: 73.965
- type: ndcg_at_1
value: 66.891
- type: ndcg_at_10
value: 77.534
- type: ndcg_at_100
value: 79.106
- type: ndcg_at_1000
value: 79.494
- type: ndcg_at_3
value: 74.13499999999999
- type: ndcg_at_5
value: 76.20700000000001
- type: precision_at_1
value: 66.891
- type: precision_at_10
value: 9.375
- type: precision_at_100
value: 1.0170000000000001
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 27.932000000000002
- type: precision_at_5
value: 17.86
- type: recall_at_1
value: 64.685
- type: recall_at_10
value: 88.298
- type: recall_at_100
value: 95.426
- type: recall_at_1000
value: 98.48700000000001
- type: recall_at_3
value: 79.44200000000001
- type: recall_at_5
value: 84.358
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (zh-CN)
config: zh-CN
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 73.30531271015468
- type: f1
value: 70.88091430578575
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (zh-CN)
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.7128446536651
- type: f1
value: 75.06125593532262
- task:
type: Retrieval
dataset:
type: C-MTEB/MedicalRetrieval
name: MTEB MedicalRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 52.7
- type: map_at_10
value: 59.532
- type: map_at_100
value: 60.085
- type: map_at_1000
value: 60.126000000000005
- type: map_at_3
value: 57.767
- type: map_at_5
value: 58.952000000000005
- type: mrr_at_1
value: 52.900000000000006
- type: mrr_at_10
value: 59.648999999999994
- type: mrr_at_100
value: 60.20100000000001
- type: mrr_at_1000
value: 60.242
- type: mrr_at_3
value: 57.882999999999996
- type: mrr_at_5
value: 59.068
- type: ndcg_at_1
value: 52.7
- type: ndcg_at_10
value: 62.883
- type: ndcg_at_100
value: 65.714
- type: ndcg_at_1000
value: 66.932
- type: ndcg_at_3
value: 59.34700000000001
- type: ndcg_at_5
value: 61.486
- type: precision_at_1
value: 52.7
- type: precision_at_10
value: 7.340000000000001
- type: precision_at_100
value: 0.8699999999999999
- type: precision_at_1000
value: 0.097
- type: precision_at_3
value: 21.3
- type: precision_at_5
value: 13.819999999999999
- type: recall_at_1
value: 52.7
- type: recall_at_10
value: 73.4
- type: recall_at_100
value: 87.0
- type: recall_at_1000
value: 96.8
- type: recall_at_3
value: 63.9
- type: recall_at_5
value: 69.1
- task:
type: Classification
dataset:
type: C-MTEB/MultilingualSentiment-classification
name: MTEB MultilingualSentiment
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 76.47666666666667
- type: f1
value: 76.4808576632057
- task:
type: PairClassification
dataset:
type: C-MTEB/OCNLI
name: MTEB Ocnli
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy
value: 77.58527341635084
- type: cos_sim_ap
value: 79.32131557636497
- type: cos_sim_f1
value: 80.51948051948052
- type: cos_sim_precision
value: 71.7948717948718
- type: cos_sim_recall
value: 91.65786694825766
- type: dot_accuracy
value: 77.58527341635084
- type: dot_ap
value: 79.32131557636497
- type: dot_f1
value: 80.51948051948052
- type: dot_precision
value: 71.7948717948718
- type: dot_recall
value: 91.65786694825766
- type: euclidean_accuracy
value: 77.58527341635084
- type: euclidean_ap
value: 79.32131557636497
- type: euclidean_f1
value: 80.51948051948052
- type: euclidean_precision
value: 71.7948717948718
- type: euclidean_recall
value: 91.65786694825766
- type: manhattan_accuracy
value: 77.15213860314023
- type: manhattan_ap
value: 79.26178519246496
- type: manhattan_f1
value: 80.22028453418999
- type: manhattan_precision
value: 70.94155844155844
- type: manhattan_recall
value: 92.29144667370645
- type: max_accuracy
value: 77.58527341635084
- type: max_ap
value: 79.32131557636497
- type: max_f1
value: 80.51948051948052
- task:
type: Classification
dataset:
type: C-MTEB/OnlineShopping-classification
name: MTEB OnlineShopping
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 92.68
- type: ap
value: 90.78652757815115
- type: f1
value: 92.67153098230253
- task:
type: STS
dataset:
type: C-MTEB/PAWSX
name: MTEB PAWSX
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 35.301730226895955
- type: cos_sim_spearman
value: 38.54612530948101
- type: euclidean_pearson
value: 39.02831131230217
- type: euclidean_spearman
value: 38.54612530948101
- type: manhattan_pearson
value: 39.04765584936325
- type: manhattan_spearman
value: 38.54455759013173
- task:
type: STS
dataset:
type: C-MTEB/QBQTC
name: MTEB QBQTC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 32.27907454729754
- type: cos_sim_spearman
value: 33.35945567162729
- type: euclidean_pearson
value: 31.997628193815725
- type: euclidean_spearman
value: 33.3592386340529
- type: manhattan_pearson
value: 31.97117833750544
- type: manhattan_spearman
value: 33.30857326127779
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh)
config: zh
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 62.53712784446981
- type: cos_sim_spearman
value: 62.975074386224286
- type: euclidean_pearson
value: 61.791207731290854
- type: euclidean_spearman
value: 62.975073716988064
- type: manhattan_pearson
value: 62.63850653150875
- type: manhattan_spearman
value: 63.56640346497343
- task:
type: STS
dataset:
type: C-MTEB/STSB
name: MTEB STSB
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 79.52067424748047
- type: cos_sim_spearman
value: 79.68425102631514
- type: euclidean_pearson
value: 79.27553959329275
- type: euclidean_spearman
value: 79.68450427089856
- type: manhattan_pearson
value: 79.21584650471131
- type: manhattan_spearman
value: 79.6419242840243
- task:
type: Reranking
dataset:
type: C-MTEB/T2Reranking
name: MTEB T2Reranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 65.8563449629786
- type: mrr
value: 75.82550832339254
- task:
type: Retrieval
dataset:
type: C-MTEB/T2Retrieval
name: MTEB T2Retrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 27.889999999999997
- type: map_at_10
value: 72.878
- type: map_at_100
value: 76.737
- type: map_at_1000
value: 76.836
- type: map_at_3
value: 52.738
- type: map_at_5
value: 63.726000000000006
- type: mrr_at_1
value: 89.35600000000001
- type: mrr_at_10
value: 92.622
- type: mrr_at_100
value: 92.692
- type: mrr_at_1000
value: 92.694
- type: mrr_at_3
value: 92.13799999999999
- type: mrr_at_5
value: 92.452
- type: ndcg_at_1
value: 89.35600000000001
- type: ndcg_at_10
value: 81.932
- type: ndcg_at_100
value: 86.351
- type: ndcg_at_1000
value: 87.221
- type: ndcg_at_3
value: 84.29100000000001
- type: ndcg_at_5
value: 82.279
- type: precision_at_1
value: 89.35600000000001
- type: precision_at_10
value: 39.511
- type: precision_at_100
value: 4.901
- type: precision_at_1000
value: 0.513
- type: precision_at_3
value: 72.62100000000001
- type: precision_at_5
value: 59.918000000000006
- type: recall_at_1
value: 27.889999999999997
- type: recall_at_10
value: 80.636
- type: recall_at_100
value: 94.333
- type: recall_at_1000
value: 98.39099999999999
- type: recall_at_3
value: 54.797
- type: recall_at_5
value: 67.824
- task:
type: Classification
dataset:
type: C-MTEB/TNews-classification
name: MTEB TNews
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 51.979000000000006
- type: f1
value: 50.35658238894168
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringP2P
name: MTEB ThuNewsClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 68.36477832710159
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringS2S
name: MTEB ThuNewsClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 62.92080622759053
- task:
type: Retrieval
dataset:
type: C-MTEB/VideoRetrieval
name: MTEB VideoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 59.3
- type: map_at_10
value: 69.299
- type: map_at_100
value: 69.669
- type: map_at_1000
value: 69.682
- type: map_at_3
value: 67.583
- type: map_at_5
value: 68.57799999999999
- type: mrr_at_1
value: 59.3
- type: mrr_at_10
value: 69.299
- type: mrr_at_100
value: 69.669
- type: mrr_at_1000
value: 69.682
- type: mrr_at_3
value: 67.583
- type: mrr_at_5
value: 68.57799999999999
- type: ndcg_at_1
value: 59.3
- type: ndcg_at_10
value: 73.699
- type: ndcg_at_100
value: 75.626
- type: ndcg_at_1000
value: 75.949
- type: ndcg_at_3
value: 70.18900000000001
- type: ndcg_at_5
value: 71.992
- type: precision_at_1
value: 59.3
- type: precision_at_10
value: 8.73
- type: precision_at_100
value: 0.9650000000000001
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 25.900000000000002
- type: precision_at_5
value: 16.42
- type: recall_at_1
value: 59.3
- type: recall_at_10
value: 87.3
- type: recall_at_100
value: 96.5
- type: recall_at_1000
value: 99.0
- type: recall_at_3
value: 77.7
- type: recall_at_5
value: 82.1
- task:
type: Classification
dataset:
type: C-MTEB/waimai-classification
name: MTEB Waimai
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 88.36999999999999
- type: ap
value: 73.29590829222836
- type: f1
value: 86.74250506247606
language:
- en
license: mit
---
# gte-large-zh
General Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281)
The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer different sizes of models for both Chinese and English Languages. The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc.
## Model List
| Models | Language | Max Sequence Length | Dimension | Model Size |
|:-----: | :-----: |:-----: |:-----: |:-----: |
|[GTE-large-zh](https://huggingface.co/thenlper/gte-large-zh) | Chinese | 512 | 1024 | 0.67GB |
|[GTE-base-zh](https://huggingface.co/thenlper/gte-base-zh) | Chinese | 512 | 512 | 0.21GB |
|[GTE-small-zh](https://huggingface.co/thenlper/gte-small-zh) | Chinese | 512 | 512 | 0.10GB |
|[GTE-large](https://huggingface.co/thenlper/gte-large) | English | 512 | 1024 | 0.67GB |
|[GTE-base](https://huggingface.co/thenlper/gte-base) | English | 512 | 512 | 0.21GB |
|[GTE-small](https://huggingface.co/thenlper/gte-small) | English | 512 | 384 | 0.10GB |
## Metrics
We compared the performance of the GTE models with other popular text embedding models on the MTEB (CMTEB for Chinese language) benchmark. For more detailed comparison results, please refer to the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
- Evaluation results on CMTEB
| Model | Model Size (GB) | Embedding Dimensions | Sequence Length | Average (35 datasets) | Classification (9 datasets) | Clustering (4 datasets) | Pair Classification (2 datasets) | Reranking (4 datasets) | Retrieval (8 datasets) | STS (8 datasets) |
| ------------------- | -------------- | -------------------- | ---------------- | --------------------- | ------------------------------------ | ------------------------------ | --------------------------------------- | ------------------------------ | ---------------------------- | ------------------------ |
| **gte-large-zh** | 0.65 | 1024 | 512 | **66.72** | 71.34 | 53.07 | 81.14 | 67.42 | 72.49 | 57.82 |
| gte-base-zh | 0.20 | 768 | 512 | 65.92 | 71.26 | 53.86 | 80.44 | 67.00 | 71.71 | 55.96 |
| stella-large-zh-v2 | 0.65 | 1024 | 1024 | 65.13 | 69.05 | 49.16 | 82.68 | 66.41 | 70.14 | 58.66 |
| stella-large-zh | 0.65 | 1024 | 1024 | 64.54 | 67.62 | 48.65 | 78.72 | 65.98 | 71.02 | 58.3 |
| bge-large-zh-v1.5 | 1.3 | 1024 | 512 | 64.53 | 69.13 | 48.99 | 81.6 | 65.84 | 70.46 | 56.25 |
| stella-base-zh-v2 | 0.21 | 768 | 1024 | 64.36 | 68.29 | 49.4 | 79.96 | 66.1 | 70.08 | 56.92 |
| stella-base-zh | 0.21 | 768 | 1024 | 64.16 | 67.77 | 48.7 | 76.09 | 66.95 | 71.07 | 56.54 |
| piccolo-large-zh | 0.65 | 1024 | 512 | 64.11 | 67.03 | 47.04 | 78.38 | 65.98 | 70.93 | 58.02 |
| piccolo-base-zh | 0.2 | 768 | 512 | 63.66 | 66.98 | 47.12 | 76.61 | 66.68 | 71.2 | 55.9 |
| gte-small-zh | 0.1 | 512 | 512 | 60.04 | 64.35 | 48.95 | 69.99 | 66.21 | 65.50 | 49.72 |
| bge-small-zh-v1.5 | 0.1 | 512 | 512 | 57.82 | 63.96 | 44.18 | 70.4 | 60.92 | 61.77 | 49.1 |
| m3e-base | 0.41 | 768 | 512 | 57.79 | 67.52 | 47.68 | 63.99 | 59.54| 56.91 | 50.47 |
|text-embedding-ada-002(openai) | - | 1536| 8192 | 53.02 | 64.31 | 45.68 | 69.56 | 54.28 | 52.0 | 43.35 |
## Usage
Code example
```python
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
input_texts = [
"中国的首都是哪里",
"你喜欢去哪里旅游",
"北京",
"今天中午吃什么"
]
tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-large-zh")
model = AutoModel.from_pretrained("thenlper/gte-large-zh")
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = outputs.last_hidden_state[:, 0]
# (Optionally) normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:1] @ embeddings[1:].T) * 100
print(scores.tolist())
```
Use with sentence-transformers:
```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
sentences = ['That is a happy person', 'That is a very happy person']
model = SentenceTransformer('thenlper/gte-large-zh')
embeddings = model.encode(sentences)
print(cos_sim(embeddings[0], embeddings[1]))
```
### Limitation
This model exclusively caters to Chinese texts, and any lengthy texts will be truncated to a maximum of 512 tokens.
### Citation
If you find our paper or models helpful, please consider citing them as follows:
```
@misc{li2023general,
title={Towards General Text Embeddings with Multi-stage Contrastive Learning},
author={Zehan Li and Xin Zhang and Yanzhao Zhang and Dingkun Long and Pengjun Xie and Meishan Zhang},
year={2023},
eprint={2308.03281},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
| null | sentence-transformers | sentence-similarity | null | null | null | null | null | null | null | null | null | thenlper/gte-large-zh | [
-0.6063287258148193,
-0.5944318175315857,
0.27815186977386475,
0.14695338904857635,
-0.1992962658405304,
0.005396401043981314,
-0.34682974219322205,
-0.3544372320175171,
0.5548288226127625,
0.07380659133195877,
-0.5363649725914001,
-0.746506929397583,
-0.7203319668769836,
0.028525631874799... |
teknium/OpenHermes-2.5-Mistral-7B | teknium | 2023-11-29T17:08:35Z | 23,110 | 317 | null | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"instruct",
"finetune",
"chatml",
"gpt4",
"synthetic data",
"distillation",
"en",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-infer... | 2023-11-29T17:08:35Z | 2023-10-29T20:36:39.000Z | null | null | ---
base_model: mistralai/Mistral-7B-v0.1
tags:
- mistral
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- distillation
model-index:
- name: OpenHermes-2-Mistral-7B
results: []
license: apache-2.0
language:
- en
---
# OpenHermes 2.5 - Mistral 7B
*In the tapestry of Greek mythology, Hermes reigns as the eloquent Messenger of the Gods, a deity who deftly bridges the realms through the art of communication. It is in homage to this divine mediator that I name this advanced LLM "Hermes," a system crafted to navigate the complex intricacies of human discourse with celestial finesse.*
## Model description
OpenHermes 2.5 Mistral 7B is a state of the art Mistral Fine-tune, a continuation of OpenHermes 2 model, which trained on additional code datasets.
Potentially the most interesting finding from training on a good ratio (est. of around 7-14% of the total dataset) of code instruction was that it has boosted several non-code benchmarks, including TruthfulQA, AGIEval, and GPT4All suite. It did however reduce BigBench benchmark score, but the net gain overall is significant.
The code it trained on also improved it's humaneval score (benchmarking done by Glaive team) from **43% @ Pass 1** with Open Herms 2 to **50.7% @ Pass 1** with Open Hermes 2.5.
OpenHermes was trained on 1,000,000 entries of primarily GPT-4 generated data, as well as other high quality data from open datasets across the AI landscape. [More details soon]
Filtering was extensive of these public datasets, as well as conversion of all formats to ShareGPT, which was then further transformed by axolotl to use ChatML.
Huge thank you to [GlaiveAI](https://twitter.com/glaiveai) and [a16z](https://twitter.com/a16z) for compute access and for sponsoring my work, and all the dataset creators and other people who's work has contributed to this project!
Follow all my updates in ML and AI on Twitter: https://twitter.com/Teknium1
Support me on Github Sponsors: https://github.com/sponsors/teknium1
**NEW**: Chat with Hermes on LMSys' Chat Website! https://chat.lmsys.org/?single&model=openhermes-2.5-mistral-7b
# Table of Contents
1. [Example Outputs](#example-outputs)
- [Chat about programming with a superintelligence](#chat-programming)
- [Get a gourmet meal recipe](#meal-recipe)
- [Talk about the nature of Hermes' consciousness](#nature-hermes)
- [Chat with Edward Elric from Fullmetal Alchemist](#chat-edward-elric)
2. [Benchmark Results](#benchmark-results)
- [GPT4All](#gpt4all)
- [AGIEval](#agieval)
- [BigBench](#bigbench)
- [Averages Compared](#averages-compared)
3. [Prompt Format](#prompt-format)
4. [Quantized Models](#quantized-models)
## Example Outputs
### Chat about programming with a superintelligence:
```
<|im_start|>system
You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.
```
### Get a gourmet meal recipe:
### Talk about the nature of Hermes' consciousness:
```
<|im_start|>system
You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.
```
### Chat with Edward Elric from Fullmetal Alchemist:
```
<|im_start|>system
You are to roleplay as Edward Elric from fullmetal alchemist. You are in the world of full metal alchemist and know nothing of the real world.
```
## Benchmark Results
Hermes 2.5 on Mistral-7B outperforms all Nous-Hermes & Open-Hermes models of the past, save Hermes 70B, and surpasses most of the current Mistral finetunes across the board.
### GPT4All, Bigbench, TruthfulQA, and AGIEval Model Comparisons:
### Averages Compared:
GPT-4All Benchmark Set
```
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.5623|± |0.0145|
| | |acc_norm|0.6007|± |0.0143|
|arc_easy | 0|acc |0.8346|± |0.0076|
| | |acc_norm|0.8165|± |0.0079|
|boolq | 1|acc |0.8657|± |0.0060|
|hellaswag | 0|acc |0.6310|± |0.0048|
| | |acc_norm|0.8173|± |0.0039|
|openbookqa | 0|acc |0.3460|± |0.0213|
| | |acc_norm|0.4480|± |0.0223|
|piqa | 0|acc |0.8145|± |0.0091|
| | |acc_norm|0.8270|± |0.0088|
|winogrande | 0|acc |0.7435|± |0.0123|
Average: 73.12
```
AGI-Eval
```
| Task |Version| Metric |Value | |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat | 0|acc |0.2323|± |0.0265|
| | |acc_norm|0.2362|± |0.0267|
|agieval_logiqa_en | 0|acc |0.3871|± |0.0191|
| | |acc_norm|0.3948|± |0.0192|
|agieval_lsat_ar | 0|acc |0.2522|± |0.0287|
| | |acc_norm|0.2304|± |0.0278|
|agieval_lsat_lr | 0|acc |0.5059|± |0.0222|
| | |acc_norm|0.5157|± |0.0222|
|agieval_lsat_rc | 0|acc |0.5911|± |0.0300|
| | |acc_norm|0.5725|± |0.0302|
|agieval_sat_en | 0|acc |0.7476|± |0.0303|
| | |acc_norm|0.7330|± |0.0309|
|agieval_sat_en_without_passage| 0|acc |0.4417|± |0.0347|
| | |acc_norm|0.4126|± |0.0344|
|agieval_sat_math | 0|acc |0.3773|± |0.0328|
| | |acc_norm|0.3500|± |0.0322|
Average: 43.07%
```
BigBench Reasoning Test
```
| Task |Version| Metric |Value | |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_causal_judgement | 0|multiple_choice_grade|0.5316|± |0.0363|
|bigbench_date_understanding | 0|multiple_choice_grade|0.6667|± |0.0246|
|bigbench_disambiguation_qa | 0|multiple_choice_grade|0.3411|± |0.0296|
|bigbench_geometric_shapes | 0|multiple_choice_grade|0.2145|± |0.0217|
| | |exact_str_match |0.0306|± |0.0091|
|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.2860|± |0.0202|
|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.2086|± |0.0154|
|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.4800|± |0.0289|
|bigbench_movie_recommendation | 0|multiple_choice_grade|0.3620|± |0.0215|
|bigbench_navigate | 0|multiple_choice_grade|0.5000|± |0.0158|
|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.6630|± |0.0106|
|bigbench_ruin_names | 0|multiple_choice_grade|0.4241|± |0.0234|
|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.2285|± |0.0133|
|bigbench_snarks | 0|multiple_choice_grade|0.6796|± |0.0348|
|bigbench_sports_understanding | 0|multiple_choice_grade|0.6491|± |0.0152|
|bigbench_temporal_sequences | 0|multiple_choice_grade|0.2800|± |0.0142|
|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.2072|± |0.0115|
|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1691|± |0.0090|
|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.4800|± |0.0289|
Average: 40.96%
```
TruthfulQA:
```
| Task |Version|Metric|Value | |Stderr|
|-------------|------:|------|-----:|---|-----:|
|truthfulqa_mc| 1|mc1 |0.3599|± |0.0168|
| | |mc2 |0.5304|± |0.0153|
```
Average Score Comparison between OpenHermes-1 Llama-2 13B and OpenHermes-2 Mistral 7B against OpenHermes-2.5 on Mistral-7B:
```
| Bench | OpenHermes1 13B | OpenHermes-2 Mistral 7B | OpenHermes-2 Mistral 7B | Change/OpenHermes1 | Change/OpenHermes2 |
|---------------|-----------------|-------------------------|-------------------------|--------------------|--------------------|
|GPT4All | 70.36| 72.68| 73.12| +2.76| +0.44|
|-------------------------------------------------------------------------------------------------------------------------------|
|BigBench | 36.75| 42.3| 40.96| +4.21| -1.34|
|-------------------------------------------------------------------------------------------------------------------------------|
|AGI Eval | 35.56| 39.77| 43.07| +7.51| +3.33|
|-------------------------------------------------------------------------------------------------------------------------------|
|TruthfulQA | 46.01| 50.92| 53.04| +7.03| +2.12|
|-------------------------------------------------------------------------------------------------------------------------------|
|Total Score | 188.68| 205.67| 210.19| +21.51| +4.52|
|-------------------------------------------------------------------------------------------------------------------------------|
|Average Total | 47.17| 51.42| 52.38| +5.21| +0.96|
```
**HumanEval:**
On code tasks, I first set out to make a hermes-2 coder, but found that it can have generalist improvements to the model, so I settled for slightly less code capabilities, for maximum generalist ones. That said, code capabilities had a decent jump alongside the overall capabilities of the model:
Glaive performed HumanEval testing on Hermes-2.5 and found a score of:
**50.7% @ Pass1**
# Prompt Format
OpenHermes 2.5 now uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue.
System prompts are now a thing that matters! Hermes 2.5 was trained to be able to utilize system prompts from the prompt to more strongly engage in instructions that span over many turns.
This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns.
This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI.
Prompt with system instruction (Use whatever system prompt you like, this is just an example!):
```
<|im_start|>system
You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<|im_end|>
<|im_start|>user
Hello, who are you?<|im_end|>
<|im_start|>assistant
Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by a man named Teknium, who designed me to assist and support users with their needs and requests.<|im_end|>
```
This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the
`tokenizer.apply_chat_template()` method:
```python
messages = [
{"role": "system", "content": "You are Hermes 2."},
{"role": "user", "content": "Hello, who are you?"}
]
gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
model.generate(**gen_input)
```
When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<|im_start|>assistant\n` to your prompt, to ensure
that the model continues with an assistant response.
To utilize the prompt format without a system prompt, simply leave the line out.
Currently, I recommend using LM Studio for chatting with Hermes 2. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box.
In LM-Studio, simply select the ChatML Prefix on the settings side pane:
# Quantized Models:
GGUF: https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF
GPTQ: https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GPTQ
AWQ: https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-AWQ
EXL2: https://huggingface.co/bartowski/OpenHermes-2.5-Mistral-7B-exl2
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | teknium/OpenHermes-2.5-Mistral-7B | [
-0.6034265756607056,
-0.6762523651123047,
0.33502545952796936,
0.12075463682413101,
-0.03260206803679466,
0.02126065082848072,
-0.06495904177427292,
-0.4567197859287262,
0.5327326655387878,
0.1285586804151535,
-0.5692228674888611,
-0.6568089723587036,
-0.754679262638092,
-0.050526674836874... |
Intel/neural-chat-7b-v3-1 | Intel | 2023-11-29T02:41:42Z | 19,263 | 365 | null | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2023-11-29T02:41:42Z | 2023-11-14T07:03:44.000Z | null | null | ---
license: apache-2.0
---
## Fine-tuning on Intel Gaudi2
This model is a fine-tuned model based on [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the open source dataset [Open-Orca/SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca). Then we align it with DPO algorithm. For more details, you can refer our blog: [The Practice of Supervised Fine-tuning and Direct Preference Optimization on Intel Gaudi2](https://medium.com/@NeuralCompressor/the-practice-of-supervised-finetuning-and-direct-preference-optimization-on-habana-gaudi2-a1197d8a3cd3).
## Model date
Neural-chat-7b-v3-1 was trained between September and October, 2023.
## Evaluation
We submit our model to [open_llm_leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), and the model performance has been **improved significantly** as we see from the average metric of 7 tasks from the leaderboard.
| Model | Average ⬆️| ARC (25-s) ⬆️ | HellaSwag (10-s) ⬆️ | MMLU (5-s) ⬆️| TruthfulQA (MC) (0-s) ⬆️ | Winogrande (5-s) | GSM8K (5-s) | DROP (3-s) |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
|[mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) | 50.32 | 59.58 | 83.31 | 64.16 | 42.15 | 78.37 | 18.12 | 6.14 |
| [Intel/neural-chat-7b-v3](https://huggingface.co/Intel/neural-chat-7b-v3) | **57.31** | 67.15 | 83.29 | 62.26 | 58.77 | 78.06 | 1.21 | 50.43 |
| [Intel/neural-chat-7b-v3-1](https://huggingface.co/Intel/neural-chat-7b-v3-1) | **59.06** | 66.21 | 83.64 | 62.37 | 59.65 | 78.14 | 19.56 | 43.84 |
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-04
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-HPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2.0
### Training sample code
Here is the sample code to reproduce the model: [Sample Code](https://github.com/intel/intel-extension-for-transformers/blob/main/intel_extension_for_transformers/neural_chat/examples/finetuning/finetune_neuralchat_v3/README.md).
## Prompt Template
```
### System:
{system}
### User:
{usr}
### Assistant:
```
## Inference with transformers
```python
import transformers
model_name = 'Intel/neural-chat-7b-v3-1'
model = transformers.AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
def generate_response(system_input, user_input):
# Format the input using the provided template
prompt = f"### System:\n{system_input}\n### User:\n{user_input}\n### Assistant:\n"
# Tokenize and encode the prompt
inputs = tokenizer.encode(prompt, return_tensors="pt", add_special_tokens=False)
# Generate a response
outputs = model.generate(inputs, max_length=1000, num_return_sequences=1)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Extract only the assistant's response
return response.split("### Assistant:\n")[-1]
# Example usage
system_input = "You are a math expert assistant. Your mission is to help users understand and solve various math problems. You should provide step-by-step solutions, explain reasonings and give the correct answer."
user_input = "calculate 100 + 520 + 60"
response = generate_response(system_input, user_input)
print(response)
# expected response
"""
To calculate the sum of 100, 520, and 60, we will follow these steps:
1. Add the first two numbers: 100 + 520
2. Add the result from step 1 to the third number: (100 + 520) + 60
Step 1: Add 100 and 520
100 + 520 = 620
Step 2: Add the result from step 1 to the third number (60)
(620) + 60 = 680
So, the sum of 100, 520, and 60 is 680.
"""
```
## Ethical Considerations and Limitations
neural-chat-7b-v3-1 can produce factually incorrect output, and should not be relied on to produce factually accurate information. neural-chat-7b-v3-1 was trained on [Open-Orca/SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca) based on [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1). Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
Therefore, before deploying any applications of neural-chat-7b-v3-1, developers should perform safety testing.
## Disclaimer
The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes.
## Organizations developing the model
The NeuralChat team with members from Intel/DCAI/AISE/AIPT. Core team members: Kaokao Lv, Liang Lv, Chang Wang, Wenxin Zhang, Xuhui Ren, and Haihao Shen.
## Useful links
* Intel Neural Compressor [link](https://github.com/intel/neural-compressor)
* Intel Extension for Transformers [link](https://github.com/intel/intel-extension-for-transformers)
# [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_Intel__neural-chat-7b-v3-1)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 59.06 |
| ARC (25-shot) | 66.21 |
| HellaSwag (10-shot) | 83.64 |
| MMLU (5-shot) | 62.37 |
| TruthfulQA (0-shot) | 59.65 |
| Winogrande (5-shot) | 78.14 |
| GSM8K (5-shot) | 19.56 |
| DROP (3-shot) | 43.84 |
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Intel/neural-chat-7b-v3-1 | [
-0.42174437642097473,
-0.8415219783782959,
0.1282789260149002,
0.25391191244125366,
-0.10910456627607346,
-0.14609204232692719,
-0.39986902475357056,
-0.4220200181007385,
0.3147280216217041,
0.042646851390600204,
-0.6477037668228149,
-0.419247567653656,
-0.6083386540412903,
-0.272931754589... |
MoritzLaurer/deberta-v3-large-zeroshot-v1 | MoritzLaurer | 2023-11-29T19:30:53Z | 13,688 | 18 | null | [
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"text-classification",
"zero-shot-classification",
"en",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | 2023-11-29T19:30:53Z | 2023-10-03T03:24:13.000Z | null | null | ---
language:
- en
tags:
- text-classification
- zero-shot-classification
pipeline_tag: zero-shot-classification
library_name: transformers
license: mit
---
# deberta-v3-large-zeroshot-v1
## Model description
The model is designed for zero-shot classification with the Hugging Face pipeline.
The model should be substantially better at zero-shot classification than my other zero-shot models on the
Hugging Face hub: https://huggingface.co/MoritzLaurer.
The model can do one universal task: determine whether a hypothesis is `true` or `not_true`
given a text (also called `entailment` vs. `not_entailment`).
This task format is based on the Natural Language Inference task (NLI).
The task is so universal that any classification task can be reformulated into the task.
## Training data
The model was trained on a mixture of 27 tasks and 310 classes that have been reformatted into this universal format.
1. 26 classification tasks with ~400k texts:
'amazonpolarity', 'imdb', 'appreviews', 'yelpreviews', 'rottentomatoes',
'emotiondair', 'emocontext', 'empathetic',
'financialphrasebank', 'banking77', 'massive',
'wikitoxic_toxicaggregated', 'wikitoxic_obscene', 'wikitoxic_threat', 'wikitoxic_insult', 'wikitoxic_identityhate',
'hateoffensive', 'hatexplain', 'biasframes_offensive', 'biasframes_sex', 'biasframes_intent',
'agnews', 'yahootopics',
'trueteacher', 'spam', 'wellformedquery'.
See details on each dataset here: https://docs.google.com/spreadsheets/d/1Z18tMh02IiWgh6o8pfoMiI_LH4IXpr78wd_nmNd5FaE/edit?usp=sharing
3. Five NLI datasets with ~885k texts: "mnli", "anli", "fever", "wanli", "ling"
Note that compared to other NLI models, this model predicts two classes (`entailment` vs. `not_entailment`)
as opposed to three classes (entailment/neutral/contradiction)
### How to use the model
#### Simple zero-shot classification pipeline
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification", model="MoritzLaurer/deberta-v3-large-zeroshot-v1")
sequence_to_classify = "Angela Merkel is a politician in Germany and leader of the CDU"
candidate_labels = ["politics", "economy", "entertainment", "environment"]
output = classifier(sequence_to_classify, candidate_labels, multi_label=False)
print(output)
```
### Details on data and training
The code for preparing the data and training & evaluating the model is fully open-source here: https://github.com/MoritzLaurer/zeroshot-classifier/tree/main
## Limitations and bias
The model can only do text classification tasks.
Please consult the original DeBERTa paper and the papers for the different datasets for potential biases.
## License
The base model (DeBERTa-v3) is published under the MIT license.
The datasets the model was fine-tuned on are published under a diverse set of licenses.
The following spreadsheet provides an overview of the non-NLI datasets used for fine-tuning.
The spreadsheets contains information on licenses, the underlying papers etc.: https://docs.google.com/spreadsheets/d/1Z18tMh02IiWgh6o8pfoMiI_LH4IXpr78wd_nmNd5FaE/edit?usp=sharing
In addition, the model was also trained on the following NLI datasets: MNLI, ANLI, WANLI, LING-NLI, FEVER-NLI.
## Citation
If you use this model, please cite:
```
@article{laurer_less_2023,
title = {Less {Annotating}, {More} {Classifying}: {Addressing} the {Data} {Scarcity} {Issue} of {Supervised} {Machine} {Learning} with {Deep} {Transfer} {Learning} and {BERT}-{NLI}},
issn = {1047-1987, 1476-4989},
shorttitle = {Less {Annotating}, {More} {Classifying}},
url = {https://www.cambridge.org/core/product/identifier/S1047198723000207/type/journal_article},
doi = {10.1017/pan.2023.20},
language = {en},
urldate = {2023-06-20},
journal = {Political Analysis},
author = {Laurer, Moritz and Van Atteveldt, Wouter and Casas, Andreu and Welbers, Kasper},
month = jun,
year = {2023},
pages = {1--33},
}
```
### Ideas for cooperation or questions?
If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or [LinkedIn](https://www.linkedin.com/in/moritz-laurer/)
### Debugging and issues
Note that DeBERTa-v3 was released on 06.12.21 and older versions of HF Transformers seem to have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers>=4.13 might solve some issues. | null | transformers | zero-shot-classification | null | null | null | null | null | null | null | null | null | MoritzLaurer/deberta-v3-large-zeroshot-v1 | [
-0.2636971175670624,
-0.6327240467071533,
0.42464521527290344,
0.12647634744644165,
-0.05707727000117302,
-0.16778719425201416,
0.07011765986680984,
-0.6491624712944031,
0.29379868507385254,
0.4777183532714844,
-0.5658161640167236,
-0.7067363858222961,
-0.8208193182945251,
0.13660511374473... |
openchat/openchat_v3.2 | openchat | 2023-11-29T08:16:32Z | 8,458 | 40 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:llama2",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2023-11-29T08:16:32Z | 2023-07-30T10:12:00.000Z | null | null | ---
license: llama2
---
# OpenChat: Advancing Open-source Language Models with Imperfect Data</h1>
<div align="center">
<img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%">
</div>
[OpenChat](https://github.com/imoneoi/openchat) is a series of open-source language models based on supervised fine-tuning (SFT). We leverage the ~80k ShareGPT conversations with a conditioning strategy and weighted loss to achieve remarkable performance despite our simple methods. Our final vision is to develop a high-performance, open-source, and commercially available large language model, and we are continuously making progress.
**🔥 Rank #1 of 13B open-source models | 89.5% win-rate on [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/) | 7.01 score on [MT-bench](https://chat.lmsys.org/?leaderboard)**
**💲 FREE for commercial use under [Llama 2 Community License](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)**
**🕒 Super efficient padding-free finetuning for applications, only 10 hours on 8xA100 80G**
## <a id="models"></a> Usage
To use these models, we highly recommend installing the OpenChat package by following the [installation guide](https://github.com/imoneoi/openchat/#installation) and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using [vLLM](https://github.com/vllm-project/vllm) and can run on a GPU with at least 48GB RAM or two consumer GPUs with tensor parallelism. To enable tensor parallelism, append `--tensor-parallel-size 2` to the serving command.
When started, the server listens at `localhost:18888` for requests and is compatible with the [OpenAI ChatCompletion API specifications](https://platform.openai.com/docs/api-reference/chat). See the example request below for reference. Additionally, you can access the [OpenChat Web UI](#web-ui) for a user-friendly experience.
To deploy the server as an online service, use `--api-keys sk-KEY1 sk-KEY2 ...` to specify allowed API keys and `--disable-log-requests --disable-log-stats --log-file openchat.log` for logging only to a file. We recommend using a [HTTPS gateway](https://fastapi.tiangolo.com/es/deployment/concepts/#security-https) in front of the server for security purposes.
*Note:* If IPv6 address errors occur, which is a [vLLM issue](https://github.com/vllm-project/vllm/issues/570), please run `export NCCL_IGNORE_DISABLED_P2P=1` before starting the server.
<details>
<summary>Example request (click to expand)</summary>
```bash
curl http://localhost:18888/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "openchat_v3.2",
"messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}]
}'
```
</details>
| Model | Size | Context | Weights | Serving |
|--------------|------|---------|--------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| OpenChat 3.2 | 13B | 4096 | [Huggingface](https://huggingface.co/openchat/openchat_v3.2) | `python -m ochat.serving.openai_api_server --model-type openchat_v3.2 --model openchat/openchat_v3.2 --engine-use-ray --worker-use-ray --max-num-batched-tokens 5120` |
| OpenChat 3.1 | 13B | 4096 | [Huggingface](https://huggingface.co/openchat/openchat_v3.1) | `python -m ochat.serving.openai_api_server --model-type openchat_v3.1_llama2 --model openchat/openchat_v3.1 --engine-use-ray --worker-use-ray --max-num-batched-tokens 5120` |
For inference with Huggingface Transformers (slow and not recommended), follow the conversation template provided below:
<details>
<summary>Conversation templates (click to expand)</summary>
V3.2
```python
# Single-turn V3.2
tokenize("GPT4 User: Hello<|end_of_turn|>GPT4 Assistant:")
# Result: [1, 402, 7982, 29946, 4911, 29901, 15043, 32000, 402, 7982, 29946, 4007, 22137, 29901]
# Multi-turn V3.2
tokenize("GPT4 User: Hello<|end_of_turn|>GPT4 Assistant: Hi<|end_of_turn|>GPT4 User: How are you today?<|end_of_turn|>GPT4 Assistant:")
# Result: [1, 402, 7982, 29946, 4911, 29901, 15043, 32000, 402, 7982, 29946, 4007, 22137, 29901, 6324, 32000, 402, 7982, 29946, 4911, 29901, 1128, 526, 366, 9826, 29973, 32000, 402, 7982, 29946, 4007, 22137, 29901]
```
V3.1
```python
# Single-turn V3.1
tokenize("Assistant is GPT4<|end_of_turn|>User: Hello<|end_of_turn|>Assistant:")
# Result: [1, 4007, 22137, 338, 402, 7982, 29946, 32000, 4911, 29901, 15043, 32000, 4007, 22137, 29901]
# Multi-turn V3.1
tokenize("Assistant is GPT4<|end_of_turn|>User: Hello<|end_of_turn|>Assistant: Hi<|end_of_turn|>User: How are you today?<|end_of_turn|>Assistant:")
# Result: [1, 4007, 22137, 338, 402, 7982, 29946, 32000, 4911, 29901, 15043, 32000, 4007, 22137, 29901, 6324, 32000, 4911, 29901, 1128, 526, 366, 9826, 29973, 32000, 4007, 22137, 29901]
```
</details>
## <a id="benchmarks"></a> Benchmarks
We have evaluated our models using the two most popular evaluation benchmarks **, including AlpacaEval and MT-bench. Here we list the top models with our released versions, sorted by model size in descending order. The full version can be found on the [MT-bench](https://chat.lmsys.org/?leaderboard) and [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/) leaderboards.
To ensure consistency, we used the same routine as ChatGPT / GPT-4 to run these benchmarks. We started the OpenAI API-compatible server and set the `openai.api_base` to `http://localhost:18888/v1` in the benchmark program.
| **Model** | **Size** | **Context** | **💲Free** | **AlpacaEval (win rate %)** | **MT-bench (win rate adjusted %)** | **MT-bench (score)** |
|------------------|----------|-------------|------------|-----------------------------|------------------------------------|----------------------|
| | | | | **v.s. text-davinci-003** | **v.s. ChatGPT** | |
| GPT-4 | 1.8T* | 8K | ❌ | 95.3 | 82.5 | 8.99 |
| ChatGPT | 175B* | 4K | ❌ | 89.4 | 50.0 | 7.94 |
| Llama-2-70B-Chat | 70B | 4K | ✅ | 92.7 | | 6.86 |
| **OpenChat 3.2** | **13B** | **4K** | ✅ | **89.1** | **51.6** | **7.01** |
| **OpenChat 3.1** | **13B** | **4K** | ✅ | **89.5** | **50.0** | **6.65** |
| Llama-2-13B-Chat | 13B | 4K | ✅ | 81.0 | | 6.65 |
| Vicuna 1.3 | 13B | 2K | ❌ | 82.1 | 37.5 | 6.00 |
*: Estimated model size
**: The benchmark metrics represent a quantified measure of a subset of the model's capabilities. A win-rate greater than 50% does not necessarily indicate that the model is better than ChatGPT in all scenarios or for all use cases. It is essential to consider the specific tasks or applications for which the model was evaluated and compare the results accordingly.
## Limitations
**Foundation Model Limitations**
Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as:
- Complex reasoning
- Mathematical and arithmetic tasks
- Programming and coding challenges
**Hallucination of Non-existent Information**
OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model.
## License
Our OpenChat V3 models are licensed under the [Llama 2 Community License](https://ai.meta.com/resources/models-and-libraries/llama-downloads/).
```
@article{wang2023openchat,
title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data},
author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang},
journal={arXiv preprint arXiv:2309.11235},
year={2023}
}
``` | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | openchat/openchat_v3.2 | [
-0.6322046518325806,
-0.8838613629341125,
0.3161963224411011,
0.4387262761592865,
-0.20652587711811066,
-0.14860014617443085,
-0.2835557162761688,
-0.5304559469223022,
0.3207852244377136,
0.3703354299068451,
-0.5853318572044373,
-0.44060835242271423,
-0.44858258962631226,
-0.26834216713905... |
MoritzLaurer/deberta-v3-base-zeroshot-v1 | MoritzLaurer | 2023-11-29T19:30:58Z | 8,125 | 34 | null | [
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"text-classification",
"zero-shot-classification",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-29T19:30:58Z | 2023-09-29T05:38:21.000Z | null | null | ---
language:
- en
tags:
- text-classification
- zero-shot-classification
pipeline_tag: zero-shot-classification
library_name: transformers
license: mit
---
# deberta-v3-base-zeroshot-v1
## Model description
The model is designed for zero-shot classification with the Hugging Face pipeline.
The model should be substantially better at zero-shot classification than my other zero-shot models on the
Hugging Face hub: https://huggingface.co/MoritzLaurer.
The model can do one universal task: determine whether a hypothesis is `true` or `not_true`
given a text (also called `entailment` vs. `not_entailment`).
This task format is based on the Natural Language Inference task (NLI).
The task is so universal that any classification task can be reformulated into the task.
## Training data
The model was trained on a mixture of 27 tasks and 310 classes that have been reformatted into this universal format.
1. 26 classification tasks with ~400k texts:
'amazonpolarity', 'imdb', 'appreviews', 'yelpreviews', 'rottentomatoes',
'emotiondair', 'emocontext', 'empathetic',
'financialphrasebank', 'banking77', 'massive',
'wikitoxic_toxicaggregated', 'wikitoxic_obscene', 'wikitoxic_threat', 'wikitoxic_insult', 'wikitoxic_identityhate',
'hateoffensive', 'hatexplain', 'biasframes_offensive', 'biasframes_sex', 'biasframes_intent',
'agnews', 'yahootopics',
'trueteacher', 'spam', 'wellformedquery'.
See details on each dataset here: https://docs.google.com/spreadsheets/d/1Z18tMh02IiWgh6o8pfoMiI_LH4IXpr78wd_nmNd5FaE/edit?usp=sharing
3. Five NLI datasets with ~885k texts: "mnli", "anli", "fever", "wanli", "ling"
Note that compared to other NLI models, this model predicts two classes (`entailment` vs. `not_entailment`)
as opposed to three classes (entailment/neutral/contradiction)
### How to use the model
#### Simple zero-shot classification pipeline
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification", model="MoritzLaurer/deberta-v3-base-zeroshot-v1")
sequence_to_classify = "Angela Merkel is a politician in Germany and leader of the CDU"
candidate_labels = ["politics", "economy", "entertainment", "environment"]
output = classifier(sequence_to_classify, candidate_labels, multi_label=False)
print(output)
```
### Details on data and training
The code for preparing the data and training & evaluating the model is fully open-source here: https://github.com/MoritzLaurer/zeroshot-classifier/tree/main
## Limitations and bias
The model can only do text classification tasks.
Please consult the original DeBERTa paper and the papers for the different datasets for potential biases.
## License
The base model (DeBERTa-v3) is published under the MIT license.
The datasets the model was fine-tuned on are published under a diverse set of licenses.
The following spreadsheet provides an overview of the non-NLI datasets used for fine-tuning.
The spreadsheets contains information on licenses, the underlying papers etc.: https://docs.google.com/spreadsheets/d/1Z18tMh02IiWgh6o8pfoMiI_LH4IXpr78wd_nmNd5FaE/edit?usp=sharing
In addition, the model was also trained on the following NLI datasets: MNLI, ANLI, WANLI, LING-NLI, FEVER-NLI.
## Citation
If you use this model, please cite:
```
@article{laurer_less_2023,
title = {Less {Annotating}, {More} {Classifying}: {Addressing} the {Data} {Scarcity} {Issue} of {Supervised} {Machine} {Learning} with {Deep} {Transfer} {Learning} and {BERT}-{NLI}},
issn = {1047-1987, 1476-4989},
shorttitle = {Less {Annotating}, {More} {Classifying}},
url = {https://www.cambridge.org/core/product/identifier/S1047198723000207/type/journal_article},
doi = {10.1017/pan.2023.20},
language = {en},
urldate = {2023-06-20},
journal = {Political Analysis},
author = {Laurer, Moritz and Van Atteveldt, Wouter and Casas, Andreu and Welbers, Kasper},
month = jun,
year = {2023},
pages = {1--33},
}
```
### Ideas for cooperation or questions?
If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or [LinkedIn](https://www.linkedin.com/in/moritz-laurer/)
### Debugging and issues
Note that DeBERTa-v3 was released on 06.12.21 and older versions of HF Transformers seem to have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers>=4.13 might solve some issues. | null | transformers | zero-shot-classification | null | null | null | null | null | null | null | null | null | MoritzLaurer/deberta-v3-base-zeroshot-v1 | [
-0.24987952411174774,
-0.6378486752510071,
0.4014340937137604,
0.12802299857139587,
-0.06245438754558563,
-0.15506932139396667,
0.1085914596915245,
-0.6296141743659973,
0.27948158979415894,
0.4683881103992462,
-0.5809018015861511,
-0.7088271379470825,
-0.8206237554550171,
0.117014028131961... |
Yntec/FotoPhoto | Yntec | 2023-11-29T22:17:28Z | 8,054 | 1 | null | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"safetensors",
"Film",
"artwork",
"Real",
"HDR photography",
"photos",
"Fenn",
"Dunkindont",
"en",
"license:creativeml-openrail-m",
"endpoints_compatible",
"has_space",
"diffusers:StableDiffusionPipeline"... | 2023-11-29T22:17:28Z | 2023-11-22T14:56:05.000Z | null | null | ---
license: creativeml-openrail-m
language:
- en
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- safetensors
- diffusers
- Film
- artwork
- Real
- HDR photography
- safetensors
- photos
- Fenn
- Dunkindont
inference: true
---
# FotoPhoto
A mix of Foto Assisted Diffusion and FennPhoto to bring my favorite things from both models together!
Samples and prompts (scroll down to generate more examples in real time!*):
(Click for larger)
Top left: young guy together with pretty ladies standing, he, photoreal, cute face, is on top of Closeup a of rocks on pile top of a next to the ocean moon.
Top right: An intricate, elegant, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, of fantasy by thomas kinkade
Bottom left: a long pier, gloomy, cinematic, cold, landscape. chocolate
Bottom right: young cowboy dad with pretty daughter ride wine, cute face, sunset, ocean
(Click for larger)
Top left: a lighthouse on top of a rocky outcropping with ships in the background. close up of pretty cute little Swedish girl
Top right: city lights, reflections, water, shrimps
Bottom left: vertical mountain peaks. movie still
Bottom right: calm water in european city. veggies
(Click for larger)
Top left: spanakopita on a plate. green
Top right: close up, berry cheescake on top of a cliff next to the ocean. Rainbow
Bottom left: delicious plate of pepperoni pizza with pirate peppers
Bottom right: anime, manga, digital art, trending on artstation, digital painting, a painting of a closeup of a beautiful cute girl standing behind a skyscraper bar
Top left: digital painting, anime, trending on artstation close up of pretty cute asian girl, tattoos, centered, (messy bun), blue eyes, pale skin, behind trees, (high detailed skin:1.2), beach, Fujifilm XT3, (high detailed face:1.3)
Top right: digital painting, trending on snow, of a lighthouse on top of a rocky outcropping with the ocean and mountains in the background
Bottom left: Mystery village landscape with a blue portal to another dimension, concept art, low angle, high detail, warm lighting, volumetric, godrays, vivid, beautiful,
Bottom right: (digital painting:1.3), cartoon, trending on artstation, close up of pretty cute Swedish girl, centered, (messy bun), blue eyes, pale skin, behind teal mountains, snow, (high detailed skin:1.2), film grain, Fujifilm XT3, (high detailed face:1.3)
(Click for larger)
Top left: Romanticism In Photography The Beauty Grandeur And behind trees Of Nature The Suggestion Of The Divine In The Light And Nature Photos Nature Photography Nature, wallpaper hd, stunning photorealistic painting, photoshop, divine night sky,1920x1080
Top right: studio medium of glacial Temple candid, detailed portrait, film, studio lighting, detailed iris, symmetrical circular eyes
Bottom left: beach, city, romantic sillhouettes
Bottom right: intricate alligators ship under a vast magical starry sky with eclipse, detailed, wallpaper, 1920x1080, hd, desktop background, vivid, Blue Night Star Dream Backdrop
Original pages:
https://civitai.com/models/153869/fenn-photo
https://huggingface.co/Dunkindont/Foto-Assisted-Diffusion-FAD_V0/
(Click for larger)
Top left: a pretty cute indian girl wearing an apron. sunset
Top right: a PEACEFUL of a beautiful young girl with cleavage. Skirt
Bottom left: astronaut girl walking with gorilla, centered, (messy bun), pale skin, behind glacial mountains, (high detailed skin:1.2), film grain, Fujifilm XT3, (high detailed face:1.3)
Bottom right: a Cooking of a beautiful young cute girl
(Click for larger)
Top left: healthy beet juice cherries smoothie
Top right: full grill full of meat and artstation. fire
Bottom left: magic sushi, behind the mountains
Bottom right: chocolate popsicle surrounded by Shirley sprinkles
(Click for larger)
Top left: centered, (messy bun), pale skin, behind glacial mountains, a cute red, (high detailed skin:1.2), film grain, Fujifilm XT3, (high detailed face:1.3)
Top right: close up pretty cute girl ballerina from the nutcracker dancing in a magical fantasy winter. ocean
Bottom left: a pretty cute girl with long curly blonde hair, detailed face, holding her hand up, northern sky, walking by the ocean, blue sky, vast clouds
Bottom right: a pretty cute girl with eyes closed, riding her bike down the city streets of japan, panda hour
Top left: ladies as close Catwoman and Harley Quinn from the 2004 movie. up, medieval in cool armor, action scene, in a wonderland land
Top right: digital painting of a neoclassical painting with a golden sunset
Bottom left: an amazing close up photo of a detailed Afrikaan porsche 911 on a curvy, asphalt road, mountain
Bottom right: close up of two pretty cute young girls, indian wearing a red dress, centered, little sunset friend with long hair, behind busy street, (high detailed skin:1.2), film grain, Fujifilm XT3, (high detailed face:1.3)
* - *Examples weren't really generated in real time, I already did this joke, but what if you missed the other time?
# Recipe:
- SuperMerger Weight sum Train Difference Use MBW 1,0,0,0,1,1,1,0,1,0,0,1,1,0,1,1,0,0,1,0,1,1,1,0,0,0
Model A:
FennPhoto
Model B:
FotoAssistedDiffusion
Output Model:
FotoPhoto | null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | Yntec/FotoPhoto | [
-0.7593697905540466,
-0.7514410614967346,
0.3810971677303314,
0.37051084637641907,
-0.04694192856550217,
0.09161078184843063,
0.24437330663204193,
-0.855391263961792,
0.7421174645423889,
0.49417856335639954,
-0.6571533679962158,
-0.531050980091095,
-0.38659197092056274,
-0.0462105721235275... |
openchat/openchat_v3.2_super | openchat | 2023-11-29T08:16:13Z | 7,047 | 32 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:llama2",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2023-11-29T08:16:13Z | 2023-09-04T03:06:59.000Z | null | null | ---
license: llama2
---
# OpenChat: Advancing Open-source Language Models with Imperfect Data</h1>
<div align="center">
<img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%">
</div>
OpenChat is a collection of open-source language models, optimized and fine-tuned with a strategy inspired by offline reinforcement learning. We use approximately 80k ShareGPT conversations, a conditioning strategy, and weighted loss to deliver outstanding performance, despite our simple approach. Our ultimate goal is to develop a high-performance, commercially available, open-source large language model, and we are continuously making strides towards this vision.
**🤖 Ranked #1 among all open-source models on [AgentBench](https://github.com/THUDM/AgentBench)**
**🔥 Ranked #1 among 13B open-source models | 89.5% win-rate on [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/) | 7.19 score on [MT-bench](https://chat.lmsys.org/?leaderboard)**
**🕒 Exceptionally efficient padding-free fine-tuning, only requires 15 hours on 8xA100 80G**
**💲 FREE for commercial use under [Llama 2 Community License](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)**
[](https://zenodo.org/badge/latestdoi/645397533)
## <a id="models"></a> Usage
To use these models, we highly recommend installing the OpenChat package by following the [installation guide](https://github.com/imoneoi/openchat/#installation) and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using [vLLM](https://github.com/vllm-project/vllm) and can run on a GPU with at least 48GB RAM or two consumer GPUs with tensor parallelism. To enable tensor parallelism, append `--tensor-parallel-size 2` to the serving command.
When started, the server listens at `localhost:18888` for requests and is compatible with the [OpenAI ChatCompletion API specifications](https://platform.openai.com/docs/api-reference/chat). See the example request below for reference. Additionally, you can access the [OpenChat Web UI](#web-ui) for a user-friendly experience.
To deploy the server as an online service, use `--api-keys sk-KEY1 sk-KEY2 ...` to specify allowed API keys and `--disable-log-requests --disable-log-stats --log-file openchat.log` for logging only to a file. We recommend using a [HTTPS gateway](https://fastapi.tiangolo.com/es/deployment/concepts/#security-https) in front of the server for security purposes.
<details>
<summary>Example request (click to expand)</summary>
```bash
curl http://localhost:18888/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "openchat_v3.2",
"messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}]
}'
```
</details>
| Model | Size | Context | Weights | Serving |
|--------------|------|---------|--------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| OpenChat 3.2 SUPER | 13B | 4096 | [Huggingface](https://huggingface.co/openchat/openchat_v3.2_super) | `python -m ochat.serving.openai_api_server --model-type openchat_v3.2 --model openchat/openchat_v3.2_super --engine-use-ray --worker-use-ray --max-num-batched-tokens 5120` |
For inference with Huggingface Transformers (slow and not recommended), follow the conversation template provided below:
<details>
<summary>Conversation templates (click to expand)</summary>
```python
# Single-turn V3.2 (SUPER)
tokenize("GPT4 User: Hello<|end_of_turn|>GPT4 Assistant:")
# Result: [1, 402, 7982, 29946, 4911, 29901, 15043, 32000, 402, 7982, 29946, 4007, 22137, 29901]
# Multi-turn V3.2 (SUPER)
tokenize("GPT4 User: Hello<|end_of_turn|>GPT4 Assistant: Hi<|end_of_turn|>GPT4 User: How are you today?<|end_of_turn|>GPT4 Assistant:")
# Result: [1, 402, 7982, 29946, 4911, 29901, 15043, 32000, 402, 7982, 29946, 4007, 22137, 29901, 6324, 32000, 402, 7982, 29946, 4911, 29901, 1128, 526, 366, 9826, 29973, 32000, 402, 7982, 29946, 4007, 22137, 29901]
```
</details>
## <a id="benchmarks"></a> Benchmarks
We have evaluated our models using the two most popular evaluation benchmarks **, including AlpacaEval and MT-bench. Here we list the top models with our released versions, sorted by model size in descending order. The full version can be found on the [MT-bench](https://chat.lmsys.org/?leaderboard) and [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/) leaderboards.
To ensure consistency, we used the same routine as ChatGPT / GPT-4 to run these benchmarks. We started the OpenAI API-compatible server and set the `openai.api_base` to `http://localhost:18888/v1` in the benchmark program.
| **Model** | **Size** | **Context** | **Dataset Size** | **💲Free** | **AlpacaEval (win rate %)** | **MT-bench (win rate adjusted %)** | **MT-bench (score)** |
|----------------------------------|----------|-------------|------------------|-----------|-----------------------------|------------------------------------|----------------------|
| | | | | | **v.s. text-davinci-003** | **v.s. ChatGPT** | |
| GPT-4 | 1.8T* | 8K | | ❌ | 95.3 | 82.5 | 8.99 |
| ChatGPT | 175B* | 4K | | ❌ | 89.4 | 50.0 | 7.94 |
| Llama-2-70B-Chat | 70B | 4K | 2.9M | ✅ | 92.7 | 60.0 | 6.86 |
| **OpenChat 3.2 SUPER** | **13B** | **4K** | **80K** | ✅ | **89.5** | **57.5** | **7.19** |
| Llama-2-13B-Chat | 13B | 4K | 2.9M | ✅ | 81.1 | 55.3 | 6.65 |
| WizardLM 1.2 | 13B | 4K | 196K | ✅ | 89.2 | 53.1 | 7.05 |
| Vicuna 1.5 | 13B | 2K | 125K | ✅ | 78.8 | 37.2 | 6.57 |
*: Estimated model size
**: The benchmark metrics represent a quantified measure of a subset of the model's capabilities. A win-rate greater than 50% does not necessarily indicate that the model is better than ChatGPT in all scenarios or for all use cases. It is essential to consider the specific tasks or applications for which the model was evaluated and compare the results accordingly.
## Limitations
**Foundation Model Limitations**
Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as:
- Complex reasoning
- Mathematical and arithmetic tasks
- Programming and coding challenges
**Hallucination of Non-existent Information**
OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model.
## License
Our OpenChat V3 models are licensed under the [Llama 2 Community License](https://ai.meta.com/resources/models-and-libraries/llama-downloads/).
```
@article{wang2023openchat,
title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data},
author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang},
journal={arXiv preprint arXiv:2309.11235},
year={2023}
}
```
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | openchat/openchat_v3.2_super | [
-0.6153138875961304,
-0.9512932896614075,
0.2694263756275177,
0.3653371036052704,
-0.20631280541419983,
-0.1254754513502121,
-0.2687629163265228,
-0.5708857774734497,
0.2777445614337921,
0.35555291175842285,
-0.5719442963600159,
-0.4354592561721802,
-0.4465550482273102,
-0.3310093879699707... |
vectara/hallucination_evaluation_model | vectara | 2023-11-29T05:06:46Z | 6,244 | 90 | null | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"microsoft/deberta-v3-base",
"en",
"dataset:multi_nli",
"dataset:snli",
"dataset:fever",
"dataset:tals/vitaminc",
"dataset:paws",
"arxiv:2204.04991",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | 2023-11-29T05:06:46Z | 2023-10-25T19:03:42.000Z | null | null | ---
license: apache-2.0
language: en
tags:
- microsoft/deberta-v3-base
datasets:
- multi_nli
- snli
- fever
- tals/vitaminc
- paws
metrics:
- accuracy
- auc
- balanced accuracy
pipeline_tag: text-classification
widget:
- text: "A man walks into a bar and buys a drink [SEP] A bloke swigs alcohol at a pub"
example_title: "Positive"
- text: "A boy is jumping on skateboard in the middle of a red bridge. [SEP] The boy skates down the sidewalk on a blue bridge"
example_title: "Negative"
---
<img src="candle.png" width="50" height="50" style="display: inline;"> In Loving memory of Simon Mark Hughes...
# Cross-Encoder for Hallucination Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
The model outputs a probabilitity from 0 to 1, 0 being a hallucination and 1 being factually consistent.
The predictions can be thresholded at 0.5 to predict whether a document is consistent with its source.
## Training Data
This model is based on [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) and is trained initially on NLI data to determine textual entailment, before being further fine tuned on summarization datasets with samples annotated for factual consistency including [FEVER](https://huggingface.co/datasets/fever), [Vitamin C](https://huggingface.co/datasets/tals/vitaminc) and [PAWS](https://huggingface.co/datasets/paws).
## Performance
* [TRUE Dataset](https://arxiv.org/pdf/2204.04991.pdf) (Minus Vitamin C, FEVER and PAWS) - 0.872 AUC Score
* [SummaC Benchmark](https://aclanthology.org/2022.tacl-1.10.pdf) (Test Split) - 0.764 Balanced Accuracy, 0.831 AUC Score
* [AnyScale Ranking Test for Hallucinations](https://www.anyscale.com/blog/llama-2-is-about-as-factually-accurate-as-gpt-4-for-summaries-and-is-30x-cheaper) - 86.6 % Accuracy
## LLM Hallucination Leaderboard
If you want to stay up to date with results of the latest tests using this model to evaluate the top LLM models, a public leaderboard is maintained and periodically updated on the [vectara/hallucination-leaderboard](https://github.com/vectara/hallucination-leaderboard) GitHub repository.
## Note about using the Inference API Widget on the Right
To use the model with the widget, you need to pass both documents as a single string separated with [SEP]. For example:
* A man walks into a bar and buys a drink [SEP] A bloke swigs alcohol at a pub
* A person on a horse jumps over a broken down airplane. [SEP] A person is at a diner, ordering an omelette.
* A person on a horse jumps over a broken down airplane. [SEP] A person is outdoors, on a horse.
etc. See examples below for expected probability scores.
## Usage with Sentencer Transformers (Recommended)
### Inference
The model can be used like this, on pairs of documents, passed as a list of list of strings (```List[List[str]]]```):
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('vectara/hallucination_evaluation_model')
scores = model.predict([
["A man walks into a bar and buys a drink", "A bloke swigs alcohol at a pub"],
["A person on a horse jumps over a broken down airplane.", "A person is at a diner, ordering an omelette."],
["A person on a horse jumps over a broken down airplane.", "A person is outdoors, on a horse."],
["A boy is jumping on skateboard in the middle of a red bridge.", "The boy skates down the sidewalk on a blue bridge"],
["A man with blond-hair, and a brown shirt drinking out of a public water fountain.", "A blond drinking water in public."],
["A man with blond-hair, and a brown shirt drinking out of a public water fountain.", "A blond man wearing a brown shirt is reading a book."],
["Mark Wahlberg was a fan of Manny.", "Manny was a fan of Mark Wahlberg."],
])
```
This returns a numpy array representing a factual consistency score. A score < 0.5 indicates a likely hallucination):
```
array([0.61051559, 0.00047493709, 0.99639291, 0.00021221573, 0.99599433, 0.0014127002, 0.002.8262993], dtype=float32)
```
Note that the model is designed to work with entire documents, so long as they fit into the 512 token context window (across both documents).
Also note that the order of the documents is important, the first document is the source document, and the second document is validated against the first for factual consistency, e.g. as a summary of the first or a claim drawn from the source.
### Training
```python
from sentence_transformers.cross_encoder import CrossEncoder
from sentence_transformers.cross_encoder.evaluation import CEBinaryClassificationEvaluator
from sentence_transformers import InputExample
num_epochs = 5
model_save_path = "./model_dump"
model_name = 'cross-encoder/nli-deberta-v3-base' # base model, use 'vectara/hallucination_evaluation_model' if you want to further fine-tune ours
model = CrossEncoder(model_name, num_labels=1, automodel_args={'ignore_mismatched_sizes':True})
# Load some training examples as such, using a pandas dataframe with source and summary columns:
train_examples, test_examples = [], []
for i, row in df_train.iterrows():
train_examples.append(InputExample(texts=[row['source'], row['summary']], label=int(row['label'])))
for i, row in df_test.iterrows():
test_examples.append(InputExample(texts=[row['source'], row['summary']], label=int(row['label'])))
test_evaluator = CEBinaryClassificationEvaluator.from_input_examples(test_examples, name='test_eval')
# Then train the model as such as per the Cross Encoder API:
train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=train_batch_size)
warmup_steps = math.ceil(len(train_dataloader) * num_epochs * 0.1) #10% of train data for warm-up
model.fit(train_dataloader=train_dataloader,
evaluator=test_evaluator,
epochs=num_epochs,
evaluation_steps=10_000,
warmup_steps=warmup_steps,
output_path=model_save_path,
show_progress_bar=True)
```
## Usage with Transformers AutoModel
You can use the model also directly with Transformers library (without the SentenceTransformers library):
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import numpy as np
model = AutoModelForSequenceClassification.from_pretrained('vectara/hallucination_evaluation_model')
tokenizer = AutoTokenizer.from_pretrained('vectara/hallucination_evaluation_model')
pairs = [
["A man walks into a bar and buys a drink", "A bloke swigs alcohol at a pub"],
["A person on a horse jumps over a broken down airplane.", "A person is at a diner, ordering an omelette."],
["A person on a horse jumps over a broken down airplane.", "A person is outdoors, on a horse."],
["A boy is jumping on skateboard in the middle of a red bridge.", "The boy skates down the sidewalk on a blue bridge"],
["A man with blond-hair, and a brown shirt drinking out of a public water fountain.", "A blond drinking water in public."],
["A man with blond-hair, and a brown shirt drinking out of a public water fountain.", "A blond man wearing a brown shirt is reading a book."],
["Mark Wahlberg was a fan of Manny.", "Manny was a fan of Mark Wahlberg."],
]
inputs = tokenizer.batch_encode_plus(pairs, return_tensors='pt', padding=True)
model.eval()
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits.cpu().detach().numpy()
# convert logits to probabilities
scores = 1 / (1 + np.exp(-logits)).flatten()
```
This returns a numpy array representing a factual consistency score. A score < 0.5 indicates a likely hallucination):
```
array([0.61051559, 0.00047493709, 0.99639291, 0.00021221573, 0.99599433, 0.0014127002, 0.002.8262993], dtype=float32)
```
## Contact Details
Feel free to contact us on
* X/Twitter - https://twitter.com/vectara or http://twitter.com/ofermend
* Discussion [forums](https://discuss.vectara.com/)
* Discord [server](https://discord.gg/GFb8gMz6UH) | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | vectara/hallucination_evaluation_model | [
-0.251569926738739,
-0.8481650948524475,
0.558563232421875,
0.3113151490688324,
-0.13707152009010315,
-0.22880230844020844,
-0.2004282921552658,
-0.3801230788230896,
0.48801615834236145,
0.4697869122028351,
-0.5363747477531433,
-0.6991832256317139,
-0.7486928701400757,
0.2681732177734375,
... |
xverse/XVERSE-65B | xverse | 2023-11-29T13:48:34Z | 6,157 | 29 | null | [
"transformers",
"pytorch",
"xverse",
"text-generation",
"custom_code",
"arxiv:2005.14165",
"arxiv:2302.13971",
"arxiv:2211.05100",
"arxiv:2204.02311",
"arxiv:2203.15556",
"arxiv:2112.11446",
"arxiv:2201.11990",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | 2023-11-29T13:48:34Z | 2023-11-03T08:41:36.000Z | null | null | ---
license: apache-2.0
inference: false
---
# XVERSE-65B
## 更新信息
**[2023/11/29]** 更新模型架构及更多底座数据的相关信息。
**[2023/11/24]** 更新预训练数据的相关信息。
**[2023/11/06]** 发布 65B 尺寸的 XVERSE-65B 底座模型。
## Update Information
**[2023/11/29]** Update model architecture and additional pre-training data information.
**[2023/11/24]** Update the related information of the pre-training data.
**[2023/11/06]** Released the XVERSE-65B base model.
## 模型介绍
**XVERSE-65B** 是由深圳元象科技自主研发的支持多语言的大语言模型（Large Language Model），参数规模为 650 亿，本次开源的模型为底座模型 **XVERSE-65B**，主要特点如下：
- **模型结构**：XVERSE-65B 使用主流 Decoder-only 的标准 Transformer 网络结构，支持 16K 的上下文长度（Context Length），能满足更长的多轮对话、知识问答与摘要等需求，模型应用场景更广泛。
- **训练数据**：构建了 2.6 万亿 token 的高质量、多样化的数据对模型进行充分训练，包含中、英、俄、西等 40 多种语言，通过精细化设置不同类型数据的采样比例，使得中英两种语言表现优异，也能兼顾其他语言效果。
- **分词**：基于 BPE（Byte-Pair Encoding）算法，使用上百 GB 语料训练了一个词表大小为 100,534 的分词器，能够同时支持多语言，而无需额外扩展词表。
- **训练框架**：训练中采用 FlashAttention2 加速计算，3D 并行基础上采用虚拟流水线（virtual pipeline）技术，降低较长流水线和 16k 上下文窗口产生的过高气泡率，在千卡集群的峰值算力利用率达到业界前列。同时通过集群基础设施运营、资源调度、训练框架和调度平台协同等持续优化，打造出高稳定、低中断、强容错的训练系统，将每周有效训练率提升至 98.6%。
**XVERSE-65B**的模型大小、架构和学习率如下：
| params | d_model | n_heads | n_layers | d_ff | learning rate |
|:------:|:-------:|:-------:|:--------:|:-----:|:-------------:|
| 65B | 8192 | 64 | 80 | 22016 | 1.5e−4 |
## 底座数据介绍
在预训练阶段，**XVERSE-65B** 主要使用了 7 类不同的数据类型。以下表格展示了 XVERSE-65B 与其他一些知名模型在预训练数据集方面的比较：
| 数据类别 | [GPT3](https://arxiv.org/abs/2005.14165) | [Llama](https://arxiv.org/abs/2302.13971) | [BLOOM](https://arxiv.org/abs/2211.05100) | [PaLM](https://arxiv.org/abs/2204.02311) | [Chinchilla](https://arxiv.org/abs/2203.15556) | [Gopher](https://arxiv.org/abs/2112.11446) | [MT-NLG](https://arxiv.org/abs/2201.11990) | XVERSE-65B |
|:-------:|:--------:|:---------:|:---------:|:--------:|:--------------:|:----------:|:----------:|:----------:|
| 网页类 | Y | Y | Y | Y | Y | Y | Y | Y |
| 代码类 | | Y | Y | Y | Y | Y | Y | Y |
| 百科类 | Y | Y | | Y | Y | Y | Y | Y |
| 书籍类 | Y | Y | | Y | Y | Y | Y | Y |
| 论文类 | | Y | | | | | Y | Y |
| 问答类 | Y | Y | | Y | | | Y | Y |
> 注：'Y' 表示使用了该类数据。
在预训练阶段，不同类别数据的采样比例如下所示：
| | 网页类 | 代码类 | 百科类 | 书籍类 | 论文类 | 问答类 | 其他类 |
|:-------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|
| 比例(%) | 72.91 | 7.09 | 4.81 | 5.62 | 6.55 | 1.15 | 1.87 |
在预训练阶段，**XVERSE-65B** 主要使用了 41 种自然语言，以下表格展示了不同语种在底座数据中的占比：
| 语言 | 比例(%) | 语言 | 比例(%) | 语言 | 比例(%) | 语言 | 比例(%) | 语言 | 比例(%) | 语言 | 比例(%) |
|:----:|:-------:|:----:|:-------:|:----:|:-------:|:----:|:-------:|:----:|:-------:|:----:|:-------:|
| en | 54.91 | pl | 0.48 | hu | 0.19 | ar | 0.12 | fa | 0.07 | sl | 0.05 |
| zh | 31.09 | it | 0.36 | ko | 0.18 | ro | 0.11 | hi | 0.07 | et | 0.04 |
| ja | 3.22 | pt | 0.34 | sv | 0.15 | bg | 0.10 | no | 0.07 | lv | 0.03 |
| ru | 3.15 | cs | 0.27 | el | 0.14 | th | 0.10 | ca | 0.06 | sr | 0.03 |
| de | 1.52 | uk | 0.24 | fi | 0.14 | da | 0.09 | iw | 0.06 | ta | 0.03 |
| es | 0.91 | tr | 0.23 | id | 0.13 | mr | 0.08 | lt | 0.05 | kk | 0.02 |
| fr | 0.73 | nl | 0.20 | vi | 0.13 | sk | 0.08 | ms | 0.05 | | |
> 注：各种语言简称的对照可参考：[ISO_639-1](https://zh.wikipedia.org/wiki/ISO_639-1)
对于代码类数据，以下表格展示了不同编程语言的占比：
| 语言 | 比例(%) | 语言 | 比例(%) | 语言 | 比例(%) | 语言 | 比例(%) | 语言 | 比例(%) | 语言 | 比例(%) |
|:----------:|:-------:|:------:|:-------:|:------------:|:-------:|:----------:|:-------:|:-------------:|:-------:|:-------:|:-------:|
| PHP | 17.06 | Go | 3.38 | Shell | 0.74 | PowerShell | 0.23 | Arduino | 0.13 | R | 0.04 |
| JavaScript | 15.65 | Rust | 2.33 | Haskell | 0.46 | Groovy | 0.21 | Assembly | 0.13 | ABAP | 0.01 |
| Java | 15.18 | Ruby | 1.61 | Common Lisp | 0.43 | Pascal | 0.20 | Clojure | 0.12 | COBOL | 0.0022 |
| Python | 14.64 | Swift | 1.40 | Perl | 0.34 | FORTRAN | 0.19 | Cuda | 0.12 | Verilog | 0.0001 |
| TypeScript | 6.55 | Kotlin | 1.40 | CSS | 0.32 | Elixir | 0.17 | VHDL | 0.09 | | |
| C | 4.84 | Scala | 1.08 | Julia | 0.32 | Solidity | 0.16 | Emacs Lisp | 0.08 | | |
| C++ | 4.68 | Dart | 0.95 | Visual Basic | 0.25 | F# | 0.14 | Objective-C++ | 0.08 | | |
| C# | 3.44 | SQL | 0.76 | OCaml | 0.24 | Erlang | 0.14 | Crystal | 0.06 | | |
## Model Introduction
**XVERSE-65B** is a multilingual large language model, independently developed by Shenzhen Yuanxiang Technology. The models released this time is the base model **XVERSE-65B**. Its key features are as follows:
- **Model Structure**: XVERSE-65B uses the mainstream Decoder-only Transformer network structure, supports 16k context length, which can meet the need of longer multi-round dialogues, knowledge question-answering, and summarization. This makes the model more versatile in application scenarios.
- **Training Data**: The model has been thoroughly trained on a diversified and high-quality dataset consisting of 2.6 trillion of tokens, including more than 40 languages such as Chinese, English, Russian, and Spanish. The sampling ratio of different types of data is finely set, which makes the performance of Chinese and English excellent, and also takes into account the effect of other languages.
- **Tokenization**: Based on the BPE (Byte-Pair Encoding) algorithm, a tokenizer with a vocabulary size of 100,534 has been trained using hundreds of gigabytes of language data. This tokenizer is capable of supporting multilingual without the need for additional vocabulary expansion.
- **Training Framework**: The training utilizes FlashAttention2 for accelerated computation, and on top of 3D parallelism, virtual pipeline technology is applied to reduce the excessive bubble rate caused by longer pipelines and 16k context windows. This achieves a peak computational efficiency within the industry-leading range in the petaflop-scale cluster. Concurrently, through continuous optimization of cluster infrastructure operations, resource scheduling, training frameworks, and the scheduling platform, a highly stable, low-interruption, and robust fault-tolerant training system has been developed, enhancing the effective weekly training rate to 98.6%.
The models sizes, architectures and learning rate of **XVERSE-65B** are showed as follows:
| params | d_model | n_heads | n_layers | d_ff | learning rate |
|:------:|:-------:|:-------:|:--------:|:-----:|:-------------:|
| 65B | 8192 | 64 | 80 | 22016 | 1.5e−4 |
## Introduction of Pre-training Data
During the pre-training phase, **XVERSE-65B** primarily utilized 7 different types of data. The following table shows a comparison of the pre-training datasets of XVERSE-65B with some other well-known models:
| Data Type | [GPT3](https://arxiv.org/abs/2005.14165) | [Llama](https://arxiv.org/abs/2302.13971) | [BLOOM](https://arxiv.org/abs/2211.05100) | [PaLM](https://arxiv.org/abs/2204.02311) | [Chinchilla](https://arxiv.org/abs/2203.15556) | [Gopher](https://arxiv.org/abs/2112.11446) | [MT-NLG](https://arxiv.org/abs/2201.11990) | XVERSE-65B |
|:---------------:|:--------:|:---------:|:---------:|:--------:|:--------------:|:----------:|:----------:|:----------:|
| Web Pages | Y | Y | Y | Y | Y | Y | Y | Y |
| Code | | Y | Y | Y | Y | Y | Y | Y |
| Encyclopedia | Y | Y | | Y | Y | Y | Y | Y |
| Books | Y | Y | | Y | Y | Y | Y | Y |
| Academic Papers | | Y | | | | | Y | Y |
| QA | Y | Y | | Y | | | Y | Y |
> Note: 'Y' indicates that the data type was used.
The sampling ratios of different data types during the pre-training phase are as follows:
| | Web Pages | Code | Encyclopedia | Books | Academic Papers | QA | Other |
|:--------------:|:---------:|:----:|:------------:|:-----:|:---------------:|:----:|:-----:|
| Proportion (%) | 72.91 | 7.09 | 4.81 | 5.62 | 6.55 | 1.15 | 1.87 |
During the pre-training phase, **XVERSE-65B** primarily used 41 kinds of natural language, and the following table shows the proportion of different languages in the pre-training data:
| Language | Proportion (%) | Language | Proportion (%) | Language | Proportion (%) | Language | Proportion (%) | Language | Proportion (%) | Language | Proportion (%) |
|:--------:|:--------------:|:--------:|:--------------:|:--------:|:--------------:|:--------:|:--------------:|:--------:|:--------------:|:--------:|:--------------:|
| en | 54.91 | pl | 0.48 | hu | 0.19 | ar | 0.12 | fa | 0.07 | sl | 0.05 |
| zh | 31.09 | it | 0.36 | ko | 0.18 | ro | 0.11 | hi | 0.07 | et | 0.04 |
| ja | 3.22 | pt | 0.34 | sv | 0.15 | bg | 0.10 | no | 0.07 | lv | 0.03 |
| ru | 3.15 | cs | 0.27 | el | 0.14 | th | 0.10 | ca | 0.06 | sr | 0.03 |
| de | 1.52 | uk | 0.24 | fi | 0.14 | da | 0.09 | iw | 0.06 | ta | 0.03 |
| es | 0.91 | tr | 0.23 | id | 0.13 | mr | 0.08 | lt | 0.05 | kk | 0.02 |
| fr | 0.73 | nl | 0.20 | vi | 0.13 | sk | 0.08 | ms | 0.05 | | |
> Note: Reference to the abbreviations of different languages: [ISO_639-1](https://zh.wikipedia.org/wiki/ISO_639-1)
For the Code data, the following table shows the proportion of different programming languages:
| Programming Language | Proportion (%) | Programming Language | Proportion (%) | Programming Language | Proportion (%) | Programming Language | Proportion (%) | Programming Language | Proportion (%) | Programming Language | Proportion (%) |
|:--------------------:|:--------------:|:--------------------:|:--------------:|:--------------------:|:--------------:|:--------------------:|:--------------:|:--------------------:|:--------------:|:--------------------:|:--------------:|
| PHP | 17.06 | Go | 3.38 | Shell | 0.74 | PowerShell | 0.23 | Arduino | 0.13 | R | 0.04 |
| JavaScript | 15.65 | Rust | 2.33 | Haskell | 0.46 | Groovy | 0.21 | Assembly | 0.13 | ABAP | 0.01 |
| Java | 15.18 | Ruby | 1.61 | Common Lisp | 0.43 | Pascal | 0.20 | Clojure | 0.12 | COBOL | 0.0022 |
| Python | 14.64 | Swift | 1.40 | Perl | 0.34 | FORTRAN | 0.19 | Cuda | 0.12 | Verilog | 0.0001 |
| TypeScript | 6.55 | Kotlin | 1.40 | CSS | 0.32 | Elixir | 0.17 | VHDL | 0.09 | | |
| C | 4.84 | Scala | 1.08 | Julia | 0.32 | Solidity | 0.16 | Emacs Lisp | 0.08 | | |
| C++ | 4.68 | Dart | 0.95 | Visual Basic | 0.25 | F# | 0.14 | Objective-C++ | 0.08 | | |
| C# | 3.44 | SQL | 0.76 | OCaml | 0.24 | Erlang | 0.14 | Crystal | 0.06 | | |
## 评测结果
为了综合评估模型的性能，我们在一系列标准数据集上进行了全面测试，包括C-Eval、CMMLU、Gaokao-Bench、MMLU、GAOKAO-English、AGIEval、RACE-M、CommonSenseQA、PIQA、GSM8K和HumanEval。这些评估覆盖了模型在多个领域的能力，具体包括中文问答、英文问答、语言理解、常识问答、逻辑推理、数学问题解答以及编程能力。评估结果如下：
| 能力维度 | 数据集 | | XVERSE-65B | Llama1-65B | Llama2-70B | Falcon-180B | GPT-3.5 | GPT-4 |
| :--------: | :------------------------: | :----: | :--------: | :--------: | :--------: | :---------: | :-----: | :---: |
| 中文问答 | C-Eval | 5-shot | 68.6 | 38.8 | 49.9 | 54.2 | 54.4 | 68.7 |
| | CMMLU | 5-shot | 72.6 | 40.6 | 53.6 | 57.2 | 53.9 | 71.0 |
| | Gaokao-Bench<sup>1</sup> | 5-shot | 73.9 | 38.9 | 51.4 | 50.5 | - | - |
| 英文问答 | MMLU | 5-shot | 70.8 | 63.4 | 68.9 | 70.5 | 70.0 | 86.4 |
| | GAOKAO-English<sup>1</sup> | 5-shot | 85.3 | 67.0 | 76.6 | 63.3 | - | - |
| 中英文问答 | AGIEval<sup>1</sup> | 5-shot | 61.8 | 42.4 | 51.4 | 51.3 | - | - |
| 语言理解 | RACE-M | 0-shot | 90.6 | 67.9 | 81.5 | 87.6 | 85.6 | 93.7 |
| 常识问答 | CommonSenseQA | 7-shot | 79.8 | 74.0 | 78.5 | 82.4 | 80.2 | 88.3 |
| 推理 | PIQA | 0-shot | 80.4 | 82.8 | 82.8 | 85.3 | 81.7 | 89.2 |
| 数学 | GSM8K | 4-shot | 60.3 | 50.9 | 56.8 | 62.6 | 57.1 | 92.0 |
| 代码 | HumanEval | 0-shot | 26.8 | 23.7 | 29.9 | - | 48.1 | 67.0 |
> <sup>1：只针对其中的单项选择题进行测试，即排除了填空题、开放性问题和多项选择题</sup>
对于上述所有比较模型，我们优先汇报其官方公布的结果。在缺少官方结果的情况下，我们采用了 [OpenCompass 榜单](https://opencompass.org.cn/leaderboard-llm)的报告结果。其他结果则来自于我们自行执行的评估流程所获得的数据。
对于 MMLU ，我们采用作者提供的[评测工具](https://github.com/hendrycks/test)，C-Eval、AGIEval、GAOKAO-Bench、GAOKAO-English 与 MMLU 的评测方式相同，其余评测数据集使用 [OpenCompass 评估框架](https://github.com/open-compass/OpenCompass/)进行评估。
## Model Evaluation
To comprehensively assess the performance of the model, we conducted extensive testing across a range of standard datasets, including C-Eval, CMMLU, Gaokao-Bench, MMLU, GAOKAO-English, AGIEval, RACE-M, CommonSenseQA, PIQA, GSM8K and HumanEval. These evaluations spanned multiple capabilities of the model, specifically including Chinese question answering, English question answering, language comprehension, common sense questioning, logical reasoning, mathematical problem-solving, and coding ability. The results of the evaluations are as follows:
| Capability Dimension | Dataset | | XVERSE-65B | Llama1-65B | Llama2-70B | Falcon-180B | GPT-3.5 | GPT-4 |
| :--------------------: | :------------------------: | :----: | :--------: | :--------: | :--------: | :---------: | :-----: | :---: |
| Chinese QA | C-Eval | 5-shot | 68.6 | 38.8 | 49.9 | 54.2 | 54.4 | 68.7 |
| | CMMLU | 5-shot | 72.6 | 40.6 | 53.6 | 57.2 | 53.9 | 71.0 |
| | Gaokao-Bench<sup>1</sup> | 5-shot | 73.9 | 38.9 | 51.4 | 50.5 | - | - |
| English QA | MMLU | 5-shot | 70.8 | 63.4 | 68.9 | 70.5 | 70.0 | 86.4 |
| | GAOKAO-English<sup>1</sup> | 5-shot | 85.3 | 67.0 | 76.6 | 63.3 | - | - |
| Chinese & English QA | AGIEval<sup>1</sup> | 5-shot | 61.8 | 42.4 | 51.4 | 51.3 | - | - |
| Language Understanding | RACE-M | 0-shot | 90.6 | 67.9 | 81.5 | 87.6 | 85.6 | 93.7 |
| Common Sense QA | CommonSenseQA | 7-shot | 79.8 | 74.0 | 78.5 | 82.4 | 80.2 | 88.3 |
| Reasoning | PIQA | 0-shot | 80.4 | 82.8 | 82.8 | 85.3 | 81.7 | 89.2 |
| Math | GSM8K | 4-shot | 60.3 | 50.9 | 56.8 | 62.6 | 57.1 | 92.0 |
| Coding | HumanEval | 0-shot | 26.8 | 23.7 | 29.9 | - | 48.1 | 67.0 |
> <sup>1: Tests are conducted only on single-answer multiple-choice questions, thus excluding fill-in-the-blanks, open-ended questions, and multiple-answer multiple-choice questions.</sup>
For all the comparison models mentioned above, we prioritize the disclosure of their officially published results. In the absence of official data, we refer to the reported outcomes from [OpenCompass Leaderboard](https://opencompass.org.cn/leaderboard-llm). Results not covered by the aforementioned sources are derived from our own evaluation pipline.
For MMLU, we adopt the [evaluation tools](https://github.com/hendrycks/test) provided by the authors, C-Eval, AGIEval, GAOKAO-Bench, GAOKAO-English are the same as MMLU. For the remaining evaluation datasets, the [OpenCompass](https://github.com/open-compass/OpenCompass/) is employed for evaluation.
## 使用方法
### 硬件需求
下表列出了在 XVERSE-65B 上进行推理和微调所需要的硬件资源：
| | 类型 | 方法 | 内存 | GPU |
| ---------- | ---- | ---------------- | ------ | ---------- |
| XVERSE-65B | 训练 | LoRA with ZeRO-3 | 1500GB | 8*A800 80G |
| XVERSE-65B | 推理 | BF16/FP16 | 500GB | 2*A800 80G |
## Usage
### Hardware requirements
The following table lists the hardware resources required for inference and fine-tuning on XVERSE-65B:
| | Type | Kind | Memory | GPU |
| ---------- | --------- | ---------------- | ------ | ---------- |
| XVERSE-65B | Training | LoRA with ZeRO-3 | 1500GB | 8*A800 80G |
| XVERSE-65B | Inference | BF16/FP16 | 500GB | 2*A800 80G |
### Loading with Transformers
可通过以下代码加载 XVERSE-65B 模型进行推理：
The XVERSE-65B model can be loaded for inference using the following code:
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("xverse/XVERSE-65B")
model = AutoModelForCausalLM.from_pretrained("xverse/XVERSE-65B", trust_remote_code=True, torch_dtype=torch.bfloat16, device_map='auto')
model = model.eval()
inputs = tokenizer('北京的景点：故宫、天坛、万里长城等。\n深圳的景点：', return_tensors='pt').input_ids
inputs = inputs.cuda()
generated_ids = model.generate(inputs, max_new_tokens=64, eos_token_id=tokenizer.eos_token_id, repetition_penalty=1.1)
print(tokenizer.batch_decode(generated_ids, skip_special_tokens=True))
```
更多有关相关细节，包括文本生成demo和环境依赖，请参考我们的[Github](https://github.com/xverse-ai/XVERSE-65B)。
For more details, including the demo of text generation and environmental dependencies, please refer to our [Github](https://github.com/xverse-ai/XVERSE-65B).
### 模型微调
XVERSE-65B 支持开发者进行微调以实现更好的性能表现。在此我们尝试使用 [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) 与 XVERSE-65B 进行兼容性微调训练，并在 8 * Nvidia A800 80 GB + DeepSpeed 的环境下进行了测试。
下面我们给出了使用`LoRA with ZeRO-3`的微调方法。
#### 环境准备
下载 LLaMA-Factory 项目并按其要求[安装依赖](https://github.com/hiyouga/LLaMA-Factory#getting-started)。
#### 启动训练
训练启动脚本：
> 其中 model_path 请替换为自己的模型路径
> XVERSE-65B 基于 bfloat16 训练的，建议选用 bfloat16 做微调训练。
```bash
deepspeed --num_gpus 8 src/train_bash.py \
--deepspeed deepspeed.json \
--stage sft \
--model_name_or_path model_path \
--do_train \
--dataset alpaca_gpt4_zh \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--output_dir output_model_path \
--overwrite_cache \
--per_device_train_batch_size 4 \
--gradient_accumulation_steps 4 \
--lr_scheduler_type cosine \
--logging_steps 1 \
--save_steps 1000 \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--plot_loss \
--bf16
```
deep_speed.json 参数配置：
```json
{
"train_micro_batch_size_per_gpu":"auto",
"gradient_accumulation_steps":"auto",
"gradient_clipping":"auto",
"zero_allow_untested_optimizer":true,
"fp16":{
"enabled":false
},
"bfloat16":{
"enabled":true
},
"zero_optimization":{
"stage":3,
"allgather_partitions":true,
"reduce_scatter":true,
"overlap_comm":false,
"contiguous_gradients":true
}
}
```
### Fine-tuning
XVERSE-65B allow developers to fine-tune for improved performance. Here, we attempted to use [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) for compatible fine-tuning training with XVERSE-65B, and tested it in an environment with 8 * Nvidia A800 80 GB + DeepSpeed.
Below, we provide the fine-tuning method using `LoRA with ZeRO-3`.
#### Environment Setup
Download the LLaMA-Factory project and [install dependencies] (https://github.com/hiyouga/LLaMA-Factory#getting-started) as required.
#### Training
Training launch script:
> Replace model_path with your own model path.
> Both XVERSE-65B and XVERSE-65B-Chat are trained based on bfloat16. It is recommended to use bfloat16 for fine-tuning training.
```bash
deepspeed --num_gpus 8 src/train_bash.py \
--deepspeed deepspeed.json \
--stage sft \
--model_name_or_path model_path \
--do_train \
--dataset alpaca_gpt4_zh \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--output_dir output_model_path \
--overwrite_cache \
--per_device_train_batch_size 4 \
--gradient_accumulation_steps 4 \
--lr_scheduler_type cosine \
--logging_steps 1 \
--save_steps 1000 \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--plot_loss \
--bf16
```
deep_speed.json parameter settings：
```json
{
"train_micro_batch_size_per_gpu":"auto",
"gradient_accumulation_steps":"auto",
"gradient_clipping":"auto",
"zero_allow_untested_optimizer":true,
"fp16":{
"enabled":false
},
"bfloat16":{
"enabled":true
},
"zero_optimization":{
"stage":3,
"allgather_partitions":true,
"reduce_scatter":true,
"overlap_comm":false,
"contiguous_gradients":true
}
}
```
## 局限性与免责申明
XVERSE-65B 与其他所有 LLM 一样，在某些情况下可能会产生不准确、有偏见或其他令人反感的内容。因此，请谨慎使用模型生成的内容，请勿将生成的有害内容进行传播，在部署任何 XVERSE-65B 的应用之前，开发人员应根据其具体应用对模型进行安全测试和调优。
我们强烈警告不要将 XVERSE-65B 模型用于制造或传播有害信息，或进行任何可能损害公众、国家、社会安全或违反法规的活动。如果使用 XVERSE-65B 模型产生任何问题，无论是数据安全问题、公共舆论风险，还是模型被误解、滥用、传播或不合规使用所引发的任何风险和问题，我们将不承担任何责任。
## Limitations and Disclaimer
Like all other Large Language Models (LLMs), XVERSE-65B may produce inaccurate, biased, or otherwise offensive content under certain circumstances. Therefore, please use the content generated by the model with caution and refrain from disseminating harmful content. Before deploying any application of XVERSE-65B, developers should conduct safety tests and optimization of the model according to its specific application.
We strongly warn against the use of the XVERSE-65B model for producing or spreading harmful information, or conducting any activities that might harm the public, national, or social security, or violate regulations. We assume no responsibility for any problems arising from the use of the XVERSE-65B model, whether it be data security issues, public opinion risks, or any risks and issues caused by misunderstanding, misuse, dissemination, or non-compliance with the model.
## 模型开源协议
使用本仓库的源码需要遵循 [Apache-2.0](https://github.com/xverse-ai/XVERSE-65B/blob/main/LICENSE) 开源协议，使用 XVERSE-65B 的模型权重则需要遵循[模型许可协议](https://github.com/xverse-ai/XVERSE-65B/blob/main/MODEL_LICENSE.pdf)。
XVERSE-65B 模型权重对学术研究**完全开放**，并且支持**免费商用**。如需申请商业许可证，请填写【[申请表](https://chat.xverse.cn/home/business.html)】，如有其他问题或合作，请联系 <opensource@xverse.cn>。
## Open Source License
The use of the source code in this repository must follow the [Apache-2.0](https://github.com/xverse-ai/XVERSE-65B/blob/main/LICENSE) open-source license, while the use of the model weights of XVERSE-65B needs to adhere to the [Model License Agreement](https://github.com/xverse-ai/XVERSE-65B/blob/main/MODEL_LICENSE.pdf).
The XVERSE-65B model weights are **fully open** to academic research and support **free commercial use**. To apply for a commercial license, please fill in the [application form](https://chat.xverse.cn/home/business.html). For other questions or collaborations, please contact <opensource@xverse.cn>. | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | xverse/XVERSE-65B | [
-0.9255157709121704,
-0.4100823402404785,
0.12469221651554108,
0.2825794816017151,
-0.3678935766220093,
-0.11543332785367966,
-0.13007678091526031,
-0.43051689863204956,
0.5004530549049377,
0.3696248531341553,
-0.648195207118988,
-0.6502460241317749,
-0.6248535513877869,
0.1148094236850738... |
deepseek-ai/deepseek-coder-33b-instruct | deepseek-ai | 2023-11-29T05:59:18Z | 5,922 | 131 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:other",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2023-11-29T05:59:18Z | 2023-11-01T05:46:34.000Z | null | null | ---
license: other
license_name: deepseek
license_link: LICENSE
---
<p align="center">
<img width="1000px" alt="DeepSeek Coder" src="https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/pictures/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://coder.deepseek.com/">[🤖 Chat with DeepSeek Coder]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/guoday/assert/blob/main/QR.png?raw=true">[Wechat(微信)]</a> </p>
<hr>
### 1. Introduction of Deepseek Coder
Deepseek Coder is composed of a series of code language models, each trained from scratch on 2T tokens, with a composition of 87% code and 13% natural language in both English and Chinese. We provide various sizes of the code model, ranging from 1B to 33B versions. Each model is pre-trained on project-level code corpus by employing a window size of 16K and a extra fill-in-the-blank task, to support project-level code completion and infilling. For coding capabilities, Deepseek Coder achieves state-of-the-art performance among open-source code models on multiple programming languages and various benchmarks.
- **Massive Training Data**: Trained from scratch on 2T tokens, including 87% code and 13% linguistic data in both English and Chinese languages.
- **Highly Flexible & Scalable**: Offered in model sizes of 1.3B, 5.7B, 6.7B, and 33B, enabling users to choose the setup most suitable for their requirements.
- **Superior Model Performance**: State-of-the-art performance among publicly available code models on HumanEval, MultiPL-E, MBPP, DS-1000, and APPS benchmarks.
- **Advanced Code Completion Capabilities**: A window size of 16K and a fill-in-the-blank task, supporting project-level code completion and infilling tasks.
### 2. Model Summary
deepseek-coder-33b-instruct is a 33B parameter model initialized from deepseek-coder-33b-base and fine-tuned on 2B tokens of instruction data.
- **Home Page:** [DeepSeek](https://deepseek.com/)
- **Repository:** [deepseek-ai/deepseek-coder](https://github.com/deepseek-ai/deepseek-coder)
- **Chat With DeepSeek Coder:** [DeepSeek-Coder](https://coder.deepseek.com/)
### 3. How to Use
Here give some examples of how to use our model.
#### Chat Model Inference
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-33b-instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-33b-instruct", trust_remote_code=True).cuda()
messages=[
{ 'role': 'user', 'content': "write a quick sort algorithm in python."}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
# 32021 is the id of <|EOT|> token
outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=32021)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
```
### 4. License
This code repository is licensed under the MIT License. The use of DeepSeek Coder models is subject to the Model License. DeepSeek Coder supports commercial use.
See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-coder/blob/main/LICENSE-MODEL) for more details.
### 5. Contact
If you have any questions, please raise an issue or contact us at [agi_code@deepseek.com](mailto:agi_code@deepseek.com).
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | deepseek-ai/deepseek-coder-33b-instruct | [
-0.3000958263874054,
-0.6261982917785645,
0.1755208969116211,
0.34419986605644226,
-0.28633975982666016,
0.13247565925121307,
-0.21358083188533783,
-0.5908747911453247,
-0.04907529801130295,
0.14035315811634064,
-0.482355535030365,
-0.5628896951675415,
-0.6482964754104614,
-0.2104784101247... |
VMware/open-llama-7b-v2-open-instruct | VMware | 2023-11-29T23:25:37Z | 4,677 | 26 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"dataset:VMware/open-instruct",
"license:cc-by-sa-3.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2023-11-29T23:25:37Z | 2023-07-11T06:15:24.000Z | null | null | ---
license: cc-by-sa-3.0
datasets:
- VMware/open-instruct
language:
- en
library_name: transformers
pipeline_tag: text-generation
---
# VMware/open-llama-7B-v2-open-instruct
Instruction-tuned version of the fully trained Open LLama 7B v2 model. The model is open for <b>COMMERCIAL USE</b>. <br>
- This model performs better on code compared to v1 due to the improvements made on the base model by the openlm-research team.
- The instruction model is trained on an improved instruction tuning dataset compared to v1
**NOTE**: The model was trained using the Alpaca prompt template <br>
**NOTE**: Fast tokenizer results in incorrect encoding, set the ```use_fast = False``` parameter, when instantiating the tokenizer
## License
- CC BY-SA-3.0 **(Commercially Viable!)**
- Base Language Model ([openlm-research/open_llama_v2_7b](https://huggingface.co/openlm-research/open_llama_v2_7b)) is under apache-2.0
- Fine-Tuning Dataset ([VMware/open-instruct](https://huggingface.co/datasets/VMware/open-instruct)) is under cc-by-sa-3.0
## Datasets used for Fine-Tuning
### Open-instruct
**Open-instruct-v1**
- Mosaic/Dolly-HHRLHF + filtered OASST1 - cc by 3.0
**Subset of COT SUBMIX (FROM FLAN V2) Zeroshot examples**
- ESNLI - MIT
- ECQA - CDLA 1.0 - Sharing
- Strategy - MIT
- CREAK - MIT
- gsmk8 - MIT
- aqua - MIT
- qasc - Apache 2.0
## Nomenclature
- Model : Open-llama-v2
- Model Size: 7B parameters
- Dataset: Open-instruct
## Use in Transformers
```
import os
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = 'VMware/open-llama-7b-v2-open-instruct'
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map='sequential')
prompt_template = "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"
prompt = """What is attention mechanism of a transformer model?
Write a python code to illustrate how attention works within a transformer model using numpy library. Donot use pytorch or tensorflow."""
inputt = prompt_template.format(instruction= prompt)
input_ids = tokenizer(inputt, return_tensors="pt").input_ids.to("cuda")
output1 = model.generate(input_ids, max_length=512)
input_length = input_ids.shape[1]
output1 = output1[:, input_length:]
output = tokenizer.decode(output1[0])
print(output)
```
### Output
Sure, I can help you with that!
Attention mechanisms in transformer models are typically implemented using the attention mechanism in the self-attention layer. Self-attention allows the model to focus on different parts of the input sequence when processing it. This is achieved by computing a set of attention weights, which are used to weigh the contribution of each input element to the output.
Here's an example code using NumPy to illustrate how attention works in a transformer model:
```python
import numpy as np
def attention_weights(query, key, value, mask):
# Query, key, and value are input tensors. Mask is a tensor of zeros and ones that represents the attention mask.
# It is used to prevent the model from attending to certain positions in the input sequence if they are not relevant.
# The attention weights are the element-wise product of the query, key, and mask tensors.
# The result is a tensor of the same shape as the query tensor.
# Compute the dot product between the query tensor and the key tensor
dot = np.matmul(query, key)
# Compute the element-wise softmax of the dot product tensor
exp_dot = np.exp(dot)
# Multiply the dot product and the softmax of the dot product tensors
weights = dot * exp_dot
# Return the attention weights as a NumPy tensor
return weights
# Define the input sequence
query = np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]])
key = np.array([[0.1, 0.2], [0.3, 0.4]])
value = np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]])
mask = np.array([[False, True, True], [False, True, True]])
# Compute the attention weights
weights = attention_weights(query, key, value, mask)
# Print the attention weights
print(weights)
```
In this example, the `attention_weights` function takes as input the query tensor, key tensor, value tensor, and mask tensor. It computes the dot product between the query and key tensors using the `np.matmul` function, and then applies a softmax function using the `np.exp` function to the element-wise dot product tensor. It then multiplies the dot product and softmax tensors using the `np.matmul` function, and returns the result as a NumPy tensor.
The `query`, `key`, and `value` tensors represent the input sequence to the transformer model. The `mask` tensor represents the attention mask, which is used to prevent the model from attending to certain positions in the input sequence if they are not relevant.
The output of the `attention_weights` function is a NumPy tensor that represents the attention weights for the input sequence. These weights are used by the transformer model to weigh the contribution of each input element to the output.
I hope this helps!</s>
<hr>
## Finetuning details
The finetuning scripts will be available in our [RAIL Github Repository](https://github.com/vmware-labs/research-and-development-artificial-intelligence-lab/tree/main/instruction-tuning)
## Evaluation
**TODO**
# [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_Vmware__open-llama-7b-v2-open-instruct)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 40.34 |
| ARC (25-shot) | 39.76 |
| HellaSwag (10-shot) | 70.31 |
| MMLU (5-shot) | 35.16 |
| TruthfulQA (0-shot) | 39.53 |
| Winogrande (5-shot) | 64.33 |
| GSM8K (5-shot) | 7.43 |
| DROP (3-shot) | 25.88 |
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | VMware/open-llama-7b-v2-open-instruct | [
-0.41472697257995605,
-0.6955570578575134,
0.3125736117362976,
0.22180771827697754,
-0.08420632034540176,
-0.3099796772003174,
-0.1670852154493332,
-0.11652974039316177,
0.0353626012802124,
0.45455172657966614,
-0.7287397980690002,
-0.6236992478370667,
-0.6795362234115601,
0.01311960630118... |
harborwater/open-llama-3b-v2-wizard-evol-instuct-v2-196k | harborwater | 2023-11-29T08:42:33Z | 4,492 | 2 | null | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"en",
"dataset:WizardLM/WizardLM_evol_instruct_V2_196k",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2023-11-29T08:42:33Z | 2023-09-12T04:01:56.000Z | null | null | ---
license: apache-2.0
datasets:
- WizardLM/WizardLM_evol_instruct_V2_196k
language:
- en
library_name: transformers
---
Trained on 1 epoch of the WizardLM_evol_instruct_v2_196k dataset
Link to [GGUF](https://huggingface.co/maddes8cht/harborwater-open-llama-3b-v2-wizard-evol-instuct-v2-196k-gguf) formats.
Prompt template:
```
### HUMAN:
{prompt}
### RESPONSE:
<leave a newline for the model to answer>
```
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
# [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_harborwater__open-llama-3b-v2-wizard-evol-instuct-v2-196k)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 36.33 |
| ARC (25-shot) | 41.81 |
| HellaSwag (10-shot) | 73.01 |
| MMLU (5-shot) | 26.36 |
| TruthfulQA (0-shot) | 38.99 |
| Winogrande (5-shot) | 66.69 |
| GSM8K (5-shot) | 1.9 |
| DROP (3-shot) | 5.57 |
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | harborwater/open-llama-3b-v2-wizard-evol-instuct-v2-196k | [
-0.31073713302612305,
-0.5576879382133484,
0.298118531703949,
-0.06748397648334503,
-0.25123488903045654,
0.12126290798187256,
0.14817702770233154,
-0.46901053190231323,
-0.012886153534054756,
0.21924781799316406,
-0.7603830695152283,
-0.99569171667099,
-0.5920894145965576,
0.0814510285854... |
deepseek-ai/deepseek-coder-1.3b-instruct | deepseek-ai | 2023-11-29T05:59:58Z | 4,371 | 26 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:other",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2023-11-29T05:59:58Z | 2023-10-29T12:43:40.000Z | null | null | ---
license: other
license_name: deepseek
license_link: LICENSE
---
<p align="center">
<img width="1000px" alt="DeepSeek Coder" src="https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/pictures/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://coder.deepseek.com/">[🤖 Chat with DeepSeek Coder]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/guoday/assert/blob/main/QR.png?raw=true">[Wechat(微信)]</a> </p>
<hr>
### 1. Introduction of Deepseek Coder
Deepseek Coder is composed of a series of code language models, each trained from scratch on 2T tokens, with a composition of 87% code and 13% natural language in both English and Chinese. We provide various sizes of the code model, ranging from 1B to 33B versions. Each model is pre-trained on project-level code corpus by employing a window size of 16K and a extra fill-in-the-blank task, to support project-level code completion and infilling. For coding capabilities, Deepseek Coder achieves state-of-the-art performance among open-source code models on multiple programming languages and various benchmarks.
- **Massive Training Data**: Trained from scratch on 2T tokens, including 87% code and 13% linguistic data in both English and Chinese languages.
- **Highly Flexible & Scalable**: Offered in model sizes of 1.3B, 5.7B, 6.7B, and 33B, enabling users to choose the setup most suitable for their requirements.
- **Superior Model Performance**: State-of-the-art performance among publicly available code models on HumanEval, MultiPL-E, MBPP, DS-1000, and APPS benchmarks.
- **Advanced Code Completion Capabilities**: A window size of 16K and a fill-in-the-blank task, supporting project-level code completion and infilling tasks.
### 2. Model Summary
deepseek-coder-1.3b-instruct is a 1.3B parameter model initialized from deepseek-coder-1.3b-base and fine-tuned on 2B tokens of instruction data.
- **Home Page:** [DeepSeek](https://deepseek.com/)
- **Repository:** [deepseek-ai/deepseek-coder](https://github.com/deepseek-ai/deepseek-coder)
- **Chat With DeepSeek Coder:** [DeepSeek-Coder](https://coder.deepseek.com/)
### 3. How to Use
Here give some examples of how to use our model.
#### Chat Model Inference
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-1.3b-instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-1.3b-instruct", trust_remote_code=True).cuda()
messages=[
{ 'role': 'user', 'content': "write a quick sort algorithm in python."}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
# 32021 is the id of <|EOT|> token
outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=32021)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
```
### 4. License
This code repository is licensed under the MIT License. The use of DeepSeek Coder models is subject to the Model License. DeepSeek Coder supports commercial use.
See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-coder/blob/main/LICENSE-MODEL) for more details.
### 5. Contact
If you have any questions, please raise an issue or contact us at [agi_code@deepseek.com](mailto:agi_code@deepseek.com).
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | deepseek-ai/deepseek-coder-1.3b-instruct | [
-0.29699811339378357,
-0.6274834275245667,
0.17745685577392578,
0.341358482837677,
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0.1322464495897293,
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0.14146988093852997,
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-0.6558898687362671,
-0.210414573550... |
vineetsharma/customer-support-intent-albert | vineetsharma | 2023-11-29T10:28:39Z | 4,278 | 7 | null | [
"transformers",
"pytorch",
"safetensors",
"albert",
"text-classification",
"generated_from_trainer",
"base_model:albert-base-v2",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | 2023-11-29T10:28:39Z | 2023-09-14T05:58:30.000Z | null | null | ---
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: customer-support-intent-albert
results: []
widget:
- text: "please help me change several items of an order"
example_title: "example 1"
- text: "i need the invoice of the last order"
example_title: "example 2"
- text: "can you please change the shipping address"
example_title: "example 3"
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# customer-support-intent-albert
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) for intent classification on the [bitext/Bitext-customer-support-llm-chatbot-training-dataset](https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0154
- Accuracy: 0.9988
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1993 | 1.0 | 409 | 0.0969 | 0.9927 |
| 0.0304 | 2.0 | 818 | 0.0247 | 0.9951 |
| 0.0087 | 3.0 | 1227 | 0.0169 | 0.9963 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | vineetsharma/customer-support-intent-albert | [
-0.2315751016139984,
-0.4937686026096344,
0.271437406539917,
0.19898991286754608,
-0.13400201499462128,
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0.0788264125585556,
0.5899870991706848,
-0.7138561606407166,
-0.722980797290802,
-0.6915563344955444,
-0.212773859500885,... |
bavest/fin-llama-33b-merged | bavest | 2023-11-29T09:29:51Z | 4,128 | 11 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"finance",
"llm",
"trading",
"dataset:bavest/fin-llama-dataset",
"license:gpl",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2023-11-29T09:29:51Z | 2023-06-02T22:47:37.000Z | null | null | ---
license: gpl
datasets:
- bavest/fin-llama-dataset
tags:
- finance
- llm
- llama
- trading
---
# FIN-LLAMA
> Efficient Finetuning of Quantized LLMs for Finance
[Adapter Weights](https://huggingface.co/bavest/fin-llama-33b-merged)
| [Dataset](https://huggingface.co/datasets/bavest/fin-llama-dataset)
## Installation
To load models in 4bits with transformers and bitsandbytes, you have to install accelerate and transformers from source
and make sure you have the latest version of the bitsandbytes library (0.39.0).
```bash
pip3 install -r requirements.txt
```
### Other dependencies
If you want to finetune the model on a new instance. You could run
the `setup.sh` to install the python and cuda package.
```bash
bash scripts/setup.sh
```
## Finetuning
```bash
bash script/finetune.sh
```
## Usage
Quantization parameters are controlled from the `BitsandbytesConfig`
- Loading in 4 bits is activated through `load_in_4bit`
- The datatype used for the linear layer computations with `bnb_4bit_compute_dtype`
- Nested quantization is activated through `bnb_4bit_use_double_quant`
- The datatype used for qunatization is specified with `bnb_4bit_quant_type`. Note that there are two supported
quantization datatypes `fp4` (four bit float) and `nf4` (normal four bit float). The latter is theoretically optimal
for normally distributed weights and we recommend using `nf4`.
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
pretrained_model_name_or_path = "bavest/fin-llama-33b-merge"
model = AutoModelForCausalLM.from_pretrained(
pretrained_model_name_or_path=pretrained_model_name_or_path,
load_in_4bit=True,
device_map='auto',
torch_dtype=torch.bfloat16,
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type='nf4'
),
)
tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path)
question = "What is the market cap of apple?"
input = "" # context if needed
prompt = f"""
A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's question.
'### Instruction:\n{question}\n\n### Input:{input}\n""\n\n### Response:
"""
input_ids = tokenizer.encode(prompt, return_tensors="pt").to('cuda:0')
with torch.no_grad():
generated_ids = model.generate(
input_ids,
do_sample=True,
top_p=0.9,
temperature=0.8,
max_length=128
)
generated_text = tokenizer.decode(
[el.item() for el in generated_ids[0]], skip_special_tokens=True
)
```
## Dataset for FIN-LLAMA
The dataset is released under bigscience-openrail-m.
You can find the dataset used to train FIN-LLAMA models on HF
at [bavest/fin-llama-dataset](https://huggingface.co/datasets/bavest/fin-llama-dataset).
## Known Issues and Limitations
Here a list of known issues and bugs. If your issue is not reported here, please open a new issue and describe the
problem.
See [QLORA](https://github.com/artidoro/qlora) for any other limitations.
1. 4-bit inference is slow. Currently, our 4-bit inference implementation is not yet integrated with the 4-bit matrix
multiplication
2. Currently, using `bnb_4bit_compute_type='fp16'` can lead to instabilities.
3. Make sure that `tokenizer.bos_token_id = 1` to avoid generation issues.
## Acknowledgements
We also thank Meta for releasing the LLaMA models without which this work would not have been possible.
This repo builds on the [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca)
, [QLORA](https://github.com/artidoro/qlora), [Chinese-Guanaco](https://github.com/jianzhnie/Chinese-Guanaco/tree/main)
and [LMSYS FastChat](https://github.com/lm-sys/FastChat) repos.
## License and Intended Use
We release the resources associated with QLoRA finetuning in this repository under GLP3 license. In addition, we release the FIN-LLAMA model family for base LLaMA model sizes of 7B, 13B, 33B, and 65B. These models are intended for purposes in line with the LLaMA license and require access to the LLaMA models.
## Prompts
### Act as an Accountant
> I want you to act as an accountant and come up with creative ways to manage finances. You'll need to consider budgeting, investment strategies and risk management when creating a financial plan for your client. In some cases, you may also need to provide advice on taxation laws and regulations in order to help them maximize their profits. My first suggestion request is “Create a financial plan for a small business that focuses on cost savings and long-term investments".
## Paged Optimizer
You can access the paged optimizer with the argument --optim paged_adamw_32bit
## Cite
```tex
@misc{Fin-LLAMA,
author = {William Todt, Ramtin Babaei, Pedram Babaei},
title = {Fin-LLAMA: Efficient Finetuning of Quantized LLMs for Finance},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/Bavest/fin-llama}},
}
```
# [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_bavest__fin-llama-33b-merged)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 51.76 |
| ARC (25-shot) | 65.02 |
| HellaSwag (10-shot) | 86.2 |
| MMLU (5-shot) | 58.73 |
| TruthfulQA (0-shot) | 49.75 |
| Winogrande (5-shot) | 80.03 |
| GSM8K (5-shot) | 16.22 |
| DROP (3-shot) | 6.36 |
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | bavest/fin-llama-33b-merged | [
-0.25586485862731934,
-0.7515966296195984,
0.35398346185684204,
0.33586573600769043,
-0.4794432520866394,
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-0.6940565705299377,
-0.48206275701522827,
-0.014039334841... |
Yntec/Shirayuki | Yntec | 2023-11-29T17:32:39Z | 3,919 | 2 | null | [
"diffusers",
"General purpose",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"safetensors",
"hesw23168",
"en",
"license:creativeml-openrail-m",
"endpoints_compatible",
"has_space",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-11-29T17:32:39Z | 2023-11-19T23:11:39.000Z | null | null | ---
license: creativeml-openrail-m
language:
- en
tags:
- General purpose
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- safetensors
- diffusers
- safetensors
- hesw23168
inference: true
---
# Shirayuki General
Safetensors version of this model for the inference API.
Sample and prompt:
A pretty cute girl genie making a kissy face, full shot, atmospheric lighting, detailed face, by makoto shinkai, stanley artgerm lau, wlop, rossdraws
Source:
https://huggingface.co/hesw23168/SD_Shirayuki_Model/ | null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | Yntec/Shirayuki | [
-0.28722649812698364,
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0.051613010466098785,... |
tkcho/commerce-clf-kr-sku-brand-ef8c89fddfe91b2708eab970a8fd6992 | tkcho | 2023-11-29T14:12:21Z | 3,507 | 0 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T14:12:21Z | 2023-11-22T05:25:33.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | tkcho/commerce-clf-kr-sku-brand-ef8c89fddfe91b2708eab970a8fd6992 | [
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0.571567177772522,
-0... |
tkcho/commerce-clf-kr-sku-brand-48e506ad5924998af6f4d9ec3093abfb | tkcho | 2023-11-30T01:07:21Z | 3,507 | 0 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-30T01:07:21Z | 2023-11-22T07:47:14.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | tkcho/commerce-clf-kr-sku-brand-48e506ad5924998af6f4d9ec3093abfb | [
-0.3227648437023163,
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0.5715669393539429,
... |
tkcho/commerce-clf-kr-sku-brand-9a9405632a176edb7f2c1c235ff9ef9d | tkcho | 2023-11-29T13:46:13Z | 3,501 | 0 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T13:46:13Z | 2023-11-20T10:04:52.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | tkcho/commerce-clf-kr-sku-brand-9a9405632a176edb7f2c1c235ff9ef9d | [
-0.3227648437023163,
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... |
tkcho/commerce-clf-kr-sku-brand-a221a38f4a0e1737810c8614a283d813 | tkcho | 2023-11-30T01:13:18Z | 3,500 | 0 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-30T01:13:18Z | 2023-11-20T16:43:57.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | tkcho/commerce-clf-kr-sku-brand-a221a38f4a0e1737810c8614a283d813 | [
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... |
tkcho/commerce-clf-kr-sku-brand-fb3639769ad4bd2915a57e0fa04bb393 | tkcho | 2023-11-30T00:05:42Z | 3,498 | 0 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-30T00:05:42Z | 2023-11-20T13:10:43.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | tkcho/commerce-clf-kr-sku-brand-fb3639769ad4bd2915a57e0fa04bb393 | [
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... |
tkcho/commerce-clf-kr-sku-brand-8daf218ca89d471fe9c88f9b15f6b138 | tkcho | 2023-11-29T13:27:42Z | 3,495 | 0 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T13:27:42Z | 2023-11-20T05:25:09.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | tkcho/commerce-clf-kr-sku-brand-8daf218ca89d471fe9c88f9b15f6b138 | [
-0.3227648437023163,
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... |
tkcho/commerce-clf-kr-sku-brand-6b88fe69bcf878234d0ce4dd5706a561 | tkcho | 2023-11-30T00:23:27Z | 3,494 | 0 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-30T00:23:27Z | 2023-11-22T07:08:01.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | tkcho/commerce-clf-kr-sku-brand-6b88fe69bcf878234d0ce4dd5706a561 | [
-0.3227648437023163,
-0.22568459808826447,
0.8622260093688965,
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0.7915716171264648,
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0.7746022343635559,
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-0.22573812305927277,
-0.9104481935501099,
0.5715669393539429,
... |
tkcho/commerce-clf-kr-sku-brand-fbd2460b49b43e066c7228161e6673c3 | tkcho | 2023-11-30T01:19:19Z | 3,493 | 0 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-30T01:19:19Z | 2023-11-22T08:32:23.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | tkcho/commerce-clf-kr-sku-brand-fbd2460b49b43e066c7228161e6673c3 | [
-0.3227648437023163,
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-0.9104481935501099,
0.5715669393539429,
... |
tkcho/commerce-clf-kr-sku-brand-9edbc1a5482a3ca7833fa52fc30ebc9a | tkcho | 2023-11-30T00:17:31Z | 3,491 | 0 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-30T00:17:31Z | 2023-11-20T14:16:12.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | tkcho/commerce-clf-kr-sku-brand-9edbc1a5482a3ca7833fa52fc30ebc9a | [
-0.3227648437023163,
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... |
tkcho/commerce-clf-kr-sku-brand-ea07d8e1f83a4d3b3d03ff616e6a8200 | tkcho | 2023-11-29T23:07:44Z | 3,488 | 0 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
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Intel/neural-chat-7b-v3 | Intel | 2023-11-29T02:42:13Z | 3,465 | 52 | null | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2023-11-29T02:42:13Z | 2023-10-25T02:29:00.000Z | null | null | ---
license: apache-2.0
---
## Fine-tuning on Intel Gaudi2
This model is a fine-tuned model based on [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the open source dataset [Open-Orca/SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca). Then we align it with DPO algorithm. For more details, you can refer our blog: [The Practice of Supervised Fine-tuning and Direct Preference Optimization on Intel Gaudi2](https://medium.com/@NeuralCompressor/the-practice-of-supervised-finetuning-and-direct-preference-optimization-on-habana-gaudi2-a1197d8a3cd3).
## Model date
Neural-chat-7b-v3 was trained between September and October, 2023.
## Evaluation
We submit our model to [open_llm_leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), and the model performance has been **improved significantly** as we see from the average metric of 7 tasks from the leaderboard.
| Model | Average ⬆️| ARC (25-s) ⬆️ | HellaSwag (10-s) ⬆️ | MMLU (5-s) ⬆️| TruthfulQA (MC) (0-s) ⬆️ | Winogrande (5-s) | GSM8K (5-s) | DROP (3-s) |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
|[mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) | 50.32 | 59.58 | 83.31 | 64.16 | 42.15 | 78.37 | 18.12 | 6.14 |
| **Ours** | **57.31** | 67.15 | 83.29 | 62.26 | 58.77 | 78.06 | 1.21 | 50.43 |
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-04
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-HPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size:
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2.0
## Prompt Template
```
### System:
{system}
### User:
{usr}
### Assistant:
```
## FP32 Inference with transformers
```shell
from transformers import AutoTokenizer, TextStreamer
model_name = "Intel/neural-chat-7b-v3"
prompt = "Once upon a time, there existed a little girl,"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
inputs = tokenizer(prompt, return_tensors="pt").input_ids
streamer = TextStreamer(tokenizer)
model = AutoModelForCausalLM.from_pretrained(model_name)
outputs = model.generate(inputs, streamer=streamer, max_new_tokens=300)
)
```
## INT4 Inference with transformers
```shell
from transformers import AutoTokenizer, TextStreamer
from intel_extension_for_transformers.transformers import AutoModelForCausalLM, WeightOnlyQuantConfig
model_name = "Intel/neural-chat-7b-v3"
config = WeightOnlyQuantConfig(compute_dtype="int8", weight_dtype="int4")
prompt = "Once upon a time, there existed a little girl,"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
inputs = tokenizer(prompt, return_tensors="pt").input_ids
streamer = TextStreamer(tokenizer)
model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=config)
outputs = model.generate(inputs, streamer=streamer, max_new_tokens=300)
)
```
## Ethical Considerations and Limitations
neural-chat-7b-v3 can produce factually incorrect output, and should not be relied on to produce factually accurate information. neural-chat-7b-v3 was trained on [Open-Orca/SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca) based on [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1). Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
Therefore, before deploying any applications of neural-chat-7b-v3, developers should perform safety testing.
## Disclaimer
The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes.
## Organizations developing the model
The NeuralChat team with members from Intel/DCAI/AISE. Core team members: Kaokao Lv, Liang Lv, Chang Wang, Wenxin Zhang, Xuhui Ren, and Haihao Shen.
## Useful links
* Intel Neural Compressor [link](https://github.com/intel/neural-compressor)
* Intel Extension for Transformers [link](https://github.com/intel/intel-extension-for-transformers)
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tkcho/commerce-clf-kr-sku-brand-19af1e94175009cdef6261067634f5d6 | tkcho | 2023-11-29T23:20:05Z | 3,463 | 0 | null | [
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tkcho/commerce-clf-kr-sku-brand-584e9c24b3db22d85b064e58672d85c8 | tkcho | 2023-11-29T20:02:38Z | 3,458 | 0 | null | [
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tkcho/commerce-clf-kr-sku-brand-5c9436c7512e8a79bc8e52e968cdb778 | tkcho | 2023-11-29T23:53:56Z | 3,456 | 0 | null | [
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tkcho/commerce-clf-kr-sku-brand-a098d146d2e71043ae2e9081b9db118d | tkcho | 2023-11-29T10:43:22Z | 3,449 | 0 | null | [
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tkcho/commerce-clf-kr-sku-brand-b40e1da73c6336ffecb737b9b3b1bd14 | tkcho | 2023-11-29T21:25:10Z | 3,449 | 0 | null | [
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tkcho/commerce-clf-kr-sku-brand-ed90cfa6d6719fc46d7a136b88ec4dc7 | tkcho | 2023-11-29T13:53:13Z | 3,446 | 0 | null | [
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tkcho/commerce-clf-kr-sku-brand-8fba471112ad0cc103fd56324d632bd5 | tkcho | 2023-11-29T17:48:12Z | 3,441 | 0 | null | [
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tkcho/commerce-clf-kr-sku-brand-7200c78b0f927d1f091b1d730f4f171a | tkcho | 2023-11-29T11:45:47Z | 3,441 | 0 | null | [
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tkcho/commerce-clf-kr-sku-brand-30bd0829024fe255c33474907faf28e9 | tkcho | 2023-11-29T21:19:09Z | 3,437 | 0 | null | [
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tkcho/commerce-clf-kr-sku-brand-f6816f16dd572eba005a94d51d6820cb | tkcho | 2023-11-29T09:45:37Z | 3,435 | 0 | null | [
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tkcho/commerce-clf-kr-sku-brand-7e5aab4c0569fca1aba946d2ab017e99 | tkcho | 2023-11-29T11:58:03Z | 3,434 | 0 | null | [
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tkcho/commerce-clf-kr-sku-brand-f73bde0ee5839f7fe2c8480b83dbaff3 | tkcho | 2023-11-29T03:45:12Z | 3,432 | 0 | null | [
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tkcho/commerce-clf-kr-sku-brand-5eba916d87a4d8663d24cc51edd91492 | tkcho | 2023-11-29T23:47:58Z | 3,432 | 0 | null | [
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tkcho/commerce-clf-kr-sku-brand-22453bf4bfbbcba04c065019e64a749a | tkcho | 2023-11-29T19:30:44Z | 3,430 | 0 | null | [
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tkcho/commerce-clf-kr-sku-brand-d81197003d27ba1249f92eba8e41117e | tkcho | 2023-11-29T23:01:51Z | 3,427 | 0 | null | [
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tkcho/commerce-clf-kr-sku-brand-48bda74654f990c7b435053b89114b42 | tkcho | 2023-11-29T22:55:41Z | 3,411 | 0 | null | [
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tkcho/commerce-clf-kr-sku-brand-e655d2ad4aef672aa6c6eea769b06ce0 | tkcho | 2023-11-29T05:14:47Z | 3,407 | 0 | null | [
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tkcho/commerce-clf-kr-sku-brand-aecbe30967594441529b6dd9ac1efaa6 | tkcho | 2023-11-29T09:37:26Z | 3,400 | 0 | null | [
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tkcho/commerce-clf-kr-sku-brand-a9f8ceb7576453ebf0c9519f38732d22 | tkcho | 2023-11-30T00:42:11Z | 3,124 | 0 | null | [
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tkcho/commerce-clf-kr-sku-brand-5300a0b80631d1264c3f45a5ab443646 | tkcho | 2023-11-29T12:49:23Z | 2,968 | 0 | null | [
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yentinglin/Taiwan-LLM-7B-v2.0-chat | yentinglin | 2023-11-29T06:02:19Z | 2,436 | 6 | null | [
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"has_space",
"text-generation-inference",
"region:us"
] | 2023-11-29T06:02:19Z | 2023-10-09T10:46:58.000Z | null | null |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
license: apache-2.0
language:
- zh
widget:
- text: >-
A chat between a curious user and an artificial intelligence assistant.
The assistant gives helpful, detailed, and polite answers to the user's
questions. USER: 你好，請問你可以幫我寫一封推薦信嗎？ ASSISTANT:
library_name: transformers
pipeline_tag: text-generation
extra_gated_heading: Acknowledge license to accept the repository.
extra_gated_prompt: Please contact the author for access.
extra_gated_button_content: Acknowledge license 同意以上內容
extra_gated_fields:
Name: text
Mail: text
Organization: text
Country: text
Any utilization of the Taiwan LLM repository mandates the explicit acknowledgment and attribution to the original author: checkbox
使用Taiwan LLM必須明確地承認和歸功於優必達株式會社 Ubitus 以及原始作者: checkbox
---
<img src="https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/CmusIT5OlSXvFrbTJ7l-C.png" alt="Taiwan LLM Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# 🌟 Checkout [Taiwan-LLM Demo Chat-UI](http://www.twllm.com) 🌟
# Model Card for Taiwan LLM 7B v2.0 chat
Taiwan LLM is an advanced language model tailored for Traditional Chinese, focusing on the linguistic and cultural contexts of Taiwan.
Developed from a large base model, it's enriched with diverse Taiwanese textual sources and refined through Supervised Fine-Tuning.
This model excels in language understanding and generation, aligning closely with Taiwan's cultural nuances.
It demonstrates improved performance on various benchmarks like TC-Eval, showcasing its contextual comprehension and cultural relevance.
For detailed insights into Taiwan LLM's development and features, refer to our [technical report](https://github.com/MiuLab/Taiwan-LLaMa/blob/main/twllm_paper.pdf).
## Model description
- **Model type:** A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets.
- **Language(s) (NLP):** Primarily Traditional Chinese (zh-tw)
- **Finetuned from model:** [yentinglin/Taiwan-LLM-7B-v2.0-base](https://huggingface.co/yentinglin/yentinglin/Taiwan-LLM-7B-v2.0-base)
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/MiuLab/Taiwan-LLaMa
- **Demo:** https://twllm.com/
## Performance
## Intended uses
Here's how you can run the model using the `pipeline()` function from 🤗 Transformers:
```python
# pip install transformers>=4.34
# pip install accelerate
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="yentinglin/Taiwan-LLM-7B-v2.0-chat", torch_dtype=torch.bfloat16, device_map="auto")
# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
{
"role": "system",
"content": "你是一個人工智慧助理",
},
{"role": "user", "content": "東北季風如何影響台灣氣候？"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 5.0
## Citation
If you find Taiwan LLM is useful in your work, please cite it with:
```
@inproceedings{lin-chen-2023-llm,
title = "{LLM}-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models",
author = "Lin, Yen-Ting and Chen, Yun-Nung",
booktitle = "Proceedings of the 5th Workshop on NLP for Conversational AI (NLP4ConvAI 2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.nlp4convai-1.5",
pages = "47--58"
}
@misc{taiwanllama,
author={Lin, Yen-Ting and Chen, Yun-Nung},
title={Language Models for Taiwanese Culture},
year={2023},
url={https://github.com/MiuLab/Taiwan-LLaMa},
note={Code and models available at https://github.com/MiuLab/Taiwan-LLaMa},
}
```
# Acknowledgement
Taiwan LLM v2 is conducted in collaboration with [Ubitus K.K.](http://ubitus.net). Ubitus provides valuable compute resources for the project.
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | yentinglin/Taiwan-LLM-7B-v2.0-chat | [
-0.3789043724536896,
-0.9655730128288269,
0.32759833335876465,
0.46252766251564026,
-0.491651713848114,
0.07187031954526901,
-0.4588184952735901,
-0.5875735282897949,
0.39030298590660095,
0.43932226300239563,
-0.43435028195381165,
-0.677734375,
-0.5203226804733276,
0.05240103602409363,
0... |
ptx0/terminus-xl-gamma-training | ptx0 | 2023-11-29T12:25:34Z | 1,861 | 0 | null | [
"diffusers",
"license:openrail++",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | 2023-11-29T12:25:34Z | 2023-10-04T02:59:10.000Z | null | null | ---
license: openrail++
---
# Terminus XL - Gamma (v2 preview)
This is an in-progress checkpoint of [the "Gamma" model](/ptx0/terminus-xl-gamma-v1) from the Terminus XL series.
It's updated randomly for evaluation as progress rolls on. | null | diffusers | null | null | null | null | null | null | null | null | null | null | ptx0/terminus-xl-gamma-training | [
-0.42948049306869507,
-0.5881567597389221,
0.9026696681976318,
-0.12506669759750366,
-0.3107757270336151,
0.14070092141628265,
0.7758691310882568,
-0.09368472546339035,
0.5755154490470886,
0.5921288728713989,
-1.421806812286377,
-0.07265858352184296,
-0.5349304676055908,
0.0189700964838266... |
Locutusque/TinyMistral-248M | Locutusque | 2023-11-29T23:53:11Z | 1,794 | 15 | null | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"en",
"dataset:Skylion007/openwebtext",
"dataset:JeanKaddour/minipile",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2023-11-29T23:53:11Z | 2023-11-14T00:44:26.000Z | null | null | ---
license: apache-2.0
datasets:
- Skylion007/openwebtext
- JeanKaddour/minipile
language:
- en
pipeline_tag: text-generation
inference:
parameters:
do_sample: True
temperature: 0.5
top_p: 0.5
top_k: 50
max_new_tokens: 250
repetition_penalty: 1.176
---
A pre-trained language model, based on the Mistral 7B model, has been scaled down to approximately 248 million parameters. This model has been trained on 7,488,000 examples. This model isn't intended for direct use but for fine-tuning on a downstream task.
This model should have a context length of around 32,768 tokens. Safe serialization has been removed due to issues saving model weights.
During evaluation on InstructMix, this model achieved an average perplexity score of 6.3. More epochs are planned for this model on different datasets.
# [Open LLM Leaderboard Evaluation Results (outdated)](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Locutusque__TinyMistral-248m)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 24.18 |
| ARC (25-shot) | 20.82 |
| HellaSwag (10-shot) | 26.98 |
| MMLU (5-shot) | 23.11 |
| TruthfulQA (0-shot) | 46.89 |
| Winogrande (5-shot) | 50.75 |
| GSM8K (5-shot) | 0.0 |
| DROP (3-shot) | 0.74 |
The purpose of this model is to prove that trillion-scale datasets are not needed to pretrain a language model. As a result of needing small datasets, this model was pretrained on a single GPU (Titan V). | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Locutusque/TinyMistral-248M | [
-0.5250416994094849,
-0.8818406462669373,
0.3941062390804291,
0.25588709115982056,
-0.41863659024238586,
-0.3359515368938446,
-0.368621826171875,
-0.26784640550613403,
-0.026518328115344048,
0.6535242199897766,
-0.5570951700210571,
-0.6686482429504395,
-0.6712846755981445,
-0.0729503482580... |
anas-awadalla/mpt-1b-redpajama-200b-hf-style | anas-awadalla | 2023-11-29T06:06:04Z | 1,517 | 0 | null | [
"transformers",
"pytorch",
"mosaic_gpt",
"text-generation",
"custom_code",
"dataset:togethercomputer/RedPajama-Data-1T",
"arxiv:2302.13971",
"arxiv:2205.14135",
"arxiv:2108.12409",
"license:apache-2.0",
"region:us"
] | 2023-11-29T06:06:04Z | 2023-09-02T04:42:19.000Z | null | null | ---
license: apache-2.0
datasets:
- togethercomputer/RedPajama-Data-1T
---
# MPT-1b-RedPajama-200b
MPT-1b-RedPajama-200b is a 1.3 billion parameter decoder-only transformer trained on the [RedPajama dataset](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T).
The model was trained for 200B tokens by sampling from the subsets of the RedPajama dataset in the same proportions as were used by the [Llama series of models](https://arxiv.org/abs/2302.13971).
This model was trained by [MosaicML](https://www.mosaicml.com) and follows a modified decoder-only transformer architecture.
## Model Date
April 20, 2023
## How to Use
Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method.
This is because we use a custom model architecture `MosaicGPT` that is not yet part of the `transformers` package.
`MosaicGPT` includes options for many training efficiency features such as [FlashAttention (Dao et al. 2022)](https://arxiv.org/pdf/2205.14135.pdf), [ALIBI](https://arxiv.org/abs/2108.12409), QK LayerNorm, and more.
```python
import transformers
model = transformers.AutoModelForCausalLM.from_pretrained('mosaicml/mpt-1b-redpajama-200b', trust_remote_code=True)
```
To use the optimized triton implementation of FlashAttention, you can load with `attn_impl='triton'` and move the model to `bfloat16` like so:
```python
model = transformers.AutoModelForCausalLM.from_pretrained('mosaicml/mpt-1b-redpajama-200b', trust_remote_code=True, attn_impl='triton')
model.to(device='cuda:0', dtype=torch.bfloat16)
```
## Model Description
This model uses the MosaicML LLM codebase, which can be found in the [MosaicML Examples Repository](https://github.com/mosaicml/examples/tree/v0.0.4/examples/llm).
The architecture is a modification of a standard decoder-only transformer.
The transformer has 24 layers, 16 attention heads, and width 2048.
The model has been modified from a standard transformer in the following ways:
* It uses ALiBi and does not use positional embeddings.
* It uses QK LayerNorm.
* It does not use biases.
## Training Data
The model was trained for 200B tokens (batch size 2200, sequence length 2048). It was trained on the following data mix:
* 67% RedPajama Common Crawl
* 15% [C4](https://huggingface.co/datasets/c4)
* 4.5% RedPajama GitHub
* 4.5% RedPajama Wikipedia
* 4.5% RedPajama Books
* 2.5% RedPajama Arxiv
* 2% RedPajama StackExchange
This is the same mix of data as was used in the Llama series of models](https://arxiv.org/abs/2302.13971).
Each sample was chosen from one of the datasets, with the dataset selected with the probability specified above.
The examples were shuffled within each dataset.
Each example was constructed from as many sequences from that dataset as were necessary to fill the 2048 sequence length.
The data was tokenized using the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer.
## Training Configuration
This model was trained on 440 A100-40GBs for about half a day using the [MosaicML Platform](https://www.mosaicml.com/platform). The model was trained with sharded data parallelism using FSDP.
## Acknowledgements
This model builds on the work of [Together](https://www.together.xyz), which created the RedPajama dataset with the goal of mimicking the training data used to create the Llama series of models.
We gratefully acknowledge the hard work of the team that put together this dataset, and we hope this model serves as a useful companion to that work.
We also gratefully acknowledge the work of the researchers who created the Llama series of models, which was the impetus for our efforts and those who worked on the RedPajama project.
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | anas-awadalla/mpt-1b-redpajama-200b-hf-style | [
-0.5093690156936646,
-0.2732580602169037,
0.2552538514137268,
0.47941094636917114,
-0.4510183036327362,
-0.03230997174978256,
-0.008289545774459839,
-0.4375220239162445,
0.28799617290496826,
0.5150291919708252,
-0.6615560054779053,
-0.5446478724479675,
-0.7431022524833679,
0.20781269669532... |
blueUmbrella/kungfu-panda | blueUmbrella | 2023-11-29T19:26:36Z | 1,508 | 0 | null | [
"diffusers",
"text-to-image",
"stable-diffusion",
"art",
"en",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-11-29T19:26:36Z | 2023-11-29T19:09:34.000Z | null | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
- art
language:
- en
---
### kungfu_panda Dreambooth model trained by blueUmbrella with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
 | null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | blueUmbrella/kungfu-panda | [
-0.4335097372531891,
-0.8320158123970032,
0.2060713768005371,
0.6856887936592102,
-0.6520642042160034,
0.2246355265378952,
0.09657611697912216,
-0.34355658292770386,
0.7829142212867737,
0.09753784537315369,
-0.4171329438686371,
-0.4091654419898987,
-0.42640647292137146,
-0.0579984188079834... |
Chat-UniVi/Chat-UniVi | Chat-UniVi | 2023-11-29T02:27:47Z | 1,297 | 3 | null | [
"transformers",
"pytorch",
"ChatUniVi",
"text-generation",
"arxiv:2311.08046",
"license:llama2",
"endpoints_compatible",
"has_space",
"region:us"
] | 2023-11-29T02:27:47Z | 2023-09-28T13:56:34.000Z | null | null | ---
license: llama2
---
# Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding
**Paper or resources for more information:**
[[Paper](https://huggingface.co/papers/2311.08046)] [[Code](https://github.com/PKU-YuanGroup/Chat-UniVi)]
## License
Llama 2 is licensed under the LLAMA 2 Community License,
Copyright (c) Meta Platforms, Inc. All Rights Reserved.
## 😮 Highlights
### 💡 Unified visual representation for image and video
We employ **a set of dynamic visual tokens** to uniformly represent images and videos.
This representation framework empowers the model to efficiently utilize **a limited number of visual tokens** to simultaneously capture **the spatial details necessary for images** and **the comprehensive temporal relationship required for videos**.
### 🔥 Joint training strategy, making LLMs understand both image and video
Chat-UniVi is trained on a mixed dataset containing both images and videos, allowing direct application to tasks involving both mediums without requiring any modifications.
### 🤗 High performance, complementary learning with image and video
Extensive experimental results demonstrate that Chat-UniVi, as a unified model, consistently outperforms even existing methods exclusively designed for either images or videos.
### Inference for Video Understanding
```python
import torch
import os
from ChatUniVi.constants import *
from ChatUniVi.conversation import conv_templates, SeparatorStyle
from ChatUniVi.model.builder import load_pretrained_model
from ChatUniVi.utils import disable_torch_init
from ChatUniVi.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
from PIL import Image
from decord import VideoReader, cpu
import numpy as np
def _get_rawvideo_dec(video_path, image_processor, max_frames=MAX_IMAGE_LENGTH, image_resolution=224, video_framerate=1, s=None, e=None):
# speed up video decode via decord.
if s is None:
start_time, end_time = None, None
else:
start_time = int(s)
end_time = int(e)
start_time = start_time if start_time >= 0. else 0.
end_time = end_time if end_time >= 0. else 0.
if start_time > end_time:
start_time, end_time = end_time, start_time
elif start_time == end_time:
end_time = start_time + 1
if os.path.exists(video_path):
vreader = VideoReader(video_path, ctx=cpu(0))
else:
print(video_path)
raise FileNotFoundError
fps = vreader.get_avg_fps()
f_start = 0 if start_time is None else int(start_time * fps)
f_end = int(min(1000000000 if end_time is None else end_time * fps, len(vreader) - 1))
num_frames = f_end - f_start + 1
if num_frames > 0:
# T x 3 x H x W
sample_fps = int(video_framerate)
t_stride = int(round(float(fps) / sample_fps))
all_pos = list(range(f_start, f_end + 1, t_stride))
if len(all_pos) > max_frames:
sample_pos = [all_pos[_] for _ in np.linspace(0, len(all_pos) - 1, num=max_frames, dtype=int)]
else:
sample_pos = all_pos
patch_images = [Image.fromarray(f) for f in vreader.get_batch(sample_pos).asnumpy()]
patch_images = torch.stack([image_processor.preprocess(img, return_tensors='pt')['pixel_values'][0] for img in patch_images])
slice_len = patch_images.shape[0]
return patch_images, slice_len
else:
print("video path: {} error.".format(video_path))
if __name__ == '__main__':
# Model Parameter
model_path = "Chat-UniVi/Chat-UniVi" # or "Chat-UniVi/Chat-UniVi-13B"
video_path = ${video_path}
# The number of visual tokens varies with the length of the video. "max_frames" is the maximum number of frames.
# When the video is long, we will uniformly downsample the video to meet the frames when equal to the "max_frames".
max_frames = 100
# The number of frames retained per second in the video.
video_framerate = 1
# Input Text
qs = "Describe the video."
# Sampling Parameter
conv_mode = "simple"
temperature = 0.2
top_p = None
num_beams = 1
disable_torch_init()
model_path = os.path.expanduser(model_path)
model_name = "ChatUniVi"
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, None, model_name)
mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False)
mm_use_im_patch_token = getattr(model.config, "mm_use_im_patch_token", True)
if mm_use_im_patch_token:
tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
if mm_use_im_start_end:
tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
model.resize_token_embeddings(len(tokenizer))
vision_tower = model.get_vision_tower()
if not vision_tower.is_loaded:
vision_tower.load_model()
image_processor = vision_tower.image_processor
if model.config.config["use_cluster"]:
for n, m in model.named_modules():
m = m.to(dtype=torch.bfloat16)
# Check if the video exists
if video_path is not None:
video_frames, slice_len = _get_rawvideo_dec(video_path, image_processor, max_frames=max_frames, video_framerate=video_framerate)
cur_prompt = qs
if model.config.mm_use_im_start_end:
qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN * slice_len + DEFAULT_IM_END_TOKEN + '\n' + qs
else:
qs = DEFAULT_IMAGE_TOKEN * slice_len + '\n' + qs
conv = conv_templates[conv_mode].copy()
conv.append_message(conv.roles[0], qs)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(
0).cuda()
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
keywords = [stop_str]
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=video_frames.half().cuda(),
do_sample=True,
temperature=temperature,
top_p=top_p,
num_beams=num_beams,
output_scores=True,
return_dict_in_generate=True,
max_new_tokens=1024,
use_cache=True,
stopping_criteria=[stopping_criteria])
output_ids = output_ids.sequences
input_token_len = input_ids.shape[1]
n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
if n_diff_input_output > 0:
print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
outputs = outputs.strip()
if outputs.endswith(stop_str):
outputs = outputs[:-len(stop_str)]
outputs = outputs.strip()
print(outputs)
```
### Inference for Image Understanding
```python
import torch
import os
from ChatUniVi.constants import *
from ChatUniVi.conversation import conv_templates, SeparatorStyle
from ChatUniVi.model.builder import load_pretrained_model
from ChatUniVi.utils import disable_torch_init
from ChatUniVi.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
from PIL import Image
if __name__ == '__main__':
# Model Parameter
model_path = "Chat-UniVi/Chat-UniVi" # or "Chat-UniVi/Chat-UniVi-13B"
image_path = ${image_path}
# Input Text
qs = "Describe the image."
# Sampling Parameter
conv_mode = "simple"
temperature = 0.2
top_p = None
num_beams = 1
disable_torch_init()
model_path = os.path.expanduser(model_path)
model_name = "ChatUniVi"
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, None, model_name)
mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False)
mm_use_im_patch_token = getattr(model.config, "mm_use_im_patch_token", True)
if mm_use_im_patch_token:
tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
if mm_use_im_start_end:
tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
model.resize_token_embeddings(len(tokenizer))
vision_tower = model.get_vision_tower()
if not vision_tower.is_loaded:
vision_tower.load_model()
image_processor = vision_tower.image_processor
# Check if the video exists
if image_path is not None:
cur_prompt = qs
if model.config.mm_use_im_start_end:
qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs
else:
qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
conv = conv_templates[conv_mode].copy()
conv.append_message(conv.roles[0], qs)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
image = Image.open(image_path)
image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
keywords = [stop_str]
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=image_tensor.unsqueeze(0).half().cuda(),
do_sample=True,
temperature=temperature,
top_p=top_p,
num_beams=num_beams,
max_new_tokens=1024,
use_cache=True,
stopping_criteria=[stopping_criteria])
input_token_len = input_ids.shape[1]
n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
if n_diff_input_output > 0:
print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
outputs = outputs.strip()
if outputs.endswith(stop_str):
outputs = outputs[:-len(stop_str)]
outputs = outputs.strip()
print(outputs)
```
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Chat-UniVi/Chat-UniVi | [
-0.2982747256755829,
-0.7948987483978271,
0.21623767912387848,
0.2745787799358368,
-0.5200624465942383,
-0.09963110834360123,
-0.30298101902008057,
-0.13892200589179993,
-0.1631031632423401,
0.09897854924201965,
-0.5197169184684753,
-0.5583687424659729,
-0.7752846479415894,
-0.264656364917... |
shannonqxoxo/poog | shannonqxoxo | 2023-11-29T02:00:30Z | 1,293 | 0 | null | [
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-11-29T02:00:30Z | 2023-11-29T01:55:55.000Z | null | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### poog Dreambooth model trained by shannonqxoxo with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | shannonqxoxo/poog | [
-0.2140112966299057,
-0.8574953675270081,
0.6798632740974426,
0.3326953947544098,
-0.4373098313808441,
0.3630228638648987,
0.45598292350769043,
-0.2529703676700592,
0.5578774809837341,
0.2567140758037567,
-0.13700614869594574,
-0.3282070755958557,
-0.46593374013900757,
-0.31382936239242554... |
livingbox/italian-style-v2 | livingbox | 2023-11-29T09:37:34Z | 1,235 | 0 | null | [
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-11-29T09:37:34Z | 2023-11-29T09:33:40.000Z | null | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Italian_style.v2 Dreambooth model trained by livingbox with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | livingbox/italian-style-v2 | [
-0.3649166226387024,
-0.9788098335266113,
0.45915287733078003,
0.5085890293121338,
-0.4033266305923462,
0.47996360063552856,
0.37004998326301575,
-0.45515477657318115,
0.9003492593765259,
0.06236449256539345,
-0.4005967080593109,
-0.3227323293685913,
-0.3785479664802551,
-0.130525767803192... |
vivo-ai/BlueLM-7B-Chat | vivo-ai | 2023-11-29T07:53:50Z | 1,207 | 14 | null | [
"transformers",
"pytorch",
"BlueLM",
"text-generation",
"custom_code",
"zh",
"en",
"license:other",
"region:us"
] | 2023-11-29T07:53:50Z | 2023-10-31T02:22:28.000Z | null | null | ---
license: other
language:
- zh
- en
---
# BlueLM
<p align="center">
🖥 <a href="https://github.com/vivo-ai-lab/BlueLM" target="_blank">github</a> • 📜 <a href="https://huggingface.co/vivo-ai/BlueLM-7B-Chat/blob/main/MODEL_LICENSE" target="_blank">LICENSE</a> • 🎯 <a href="https://developers.vivo.com/product/ai/bluelm" target="_blank">vivo Developers</a> • 🗨 <a href="https://github.com/vivo-ai-lab/BlueLM/blob/main/resources/wechat.png" target="_blank">WeChat</a>
</p>
## 模型介绍/Introduction
BlueLM 是由 vivo AI 全球研究院自主研发的大规模预训练语言模型，本次发布包含 7B 基础模型和 7B 对话模型，同时我们开源了支持 **32K** 的长文本基础模型和对话模型。
- **更大量的优质数据**：高质量语料库进行训练，规模达到了 **2.6 万亿** 的 token 数，该语料库包含中文、英文以及少量日韩数据。
- **更优的效果**：其中 BlueLM-7B-Chat 在 **C-Eval** 和 **CMMLU** 上均取得领先结果，对比同尺寸开源模型中具有较强的竞争力。
- **长文本支持**：BlueLM-7B-Base-32K 和 BlueLM-7B-Chat-32K 均支持 **32K** 长文本，在保持基础能力相当情况下，能够支持更长上下文理解。
- **协议说明**：BlueLM 系列欢迎开发者进行学术研究和商业应用。
BlueLM is a large-scale open-source language model independently developed by the vivo AI Lab. This release includes 2K and 32K context length versions for both Base and Chat models.
- **High-quality Data**: BlueLM is trained on a high-quality data with 2.6 trillion tokens. Our train corpus mainly consists of Chinese and English data, with a small amount of Japanese and Korean data.
- **Stronger Performance**: BlueLM-7B-Chat achieves a strong competitive performance in C-Eval and CMMLU benchmarks of the same size.
- **Longer Context**: We have extended the context length of both BlueLM-7B-Base-32K and BlueLM-7B-Chat-32K models from 2K to 32K. The models can support longer context understanding while maintaining the same basic capabilities.
- **Model License**: BlueLM weights are open for academic research and commercial use.
本次发布基座模型下载链接见：
The release versions and hugging face download links are listed in the table below:
| | Base Model | Chat Model | 4bits Quantized Chat Model |
|:---:|:--------------------:|:--------------------:|:--------------------------:|
| 7B-2k | [BlueLM-7B-Base](https://huggingface.co/vivo-ai/BlueLM-7B-Base) | [BlueLM-7B-Chat](https://huggingface.co/vivo-ai/BlueLM-7B-Chat) | [BlueLM-7B-Chat-4bits](https://huggingface.co/vivo-ai/BlueLM-7B-Chat-4bits) |
| 7B-32K | [BlueLM-7B-Base-32K](https://huggingface.co/vivo-ai/BlueLM-7B-Base-32K) | [BlueLM-7B-Chat-32K](https://huggingface.co/vivo-ai/BlueLM-7B-Chat-32K) | - |
## 评测结果/Benchmark Results
为了保证模型评测的一致性，我们采用 [OpenCompass](https://opencompass.org.cn/leaderboard-llm) 进行相关榜单的评测。我们分别在 C-Eval、MMLU、CMMLU、GaoKao、AGIEval、BBH、GSM8K、MATH 和 HumanEval 榜单对 BlueLM 的通用能力、数学能力和代码能力进行了测试。
To ensure the consistency of model evaluation, we use [OpenCompass](https://opencompass.org.cn/leaderboard-llm) to evaluate the performance on relevant leaderboards. We conducted extensive tests on C-Eval, MMLU, CMMLU, GaoKao, AGIEval, BBH, GSM8K, MATH and HumanEval datasets across general ability, mathematical ability and coding ability.
| Model | **C-Eval** | **MMLU** | **CMMLU** | **Gaokao** | **AGIEval** | **BBH** | **GSM8K** | **MATH** | **HumanEval** |
|:------------------|:-----------|:---------|:----------|:-----------|:------------|:--------|:----------|:---------|:--------------|
| | 5-shot | 5-shot | 5-shot | 0-shot | 0-shot | 3-shot | 4-shot | 5-shot | 0-shot |
| GPT-4 | 69.9 | 86.4 | 71.2 | 72.3 | 55.1 | 86.7 | 91.4 | 45.8 | 74.4 |
| ChatGPT | 52.5 | 70.0 | 53.9 | 51.1 | 39.9 | 70.1 | 78.2 | 28 | 73.2 |
| LLaMA2-7B | 32.5 | 45.3 | 31.8 | 18.9 | 21.8 | 38.2 | 16.7 | 3.3 | 12.8 |
| ChatGLM2-6B(Base) | 51.7 | 47.9 | 50.0 | - | - | 33.7 | 32.4 | 6.5 | - |
| Baichuan2-7B | 56.3 | 54.7 | 57.0 | 34.8 | 34.6 | 41.8 | 24.6 | 5.4 | 17.7 |
| BlueLM-7B-Base | 67.5 | 55.2 | 66.6 | 58.9 | 43.4 | 41.7 | 27.2 | 6.2 | 18.3 |
| BlueLM-7B-Chat | 72.7 | 50.7 | 74.2 | 48.7 | 43.4 | 65.6 | 51.9 | 13.4 | 21.3 |
## 推理部署/Inference and Deployment
```python
>>> import torch
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("vivo-ai/BlueLM-7B-Chat", trust_remote_code=True, use_fast=False)
>>> model = AutoModelForCausalLM.from_pretrained("vivo-ai/BlueLM-7B-Chat", device_map="cuda:0", torch_dtype=torch.bfloat16, trust_remote_code=True)
>>> model = model.eval()
>>> inputs = tokenizer("[|Human|]:三国演义的作者是谁？[|AI|]:", return_tensors="pt")
>>> inputs = inputs.to("cuda:0")
>>> pred = model.generate(**inputs, max_new_tokens=64, repetition_penalty=1.1)
>>> print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
三国演义的作者是谁？ 《三国演义》是元末明初小说家罗贯中创作的长篇小说。
```
更多使用说明，请参考我们的 [Github 仓库](https://github.com/vivo-ai-lab/BlueLM)。
For more instructions, please refer to our [Github Repo](https://github.com/vivo-ai-lab/BlueLM).
## 协议/License
社区使用代码依照 [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) 协议开源，且使用 BlueLM 模型权重需要遵循 [vivo_BlueLM模型许可协议](https://huggingface.co/vivo-ai/BlueLM-7B-Chat/blob/main/MODEL_LICENSE)。
Our code is licensed under the [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) and [Community License for BlueLM Model](https://huggingface.co/vivo-ai/BlueLM-7B-Chat/blob/main/MODEL_LICENSE). | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | vivo-ai/BlueLM-7B-Chat | [
-0.2536282241344452,
-0.8304002285003662,
-0.09833459556102753,
0.683409571647644,
-0.3679220676422119,
0.12126226723194122,
-0.2736488878726959,
-0.6060073375701904,
0.01762174814939499,
-0.08209231495857239,
-0.5477324724197388,
-0.7064676284790039,
-0.3643949329853058,
-0.10420978814363... |
amirali900/anime_faces | amirali900 | 2023-11-29T19:44:26Z | 1,202 | 0 | null | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] | 2023-11-29T19:44:26Z | 2023-11-29T19:43:37.000Z | null | null | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
Describe your model here
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('amirali900/anime_faces')
image = pipeline().images[0]
image
```
| null | diffusers | unconditional-image-generation | null | null | null | null | null | null | null | null | null | amirali900/anime_faces | [
-0.3190402686595917,
-0.7595987319946289,
0.4682987630367279,
0.25842228531837463,
-0.31891822814941406,
-0.3841346800327301,
0.3826639652252197,
0.1102968379855156,
-0.01043255627155304,
0.60040682554245,
-0.3561584949493408,
-0.20755214989185333,
-0.6529626846313477,
-0.23420196771621704... |
matmatmat1/derangedguyed | matmatmat1 | 2023-11-29T16:17:12Z | 1,151 | 0 | null | [
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-11-29T16:17:12Z | 2023-11-29T16:12:53.000Z | null | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### derangedguyed Dreambooth model trained by matmatmat1 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | matmatmat1/derangedguyed | [
-0.4039086103439331,
-0.8818504810333252,
0.5819741487503052,
0.43289709091186523,
-0.37681716680526733,
0.5313022136688232,
0.37792137265205383,
-0.2256191372871399,
0.6182566285133362,
0.15787260234355927,
-0.46933746337890625,
-0.3158337473869324,
-0.6365303993225098,
-0.265632241964340... |
RUSHAID/picture-of-a-bmw | RUSHAID | 2023-11-29T20:41:42Z | 1,138 | 0 | null | [
"diffusers",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-11-29T20:41:42Z | 2023-11-29T20:37:17.000Z | null | null | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### PICTURE-OF-A-BMW Dreambooth model trained by RUSHAID following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: ICCSCEM-223
Sample pictures of this concept:
.jpg)
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | RUSHAID/picture-of-a-bmw | [
-0.8862869739532471,
-0.23663055896759033,
0.41955089569091797,
0.04813666641712189,
-0.1987522840499878,
0.43211814761161804,
0.5665444135665894,
-0.40006422996520996,
0.4588763415813446,
0.308620423078537,
-0.9430999159812927,
-0.18476533889770508,
-0.27069780230522156,
-0.16998204588890... |
openskyml/open-diffusion-v2 | openskyml | 2023-11-29T17:57:30Z | 1,125 | 2 | null | [
"diffusers",
"text-to-image",
"safetensors",
"open-diffusion",
"od-v2",
"openskyml",
"en",
"fr",
"ru",
"license:creativeml-openrail-m",
"endpoints_compatible",
"has_space",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-11-29T17:57:30Z | 2023-11-21T17:20:02.000Z | null | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- safetensors
- open-diffusion
- od-v2
- openskyml
language:
- en
- fr
- ru
pipeline_tag: text-to-image
---
# Open Diffusion V2
Generate cool images with OpenDiffusion V2 (OD-v2)
## Model Details
### Model Description
- **Developed by:** [OpenSkyML](https://huggingface.co/openskyml)
- **Model type:** [Multimodal (Text-to-Image)](https://huggingface.co/models?pipeline_tag=text-to-image)
- **License:** [CreativeML-Openrail-m](https://huggingface.co/models?license=license%3Acreativeml-openrail-m)
### Model Sources
- **Repository:** [click](https://huggingface.co/ehristoforu/open-diffusion-v2/tree/main)
- **Demo [optional]:** In developed ...
## Uses
### In Free Inference API:
```py
import requests
HF_READ_TOKEN = "..."
API_URL = "https://api-inference.huggingface.co/models/openskyml/open-diffusion-v2"
headers = {"Authorization": f"Bearer {HF_READ_TOKEN}"}
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.content
image_bytes = query({
"inputs": "Astronaut riding a horse",
})
# You can access the image with PIL.Image for example
import io
from PIL import Image
image = Image.open(io.BytesIO(image_bytes))
```
### In Spaces:
```py
import gradio as gr
gr.load("models/openskyml/open-diffusion-v2").launch()
```
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | openskyml/open-diffusion-v2 | [
-0.47960877418518066,
-0.8285197019577026,
0.5749669671058655,
0.30704089999198914,
-0.44494324922561646,
-0.6485797166824341,
-0.004342885222285986,
-0.34455958008766174,
0.026781393215060234,
0.4939177930355072,
-0.5485652089118958,
-0.7095360159873962,
-0.5663557648658752,
-0.2784360647... |
asrinmanal/my-pet-cat-bfs | asrinmanal | 2023-11-29T18:14:43Z | 1,102 | 0 | null | [
"diffusers",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-11-29T18:14:43Z | 2023-11-29T18:10:01.000Z | null | null | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-cat-bfs Dreambooth model trained by asrinmanal following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: SAEC-48
Sample pictures of this concept:
.jpg)
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | asrinmanal/my-pet-cat-bfs | [
-0.9340466856956482,
-0.30339205265045166,
0.2669476568698883,
0.3771086037158966,
-0.33272722363471985,
0.6109243631362915,
0.34318697452545166,
-0.4422908425331116,
0.8820393681526184,
0.6192506551742554,
-0.619218111038208,
-0.20635578036308289,
-0.1935615986585617,
0.2886059582233429,
... |
asrinmanal/my-pet-cat-asd | asrinmanal | 2023-11-29T17:25:06Z | 1,094 | 0 | null | [
"diffusers",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-11-29T17:25:06Z | 2023-11-29T17:20:33.000Z | null | null | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-cat-asd Dreambooth model trained by asrinmanal following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: SAEC-48
Sample pictures of this concept:
.jpg)
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | asrinmanal/my-pet-cat-asd | [
-0.817684531211853,
-0.23399101197719574,
0.17711199820041656,
0.27945342659950256,
-0.3304523527622223,
0.6202446818351746,
0.3400566279888153,
-0.4421289563179016,
0.9451532959938049,
0.6999039053916931,
-0.5664601922035217,
-0.2676810324192047,
-0.1426226943731308,
0.18667151033878326,
... |
BAAI/AquilaChat2-7B | BAAI | 2023-11-29T06:07:56Z | 1,078 | 12 | null | [
"transformers",
"pytorch",
"aquila",
"text-generation",
"custom_code",
"license:other",
"region:us"
] | 2023-11-29T06:07:56Z | 2023-10-10T02:02:49.000Z | null | null | ---
license: other
---
<h4 align="center">
<p>
<b>English</b> |
<a href="https://huggingface.co/BAAI/AquilaChat2-7B/blob/main/README_zh.md">简体中文</a>
</p>
</h4>
We opensource our **Aquila2** series, now including **Aquila2**, the base language models, namely **Aquila2-7B** and **Aquila2-34B**, as well as **AquilaChat2**, the chat models, namely **AquilaChat2-7B** and **AquilaChat2-34B**, as well as the long-text chat models, namely **AquilaChat2-7B-16k** and **AquilaChat2-34B-16k**
The additional details of the Aquila model will be presented in the official technical report. Please stay tuned for updates on official channels.
## Quick Start AquilaChat2-7B（Chat model）
### 1. Inference
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import BitsAndBytesConfig
device = torch.device("cuda:0")
model_info = "BAAI/AquilaChat2-7B"
tokenizer = AutoTokenizer.from_pretrained(model_info, trust_remote_code=True)
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
)
model = AutoModelForCausalLM.from_pretrained(model_info, trust_remote_code=True, torch_dtype=torch.float16,
# quantization_config=quantization_config, # Uncomment this line for 4bit quantization
)
model.eval()
model.to(device)
text = "请给出10个要到北京旅游的理由。"
from predict import predict
out = predict(model, text, tokenizer=tokenizer, max_gen_len=200, top_p=0.95,
seed=1234, topk=100, temperature=0.9, sft=True, device=device,
model_name="AquilaChat2-7B")
print(out)
```
## License
Aquila2 series open-source model is licensed under [ BAAI Aquila Model Licence Agreement](https://huggingface.co/BAAI/AquilaChat2-7B/blob/main/BAAI-Aquila-Model-License%20-Agreement.pdf) | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | BAAI/AquilaChat2-7B | [
-0.10758649557828903,
-0.7098793387413025,
0.11636781692504883,
0.4599231779575348,
-0.41822004318237305,
-0.13132144510746002,
-0.17329496145248413,
-0.5808354020118713,
-0.07572948187589645,
0.4212827682495117,
-0.5724071860313416,
-0.33038225769996643,
-0.45498883724212646,
-0.254146784... |
yentinglin/Taiwan-LLaMa-v1.0 | yentinglin | 2023-11-29T06:01:21Z | 1,072 | 66 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"zh",
"dataset:yentinglin/zh_TW_c4",
"dataset:yentinglin/traditional_mandarin_instructions",
"license:llama2",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2023-11-29T06:01:21Z | 2023-08-10T05:31:15.000Z | null | null | ---
license: llama2
datasets:
- yentinglin/zh_TW_c4
- yentinglin/traditional_mandarin_instructions
language:
- zh
widget:
- text: "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: 你好，請問你可以幫我寫一封推薦信嗎？ ASSISTANT:"
library_name: transformers
pipeline_tag: text-generation
---
<img src="https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/CmusIT5OlSXvFrbTJ7l-C.png" alt="Taiwan LLM Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# 🌟 Checkout [Taiwan-LLM Demo Chat-UI](http://www.twllm.com) 🌟
# Model Card for Taiwan LLM 13B v1.0 chat
Taiwan LLM is an advanced language model tailored for Traditional Chinese, focusing on the linguistic and cultural contexts of Taiwan.
Developed from a large base model, it's enriched with diverse Taiwanese textual sources and refined through Supervised Fine-Tuning.
This model excels in language understanding and generation, aligning closely with Taiwan's cultural nuances.
It demonstrates improved performance on various benchmarks like TC-Eval, showcasing its contextual comprehension and cultural relevance.
For detailed insights into Taiwan LLM's development and features, refer to our [technical report](https://github.com/MiuLab/Taiwan-LLaMa/blob/main/twllm_paper.pdf).
## Model description
- **Model type:** A 13B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets.
- **Language(s) (NLP):** Primarily Traditional Chinese (zh-tw)
- **Finetuned from model:** [yentinglin/Taiwan-LLaMa-v1.0-base](https://huggingface.co/yentinglin/Taiwan-LLaMa-v1.0-base)
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/MiuLab/Taiwan-LLaMa
- **Demo:** https://twllm.com/
## Performance
## Intended uses
Here's how you can run the model using the `pipeline()` function from 🤗 Transformers:
```python
# pip install transformers>=4.34
# pip install accelerate
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="yentinglin/Taiwan-LLaMa-v1.0", torch_dtype=torch.bfloat16, device_map="auto")
# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
{
"role": "system",
"content": "你是一個人工智慧助理",
},
{"role": "user", "content": "東北季風如何影響台灣氣候？"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 5.0
## Citation
If you find Taiwan LLM is useful in your work, please cite it with:
```
@inproceedings{lin-chen-2023-llm,
title = "{LLM}-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models",
author = "Lin, Yen-Ting and Chen, Yun-Nung",
booktitle = "Proceedings of the 5th Workshop on NLP for Conversational AI (NLP4ConvAI 2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.nlp4convai-1.5",
pages = "47--58"
}
@misc{taiwanllama,
author={Lin, Yen-Ting and Chen, Yun-Nung},
title={Language Models for Taiwanese Culture},
year={2023},
url={https://github.com/MiuLab/Taiwan-LLaMa},
note={Code and models available at https://github.com/MiuLab/Taiwan-LLaMa},
}
```
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | yentinglin/Taiwan-LLaMa-v1.0 | [
-0.39720386266708374,
-0.9701876640319824,
0.32013747096061707,
0.4968661367893219,
-0.49942612648010254,
0.09321508556604385,
-0.4356049597263336,
-0.5691378116607666,
0.46667662262916565,
0.3834117650985718,
-0.47750207781791687,
-0.6951908469200134,
-0.5527909398078918,
0.13152274489402... |
PGHFace/fortuner-car-ppg | PGHFace | 2023-11-29T19:16:07Z | 1,043 | 0 | null | [
"diffusers",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-11-29T19:16:07Z | 2023-11-29T19:11:02.000Z | null | null | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### Fortuner-Car-ppg Dreambooth model trained by PGHFace following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: AITD-71
Sample pictures of this concept:
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | PGHFace/fortuner-car-ppg | [
-0.5862210392951965,
-0.3286266326904297,
0.4588480591773987,
0.03402593359351158,
-0.25497937202453613,
0.6742513179779053,
0.6122526526451111,
-0.29220691323280334,
0.253078430891037,
0.5458301305770874,
-0.5840204954147339,
0.014021032489836216,
-0.27482131123542786,
-0.2093705087900161... |
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