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train · 878k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-12 18:27:22	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 518 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-12 18:26:55	card stringlengths 11 1.01M
prajwalJumde/QA_REDACTION_NOV1_19	prajwalJumde	2023-11-01T20:14:00Z	3	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-11-01T20:01:03Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer model-index: - name: QA_REDACTION_NOV1_19 results: [] --- <!-- 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. --> # QA_REDACTION_NOV1_19 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0174 ## 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: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.0179 \| 1.0 \| 945 \| 0.0366 \| \| 0.1597 \| 2.0 \| 1890 \| 0.0217 \| \| 0.0651 \| 3.0 \| 2835 \| 0.0264 \| \| 0.0815 \| 4.0 \| 3780 \| 0.0241 \| \| 0.0503 \| 5.0 \| 4725 \| 0.0213 \| \| 0.0215 \| 6.0 \| 5670 \| 0.0160 \| \| 0.0257 \| 7.0 \| 6615 \| 0.0195 \| \| 0.0268 \| 8.0 \| 7560 \| 0.0194 \| \| 0.0323 \| 9.0 \| 8505 \| 0.0179 \| \| 0.0215 \| 10.0 \| 9450 \| 0.0174 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.0.1 - Datasets 2.14.6 - Tokenizers 0.14.1
lmg-anon/TLAssist-llama2-7b-experiment-qlora	lmg-anon	2023-11-01T20:08:22Z	0	0	null	[ "translation", "en", "license:llama2", "region:us" ]	translation	2023-11-01T19:01:56Z	--- language: - en license: llama2 pipeline_tag: translation tags: - translation --- TLAssist is a qlora trained for 2 epochs using all the lines of the final chapter of the Visual Novel "Rewrite" in groups of "Original Text" (Japanese), "Baseline Translation" (MTL), "Improved Translation" (Fan TL). The purpose of this experiment being to find out if it's possible to create a model that can aid improve the quality of MTL. ## Prompt ``` Original Text: {Japanese Text} Baseline Translation: {MTL} Improved Translation: ``` For multi-turn, each translation group is separated by `---`. ## v1 The results I got from v1 seem to be promising, however, It still isn't ideal, some examples of things that I didn't like are: ``` Original Text: 「…う、うん」 Baseline Translation: "…No" Improved Translation: 「...no.」 // The model seems to trust the baseline too much. ``` ``` Original Text: その一　いつもと違う髪形に気がつくこと Baseline Translation: One thing is noticing a different hairstyle than usual Improved Translation: One: you must notice when I've changed my hairstyle --- Original Text: その二　ちゃんと靴までみることいいね？ Baseline Translation: The second thing, it's good to pay attention to the shoes as well, right? Improved Translation: Two: make sure you look at everything from head to toe. // The model seems to omit things for no reason. (in this case the "right?") ```
sofia-todeschini/BioELECTRA-LitCovid-v1.3.1	sofia-todeschini	2023-11-01T20:02:58Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "electra", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-11-01T18:21:47Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: BioELECTRA-LitCovid-v1.3.1 results: [] --- <!-- 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. --> # BioELECTRA-LitCovid-v1.3.1 This model is a fine-tuned version of [kamalkraj/bioelectra-base-discriminator-pubmed](https://huggingface.co/kamalkraj/bioelectra-base-discriminator-pubmed) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6749 - Hamming loss: 0.0257 - F1 micro: 0.7955 - F1 macro: 0.3005 - F1 weighted: 0.8714 - F1 samples: 0.8642 - Precision micro: 0.6936 - Precision macro: 0.2470 - Precision weighted: 0.8294 - Precision samples: 0.8463 - Recall micro: 0.9326 - Recall macro: 0.7358 - Recall weighted: 0.9326 - Recall samples: 0.9427 - Roc Auc: 0.9546 - Accuracy: 0.6664 ## 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Hamming loss \| F1 micro \| F1 macro \| F1 weighted \| F1 samples \| Precision micro \| Precision macro \| Precision weighted \| Precision samples \| Recall micro \| Recall macro \| Recall weighted \| Recall samples \| Roc Auc \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------------:\|:--------:\|:--------:\|:-----------:\|:----------:\|:---------------:\|:---------------:\|:------------------:\|:-----------------:\|:------------:\|:------------:\|:---------------:\|:--------------:\|:-------:\|:--------:\| \| 1.385 \| 1.0 \| 2272 \| 0.6961 \| 0.0592 \| 0.6188 \| 0.2176 \| 0.7539 \| 0.7422 \| 0.4725 \| 0.1706 \| 0.6672 \| 0.6890 \| 0.8965 \| 0.6896 \| 0.8965 \| 0.9065 \| 0.9199 \| 0.3887 \| \| 1.2034 \| 2.0 \| 4544 \| 0.6242 \| 0.0342 \| 0.7421 \| 0.2668 \| 0.8404 \| 0.8354 \| 0.6231 \| 0.2180 \| 0.7922 \| 0.8120 \| 0.9172 \| 0.6872 \| 0.9172 \| 0.9319 \| 0.9429 \| 0.5906 \| \| 1.0857 \| 3.0 \| 6816 \| 0.6185 \| 0.0270 \| 0.7869 \| 0.2949 \| 0.8615 \| 0.8587 \| 0.6815 \| 0.2402 \| 0.8153 \| 0.8382 \| 0.9308 \| 0.7164 \| 0.9308 \| 0.9437 \| 0.9531 \| 0.6444 \| \| 0.8846 \| 4.0 \| 9088 \| 0.6143 \| 0.0260 \| 0.7936 \| 0.2994 \| 0.8677 \| 0.8626 \| 0.6916 \| 0.2460 \| 0.8237 \| 0.8444 \| 0.9309 \| 0.7254 \| 0.9309 \| 0.9421 \| 0.9537 \| 0.6594 \| \| 0.6753 \| 5.0 \| 11360 \| 0.6749 \| 0.0257 \| 0.7955 \| 0.3005 \| 0.8714 \| 0.8642 \| 0.6936 \| 0.2470 \| 0.8294 \| 0.8463 \| 0.9326 \| 0.7358 \| 0.9326 \| 0.9427 \| 0.9546 \| 0.6664 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.13.3
Arabic-Clip-Archive/arabertv2-Vit-B-16-plus-epoch-11-trained-mscoco-training	Arabic-Clip-Archive	2023-11-01T19:51:48Z	3	0	transformers	[ "transformers", "pytorch", "tf", "bert", "feature-extraction", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2023-11-01T19:21:11Z	https://wandb.ai/uos_mlalp/mscoco_teacher_learning_full_data/runs/r0iknp6k
Trelis/falcon-7b-4k-chat-commercial-use-adapters	Trelis	2023-11-01T19:44:50Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:tiiuae/falcon-7b", "base_model:adapter:tiiuae/falcon-7b", "region:us" ]	null	2023-11-01T19:44:45Z	--- library_name: peft base_model: tiiuae/falcon-7b --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure ### Framework versions - PEFT 0.6.0.dev0
M0F0IAm/RL_PBL_URK20CS1158	M0F0IAm	2023-11-01T19:42:54Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-11-01T18:51:27Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_defend_the_line type: doom_defend_the_line metrics: - type: mean_reward value: 20.70 +/- 4.36 name: mean_reward verified: false --- A(n) APPO model trained on the doom_defend_the_line environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r M0F0IAm/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_defend_the_line --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_defend_the_line --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
gokuls/HBERTv1_48_L2_H128_A2_ffn_5	gokuls	2023-11-01T19:40:11Z	4	0	transformers	[ "transformers", "pytorch", "hybridbert", "fill-mask", "generated_from_trainer", "dataset:gokuls/wiki_book_corpus_complete_processed_bert_dataset", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-10-30T11:45:54Z	--- tags: - generated_from_trainer datasets: - gokuls/wiki_book_corpus_complete_processed_bert_dataset metrics: - accuracy model-index: - name: HBERTv1_48_L2_H128_A2_ffn_5 results: - task: name: Masked Language Modeling type: fill-mask dataset: name: gokuls/wiki_book_corpus_complete_processed_bert_dataset type: gokuls/wiki_book_corpus_complete_processed_bert_dataset metrics: - name: Accuracy type: accuracy value: 0.14995151698763923 --- <!-- 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. --> # HBERTv1_48_L2_H128_A2_ffn_5 This model is a fine-tuned version of [](https://huggingface.co/) on the gokuls/wiki_book_corpus_complete_processed_bert_dataset dataset. It achieves the following results on the evaluation set: - Loss: 6.0220 - Accuracy: 0.1500 ## 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: 1e-05 - train_batch_size: 160 - eval_batch_size: 160 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10000 - num_epochs: 100 ### Training results ### Framework versions - Transformers 4.34.1 - Pytorch 1.14.0a0+410ce96 - Datasets 2.14.6 - Tokenizers 0.14.1
nlamot/q-Taxi-v3	nlamot	2023-11-01T19:08:16Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-11-01T15:30:24Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="nlamot/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
ProHax/sft_final_2	ProHax	2023-11-01T19:07:49Z	0	0	peft	[ "peft", "region:us" ]	null	2023-11-01T19:05:58Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0
gokuls/HBERTv1_48_L2_H128_A2_ffn_1	gokuls	2023-11-01T18:54:19Z	4	0	transformers	[ "transformers", "pytorch", "hybridbert", "fill-mask", "generated_from_trainer", "dataset:gokuls/wiki_book_corpus_complete_processed_bert_dataset", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-10-30T11:45:57Z	--- tags: - generated_from_trainer datasets: - gokuls/wiki_book_corpus_complete_processed_bert_dataset metrics: - accuracy model-index: - name: HBERTv1_48_L2_H128_A2_ffn_1 results: - task: name: Masked Language Modeling type: fill-mask dataset: name: gokuls/wiki_book_corpus_complete_processed_bert_dataset type: gokuls/wiki_book_corpus_complete_processed_bert_dataset metrics: - name: Accuracy type: accuracy value: 0.1496531706247267 --- <!-- 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. --> # HBERTv1_48_L2_H128_A2_ffn_1 This model is a fine-tuned version of [](https://huggingface.co/) on the gokuls/wiki_book_corpus_complete_processed_bert_dataset dataset. It achieves the following results on the evaluation set: - Loss: 6.0273 - Accuracy: 0.1497 ## 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: 1e-05 - train_batch_size: 180 - eval_batch_size: 180 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10000 - num_epochs: 100 ### Training results ### Framework versions - Transformers 4.34.1 - Pytorch 1.14.0a0+410ce96 - Datasets 2.14.6 - Tokenizers 0.14.1
am-infoweb/QA_REDACTION_NOV1_17	am-infoweb	2023-11-01T18:39:38Z	3	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-11-01T18:13:59Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer model-index: - name: QA_REDACTION_NOV1_17 results: [] --- <!-- 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. --> # QA_REDACTION_NOV1_17 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0463 ## 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: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 0.0217 \| 1.0 \| 1913 \| 0.0561 \| \| 0.076 \| 2.0 \| 3826 \| 0.0338 \| \| 0.0732 \| 3.0 \| 5739 \| 0.0277 \| \| 0.0483 \| 4.0 \| 7652 \| 0.0250 \| \| 0.0282 \| 5.0 \| 9565 \| 0.1352 \| \| 0.0255 \| 6.0 \| 11478 \| 0.0270 \| \| 0.0227 \| 7.0 \| 13391 \| 0.0282 \| \| 0.0038 \| 8.0 \| 15304 \| 0.0499 \| \| 0.0178 \| 9.0 \| 17217 \| 0.0439 \| \| 0.0052 \| 10.0 \| 19130 \| 0.0463 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.0.1 - Datasets 2.14.6 - Tokenizers 0.14.1
albertakn/dreambooth_output_v5	albertakn	2023-11-01T18:36:54Z	1	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:runwayml/stable-diffusion-v1-5", "base_model:finetune:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-11-01T17:32:20Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - albertakn/dreambooth_output_v5 This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
VoidZeroe/llama8-model	VoidZeroe	2023-11-01T18:35:05Z	0	0	peft	[ "peft", "region:us" ]	null	2023-11-01T18:34:31Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0
Derni/blender	Derni	2023-11-01T18:32:48Z	0	0	null	[ "license:bigcode-openrail-m", "region:us" ]	null	2023-11-01T18:31:01Z	--- license: bigcode-openrail-m ---
alibidaran/falcon7b_demo	alibidaran	2023-11-01T18:31:47Z	7	0	peft	[ "peft", "region:us" ]	null	2023-08-01T10:45:42Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0 + base_model:ybelkada/falcon-7b-sharded-bf16
GiantTreeG/german-jeopardy-mt5-large-256	GiantTreeG	2023-11-01T18:29:02Z	6	0	transformers	[ "transformers", "safetensors", "mt5", "text2text-generation", "question-generation", "german", "generated_from_trainer", "de", "dataset:lmqg/qg_dequad", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-11-01T18:28:16Z	--- language: - de tags: - question-generation - german - text2text-generation - generated_from_trainer datasets: - lmqg/qg_dequad metrics: - bleu4 - f1 - rouge - exact_match model-index: - name: german-jeopardy-mt5-large-256 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: lmqg/qg_dequad type: default args: default metrics: - name: BLEU-4 type: bleu4 value: 16.43 - name: F1 type: f1 value: 42.48 - name: ROUGE-1 type: rouge1 value: 43.56 - name: ROUGE-2 type: rouge2 value: 23.78 - name: ROUGE-L type: rougel value: 41.81 - name: ROUGE-Lsum type: rougelsum value: 41.80 - name: Exact Match type: exact_match value: 3.13 --- <!-- 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. --> # german-jeopardy-mt5-large-256 This model is a fine-tuned version of [google/mt5-large](https://huggingface.co/google/mt5-large) on the [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) dataset. It achieves the following results on the evaluation set: - Loss: 1.3943 - Brevity Penalty: 0.9201 - System Length: 19195 - Reference Length: 20793 - ROUGE-1: 43.56 - ROUGE-2: 23.78 - ROUGE-L: 41.81 - ROUGE-Lsum: 41.80 - Exact Match: 3.13 - BLEU: 16.43 - F1: 42.48 ## Model description See [google/mt5-large](https://huggingface.co/google/mt5-large) for the model architecture. The model was trained on a single NVIDIA RTX 3090 GPU with 24GB of VRAM. ## Intended uses & limitations This model can be used for question generation on German text. ## Training and evaluation data See [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad). ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 7 - gradient_accumulation_steps: 256 - total_train_batch_size: 256 - optimizer: Adafactor - lr_scheduler_type: constant - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Counts 1 \| Counts 2 \| Counts 3 \| Counts 4 \| Totals 1 \| Totals 2 \| Totals 3 \| Totals 4 \| Precisions 1 \| Precisions 2 \| Precisions 3 \| Precisions 4 \| Brevity Penalty \| System Length \| Reference Length \| ROUGE-1 \| ROUGE-2 \| ROUGE-L \| ROUGE-Lsum \| Exact Match \| BLEU \| Mean Generated Length \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:------------:\|:------------:\|:------------:\|:------------:\|:---------------:\|:-------------:\|:----------------:\|:-------:\|:-------:\|:-------:\|:----------:\|:-----------:\|:-------:\|:---------------------:\|:------:\| \| 5.932 \| 0.99 \| 36 \| 2.4510 \| 5614 \| 1426 \| 527 \| 204 \| 28835 \| 26631 \| 24427 \| 22223 \| 19.4694 \| 5.3547 \| 2.1574 \| 0.918 \| 1.0 \| 28835 \| 21250 \| 0.1946 \| 0.0763 \| 0.1843 \| 0.1843 \| 0.0 \| 3.7906 \| 11.4306 \| 0.2127 \| \| 2.3089 \| 1.98 \| 72 \| 1.3964 \| 7578 \| 2696 \| 1244 \| 580 \| 17203 \| 14999 \| 12795 \| 10591 \| 44.0505 \| 17.9745 \| 9.7225 \| 5.4763 \| 0.7904 \| 17203 \| 21250 \| 0.3312 \| 0.1655 \| 0.316 \| 0.3162 \| 0.01 \| 11.3254 \| 12.6583 \| 0.3246 \| \| 1.6778 \| 3.0 \| 109 \| 1.2660 \| 7961 \| 3020 \| 1480 \| 747 \| 17067 \| 14863 \| 12659 \| 10455 \| 46.6456 \| 20.3189 \| 11.6913 \| 7.1449 \| 0.7826 \| 17067 \| 21250 \| 0.3608 \| 0.1881 \| 0.3456 \| 0.3454 \| 0.0195 \| 13.128 \| 12.4682 \| 0.3517 \| \| 1.5383 \| 3.99 \| 145 \| 1.2212 \| 7948 \| 3121 \| 1558 \| 796 \| 16694 \| 14490 \| 12286 \| 10082 \| 47.6099 \| 21.539 \| 12.6811 \| 7.8953 \| 0.7612 \| 16694 \| 21250 \| 0.3663 \| 0.1989 \| 0.3523 \| 0.352 \| 0.024 \| 13.625 \| 12.221 \| 0.3554 \| \| 1.423 \| 4.97 \| 181 \| 1.1706 \| 8746 \| 3590 \| 1840 \| 963 \| 17765 \| 15561 \| 13357 \| 11153 \| 49.2316 \| 23.0705 \| 13.7755 \| 8.6344 \| 0.8219 \| 17765 \| 21250 \| 0.4033 \| 0.2224 \| 0.3876 \| 0.3874 \| 0.0304 \| 15.7567 \| 13.0277 \| 0.3941 \| \| 1.2861 \| 5.99 \| 218 \| 1.1327 \| 8885 \| 3646 \| 1864 \| 1005 \| 17406 \| 15202 \| 12998 \| 10794 \| 51.0456 \| 23.9837 \| 14.3407 \| 9.3107 \| 0.8018 \| 17406 \| 21250 \| 0.4181 \| 0.2295 \| 0.4022 \| 0.402 \| 0.0331 \| 16.123 \| 12.9142 \| 0.4092 \| \| 1.2372 \| 6.98 \| 254 \| 1.1248 \| 9122 \| 3824 \| 1997 \| 1084 \| 17310 \| 15106 \| 12902 \| 10698 \| 52.6979 \| 25.3144 \| 15.4782 \| 10.1327 \| 0.7964 \| 17310 \| 21250 \| 0.4313 \| 0.239 \| 0.4175 \| 0.4172 \| 0.0358 \| 17.0334 \| 12.8412 \| 0.4236 \| \| 1.1307 \| 8.0 \| 291 \| 1.0998 \| 9423 \| 4019 \| 2136 \| 1190 \| 18074 \| 15870 \| 13666 \| 11462 \| 52.1357 \| 25.3245 \| 15.63 \| 10.3821 \| 0.8389 \| 18074 \| 21250 \| 0.441 \| 0.249 \| 0.4255 \| 0.4252 \| 0.0404 \| 18.0474 \| 13.4138 \| 0.4327 \| \| 1.0982 \| 8.99 \| 327 \| 1.1052 \| 9450 \| 4003 \| 2147 \| 1184 \| 18145 \| 15941 \| 13737 \| 11533 \| 52.0805 \| 25.1113 \| 15.6293 \| 10.2662 \| 0.8427 \| 18145 \| 21250 \| 0.4427 \| 0.2492 \| 0.4266 \| 0.4261 \| 0.0426 \| 18.0367 \| 13.4465 \| 0.4344 \| \| 1.0449 \| 9.98 \| 363 \| 1.0996 \| 9471 \| 4036 \| 2149 \| 1180 \| 18067 \| 15863 \| 13659 \| 11455 \| 52.4215 \| 25.4429 \| 15.7332 \| 10.3012 \| 0.8385 \| 18067 \| 21250 \| 0.4422 \| 0.2477 \| 0.4261 \| 0.4257 \| 0.0404 \| 18.0793 \| 13.333 \| 0.4341 \| \| 0.9686 \| 10.99 \| 400 \| 1.1012 \| 9612 \| 4165 \| 2240 \| 1233 \| 17983 \| 15779 \| 13575 \| 11371 \| 53.4505 \| 26.3958 \| 16.5009 \| 10.8434 \| 0.8339 \| 17983 \| 21250 \| 0.4534 \| 0.2591 \| 0.4381 \| 0.4378 \| 0.0449 \| 18.6914 \| 13.3534 \| 0.4458 \| \| 0.9465 \| 11.98 \| 436 \| 1.1027 \| 9670 \| 4154 \| 2229 \| 1239 \| 18217 \| 16013 \| 13809 \| 11605 \| 53.0823 \| 25.9414 \| 16.1416 \| 10.6764 \| 0.8466 \| 18217 \| 21250 \| 0.4531 \| 0.258 \| 0.4377 \| 0.4374 \| 0.0445 \| 18.6863 \| 13.5912 \| 0.4452 \| \| 0.9025 \| 12.97 \| 472 \| 1.1124 \| 9627 \| 4155 \| 2241 \| 1247 \| 18076 \| 15872 \| 13668 \| 11464 \| 53.2585 \| 26.1782 \| 16.396 \| 10.8775 \| 0.839 \| 18076 \| 21250 \| 0.4531 \| 0.2583 \| 0.4386 \| 0.4382 \| 0.0436 \| 18.7344 \| 13.5259 \| 0.4452 \| \| 0.8402 \| 13.99 \| 509 \| 1.1392 \| 9425 \| 4071 \| 2176 \| 1207 \| 17339 \| 15135 \| 12931 \| 10727 \| 54.3572 \| 26.8979 \| 16.8278 \| 11.252 \| 0.7981 \| 17339 \| 21250 \| 0.4495 \| 0.2568 \| 0.4365 \| 0.4358 \| 0.0445 \| 18.3062 \| 12.9129 \| 0.4417 \| \| 0.8282 \| 14.98 \| 545 \| 1.1227 \| 9803 \| 4274 \| 2316 \| 1305 \| 18652 \| 16448 \| 14244 \| 12040 \| 52.5574 \| 25.9849 \| 16.2595 \| 10.8389 \| 0.87 \| 18652 \| 21250 \| 0.4573 \| 0.2627 \| 0.4418 \| 0.4414 \| 0.0463 \| 19.2695 \| 14.0104 \| 0.4496 \| \| 0.7694 \| 16.0 \| 582 \| 1.1394 \| 9740 \| 4240 \| 2299 \| 1296 \| 18281 \| 16077 \| 13873 \| 11669 \| 53.2794 \| 26.3731 \| 16.5718 \| 11.1064 \| 0.8501 \| 18281 \| 21250 \| 0.4572 \| 0.2629 \| 0.4411 \| 0.4412 \| 0.0476 \| 19.1704 \| 13.6475 \| 0.4492 \| \| 0.7589 \| 16.99 \| 618 \| 1.1497 \| 9663 \| 4140 \| 2214 \| 1232 \| 18412 \| 16208 \| 14004 \| 11800 \| 52.4821 \| 25.5429 \| 15.8098 \| 10.4407 \| 0.8572 \| 18412 \| 21250 \| 0.4515 \| 0.2561 \| 0.4359 \| 0.4358 \| 0.044 \| 18.5906 \| 13.7926 \| 0.4432 \| \| 0.724 \| 17.98 \| 654 \| 1.1680 \| 9743 \| 4246 \| 2316 \| 1300 \| 18402 \| 16198 \| 13994 \| 11790 \| 52.9453 \| 26.2131 \| 16.5499 \| 11.0263 \| 0.8566 \| 18402 \| 21250 \| 0.4562 \| 0.2625 \| 0.4408 \| 0.441 \| 0.0472 \| 19.2167 \| 13.7214 \| 0.4474 \| \| 0.6755 \| 18.99 \| 691 \| 1.1874 \| 9722 \| 4266 \| 2351 \| 1341 \| 18272 \| 16068 \| 13864 \| 11660 \| 53.2071 \| 26.5497 \| 16.9576 \| 11.5009 \| 0.8496 \| 18272 \| 21250 \| 0.4559 \| 0.2639 \| 0.4417 \| 0.4413 \| 0.0495 \| 19.4647 \| 13.6071 \| 0.4469 \| \| 0.657 \| 19.79 \| 720 \| 1.1845 \| 9920 \| 4361 \| 2402 \| 1373 \| 18884 \| 16680 \| 14476 \| 12272 \| 52.5312 \| 26.1451 \| 16.593 \| 11.1881 \| 0.8822 \| 18884 \| 21250 \| 0.4594 \| 0.2647 \| 0.4423 \| 0.4421 \| 0.0467 \| 19.8248 \| 14.2001 \| 0.4508 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.0 - Datasets 2.12.0 - Tokenizers 0.13.3
GerardMR/CartPole	GerardMR	2023-11-01T18:21:40Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-11-01T18:20:47Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: CartPole results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
GiantTreeG/german-jeopardy-mt5-large-128	GiantTreeG	2023-11-01T18:21:12Z	6	0	transformers	[ "transformers", "safetensors", "mt5", "text2text-generation", "question-generation", "german", "generated_from_trainer", "de", "dataset:lmqg/qg_dequad", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-11-01T18:20:29Z	--- language: - de tags: - question-generation - german - text2text-generation - generated_from_trainer datasets: - lmqg/qg_dequad metrics: - bleu4 - f1 - rouge - exact_match model-index: - name: german-jeopardy-mt5-large-128 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: lmqg/qg_dequad type: default args: default metrics: - name: BLEU-4 type: bleu4 value: 16.06 - name: F1 type: f1 value: 42.29 - name: ROUGE-1 type: rouge1 value: 43.40 - name: ROUGE-2 type: rouge2 value: 23.68 - name: ROUGE-L type: rougel value: 41.78 - name: ROUGE-Lsum type: rougelsum value: 41.79 - name: Exact Match type: exact_match value: 3.18 --- <!-- 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. --> # german-jeopardy-mt5-large-128 This model is a fine-tuned version of [google/mt5-large](https://huggingface.co/google/mt5-large) on the [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) dataset. It achieves the following results on the evaluation set: - Loss: 1.5487 - Brevity Penalty: 0.9115 - System Length: 19029 - Reference Length: 20793 - ROUGE-1: 43.40 - ROUGE-2: 23.68 - ROUGE-L: 41.78 - ROUGE-Lsum: 41.79 - Exact Match: 3.18 - BLEU: 16.06 - F1: 42.29 ## Model description See [google/mt5-large](https://huggingface.co/google/mt5-large) for the model architecture. The model was trained on a single NVIDIA RTX 3090 GPU with 24GB of VRAM. ## Intended uses & limitations This model can be used for question generation on German text. ## Training and evaluation data See [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad). ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 7 - gradient_accumulation_steps: 128 - total_train_batch_size: 128 - optimizer: Adafactor - lr_scheduler_type: constant - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Counts 1 \| Counts 2 \| Counts 3 \| Counts 4 \| Totals 1 \| Totals 2 \| Totals 3 \| Totals 4 \| Precisions 1 \| Precisions 2 \| Precisions 3 \| Precisions 4 \| Brevity Penalty \| System Length \| Reference Length \| ROUGE-1 \| ROUGE-2 \| ROUGE-L \| ROUGE-Lsum \| Exact Match \| BLEU \| Mean Generated Length \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:------------:\|:------------:\|:------------:\|:------------:\|:---------------:\|:-------------:\|:----------------:\|:-------:\|:-------:\|:-------:\|:----------:\|:-----------:\|:-------:\|:---------------------:\|:------:\| \| 3.9659 \| 0.99 \| 72 \| 1.4145 \| 7244 \| 2547 \| 1183 \| 565 \| 16296 \| 14092 \| 11888 \| 9684 \| 44.4526 \| 18.0741 \| 9.9512 \| 5.8344 \| 0.7379 \| 16296 \| 21250 \| 0.3213 \| 0.1608 \| 0.3091 \| 0.309 \| 0.0136 \| 10.8438 \| 11.7786 \| 0.3139 \| \| 1.7081 \| 1.99 \| 145 \| 1.2632 \| 7865 \| 3037 \| 1498 \| 759 \| 16841 \| 14637 \| 12433 \| 10229 \| 46.7015 \| 20.7488 \| 12.0486 \| 7.4201 \| 0.7697 \| 16841 \| 21250 \| 0.3577 \| 0.189 \| 0.3438 \| 0.3439 \| 0.0181 \| 13.2044 \| 12.225 \| 0.3481 \| \| 1.4856 \| 3.0 \| 218 \| 1.1974 \| 8608 \| 3519 \| 1818 \| 969 \| 17627 \| 15423 \| 13219 \| 11015 \| 48.8342 \| 22.8166 \| 13.7529 \| 8.7971 \| 0.8142 \| 17627 \| 21250 \| 0.3969 \| 0.2181 \| 0.381 \| 0.3812 \| 0.0268 \| 15.6014 \| 13.0027 \| 0.3882 \| \| 1.3277 \| 4.0 \| 291 \| 1.1394 \| 9018 \| 3702 \| 1907 \| 1029 \| 17465 \| 15261 \| 13057 \| 10853 \| 51.6347 \| 24.2579 \| 14.6052 \| 9.4812 \| 0.8052 \| 17465 \| 21250 \| 0.424 \| 0.2321 \| 0.4087 \| 0.4085 \| 0.0313 \| 16.4313 \| 12.8716 \| 0.4156 \| \| 1.2314 \| 4.99 \| 363 \| 1.1193 \| 9240 \| 3869 \| 1994 \| 1076 \| 17794 \| 15590 \| 13386 \| 11182 \| 51.9276 \| 24.8172 \| 14.8962 \| 9.6226 \| 0.8235 \| 17794 \| 21250 \| 0.4336 \| 0.2413 \| 0.4183 \| 0.418 \| 0.0363 \| 17.0718 \| 13.2137 \| 0.4256 \| \| 1.1264 \| 5.99 \| 436 \| 1.1086 \| 9263 \| 3908 \| 2055 \| 1127 \| 17502 \| 15298 \| 13094 \| 10890 \| 52.9254 \| 25.5458 \| 15.6942 \| 10.3489 \| 0.8072 \| 17502 \| 21250 \| 0.4383 \| 0.2452 \| 0.4239 \| 0.4237 \| 0.0372 \| 17.4744 \| 13.034 \| 0.4309 \| \| 1.0469 \| 7.0 \| 509 \| 1.1038 \| 9434 \| 4034 \| 2146 \| 1189 \| 18028 \| 15824 \| 13620 \| 11416 \| 52.3297 \| 25.4929 \| 15.7562 \| 10.4152 \| 0.8363 \| 18028 \| 21250 \| 0.4433 \| 0.2505 \| 0.4286 \| 0.4282 \| 0.039 \| 18.0906 \| 13.422 \| 0.4348 \| \| 0.9874 \| 8.0 \| 582 \| 1.0990 \| 9746 \| 4265 \| 2287 \| 1285 \| 18351 \| 16147 \| 13943 \| 11739 \| 53.1088 \| 26.4136 \| 16.4025 \| 10.9464 \| 0.8539 \| 18351 \| 21250 \| 0.457 \| 0.2627 \| 0.4417 \| 0.4416 \| 0.0454 \| 19.1287 \| 13.6466 \| 0.4498 \| \| 0.9488 \| 8.99 \| 654 \| 1.1175 \| 9484 \| 4062 \| 2158 \| 1197 \| 17831 \| 15627 \| 13423 \| 11219 \| 53.1883 \| 25.9935 \| 16.0769 \| 10.6694 \| 0.8255 \| 17831 \| 21250 \| 0.4482 \| 0.2548 \| 0.4338 \| 0.4333 \| 0.0431 \| 18.2172 \| 13.2763 \| 0.4399 \| \| 0.8893 \| 9.99 \| 727 \| 1.1222 \| 9650 \| 4205 \| 2289 \| 1289 \| 18017 \| 15813 \| 13609 \| 11405 \| 53.5605 \| 26.592 \| 16.8198 \| 11.3021 \| 0.8357 \| 18017 \| 21250 \| 0.4543 \| 0.262 \| 0.4396 \| 0.4394 \| 0.0463 \| 19.064 \| 13.4251 \| 0.4472 \| \| 0.8362 \| 10.99 \| 800 \| 1.1342 \| 9706 \| 4232 \| 2279 \| 1281 \| 18232 \| 16028 \| 13824 \| 11620 \| 53.2361 \| 26.4038 \| 16.4858 \| 11.0241 \| 0.8474 \| 18232 \| 21250 \| 0.4551 \| 0.2632 \| 0.4395 \| 0.4393 \| 0.0472 \| 19.052 \| 13.6021 \| 0.4473 \| \| 0.7835 \| 12.0 \| 873 \| 1.1427 \| 9802 \| 4280 \| 2292 \| 1285 \| 18491 \| 16287 \| 14083 \| 11879 \| 53.0096 \| 26.2786 \| 16.2749 \| 10.8174 \| 0.8614 \| 18491 \| 21250 \| 0.458 \| 0.2634 \| 0.4414 \| 0.4412 \| 0.0472 \| 19.169 \| 14.0168 \| 0.4497 \| \| 0.7441 \| 12.99 \| 945 \| 1.1669 \| 9816 \| 4323 \| 2334 \| 1294 \| 18498 \| 16294 \| 14090 \| 11886 \| 53.0652 \| 26.5312 \| 16.5649 \| 10.8868 \| 0.8618 \| 18498 \| 21250 \| 0.4577 \| 0.2659 \| 0.4418 \| 0.4417 \| 0.0463 \| 19.3443 \| 13.8348 \| 0.4493 \| \| 0.7012 \| 13.99 \| 1018 \| 1.1740 \| 9856 \| 4364 \| 2375 \| 1360 \| 18537 \| 16333 \| 14129 \| 11925 \| 53.1693 \| 26.7189 \| 16.8094 \| 11.4046 \| 0.8639 \| 18537 \| 21250 \| 0.4591 \| 0.2653 \| 0.443 \| 0.4428 \| 0.0476 \| 19.7341 \| 13.976 \| 0.4514 \| \| 0.6597 \| 14.99 \| 1091 \| 1.1987 \| 9780 \| 4292 \| 2336 \| 1302 \| 18468 \| 16264 \| 14060 \| 11856 \| 52.9565 \| 26.3896 \| 16.6145 \| 10.9818 \| 0.8602 \| 18468 \| 21250 \| 0.457 \| 0.2633 \| 0.4418 \| 0.4416 \| 0.0485 \| 19.3289 \| 13.8802 \| 0.4492 \| \| 0.6236 \| 16.0 \| 1164 \| 1.2135 \| 9931 \| 4388 \| 2390 \| 1359 \| 18717 \| 16513 \| 14309 \| 12105 \| 53.0587 \| 26.573 \| 16.7028 \| 11.2268 \| 0.8734 \| 18717 \| 21250 \| 0.4618 \| 0.2682 \| 0.4452 \| 0.445 \| 0.0495 \| 19.8055 \| 14.044 \| 0.4538 \| \| 0.5933 \| 17.0 \| 1237 \| 1.2305 \| 9806 \| 4316 \| 2366 \| 1348 \| 18566 \| 16362 \| 14158 \| 11954 \| 52.817 \| 26.3782 \| 16.7114 \| 11.2766 \| 0.8654 \| 18566 \| 21250 \| 0.4571 \| 0.2628 \| 0.4407 \| 0.4409 \| 0.049 \| 19.5893 \| 14.0622 \| 0.4485 \| \| 0.5622 \| 17.99 \| 1309 \| 1.2796 \| 9787 \| 4306 \| 2346 \| 1338 \| 18559 \| 16355 \| 14151 \| 11947 \| 52.7345 \| 26.3283 \| 16.5783 \| 11.1995 \| 0.865 \| 18559 \| 21250 \| 0.4549 \| 0.2609 \| 0.4383 \| 0.4382 \| 0.0476 \| 19.4914 \| 13.7763 \| 0.447 \| \| 0.5275 \| 18.99 \| 1382 \| 1.2833 \| 9918 \| 4363 \| 2374 \| 1355 \| 18950 \| 16746 \| 14542 \| 12338 \| 52.3377 \| 26.054 \| 16.3251 \| 10.9823 \| 0.8857 \| 18950 \| 21250 \| 0.4573 \| 0.2624 \| 0.441 \| 0.4408 \| 0.0508 \| 19.6947 \| 14.1647 \| 0.4499 \| \| 0.4986 \| 19.79 \| 1440 \| 1.3059 \| 9879 \| 4315 \| 2347 \| 1324 \| 18931 \| 16727 \| 14523 \| 12319 \| 52.1842 \| 25.7966 \| 16.1606 \| 10.7476 \| 0.8847 \| 18931 \| 21250 \| 0.4564 \| 0.2622 \| 0.4407 \| 0.4403 \| 0.0495 \| 19.4544 \| 14.2827 \| 0.4478 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.0 - Datasets 2.12.0 - Tokenizers 0.13.3
oblivious/Refact-1.6B-fim-GGUF	oblivious	2023-11-01T18:13:32Z	140	4	null	[ "gguf", "code", "text-generation", "en", "dataset:bigcode/the-stack-dedup", "dataset:rombodawg/2XUNCENSORED_MegaCodeTraining188k", "dataset:bigcode/commitpackft", "base_model:smallcloudai/Refact-1_6B-fim", "base_model:quantized:smallcloudai/Refact-1_6B-fim", "license:bigscience-openrail-m", "endpoints_compatible", "region:us" ]	text-generation	2023-11-01T17:20:10Z	--- base_model: smallcloudai/Refact-1_6B-fim license: bigscience-openrail-m model_creator: Small Magellanic Cloud AI model_name: Refact-1.6B pipeline_tag: text-generation prompt_template: '<fim_prefix>{prefix}<fim_suffix>{suffix}<fim_middle>' pretrain-datasets: - books - arxiv - c4 - falcon-refinedweb - wiki - github-issues - stack_markdown - self-made dataset of permissive github code datasets: - bigcode/the-stack-dedup - rombodawg/2XUNCENSORED_MegaCodeTraining188k - bigcode/commitpackft tags: - code language: - en --- # Refact-1.6B-fim-GGUF - Model creator: [Small Magellanic Cloud AI](https://huggingface.co/smallcloudai) - Original model: [Refact-1.6B](https://huggingface.co/smallcloudai/Refact-1_6B-fim) ## Description This repository contains quantized GGUF format model files for [Refact-1.6B](https://huggingface.co/smallcloudai/Refact-1_6B-fim). ## Prompt: fill in the middle ``` <fim_prefix>def print_hello_world():\n """<fim_suffix>\n print("Hello world!")<fim_middle> ``` ## Prompt: chat (experimental) ``` <empty_output>SYSTEM You are a programming assistant <empty_output>USER How do I sort a list in Python? <empty_output>ASSISTANT ``` ## Example `llama.cpp` command ```shell ./main -m refact-1_6b-Q4_K_M.gguf -c 4096 -n -1 -p '<fim_prefix>{prefix}<fim_suffix>{suffix}<fim_middle>' ``` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
floressullon/distilbert-base-uncased-finetuned-pos	floressullon	2023-11-01T18:02:25Z	5	0	transformers	[ "transformers", "pytorch", "tf", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "dataset:conll2003", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-10-30T20:23:23Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-pos results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: validation args: conll2003 metrics: - name: Precision type: precision value: 0.9111159430794133 - name: Recall type: recall value: 0.9150066723280359 - name: F1 type: f1 value: 0.9130571629179478 - name: Accuracy type: accuracy value: 0.9249209652564856 --- <!-- 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. --> # distilbert-base-uncased-finetuned-pos This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.3205 - Precision: 0.9111 - Recall: 0.9150 - F1: 0.9131 - Accuracy: 0.9249 ## 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 \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.8042 \| 1.0 \| 878 \| 0.3449 \| 0.8981 \| 0.9028 \| 0.9004 \| 0.9168 \| \| 0.256 \| 2.0 \| 1756 \| 0.3195 \| 0.9069 \| 0.9125 \| 0.9097 \| 0.9232 \| \| 0.2075 \| 3.0 \| 2634 \| 0.3205 \| 0.9111 \| 0.9150 \| 0.9131 \| 0.9249 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1
karanthgreeshma/wav2vec2-large-300m-colab-only-gn	karanthgreeshma	2023-11-01T18:01:23Z	4	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_13_0", "base_model:facebook/wav2vec2-xls-r-300m", "base_model:finetune:facebook/wav2vec2-xls-r-300m", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-10-31T21:23:57Z	--- license: apache-2.0 base_model: facebook/wav2vec2-xls-r-300m tags: - generated_from_trainer datasets: - common_voice_13_0 metrics: - wer model-index: - name: wav2vec2-large-300m-colab-only-gn results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_13_0 type: common_voice_13_0 config: gn split: test args: gn metrics: - name: Wer type: wer value: 0.5229303156640858 --- <!-- 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. --> # wav2vec2-large-300m-colab-only-gn This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_13_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.5274 - Wer: 0.5229 ## 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: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 20.8148 \| 0.45 \| 25 \| 13.5976 \| 1.0 \| \| 7.0188 \| 0.9 \| 50 \| 5.5263 \| 1.0 \| \| 4.1285 \| 1.35 \| 75 \| 3.6078 \| 1.0 \| \| 3.338 \| 1.8 \| 100 \| 3.3217 \| 1.0 \| \| 3.2829 \| 2.25 \| 125 \| 3.2781 \| 1.0 \| \| 3.272 \| 2.7 \| 150 \| 3.2601 \| 1.0 \| \| 3.2224 \| 3.15 \| 175 \| 3.2234 \| 1.0 \| \| 3.1949 \| 3.6 \| 200 \| 3.1998 \| 1.0 \| \| 3.1846 \| 4.05 \| 225 \| 3.1841 \| 1.0 \| \| 3.1615 \| 4.5 \| 250 \| 3.1719 \| 1.0 \| \| 3.1367 \| 4.95 \| 275 \| 3.1132 \| 1.0 \| \| 3.0111 \| 5.41 \| 300 \| 2.9344 \| 1.0 \| \| 2.7786 \| 5.86 \| 325 \| 2.5643 \| 1.0 \| \| 2.2106 \| 6.31 \| 350 \| 1.8132 \| 1.0 \| \| 1.6365 \| 6.76 \| 375 \| 1.4008 \| 0.9982 \| \| 1.178 \| 7.21 \| 400 \| 1.0678 \| 0.9845 \| \| 0.8903 \| 7.66 \| 425 \| 0.8744 \| 0.9369 \| \| 0.7429 \| 8.11 \| 450 \| 0.7213 \| 0.8752 \| \| 0.5931 \| 8.56 \| 475 \| 0.6681 \| 0.8189 \| \| 0.5592 \| 9.01 \| 500 \| 0.6622 \| 0.7895 \| \| 0.4316 \| 9.46 \| 525 \| 0.6177 \| 0.7644 \| \| 0.4098 \| 9.91 \| 550 \| 0.5599 \| 0.7874 \| \| 0.3176 \| 10.36 \| 575 \| 0.5649 \| 0.7001 \| \| 0.3142 \| 10.81 \| 600 \| 0.5828 \| 0.6867 \| \| 0.3227 \| 11.26 \| 625 \| 0.5505 \| 0.6736 \| \| 0.275 \| 11.71 \| 650 \| 0.5432 \| 0.6540 \| \| 0.2783 \| 12.16 \| 675 \| 0.5372 \| 0.6462 \| \| 0.2316 \| 12.61 \| 700 \| 0.5078 \| 0.6379 \| \| 0.2281 \| 13.06 \| 725 \| 0.5059 \| 0.6161 \| \| 0.2191 \| 13.51 \| 750 \| 0.5175 \| 0.5956 \| \| 0.1911 \| 13.96 \| 775 \| 0.5216 \| 0.5929 \| \| 0.1731 \| 14.41 \| 800 \| 0.5069 \| 0.5789 \| \| 0.1743 \| 14.86 \| 825 \| 0.5207 \| 0.5971 \| \| 0.1755 \| 15.32 \| 850 \| 0.5436 \| 0.6307 \| \| 0.1568 \| 15.77 \| 875 \| 0.5374 \| 0.6001 \| \| 0.1629 \| 16.22 \| 900 \| 0.5429 \| 0.6102 \| \| 0.1418 \| 16.67 \| 925 \| 0.5089 \| 0.5762 \| \| 0.136 \| 17.12 \| 950 \| 0.5291 \| 0.5878 \| \| 0.1354 \| 17.57 \| 975 \| 0.5381 \| 0.5840 \| \| 0.1351 \| 18.02 \| 1000 \| 0.5511 \| 0.5947 \| \| 0.1252 \| 18.47 \| 1025 \| 0.5204 \| 0.5643 \| \| 0.1215 \| 18.92 \| 1050 \| 0.5385 \| 0.5613 \| \| 0.1188 \| 19.37 \| 1075 \| 0.5063 \| 0.5718 \| \| 0.1209 \| 19.82 \| 1100 \| 0.5211 \| 0.5488 \| \| 0.1091 \| 20.27 \| 1125 \| 0.5245 \| 0.5557 \| \| 0.112 \| 20.72 \| 1150 \| 0.4910 \| 0.5587 \| \| 0.102 \| 21.17 \| 1175 \| 0.5192 \| 0.5581 \| \| 0.0947 \| 21.62 \| 1200 \| 0.5500 \| 0.5718 \| \| 0.1066 \| 22.07 \| 1225 \| 0.5288 \| 0.5488 \| \| 0.1011 \| 22.52 \| 1250 \| 0.5180 \| 0.5438 \| \| 0.0974 \| 22.97 \| 1275 \| 0.5089 \| 0.5277 \| \| 0.0926 \| 23.42 \| 1300 \| 0.5222 \| 0.5301 \| \| 0.0871 \| 23.87 \| 1325 \| 0.5135 \| 0.5366 \| \| 0.0808 \| 24.32 \| 1350 \| 0.4990 \| 0.5331 \| \| 0.0739 \| 24.77 \| 1375 \| 0.5281 \| 0.5351 \| \| 0.0841 \| 25.23 \| 1400 \| 0.5321 \| 0.5360 \| \| 0.0743 \| 25.68 \| 1425 \| 0.5508 \| 0.5447 \| \| 0.0809 \| 26.13 \| 1450 \| 0.5228 \| 0.5396 \| \| 0.0631 \| 26.58 \| 1475 \| 0.5284 \| 0.5351 \| \| 0.0788 \| 27.03 \| 1500 \| 0.5250 \| 0.5289 \| \| 0.0754 \| 27.48 \| 1525 \| 0.5204 \| 0.5259 \| \| 0.0663 \| 27.93 \| 1550 \| 0.5275 \| 0.5313 \| \| 0.0645 \| 28.38 \| 1575 \| 0.5288 \| 0.5259 \| \| 0.0729 \| 28.83 \| 1600 \| 0.5268 \| 0.5259 \| \| 0.0656 \| 29.28 \| 1625 \| 0.5277 \| 0.5232 \| \| 0.0703 \| 29.73 \| 1650 \| 0.5274 \| 0.5229 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
tkay264/model-test	tkay264	2023-11-01T17:53:33Z	0	0	peft	[ "peft", "llama", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "4-bit", "bitsandbytes", "region:us" ]	null	2023-11-01T17:25:23Z	--- library_name: peft base_model: meta-llama/Llama-2-7b-chat-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0
Shiko07/bert-base-uncased	Shiko07	2023-11-01T17:52:53Z	3	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-11-01T17:05:04Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: bert-base-uncased results: [] --- <!-- 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. --> # bert-base-uncased This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7672 - Rmse: 0.6779 ## 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: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rmse \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.7744 \| 0.5 \| 500 \| 0.7366 \| 0.7180 \| \| 0.7071 \| 1.0 \| 1000 \| 0.6900 \| 0.7068 \| \| 0.5951 \| 1.5 \| 1500 \| 0.6372 \| 0.6764 \| \| 0.5814 \| 2.0 \| 2000 \| 0.6076 \| 0.6986 \| \| 0.4345 \| 2.5 \| 2500 \| 0.7680 \| 0.6793 \| \| 0.404 \| 3.0 \| 3000 \| 0.7672 \| 0.6779 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
TheBloke/vietnamese-llama2-7B-40GB-GPTQ	TheBloke	2023-11-01T17:52:09Z	23	4	transformers	[ "transformers", "safetensors", "llama", "text-generation", "vi", "en", "dataset:vietgpt/wikipedia_vi", "dataset:wikipedia", "dataset:pg19", "dataset:mc4", "base_model:bkai-foundation-models/vietnamese-llama2-7b-40GB", "base_model:quantized:bkai-foundation-models/vietnamese-llama2-7b-40GB", "license:other", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]	text-generation	2023-11-01T16:53:58Z	--- base_model: bkai-foundation-models/vietnamese-llama2-7b-40GB datasets: - vietgpt/wikipedia_vi - wikipedia - pg19 - mc4 inference: false language: - vi - en license: other model_creator: BKAI-HUST Foundation Models Lab model_name: Vietnamese Llama2 7B 40GB model_type: llama prompt_template: '{prompt} ' quantized_by: TheBloke --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Vietnamese Llama2 7B 40GB - GPTQ - Model creator: [BKAI-HUST Foundation Models Lab](https://huggingface.co/bkai-foundation-models) - Original model: [Vietnamese Llama2 7B 40GB](https://huggingface.co/bkai-foundation-models/vietnamese-llama2-7b-40GB) <!-- description start --> ## Description This repo contains GPTQ model files for [BKAI-HUST Foundation Models Lab's Vietnamese Llama2 7B 40GB](https://huggingface.co/bkai-foundation-models/vietnamese-llama2-7b-40GB). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GGUF) * [BKAI-HUST Foundation Models Lab's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/bkai-foundation-models/vietnamese-llama2-7b-40GB) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: None ``` {prompt} ``` <!-- prompt-template end --> <!-- licensing start --> ## Licensing The creator of the source model has listed its license as `other`, and this quantization has therefore used that same license. As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly. In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [BKAI-HUST Foundation Models Lab's Vietnamese Llama2 7B 40GB](https://huggingface.co/bkai-foundation-models/vietnamese-llama2-7b-40GB). <!-- licensing end --> <!-- README_GPTQ.md-compatible clients start --> ## Known compatible clients / servers These GPTQ models are known to work in the following inference servers/webuis. - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) - [KobaldAI United](https://github.com/henk717/koboldai) - [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) This may not be a complete list; if you know of others, please let me know! <!-- README_GPTQ.md-compatible clients end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit. </details> \| Branch \| Bits \| GS \| Act Order \| Damp % \| GPTQ Dataset \| Seq Len \| Size \| ExLlama \| Desc \| \| ------ \| ---- \| -- \| --------- \| ------ \| ------------ \| ------- \| ---- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GPTQ/tree/main) \| 4 \| 128 \| Yes \| 0.1 \| [Alpaca Vietnamese](https://huggingface.co/datasets/nRuaif/Vietnamese_x_Alpaca) \| 2048 \| 4.13 GB \| Yes \| 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. \| \| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GPTQ/tree/gptq-4bit-32g-actorder_True) \| 4 \| 32 \| Yes \| 0.1 \| [Alpaca Vietnamese](https://huggingface.co/datasets/nRuaif/Vietnamese_x_Alpaca) \| 2048 \| 4.51 GB \| Yes \| 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. \| \| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GPTQ/tree/gptq-8bit--1g-actorder_True) \| 8 \| None \| Yes \| 0.1 \| [Alpaca Vietnamese](https://huggingface.co/datasets/nRuaif/Vietnamese_x_Alpaca) \| 2048 \| 7.25 GB \| No \| 8-bit, with Act Order. No group size, to lower VRAM requirements. \| \| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GPTQ/tree/gptq-8bit-128g-actorder_True) \| 8 \| 128 \| Yes \| 0.1 \| [Alpaca Vietnamese](https://huggingface.co/datasets/nRuaif/Vietnamese_x_Alpaca) \| 2048 \| 7.39 GB \| No \| 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. \| \| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GPTQ/tree/gptq-8bit-32g-actorder_True) \| 8 \| 32 \| Yes \| 0.1 \| [Alpaca Vietnamese](https://huggingface.co/datasets/nRuaif/Vietnamese_x_Alpaca) \| 2048 \| 7.85 GB \| No \| 8-bit, with group size 32g and Act Order for maximum inference quality. \| \| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GPTQ/tree/gptq-4bit-64g-actorder_True) \| 4 \| 64 \| Yes \| 0.1 \| [Alpaca Vietnamese](https://huggingface.co/datasets/nRuaif/Vietnamese_x_Alpaca) \| 2048 \| 4.26 GB \| Yes \| 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. \| <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/vietnamese-llama2-7B-40GB-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/vietnamese-llama2-7B-40GB-GPTQ:gptq-4bit-32g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `vietnamese-llama2-7B-40GB-GPTQ`: ```shell mkdir vietnamese-llama2-7B-40GB-GPTQ huggingface-cli download TheBloke/vietnamese-llama2-7B-40GB-GPTQ --local-dir vietnamese-llama2-7B-40GB-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir vietnamese-llama2-7B-40GB-GPTQ huggingface-cli download TheBloke/vietnamese-llama2-7B-40GB-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir vietnamese-llama2-7B-40GB-GPTQ --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir vietnamese-llama2-7B-40GB-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/vietnamese-llama2-7B-40GB-GPTQ --local-dir vietnamese-llama2-7B-40GB-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### With `git` (not recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.) <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/vietnamese-llama2-7B-40GB-GPTQ`. - To download from a specific branch, enter for example `TheBloke/vietnamese-llama2-7B-40GB-GPTQ:gptq-4bit-32g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `vietnamese-llama2-7B-40GB-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. - Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the Text Generation tab and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-tgi start --> ## Serving this model from Text Generation Inference (TGI) It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/vietnamese-llama2-7B-40GB-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''{prompt} ''' client = InferenceClient(endpoint_url) response = client.text_generation(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1) print(f"Model output: {response}") ``` <!-- README_GPTQ.md-use-from-tgi end --> <!-- README_GPTQ.md-use-from-python start --> ## How to use this GPTQ model from Python code ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install transformers optimum pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7 ``` If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.4.2 pip3 install . ``` ### You can then use the following code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/vietnamese-llama2-7B-40GB-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-32g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''{prompt} ''' print("\n\n* Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("* Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly. [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility. For a list of clients/servers, please see "Known compatible clients / servers", above. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: BKAI-HUST Foundation Models Lab's Vietnamese Llama2 7B 40GB We employed [SentencePiece](https://github.com/google/sentencepiece) to retrain a Vietnamese tokenizer with a vocabulary size of 20K. No Vietnamese word segmentation was used. We then merged this vocabulary with the original one of Llama2, removing duplicate tokens. The new tokenizer significantly improves when encoding Vietnamese text, reducing the number of tokens by 50% compared to ChatGPT and approximately 70% compared to the original Llama2. We conducted a single-epoch continual pretraining, also known as incremental pretraining, using the Llama2-chat 7B model on a mixed dataset totaling 40.5 GB, comprised of: - 19 GB [NewsCorpus](https://github.com/binhvq/news-corpus) - 1.1 GB Vietnamese Wikipedia - 1.6 GB [Vietnamese books](https://www.kaggle.com/datasets/iambestfeeder/10000-vietnamese-books) - 4.5 GB Vietnamese legal documents (crawled from thuvienphapluat and processed by ourselves) - 2.1 GB Vietnamese legal text (from [C4-vi](https://huggingface.co/datasets/c4)) - 1.1 GB English Books (sub-sampled from [pg19](https://huggingface.co/datasets/pg19)) - 1.1 GB English Wikipedia (sub-sampled from 20220301.en wikipedia) - 10 GB English Text (sub-sampled from [C4-en](https://huggingface.co/datasets/c4)) We trained the model on a DGX A100 system, utilizing four GPU A100 in 10 days (about 1000 GPU hours). Hyperparameters are set as follows: - Training Regime: BFloat16 mixed precision - Lora Config: ``` { "base_model_name_or_path": "meta-llama/Llama-2-7b-chat-hf", "bias": "none", "enable_lora": null, "fan_in_fan_out": false, "inference_mode": true, "lora_alpha": 32.0, "lora_dropout": 0.05, "merge_weights": false, "modules_to_save": [ "embed_tokens", "lm_head" ], "peft_type": "LORA", "r": 8, "target_modules": [ "q_proj", "v_proj", "k_proj", "o_proj", "gate_proj", "down_proj", "up_proj" ], "task_type": "CAUSAL_LM" } ``` We also provide the [LoRA part](https://huggingface.co/bkai-foundation-models/vietnamese-llama2-7b-40GB/tree/main/pt_lora_model) so that you can integrate it with the original Llama2-chat-7b by yourself. Please note that this model requires further supervised fine-tuning (SFT) to be used in practice! Usage and other considerations: We refer to the [Llama 2](https://github.com/facebookresearch/llama) Training loss: <img src="figure/training_loss.png" alt="Training Loss Curve"/> Disclaimer This project is built upon Meta's Llama-2 model. It is essential to strictly adhere to the open-source license agreement of Llama-2 when using this model. If you incorporate third-party code, please ensure compliance with the relevant open-source license agreements. It's important to note that the content generated by the model may be influenced by various factors, such as calculation methods, random elements, and potential inaccuracies in quantification. Consequently, this project does not offer any guarantees regarding the accuracy of the model's outputs, and it disclaims any responsibility for consequences resulting from the use of the model's resources and its output. For those employing the models from this project for commercial purposes, developers must adhere to local laws and regulations to ensure the compliance of the model's output content. This project is not accountable for any products or services derived from such usage. Acknowledgments We extend our gratitude to PHPC - Phenikaa University and NVIDIA for their generous provision of computing resources for model training. Our appreciation also goes out to binhvq and the other authors for their diligent efforts in collecting and preparing the Vietnamese text corpus.
ruchita1010/LED_billsum_model	ruchita1010	2023-11-01T17:42:56Z	6	0	transformers	[ "transformers", "pytorch", "led", "text2text-generation", "generated_from_trainer", "dataset:billsum", "base_model:allenai/led-base-16384", "base_model:finetune:allenai/led-base-16384", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-11-01T16:45:12Z	--- license: apache-2.0 base_model: allenai/led-base-16384 tags: - generated_from_trainer datasets: - billsum metrics: - rouge model-index: - name: LED_billsum_model results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: billsum type: billsum config: default split: ca_test args: default metrics: - name: Rouge1 type: rouge value: 0.1447 --- <!-- 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. --> # LED_billsum_model This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 1.6576 - Rouge1: 0.1447 - Rouge2: 0.0854 - Rougel: 0.1292 - Rougelsum: 0.1339 - Gen Len: 20.0 ## 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: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| 1.4849 \| 1.0 \| 330 \| 1.6511 \| 0.1463 \| 0.0827 \| 0.1276 \| 0.1337 \| 20.0 \| \| 1.3361 \| 2.0 \| 660 \| 1.6056 \| 0.148 \| 0.0799 \| 0.1268 \| 0.1336 \| 20.0 \| \| 1.1727 \| 3.0 \| 990 \| 1.5833 \| 0.1459 \| 0.0827 \| 0.1289 \| 0.1341 \| 20.0 \| \| 1.0601 \| 4.0 \| 1320 \| 1.5987 \| 0.1462 \| 0.0859 \| 0.1299 \| 0.1344 \| 20.0 \| \| 0.9789 \| 5.0 \| 1650 \| 1.6030 \| 0.1414 \| 0.0794 \| 0.125 \| 0.1302 \| 20.0 \| \| 0.8724 \| 6.0 \| 1980 \| 1.6060 \| 0.1476 \| 0.0868 \| 0.1298 \| 0.1356 \| 20.0 \| \| 0.7994 \| 7.0 \| 2310 \| 1.6295 \| 0.1348 \| 0.0758 \| 0.1198 \| 0.1253 \| 20.0 \| \| 0.7762 \| 8.0 \| 2640 \| 1.6317 \| 0.1422 \| 0.0831 \| 0.1261 \| 0.1312 \| 20.0 \| \| 0.7087 \| 9.0 \| 2970 \| 1.6501 \| 0.1421 \| 0.0825 \| 0.1264 \| 0.1311 \| 20.0 \| \| 0.7014 \| 10.0 \| 3300 \| 1.6576 \| 0.1447 \| 0.0854 \| 0.1292 \| 0.1339 \| 20.0 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
GiantTreeG/german-jeopardy-longt5-base-256	GiantTreeG	2023-11-01T17:41:10Z	6	0	transformers	[ "transformers", "safetensors", "longt5", "text2text-generation", "question-generation", "german", "generated_from_trainer", "de", "dataset:lmqg/qg_dequad", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-11-01T17:39:32Z	--- language: - de tags: - question-generation - german - text2text-generation - generated_from_trainer datasets: - lmqg/qg_dequad metrics: - bleu4 - f1 - rouge - exact_match model-index: - name: german-jeopardy-longt5-base-256 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: lmqg/qg_dequad type: default args: default metrics: - name: BLEU-4 type: bleu4 value: 10.52 - name: F1 type: f1 value: 33.92 - name: ROUGE-1 type: rouge1 value: 34.80 - name: ROUGE-2 type: rouge2 value: 16.54 - name: ROUGE-L type: rougel value: 33.69 - name: ROUGE-Lsum type: rougelsum value: 33.70 - name: Exact Match type: exact_match value: 1.50 --- <!-- 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. --> # german-jeopardy-longt5-base-256 This model is a fine-tuned version of [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) on the [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) dataset. It achieves the following results on the evaluation set: - Loss: 1.7833 - Brevity Penalty: 0.8244 - System Length: 17427 - Reference Length: 20793 - ROUGE-1: 34.80 - ROUGE-2: 16.54 - ROUGE-L: 33.69 - ROUGE-Lsum: 33.70 - Exact Match: 1.50 - BLEU: 10.52 - F1: 33.92 ## Model description See [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) for more information about the model architecture. The model was trained on a single NVIDIA RTX 3090 GPU with 24GB of VRAM. ## Intended uses & limitations This model can be used for question generation on German text. ## Training and evaluation data See [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad). ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 4 - seed: 7 - gradient_accumulation_steps: 32 - total_train_batch_size: 256 - optimizer: Adafactor - lr_scheduler_type: constant - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Counts 1 \| Counts 2 \| Counts 3 \| Counts 4 \| Totals 1 \| Totals 2 \| Totals 3 \| Totals 4 \| Precisions 1 \| Precisions 2 \| Precisions 3 \| Precisions 4 \| Brevity Penalty \| System Length \| Reference Length \| ROUGE-1 \| ROUGE-2 \| ROUGE-L \| ROUGE-Lsum \| Exact Match \| BLEU \| Mean Generated Length \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:------------:\|:------------:\|:------------:\|:------------:\|:---------------:\|:-------------:\|:----------------:\|:-------:\|:-------:\|:-------:\|:----------:\|:-----------:\|:-------:\|:---------------------:\|:------:\| \| 3.6024 \| 0.99 \| 36 \| 2.4682 \| 5645 \| 1343 \| 424 \| 109 \| 15388 \| 13184 \| 10980 \| 8776 \| 36.6844 \| 10.1866 \| 3.8616 \| 1.242 \| 0.6832 \| 15388 \| 21250 \| 0.2285 \| 0.0824 \| 0.2192 \| 0.2188 \| 0.0005 \| 4.4454 \| 11.6338 \| 0.2236 \| \| 2.9671 \| 1.98 \| 72 \| 2.2445 \| 5988 \| 1562 \| 569 \| 179 \| 16094 \| 13890 \| 11686 \| 9482 \| 37.2064 \| 11.2455 \| 4.8691 \| 1.8878 \| 0.7259 \| 16094 \| 21250 \| 0.2465 \| 0.0971 \| 0.2371 \| 0.2371 \| 0.0018 \| 5.7163 \| 12.314 \| 0.2401 \| \| 2.6324 \| 2.99 \| 109 \| 2.1227 \| 6539 \| 1846 \| 702 \| 240 \| 17173 \| 14969 \| 12765 \| 10561 \| 38.0772 \| 12.3322 \| 5.4994 \| 2.2725 \| 0.7887 \| 17173 \| 21250 \| 0.2729 \| 0.1154 \| 0.2601 \| 0.2604 \| 0.0027 \| 6.9028 \| 13.2319 \| 0.2663 \| \| 2.5557 \| 3.98 \| 145 \| 2.0357 \| 6491 \| 1923 \| 752 \| 275 \| 15961 \| 13757 \| 11553 \| 9349 \| 40.6679 \| 13.9783 \| 6.5091 \| 2.9415 \| 0.7179 \| 15961 \| 21250 \| 0.2783 \| 0.1214 \| 0.2676 \| 0.2678 \| 0.0059 \| 7.3331 \| 12.0962 \| 0.2729 \| \| 2.3785 \| 5.0 \| 182 \| 1.9824 \| 6808 \| 2113 \| 855 \| 328 \| 16439 \| 14235 \| 12031 \| 9827 \| 41.4137 \| 14.8437 \| 7.1066 \| 3.3377 \| 0.7463 \| 16439 \| 21250 \| 0.2948 \| 0.1326 \| 0.2825 \| 0.2825 \| 0.0064 \| 8.2007 \| 12.6819 \| 0.2892 \| \| 2.3396 \| 5.99 \| 218 \| 1.9449 \| 7033 \| 2194 \| 886 \| 364 \| 16851 \| 14647 \| 12443 \| 10239 \| 41.7364 \| 14.9792 \| 7.1205 \| 3.555 \| 0.7702 \| 16851 \| 21250 \| 0.3044 \| 0.1373 \| 0.292 \| 0.2922 \| 0.0086 \| 8.639 \| 13.0254 \| 0.3 \| \| 2.2557 \| 6.98 \| 254 \| 1.8938 \| 7167 \| 2285 \| 939 \| 389 \| 16529 \| 14325 \| 12121 \| 9917 \| 43.3602 \| 15.9511 \| 7.7469 \| 3.9226 \| 0.7515 \| 16529 \| 21250 \| 0.3166 \| 0.1428 \| 0.3043 \| 0.3046 \| 0.0095 \| 9.049 \| 12.7119 \| 0.3119 \| \| 2.1168 \| 7.99 \| 291 \| 1.8575 \| 7347 \| 2425 \| 1021 \| 425 \| 16860 \| 14656 \| 12452 \| 10248 \| 43.5765 \| 16.5461 \| 8.1995 \| 4.1472 \| 0.7708 \| 16860 \| 21250 \| 0.3258 \| 0.1505 \| 0.3137 \| 0.3142 \| 0.0104 \| 9.6447 \| 12.9374 \| 0.3211 \| \| 2.1105 \| 8.98 \| 327 \| 1.8284 \| 7460 \| 2461 \| 1061 \| 449 \| 17034 \| 14830 \| 12626 \| 10422 \| 43.7948 \| 16.5947 \| 8.4033 \| 4.3082 \| 0.7807 \| 17034 \| 21250 \| 0.3317 \| 0.1521 \| 0.3187 \| 0.3191 \| 0.0095 \| 9.9436 \| 13.1828 \| 0.3267 \| \| 1.9913 \| 10.0 \| 364 \| 1.8057 \| 7547 \| 2537 \| 1105 \| 487 \| 17005 \| 14801 \| 12597 \| 10393 \| 44.3811 \| 17.1407 \| 8.7719 \| 4.6858 \| 0.7791 \| 17005 \| 21250 \| 0.335 \| 0.1566 \| 0.323 \| 0.3233 \| 0.0113 \| 10.3601 \| 13.0358 \| 0.3316 \| \| 1.9943 \| 10.99 \| 400 \| 1.7973 \| 7629 \| 2574 \| 1131 \| 496 \| 16842 \| 14638 \| 12434 \| 10230 \| 45.2975 \| 17.5844 \| 9.096 \| 4.8485 \| 0.7697 \| 16842 \| 21250 \| 0.343 \| 0.1594 \| 0.3296 \| 0.33 \| 0.0113 \| 10.5378 \| 13.0154 \| 0.3385 \| \| 1.941 \| 11.98 \| 436 \| 1.7773 \| 7681 \| 2606 \| 1164 \| 528 \| 17105 \| 14901 \| 12697 \| 10493 \| 44.905 \| 17.4888 \| 9.1675 \| 5.0319 \| 0.7848 \| 17105 \| 21250 \| 0.3421 \| 0.1607 \| 0.3295 \| 0.3294 \| 0.0132 \| 10.8273 \| 13.1361 \| 0.3385 \| \| 1.8453 \| 12.99 \| 473 \| 1.7595 \| 7817 \| 2700 \| 1224 \| 560 \| 17324 \| 15120 \| 12916 \| 10712 \| 45.1224 \| 17.8571 \| 9.4766 \| 5.2278 \| 0.7972 \| 17324 \| 21250 \| 0.3492 \| 0.1662 \| 0.3367 \| 0.3367 \| 0.0127 \| 11.2687 \| 13.5018 \| 0.3447 \| \| 1.85 \| 13.98 \| 509 \| 1.7414 \| 7792 \| 2642 \| 1182 \| 537 \| 17417 \| 15213 \| 13009 \| 10805 \| 44.7379 \| 17.3667 \| 9.086 \| 4.9699 \| 0.8025 \| 17417 \| 21250 \| 0.3458 \| 0.1632 \| 0.3322 \| 0.3322 \| 0.0127 \| 10.9825 \| 13.5395 \| 0.3416 \| \| 1.7588 \| 15.0 \| 546 \| 1.7346 \| 7827 \| 2702 \| 1223 \| 569 \| 17265 \| 15061 \| 12857 \| 10653 \| 45.3345 \| 17.9404 \| 9.5123 \| 5.3412 \| 0.7939 \| 17265 \| 21250 \| 0.3487 \| 0.1661 \| 0.3355 \| 0.3354 \| 0.015 \| 11.3189 \| 13.3026 \| 0.3446 \| \| 1.7663 \| 15.99 \| 582 \| 1.7191 \| 7946 \| 2757 \| 1245 \| 581 \| 17431 \| 15227 \| 13023 \| 10819 \| 45.5855 \| 18.106 \| 9.56 \| 5.3702 \| 0.8032 \| 17431 \| 21250 \| 0.3544 \| 0.1695 \| 0.3418 \| 0.3416 \| 0.0154 \| 11.5245 \| 13.4515 \| 0.3501 \| \| 1.7317 \| 16.98 \| 618 \| 1.7133 \| 8068 \| 2844 \| 1325 \| 633 \| 17752 \| 15548 \| 13344 \| 11140 \| 45.4484 \| 18.2917 \| 9.9296 \| 5.6822 \| 0.8212 \| 17752 \| 21250 \| 0.3575 \| 0.1746 \| 0.3445 \| 0.3447 \| 0.0163 \| 12.0845 \| 13.77 \| 0.3527 \| \| 1.6421 \| 17.99 \| 655 \| 1.7198 \| 8003 \| 2823 \| 1301 \| 609 \| 17535 \| 15331 \| 13127 \| 10923 \| 45.6401 \| 18.4137 \| 9.9109 \| 5.5754 \| 0.8091 \| 17535 \| 21250 \| 0.3576 \| 0.1737 \| 0.3447 \| 0.3448 \| 0.015 \| 11.877 \| 13.4669 \| 0.353 \| \| 1.6543 \| 18.98 \| 691 \| 1.7151 \| 8031 \| 2817 \| 1294 \| 612 \| 17803 \| 15599 \| 13395 \| 11191 \| 45.1104 \| 18.0588 \| 9.6603 \| 5.4687 \| 0.824 \| 17803 \| 21250 \| 0.3567 \| 0.1734 \| 0.3435 \| 0.3431 \| 0.015 \| 11.8679 \| 13.8648 \| 0.351 \| \| 1.5702 \| 19.78 \| 720 \| 1.7079 \| 7996 \| 2850 \| 1330 \| 639 \| 17275 \| 15071 \| 12867 \| 10663 \| 46.2865 \| 18.9105 \| 10.3365 \| 5.9927 \| 0.7945 \| 17275 \| 21250 \| 0.3618 \| 0.1769 \| 0.3485 \| 0.348 \| 0.0168 \| 12.1229 \| 13.3367 \| 0.3569 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.0 - Datasets 2.12.0 - Tokenizers 0.13.3
reneeshdenny/q-taxi-v3	reneeshdenny	2023-11-01T17:40:27Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-11-01T17:40:25Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.46 +/- 2.74 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="reneeshdenny/q-taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
gokuls/HBERTv1_L12_H768_A12_ffn_0.5	gokuls	2023-11-01T17:33:07Z	3	0	transformers	[ "transformers", "pytorch", "hybridbert", "fill-mask", "generated_from_trainer", "dataset:gokuls/wiki_book_corpus_complete_processed_bert_dataset", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-10-30T10:05:54Z	--- tags: - generated_from_trainer datasets: - gokuls/wiki_book_corpus_complete_processed_bert_dataset metrics: - accuracy model-index: - name: HBERTv1_L12_H768_A12_ffn_0.5 results: - task: name: Masked Language Modeling type: fill-mask dataset: name: gokuls/wiki_book_corpus_complete_processed_bert_dataset type: gokuls/wiki_book_corpus_complete_processed_bert_dataset metrics: - name: Accuracy type: accuracy value: 0.42137106830668214 --- <!-- 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. --> # HBERTv1_L12_H768_A12_ffn_0.5 This model is a fine-tuned version of [](https://huggingface.co/) on the gokuls/wiki_book_corpus_complete_processed_bert_dataset dataset. It achieves the following results on the evaluation set: - Loss: 3.4716 - Accuracy: 0.4214 ## 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: 1e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10000 - num_epochs: 100 ### Training results ### Framework versions - Transformers 4.34.1 - Pytorch 1.14.0a0+410ce96 - Datasets 2.14.6 - Tokenizers 0.14.1
GiantTreeG/german-jeopardy-longt5-base-128	GiantTreeG	2023-11-01T17:27:51Z	7	0	transformers	[ "transformers", "safetensors", "longt5", "text2text-generation", "question-generation", "german", "generated_from_trainer", "de", "dataset:lmqg/qg_dequad", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-11-01T17:23:07Z	--- language: - de tags: - question-generation - german - text2text-generation - generated_from_trainer datasets: - lmqg/qg_dequad metrics: - bleu4 - f1 - rouge - exact_match model-index: - name: german-jeopardy-longt5-base-128 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: lmqg/qg_dequad type: default args: default metrics: - name: BLEU-4 type: bleu4 value: 10.73 - name: F1 type: f1 value: 34.55 - name: ROUGE-1 type: rouge1 value: 35.34 - name: ROUGE-2 type: rouge2 value: 16.82 - name: ROUGE-L type: rougel value: 34.13 - name: ROUGE-Lsum type: rougelsum value: 34.14 - name: Exact Match type: exact_match value: 1.41 --- <!-- 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. --> # german-jeopardy-longt5-base-128 This model is a fine-tuned version of [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) on the [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) dataset. It achieves the following results on the evaluation set: - Loss: 1.8010 - Brevity Penalty: 0.8577 - System Length: 18026 - Reference Length: 20793 - ROUGE-1: 35.34 - ROUGE-2: 16.82 - ROUGE-L: 34.13 - ROUGE-Lsum: 34.14 - Exact Match: 1.41 - BLEU: 10.73 - F1: 34.55 ## Model description See [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) for more information about the model architecture. The model was trained on a single NVIDIA RTX 3090 GPU with 24GB of VRAM. ## Intended uses & limitations This model can be used for question generation on German text. ## Training and evaluation data See [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad). ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 4 - seed: 7 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adafactor - lr_scheduler_type: constant - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Counts 1 \| Counts 2 \| Counts 3 \| Counts 4 \| Totals 1 \| Totals 2 \| Totals 3 \| Totals 4 \| Precisions 1 \| Precisions 2 \| Precisions 3 \| Precisions 4 \| Brevity Penalty \| System Length \| Reference Length \| ROUGE-1 \| ROUGE-2 \| ROUGE-L \| ROUGE-Lsum \| Exact Match \| BLEU \| Mean Generated Length \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:------------:\|:------------:\|:------------:\|:------------:\|:---------------:\|:-------------:\|:----------------:\|:-------:\|:-------:\|:-------:\|:----------:\|:-----------:\|:-------:\|:---------------------:\|:------:\| \| 3.458 \| 0.99 \| 72 \| 2.3696 \| 5618 \| 1383 \| 463 \| 116 \| 15080 \| 12876 \| 10672 \| 8468 \| 37.2546 \| 10.7409 \| 4.3385 \| 1.3699 \| 0.6642 \| 15080 \| 21250 \| 0.2266 \| 0.0841 \| 0.2197 \| 0.2196 \| 0.0005 \| 4.6384 \| 11.3013 \| 0.2226 \| \| 2.7548 \| 1.99 \| 145 \| 2.1310 \| 6361 \| 1807 \| 700 \| 254 \| 16130 \| 13926 \| 11722 \| 9518 \| 39.4358 \| 12.9757 \| 5.9717 \| 2.6686 \| 0.728 \| 16130 \| 21250 \| 0.2706 \| 0.1122 \| 0.2596 \| 0.2596 \| 0.0036 \| 6.9183 \| 12.206 \| 0.2635 \| \| 2.5084 \| 2.99 \| 218 \| 2.0244 \| 6758 \| 2001 \| 780 \| 285 \| 16871 \| 14667 \| 12463 \| 10259 \| 40.0569 \| 13.6429 \| 6.2585 \| 2.778 \| 0.7714 \| 16871 \| 21250 \| 0.2888 \| 0.1258 \| 0.2766 \| 0.2767 \| 0.0045 \| 7.616 \| 12.8825 \| 0.2832 \| \| 2.3562 \| 4.0 \| 291 \| 1.9501 \| 7011 \| 2193 \| 908 \| 360 \| 16796 \| 14592 \| 12388 \| 10184 \| 41.7421 \| 15.0288 \| 7.3297 \| 3.535 \| 0.7671 \| 16796 \| 21250 \| 0.303 \| 0.1375 \| 0.2892 \| 0.2894 \| 0.0077 \| 8.6611 \| 12.9142 \| 0.2978 \| \| 2.2383 \| 5.0 \| 364 \| 1.8874 \| 7245 \| 2386 \| 1015 \| 435 \| 16708 \| 14504 \| 12300 \| 10096 \| 43.3625 \| 16.4506 \| 8.252 \| 4.3086 \| 0.762 \| 16708 \| 21250 \| 0.3198 \| 0.1498 \| 0.3077 \| 0.3079 \| 0.0113 \| 9.6159 \| 12.8417 \| 0.3155 \| \| 2.1576 \| 5.99 \| 436 \| 1.8593 \| 7378 \| 2382 \| 997 \| 429 \| 17014 \| 14810 \| 12606 \| 10402 \| 43.3643 \| 16.0837 \| 7.9089 \| 4.1242 \| 0.7796 \| 17014 \| 21250 \| 0.326 \| 0.1497 \| 0.3132 \| 0.3132 \| 0.0109 \| 9.5745 \| 13.2187 \| 0.3215 \| \| 2.0356 \| 6.99 \| 509 \| 1.8133 \| 7570 \| 2520 \| 1097 \| 482 \| 16999 \| 14795 \| 12591 \| 10387 \| 44.532 \| 17.0328 \| 8.7126 \| 4.6404 \| 0.7787 \| 16999 \| 21250 \| 0.3384 \| 0.158 \| 0.3258 \| 0.3257 \| 0.0123 \| 10.3053 \| 13.0368 \| 0.3339 \| \| 1.9575 \| 7.99 \| 582 \| 1.7856 \| 7764 \| 2637 \| 1175 \| 545 \| 17379 \| 15175 \| 12971 \| 10767 \| 44.6746 \| 17.3773 \| 9.0587 \| 5.0618 \| 0.8003 \| 17379 \| 21250 \| 0.345 \| 0.1625 \| 0.3322 \| 0.3324 \| 0.0136 \| 10.993 \| 13.4719 \| 0.3407 \| \| 1.8889 \| 9.0 \| 655 \| 1.7666 \| 7766 \| 2644 \| 1184 \| 532 \| 17102 \| 14898 \| 12694 \| 10490 \| 45.4099 \| 17.7473 \| 9.3272 \| 5.0715 \| 0.7846 \| 17102 \| 21250 \| 0.3487 \| 0.1636 \| 0.3348 \| 0.335 \| 0.0123 \| 10.9637 \| 13.2164 \| 0.3438 \| \| 1.8201 \| 10.0 \| 728 \| 1.7415 \| 7737 \| 2680 \| 1238 \| 587 \| 17156 \| 14952 \| 12748 \| 10544 \| 45.0979 \| 17.924 \| 9.7113 \| 5.5671 \| 0.7877 \| 17156 \| 21250 \| 0.3453 \| 0.1666 \| 0.3332 \| 0.3333 \| 0.0163 \| 11.3891 \| 13.1388 \| 0.3406 \| \| 1.7882 \| 10.99 \| 800 \| 1.7331 \| 7859 \| 2722 \| 1241 \| 572 \| 17364 \| 15160 \| 12956 \| 10752 \| 45.2603 \| 17.9551 \| 9.5786 \| 5.3199 \| 0.7995 \| 17364 \| 21250 \| 0.3524 \| 0.1673 \| 0.3387 \| 0.3385 \| 0.0145 \| 11.4047 \| 13.4052 \| 0.3473 \| \| 1.7095 \| 11.99 \| 873 \| 1.7194 \| 7968 \| 2783 \| 1292 \| 625 \| 17467 \| 15263 \| 13059 \| 10855 \| 45.6175 \| 18.2336 \| 9.8936 \| 5.7577 \| 0.8053 \| 17467 \| 21250 \| 0.3547 \| 0.1708 \| 0.3418 \| 0.3414 \| 0.0154 \| 11.8807 \| 13.4437 \| 0.3495 \| \| 1.6619 \| 12.99 \| 946 \| 1.7032 \| 8011 \| 2796 \| 1286 \| 604 \| 17433 \| 15229 \| 13025 \| 10821 \| 45.9531 \| 18.3597 \| 9.8733 \| 5.5817 \| 0.8034 \| 17433 \| 21250 \| 0.3584 \| 0.1736 \| 0.3454 \| 0.3454 \| 0.0154 \| 11.7968 \| 13.4964 \| 0.3526 \| \| 1.6103 \| 13.99 \| 1019 \| 1.7028 \| 8154 \| 2891 \| 1347 \| 636 \| 17665 \| 15461 \| 13257 \| 11053 \| 46.1591 \| 18.6987 \| 10.1607 \| 5.7541 \| 0.8163 \| 17665 \| 21250 \| 0.3659 \| 0.1795 \| 0.3509 \| 0.3508 \| 0.015 \| 12.235 \| 13.7223 \| 0.3602 \| \| 1.565 \| 15.0 \| 1092 \| 1.6955 \| 8135 \| 2897 \| 1362 \| 665 \| 17530 \| 15326 \| 13122 \| 10918 \| 46.4062 \| 18.9025 \| 10.3795 \| 6.0909 \| 0.8088 \| 17530 \| 21250 \| 0.3668 \| 0.1808 \| 0.3518 \| 0.3516 \| 0.02 \| 12.4116 \| 13.6107 \| 0.3603 \| \| 1.522 \| 16.0 \| 1165 \| 1.6793 \| 8271 \| 2982 \| 1414 \| 697 \| 17946 \| 15742 \| 13538 \| 11334 \| 46.0883 \| 18.943 \| 10.4447 \| 6.1496 \| 0.8318 \| 17946 \| 21250 \| 0.3695 \| 0.1828 \| 0.354 \| 0.354 \| 0.0191 \| 12.8008 \| 13.9192 \| 0.3632 \| \| 1.5022 \| 16.99 \| 1237 \| 1.6849 \| 8244 \| 2967 \| 1392 \| 680 \| 17510 \| 15306 \| 13102 \| 10898 \| 47.0817 \| 19.3846 \| 10.6243 \| 6.2397 \| 0.8077 \| 17510 \| 21250 \| 0.3728 \| 0.184 \| 0.3569 \| 0.3569 \| 0.0191 \| 12.6672 \| 13.6243 \| 0.366 \| \| 1.4359 \| 17.99 \| 1310 \| 1.6862 \| 8328 \| 3050 \| 1448 \| 717 \| 17873 \| 15669 \| 13465 \| 11261 \| 46.5954 \| 19.4652 \| 10.7538 \| 6.3671 \| 0.8278 \| 17873 \| 21250 \| 0.3742 \| 0.1866 \| 0.3582 \| 0.3583 \| 0.0181 \| 13.0683 \| 13.7255 \| 0.3671 \| \| 1.3994 \| 18.99 \| 1383 \| 1.6775 \| 8272 \| 2998 \| 1417 \| 704 \| 17645 \| 15441 \| 13237 \| 11033 \| 46.8801 \| 19.4158 \| 10.7048 \| 6.3809 \| 0.8152 \| 17645 \| 21250 \| 0.3739 \| 0.1866 \| 0.3583 \| 0.3581 \| 0.0213 \| 12.8728 \| 13.6956 \| 0.3673 \| \| 1.3609 \| 19.78 \| 1440 \| 1.6884 \| 8347 \| 3062 \| 1465 \| 723 \| 17823 \| 15619 \| 13415 \| 11211 \| 46.8327 \| 19.6043 \| 10.9206 \| 6.449 \| 0.8251 \| 17823 \| 21250 \| 0.3761 \| 0.1886 \| 0.3601 \| 0.3596 \| 0.0204 \| 13.1569 \| 13.7328 \| 0.3692 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.0 - Datasets 2.12.0 - Tokenizers 0.13.3
bartoszmaj/falcon_7b_qlora	bartoszmaj	2023-11-01T17:21:57Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:tiiuae/falcon-7b", "base_model:adapter:tiiuae/falcon-7b", "region:us" ]	null	2023-11-01T17:21:55Z	--- library_name: peft base_model: tiiuae/falcon-7b --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.0.dev0
bartoszmaj/my-finetuned-bert	bartoszmaj	2023-11-01T17:21:39Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:tiiuae/falcon-7b", "base_model:adapter:tiiuae/falcon-7b", "region:us" ]	null	2023-11-01T17:21:37Z	--- library_name: peft base_model: tiiuae/falcon-7b --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.0.dev0
daochf/Lora-Meta-Llama2-7b-hf-QandA_2g_v01-v02	daochf	2023-11-01T17:20:03Z	0	0	peft	[ "peft", "region:us" ]	null	2023-11-01T17:20:00Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0
Adminhuggingface/OUTPUTA1	Adminhuggingface	2023-11-01T17:10:24Z	1	1	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-11-01T17:08:38Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - Adminhuggingface/OUTPUTA1 These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the Adminhuggingface/LORA_ONE dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)
am-infoweb/QA_REDACTION_NOV1_16	am-infoweb	2023-11-01T16:59:15Z	4	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-11-01T16:33:25Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer model-index: - name: QA_REDACTION_NOV1_16 results: [] --- <!-- 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. --> # QA_REDACTION_NOV1_16 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0383 ## 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: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 0.0454 \| 1.0 \| 1800 \| 0.0552 \| \| 0.0534 \| 2.0 \| 3600 \| 0.0423 \| \| 0.3243 \| 3.0 \| 5400 \| 0.0335 \| \| 0.051 \| 4.0 \| 7200 \| 0.0482 \| \| 0.033 \| 5.0 \| 9000 \| 0.0260 \| \| 0.0378 \| 6.0 \| 10800 \| 0.0233 \| \| 0.0206 \| 7.0 \| 12600 \| 0.0282 \| \| 0.0399 \| 8.0 \| 14400 \| 0.0490 \| \| 0.009 \| 9.0 \| 16200 \| 0.0282 \| \| 0.0239 \| 10.0 \| 18000 \| 0.0383 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.0.1 - Datasets 2.14.6 - Tokenizers 0.14.1
TheBloke/vietnamese-llama2-7B-40GB-GGUF	TheBloke	2023-11-01T16:58:03Z	131	3	transformers	[ "transformers", "gguf", "llama", "vi", "en", "dataset:vietgpt/wikipedia_vi", "dataset:wikipedia", "dataset:pg19", "dataset:mc4", "base_model:bkai-foundation-models/vietnamese-llama2-7b-40GB", "base_model:quantized:bkai-foundation-models/vietnamese-llama2-7b-40GB", "license:other", "region:us" ]	null	2023-11-01T16:53:58Z	--- base_model: bkai-foundation-models/vietnamese-llama2-7b-40GB datasets: - vietgpt/wikipedia_vi - wikipedia - pg19 - mc4 inference: false language: - vi - en license: other model_creator: BKAI-HUST Foundation Models Lab model_name: Vietnamese Llama2 7B 40GB model_type: llama prompt_template: '{prompt} ' quantized_by: TheBloke --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Vietnamese Llama2 7B 40GB - GGUF - Model creator: [BKAI-HUST Foundation Models Lab](https://huggingface.co/bkai-foundation-models) - Original model: [Vietnamese Llama2 7B 40GB](https://huggingface.co/bkai-foundation-models/vietnamese-llama2-7b-40GB) <!-- description start --> ## Description This repo contains GGUF format model files for [BKAI-HUST Foundation Models Lab's Vietnamese Llama2 7B 40GB](https://huggingface.co/bkai-foundation-models/vietnamese-llama2-7b-40GB). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplate list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GGUF) * [BKAI-HUST Foundation Models Lab's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/bkai-foundation-models/vietnamese-llama2-7b-40GB) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: None ``` {prompt} ``` <!-- prompt-template end --> <!-- licensing start --> ## Licensing The creator of the source model has listed its license as `other`, and this quantization has therefore used that same license. As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly. In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [BKAI-HUST Foundation Models Lab's Vietnamese Llama2 7B 40GB](https://huggingface.co/bkai-foundation-models/vietnamese-llama2-7b-40GB). <!-- licensing end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [vietnamese-llama2-7b-40gb.Q2_K.gguf](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GGUF/blob/main/vietnamese-llama2-7b-40gb.Q2_K.gguf) \| Q2_K \| 2 \| 2.89 GB\| 5.39 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [vietnamese-llama2-7b-40gb.Q3_K_S.gguf](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GGUF/blob/main/vietnamese-llama2-7b-40gb.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 3.02 GB\| 5.52 GB \| very small, high quality loss \| \| [vietnamese-llama2-7b-40gb.Q3_K_M.gguf](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GGUF/blob/main/vietnamese-llama2-7b-40gb.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 3.37 GB\| 5.87 GB \| very small, high quality loss \| \| [vietnamese-llama2-7b-40gb.Q3_K_L.gguf](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GGUF/blob/main/vietnamese-llama2-7b-40gb.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 3.67 GB\| 6.17 GB \| small, substantial quality loss \| \| [vietnamese-llama2-7b-40gb.Q4_0.gguf](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GGUF/blob/main/vietnamese-llama2-7b-40gb.Q4_0.gguf) \| Q4_0 \| 4 \| 3.91 GB\| 6.41 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [vietnamese-llama2-7b-40gb.Q4_K_S.gguf](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GGUF/blob/main/vietnamese-llama2-7b-40gb.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 3.94 GB\| 6.44 GB \| small, greater quality loss \| \| [vietnamese-llama2-7b-40gb.Q4_K_M.gguf](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GGUF/blob/main/vietnamese-llama2-7b-40gb.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 4.16 GB\| 6.66 GB \| medium, balanced quality - recommended \| \| [vietnamese-llama2-7b-40gb.Q5_0.gguf](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GGUF/blob/main/vietnamese-llama2-7b-40gb.Q5_0.gguf) \| Q5_0 \| 5 \| 4.74 GB\| 7.24 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [vietnamese-llama2-7b-40gb.Q5_K_S.gguf](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GGUF/blob/main/vietnamese-llama2-7b-40gb.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 4.74 GB\| 7.24 GB \| large, low quality loss - recommended \| \| [vietnamese-llama2-7b-40gb.Q5_K_M.gguf](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GGUF/blob/main/vietnamese-llama2-7b-40gb.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 4.87 GB\| 7.37 GB \| large, very low quality loss - recommended \| \| [vietnamese-llama2-7b-40gb.Q6_K.gguf](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GGUF/blob/main/vietnamese-llama2-7b-40gb.Q6_K.gguf) \| Q6_K \| 6 \| 5.63 GB\| 8.13 GB \| very large, extremely low quality loss \| \| [vietnamese-llama2-7b-40gb.Q8_0.gguf](https://huggingface.co/TheBloke/vietnamese-llama2-7B-40GB-GGUF/blob/main/vietnamese-llama2-7b-40gb.Q8_0.gguf) \| Q8_0 \| 8 \| 7.29 GB\| 9.79 GB \| very large, extremely low quality loss - not recommended \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/vietnamese-llama2-7B-40GB-GGUF and below it, a specific filename to download, such as: vietnamese-llama2-7b-40gb.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/vietnamese-llama2-7B-40GB-GGUF vietnamese-llama2-7b-40gb.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/vietnamese-llama2-7B-40GB-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/vietnamese-llama2-7B-40GB-GGUF vietnamese-llama2-7b-40gb.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 32 -m vietnamese-llama2-7b-40gb.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. ### How to load this model in Python code, using ctransformers #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install ctransformers # Or with CUDA GPU acceleration pip install ctransformers[cuda] # Or with AMD ROCm GPU acceleration (Linux only) CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers # Or with Metal GPU acceleration for macOS systems only CT_METAL=1 pip install ctransformers --no-binary ctransformers ``` #### Simple ctransformers example code ```python from ctransformers import AutoModelForCausalLM # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = AutoModelForCausalLM.from_pretrained("TheBloke/vietnamese-llama2-7B-40GB-GGUF", model_file="vietnamese-llama2-7b-40gb.Q4_K_M.gguf", model_type="llama", gpu_layers=50) print(llm("AI is going to")) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: BKAI-HUST Foundation Models Lab's Vietnamese Llama2 7B 40GB We employed [SentencePiece](https://github.com/google/sentencepiece) to retrain a Vietnamese tokenizer with a vocabulary size of 20K. No Vietnamese word segmentation was used. We then merged this vocabulary with the original one of Llama2, removing duplicate tokens. The new tokenizer significantly improves when encoding Vietnamese text, reducing the number of tokens by 50% compared to ChatGPT and approximately 70% compared to the original Llama2. We conducted a single-epoch continual pretraining, also known as incremental pretraining, using the Llama2-chat 7B model on a mixed dataset totaling 40.5 GB, comprised of: - 19 GB [NewsCorpus](https://github.com/binhvq/news-corpus) - 1.1 GB Vietnamese Wikipedia - 1.6 GB [Vietnamese books](https://www.kaggle.com/datasets/iambestfeeder/10000-vietnamese-books) - 4.5 GB Vietnamese legal documents (crawled from thuvienphapluat and processed by ourselves) - 2.1 GB Vietnamese legal text (from [C4-vi](https://huggingface.co/datasets/c4)) - 1.1 GB English Books (sub-sampled from [pg19](https://huggingface.co/datasets/pg19)) - 1.1 GB English Wikipedia (sub-sampled from 20220301.en wikipedia) - 10 GB English Text (sub-sampled from [C4-en](https://huggingface.co/datasets/c4)) We trained the model on a DGX A100 system, utilizing four GPU A100 in 10 days (about 1000 GPU hours). Hyperparameters are set as follows: - Training Regime: BFloat16 mixed precision - Lora Config: ``` { "base_model_name_or_path": "meta-llama/Llama-2-7b-chat-hf", "bias": "none", "enable_lora": null, "fan_in_fan_out": false, "inference_mode": true, "lora_alpha": 32.0, "lora_dropout": 0.05, "merge_weights": false, "modules_to_save": [ "embed_tokens", "lm_head" ], "peft_type": "LORA", "r": 8, "target_modules": [ "q_proj", "v_proj", "k_proj", "o_proj", "gate_proj", "down_proj", "up_proj" ], "task_type": "CAUSAL_LM" } ``` We also provide the [LoRA part](https://huggingface.co/bkai-foundation-models/vietnamese-llama2-7b-40GB/tree/main/pt_lora_model) so that you can integrate it with the original Llama2-chat-7b by yourself. Please note that this model requires further supervised fine-tuning (SFT) to be used in practice! Usage and other considerations: We refer to the [Llama 2](https://github.com/facebookresearch/llama) Training loss: <img src="figure/training_loss.png" alt="Training Loss Curve"/> Disclaimer This project is built upon Meta's Llama-2 model. It is essential to strictly adhere to the open-source license agreement of Llama-2 when using this model. If you incorporate third-party code, please ensure compliance with the relevant open-source license agreements. It's important to note that the content generated by the model may be influenced by various factors, such as calculation methods, random elements, and potential inaccuracies in quantification. Consequently, this project does not offer any guarantees regarding the accuracy of the model's outputs, and it disclaims any responsibility for consequences resulting from the use of the model's resources and its output. For those employing the models from this project for commercial purposes, developers must adhere to local laws and regulations to ensure the compliance of the model's output content. This project is not accountable for any products or services derived from such usage. Acknowledgments We extend our gratitude to PHPC - Phenikaa University and NVIDIA for their generous provision of computing resources for model training. Our appreciation also goes out to binhvq and the other authors for their diligent efforts in collecting and preparing the Vietnamese text corpus. <!-- original-model-card end -->
Loriiis/rl_course_vizdoom_health_gathering_supreme	Loriiis	2023-11-01T16:53:33Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-11-01T16:53:27Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 11.94 +/- 4.73 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r Loriiis/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
juliajoanna/sdxl-one_hot_encoding	juliajoanna	2023-11-01T16:52:31Z	2	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2023-11-01T16:41:17Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-xl-base-1.0 dataset: None tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers inference: true --- # Text-to-image finetuning - juliajoanna/sdxl-one_hot_encoding This pipeline was finetuned from stabilityai/stable-diffusion-xl-base-1.0 on the None dataset. Below are some example images generated with the finetuned pipeline using the following prompt: Fred is driving a car: ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
minoosh/finetuned_bert-base_on_shEMO_transcripts	minoosh	2023-11-01T16:37:55Z	14	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-11-01T16:21:14Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: finetuned_bert-base results: [] --- <!-- 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. --> # finetuned_bert-base This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2046 - Accuracy: 0.52 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.413 \| 1.0 \| 75 \| 1.3023 \| 0.4667 \| \| 1.2772 \| 2.0 \| 150 \| 1.2043 \| 0.52 \| \| 1.2019 \| 3.0 \| 225 \| 1.0879 \| 0.5733 \| \| 1.1463 \| 4.0 \| 300 \| 1.1124 \| 0.57 \| \| 1.1566 \| 5.0 \| 375 \| 1.1220 \| 0.5367 \| \| 1.1096 \| 6.0 \| 450 \| 1.0675 \| 0.5967 \| \| 0.9806 \| 7.0 \| 525 \| 1.0315 \| 0.64 \| \| 0.8715 \| 8.0 \| 600 \| 1.0616 \| 0.6 \| \| 0.8788 \| 9.0 \| 675 \| 1.1211 \| 0.59 \| \| 0.8071 \| 10.0 \| 750 \| 1.1400 \| 0.6 \| \| 0.6908 \| 11.0 \| 825 \| 1.1848 \| 0.6033 \| \| 0.6244 \| 12.0 \| 900 \| 1.2255 \| 0.59 \| \| 0.628 \| 13.0 \| 975 \| 1.2264 \| 0.6 \| \| 0.6003 \| 14.0 \| 1050 \| 1.2270 \| 0.6033 \| \| 0.5283 \| 15.0 \| 1125 \| 1.2399 \| 0.5933 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
JDCollier89/ppo-LunarLander-v2	JDCollier89	2023-11-01T16:34:58Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-11-01T16:34:38Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 258.01 +/- 13.46 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
minoosh/finetuned_roberta-base_on_shEMO_transcripts	minoosh	2023-11-01T16:15:49Z	4	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-11-01T15:59:36Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: finetuned_roberta-base results: [] --- <!-- 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. --> # finetuned_roberta-base This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2574 - Accuracy: 0.6033 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.48 \| 1.0 \| 75 \| 1.4001 \| 0.41 \| \| 1.2847 \| 2.0 \| 150 \| 1.1993 \| 0.58 \| \| 1.1522 \| 3.0 \| 225 \| 1.0007 \| 0.6333 \| \| 0.9921 \| 4.0 \| 300 \| 0.9189 \| 0.66 \| \| 0.9104 \| 5.0 \| 375 \| 0.8855 \| 0.69 \| \| 0.8371 \| 6.0 \| 450 \| 0.9431 \| 0.6767 \| \| 0.699 \| 7.0 \| 525 \| 0.9500 \| 0.6633 \| \| 0.6872 \| 8.0 \| 600 \| 0.9728 \| 0.7033 \| \| 0.5867 \| 9.0 \| 675 \| 0.9939 \| 0.6867 \| \| 0.5323 \| 10.0 \| 750 \| 1.1115 \| 0.69 \| \| 0.4066 \| 11.0 \| 825 \| 1.2031 \| 0.6667 \| \| 0.3517 \| 12.0 \| 900 \| 1.2193 \| 0.65 \| \| 0.3114 \| 13.0 \| 975 \| 1.2281 \| 0.67 \| \| 0.3102 \| 14.0 \| 1050 \| 1.2691 \| 0.67 \| \| 0.2681 \| 15.0 \| 1125 \| 1.2818 \| 0.6633 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
trymtv/bert_parliament_classifier_large	trymtv	2023-11-01T16:14:52Z	0	0	peft	[ "peft", "region:us" ]	null	2023-11-01T16:14:21Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0
MakAttack/6540dc2059df1fe983c03af2	MakAttack	2023-11-01T16:11:52Z	3	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2023-11-01T15:16:02Z	--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks dog tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - MakAttack/6540dc2059df1fe983c03af2 These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
Pradeep/Twitter-777	Pradeep	2023-11-01T16:09:52Z	3	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-11-01T16:04:04Z	--- license: apache-2.0 --- Used Customer Support chat texts to build this model
GluonsGluonsGluons/Taxi-v3	GluonsGluonsGluons	2023-11-01T15:59:52Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-11-01T15:59:51Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.76 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="GluonsGluonsGluons/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
arodriguezmiraltabank/LLM_toy	arodriguezmiraltabank	2023-11-01T15:59:00Z	0	0	null	[ "license:cc-by-nc-sa-4.0", "region:us" ]	null	2023-11-01T15:59:00Z	--- license: cc-by-nc-sa-4.0 ---
ai-maker-space/Transformers-Workshop-BERT-NewsGroupClassification	ai-maker-space	2023-11-01T15:50:36Z	5	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-11-01T15:50:15Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: Transformers-Workshop-BERT-NewsGroupClassification results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Transformers-Workshop-BERT-NewsGroupClassification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: ## 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1908, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results ### Framework versions - Transformers 4.34.1 - TensorFlow 2.14.0 - Datasets 2.14.6 - Tokenizers 0.14.1
ai-maker-space/Transformers-Workshop-BART-Summarization	ai-maker-space	2023-11-01T15:47:40Z	13	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "generated_from_trainer", "summarization", "dataset:cnn_dailymail", "base_model:facebook/bart-base", "base_model:finetune:facebook/bart-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2023-11-01T15:23:16Z	--- license: apache-2.0 base_model: facebook/bart-base tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-base-finetuned-CNN-DailyNews results: [] pipeline_tag: summarization datasets: - cnn_dailymail --- <!-- 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. --> # bart-base-finetuned-CNN-DailyNews This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9584 - Rouge1: 0.1977 - Rouge2: 0.1321 - Rougel: 0.1792 - Rougelsum: 0.1884 ## 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: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\| \| 2.6767 \| 1.0 \| 63 \| 1.8911 \| 0.1745 \| 0.0915 \| 0.1536 \| 0.1644 \| \| 2.0691 \| 2.0 \| 126 \| 1.5904 \| 0.1777 \| 0.1003 \| 0.1579 \| 0.1677 \| \| 1.8047 \| 3.0 \| 189 \| 1.3652 \| 0.1778 \| 0.1029 \| 0.1587 \| 0.1663 \| \| 1.6345 \| 4.0 \| 252 \| 1.2317 \| 0.1959 \| 0.1226 \| 0.1751 \| 0.1842 \| \| 1.4837 \| 5.0 \| 315 \| 1.1099 \| 0.2015 \| 0.1265 \| 0.1796 \| 0.1911 \| \| 1.3904 \| 6.0 \| 378 \| 1.0267 \| 0.2004 \| 0.1278 \| 0.1799 \| 0.1893 \| \| 1.2876 \| 7.0 \| 441 \| 0.9788 \| 0.1978 \| 0.1307 \| 0.1784 \| 0.1878 \| \| 1.2578 \| 8.0 \| 504 \| 0.9584 \| 0.1977 \| 0.1321 \| 0.1792 \| 0.1884 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
gokuls/HBERTv1_L12_H768_A12_ffn_2	gokuls	2023-11-01T15:47:04Z	3	0	transformers	[ "transformers", "pytorch", "hybridbert", "fill-mask", "generated_from_trainer", "dataset:gokuls/wiki_book_corpus_complete_processed_bert_dataset", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-10-30T08:18:01Z	--- tags: - generated_from_trainer datasets: - gokuls/wiki_book_corpus_complete_processed_bert_dataset metrics: - accuracy model-index: - name: HBERTv1_L12_H768_A12_ffn_2 results: - task: name: Masked Language Modeling type: fill-mask dataset: name: gokuls/wiki_book_corpus_complete_processed_bert_dataset type: gokuls/wiki_book_corpus_complete_processed_bert_dataset metrics: - name: Accuracy type: accuracy value: 0.4755208001727227 --- <!-- 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. --> # HBERTv1_L12_H768_A12_ffn_2 This model is a fine-tuned version of [](https://huggingface.co/) on the gokuls/wiki_book_corpus_complete_processed_bert_dataset dataset. It achieves the following results on the evaluation set: - Loss: 3.0254 - Accuracy: 0.4755 ## 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: 1e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10000 - num_epochs: 100 ### Training results ### Framework versions - Transformers 4.34.1 - Pytorch 1.14.0a0+410ce96 - Datasets 2.14.6 - Tokenizers 0.14.1
Beaverlennium/Karlijn	Beaverlennium	2023-11-01T15:44:55Z	0	0	null	[ "nl", "en", "license:unlicense", "region:us" ]	null	2023-11-01T15:41:52Z	--- license: unlicense language: - nl - en ---
x6381518/project2	x6381518	2023-11-01T15:43:11Z	1	0	peft	[ "peft", "region:us" ]	null	2023-10-05T21:04:33Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0 - PEFT 0.5.0
longface/logicalreasoning-task-model-flanT5	longface	2023-11-01T15:41:05Z	0	0	peft	[ "peft", "region:us" ]	null	2023-11-01T15:41:04Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0
maddes8cht/mosaicml-mpt-7b-gguf	maddes8cht	2023-11-01T15:36:58Z	78	0	null	[ "gguf", "Composer", "MosaicML", "llm-foundry", "StreamingDatasets", "dataset:mc4", "dataset:c4", "dataset:togethercomputer/RedPajama-Data-1T", "dataset:bigcode/the-stack", "dataset:allenai/s2orc", "arxiv:2108.12409", "arxiv:2302.13971", "arxiv:2205.14135", "arxiv:2010.04245", "arxiv:1909.08053", "arxiv:2302.06675", "license:apache-2.0", "region:us" ]	null	2023-10-25T13:10:11Z	--- license: apache-2.0 tags: - Composer - MosaicML - llm-foundry - StreamingDatasets datasets: - mc4 - c4 - togethercomputer/RedPajama-Data-1T - bigcode/the-stack - allenai/s2orc inference: false --- [![banner](https://maddes8cht.github.io/assets/buttons/Huggingface-banner.jpg)]() I'm constantly enhancing these model descriptions to provide you with the most relevant and comprehensive information # mpt-7b - GGUF - Model creator: [mosaicml](https://huggingface.co/mosaicml) - Original model: [mpt-7b](https://huggingface.co/mosaicml/mpt-7b) MPT-7b and MPT-30B are part of the family of Mosaic Pretrained Transformer (MPT) models, which use a modified transformer architecture optimized for efficient training and inference. --- # Brief MPT-7B and MPT-30B are the Base models of the MPT Family. --- # About GGUF format `gguf` is the current file format used by the [`ggml`](https://github.com/ggerganov/ggml) library. A growing list of Software is using it and can therefore use this model. The core project making use of the ggml library is the [llama.cpp](https://github.com/ggerganov/llama.cpp) project by Georgi Gerganov # Quantization variants There is a bunch of quantized files available to cater to your specific needs. Here's how to choose the best option for you: # Legacy quants Q4_0, Q4_1, Q5_0, Q5_1 and Q8 are `legacy` quantization types. Nevertheless, they are fully supported, as there are several circumstances that cause certain model not to be compatible with the modern K-quants. ## Note: Now there's a new option to use K-quants even for previously 'incompatible' models, although this involves some fallback solution that makes them not real K-quants. More details can be found in affected model descriptions. (This mainly refers to Falcon 7b and Starcoder models) # K-quants K-quants are designed with the idea that different levels of quantization in specific parts of the model can optimize performance, file size, and memory load. So, if possible, use K-quants. With a Q6_K, you'll likely find it challenging to discern a quality difference from the original model - ask your model two times the same question and you may encounter bigger quality differences. --- # Original Model Card: # MPT-7B MPT-7B is a decoder-style transformer pretrained from scratch on 1T tokens of English text and code. This model was trained by [MosaicML](https://www.mosaicml.com). MPT-7B is part of the family of MosaicPretrainedTransformer (MPT) models, which use a modified transformer architecture optimized for efficient training and inference. These architectural changes include performance-optimized layer implementations and the elimination of context length limits by replacing positional embeddings with Attention with Linear Biases ([ALiBi](https://arxiv.org/abs/2108.12409)). Thanks to these modifications, MPT models can be trained with high throughput efficiency and stable convergence. MPT models can also be served efficiently with both standard HuggingFace pipelines and NVIDIA's [FasterTransformer](https://github.com/NVIDIA/FasterTransformer). This model uses the MosaicML LLM codebase, which can be found in the [llm-foundry repository](https://github.com/mosaicml/llm-foundry). It was trained by MosaicML’s NLP team on the [MosaicML platform](https://www.mosaicml.com/training) for LLM pretraining, finetuning, and inference. ### How is this model different? MPT-7B is * Licensed for the possibility of commercial use (unlike [LLaMA](https://arxiv.org/abs/2302.13971)). * Trained on a large amount of data (1T tokens like [LLaMA](https://arxiv.org/abs/2302.13971) vs. 300B for [Pythia](https://github.com/EleutherAI/pythia), 300B for [OpenLLaMA](https://github.com/openlm-research/open_llama), and 800B for [StableLM](https://github.com/Stability-AI/StableLM)). * Prepared to handle extremely long inputs thanks to [ALiBi](https://arxiv.org/abs/2108.12409) (we finetuned [MPT-7B-StoryWriter-65k+](https://huggingface.co/mosaicml/mpt-7b-storywriter) on up to 65k inputs and can handle up to 84k vs. 2k-4k for other open source models). * Capable of fast training and inference (via [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) and [FasterTransformer](https://github.com/NVIDIA/FasterTransformer)) * Equipped with highly efficient open-source training code via the [llm-foundry repository](https://github.com/mosaicml/llm-foundry) ### Models finetuned off MPT-7B: The following models are finetuned on MPT-7B: * [MPT-7B-StoryWriter-65k+](https://huggingface.co/mosaicml/mpt-7b-storywriter): a model designed to read and write fictional stories with super long context lengths. Built by finetuning MPT-7B with a context length of 65k tokens on a filtered fiction subset of the [books3 dataset](https://huggingface.co/datasets/the_pile_books3). At inference time, thanks to [ALiBi](https://arxiv.org/abs/2108.12409), MPT-7B-StoryWriter-65k+ can extrapolate even beyond 65k tokens. We demonstrate generations as long as 80k tokens on a single A100-80GB GPU in our [blogpost](www.mosaicml.com/blog/mpt-7b). * License: Apache 2.0 * [MPT-7B-Instruct](https://huggingface.co/mosaicml/mpt-7b-instruct): a model for short-form instruction following. Built by finetuning MPT-7B on a [dataset](https://huggingface.co/datasets/mosaicml/dolly_hhrlhf) we also release, derived from the [Databricks Dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) and the [Anthropic Helpful and Harmless (HH-RLHF)](https://huggingface.co/datasets/Anthropic/hh-rlhf) datasets. * License: _CC-By-SA-3.0_ * [MPT-7B-Chat](https://huggingface.co/mosaicml/mpt-7b-chat): a chatbot-like model for dialogue generation. Built by finetuning MPT-7B on the [ShareGPT-Vicuna](https://huggingface.co/datasets/jeffwan/sharegpt_vicuna), [HC3](https://huggingface.co/datasets/Hello-SimpleAI/HC3), [Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca), [HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf), and [Evol-Instruct](https://huggingface.co/datasets/victor123/evol_instruct_70k) datasets. * License: _CC-By-NC-SA-4.0_ ## Model Date May 5, 2023 ## Model License Apache-2.0 ## Documentation * [Blog post: Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs](https://www.mosaicml.com/blog/mpt-7b) * [Codebase (mosaicml/llm-foundry repo)](https://github.com/mosaicml/llm-foundry/) * Questions: Feel free to contact us via the [MosaicML Community Slack](https://mosaicml.me/slack)! ## How to Use This model is best used with the MosaicML [llm-foundry repository](https://github.com/mosaicml/llm-foundry) for training and finetuning. ```python import transformers model = transformers.AutoModelForCausalLM.from_pretrained( 'mosaicml/mpt-7b', trust_remote_code=True ) ``` Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method. This is because we use a custom `MPT` model architecture that is not yet part of the Hugging Face `transformers` package. `MPT` includes options for many training efficiency features such as [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi](https://arxiv.org/abs/2108.12409), [QK LayerNorm](https://arxiv.org/abs/2010.04245), and more. To use the optimized [triton implementation](https://github.com/openai/triton) of FlashAttention, you can load the model on GPU (`cuda:0`) with `attn_impl='triton'` and with `bfloat16` precision: ```python import torch import transformers name = 'mosaicml/mpt-7b' config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True) config.attn_config['attn_impl'] = 'triton' config.init_device = 'cuda:0' # For fast initialization directly on GPU! model = transformers.AutoModelForCausalLM.from_pretrained( name, config=config, torch_dtype=torch.bfloat16, # Load model weights in bfloat16 trust_remote_code=True ) ``` Although the model was trained with a sequence length of 2048, ALiBi enables users to increase the maximum sequence length during finetuning and/or inference. For example: ```python import transformers name = 'mosaicml/mpt-7b' config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True) config.max_seq_len = 4096 # (input + output) tokens can now be up to 4096 model = transformers.AutoModelForCausalLM.from_pretrained( name, config=config, trust_remote_code=True ) ``` This model was trained with the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. ```python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b') ``` The model can then be used, for example, within a text-generation pipeline. Note: when running Torch modules in lower precision, it is best practice to use the [torch.autocast context manager](https://pytorch.org/docs/stable/amp.html). ```python from transformers import pipeline pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, device='cuda:0') with torch.autocast('cuda', dtype=torch.bfloat16): print( pipe('Here is a recipe for vegan banana bread:\n', max_new_tokens=100, do_sample=True, use_cache=True)) ``` ## Model Description The architecture is a modification of a standard decoder-only transformer. The model has been modified from a standard transformer in the following ways: * It uses [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) * It uses [ALiBi (Attention with Linear Biases)](https://arxiv.org/abs/2108.12409) and does not use positional embeddings * It does not use biases \| Hyperparameter \| Value \| \|----------------\|-------\| \|n_parameters \| 6.7B \| \|n_layers \| 32 \| \| n_heads \| 32 \| \| d_model \| 4096 \| \| vocab size \| 50432 \| \| sequence length \| 2048 \| ## Training Data ### Streaming Datasets Data was formatted using the MosaicML [StreamingDataset](https://github.com/mosaicml/streaming) library to host our data in object storage and efficiently stream it to our compute cluster during training. StreamingDataset obviates the need to download the whole dataset before starting training, and allows instant resumption of training from any point in the dataset. ### Data Mix The model was trained for 1T tokens (with batch size 1760 and sequence length 2048). It was trained on the following data mix: \| Data Source \| Number of Tokens in Source \| Proportion \| Effective Number of Tokens \| Epochs \| \|-------------\|----------------------------\|------------\|----------------------------\|--------\| \| mC4 3.1.0 - English \| 417.99 B \| 0.33 \| 330 B \| 0.14 \| \| C4 - English - SemDedup 80% \| 100.42 B \| 0.299 \| 299 B \| 2.98 \| \| RedPajama - CommonCrawl \| 878.45 B \| 0.1 \| 100 B \| 0.11 \| \| The Stack - Selected Languages \| 463.78 B \| 0.1 \| 100 B \| 0.22 \| \| RedPajama - Wikipedia - En \| 4.87 B \| 0.04 \| 40 B \| 8.21 \| \| The Stack - Markdown \| 107.07 B \| 0.035 \| 35 B \| 0.33 \| \| S2ORC \| 48.85 B \| 0.033 \| 33 B \| 0.68 \| \| RedPajama - Books \| 26.02 B \| 0.03 \| 30B \| 1.15 \| \| RedPajama - arXiv \| 28.10 B \| 0.019 \| 19 B \| 0.68 \| \| RedPajama - StackExchange \| 20.54 B \| 0.014 \| 14 B \|0.68 \| Samples for each batch were selected from one of the datasets with the probability specified above. The examples were shuffled within each dataset, and 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. This BPE tokenizer has a number of desirable characteristics, most of which are relevant for tokenizing code: (1) It was trained on a diverse mix of data that includes code (The Pile) (2) It applies consistent space delimitation, unlike the GPT2 tokenizer which tokenizes inconsistently depending on the presence of prefix spaces (3) It contains tokens for repeated space characters, which allows superior compression of text with large amounts of repeated space characters. The model vocabulary size of 50432 was set to be a multiple of 128 (as in [MEGATRON-LM](https://arxiv.org/abs/1909.08053)), model flop utilization (MFU) increased by up to four percentage points. ### Training Configuration This model was trained on 440 A100-40GBs for about 9.5 days using the [MosaicML Platform](https://www.mosaicml.com/platform). The model was trained with sharded data parallelism using [FSDP](https://pytorch.org/docs/stable/fsdp.html) and used the [LION](https://arxiv.org/abs/2302.06675) optimizer. ## Limitations and Biases _The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_ MPT-7B (Base) is not intended for deployment without finetuning. It should not be used for human-facing interactions without further guardrails and user consent. MPT-7B can produce factually incorrect output, and should not be relied on to produce factually accurate information. MPT-7B was trained on various public datasets. While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs. ## MosaicML Platform If you're interested in [training](https://www.mosaicml.com/training) and [deploying](https://www.mosaicml.com/inference) your own MPT or LLMs on the MosaicML Platform, [sign up here](https://forms.mosaicml.com/demo?utm_source=huggingface&utm_medium=referral&utm_campaign=mpt-7b). ## 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. ## Citation Please cite this model using the following format: ``` @online{MosaicML2023Introducing, author = {MosaicML NLP Team}, title = {Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs}, year = {2023}, url = {www.mosaicml.com/blog/mpt-7b}, note = {Accessed: 2023-05-05}, urldate = {2023-05-05} } ``` *End of original Model File* --- ## Please consider to support my work Coming Soon: I'm in the process of launching a sponsorship/crowdfunding campaign for my work. I'm evaluating Kickstarter, Patreon, or the new GitHub Sponsors platform, and I am hoping for some support and contribution to the continued availability of these kind of models. Your support will enable me to provide even more valuable resources and maintain the models you rely on. Your patience and ongoing support are greatly appreciated as I work to make this page an even more valuable resource for the community. <center> [![GitHub](https://maddes8cht.github.io/assets/buttons/github-io-button.png)](https://maddes8cht.github.io) [![Stack Exchange](https://stackexchange.com/users/flair/26485911.png)](https://stackexchange.com/users/26485911) [![GitHub](https://maddes8cht.github.io/assets/buttons/github-button.png)](https://github.com/maddes8cht) [![HuggingFace](https://maddes8cht.github.io/assets/buttons/huggingface-button.png)](https://huggingface.co/maddes8cht) [![Twitter](https://maddes8cht.github.io/assets/buttons/twitter-button.png)](https://twitter.com/maddes1966) </center>
maddes8cht/mosaicml-mpt-7b-storywriter-gguf	maddes8cht	2023-11-01T15:36:56Z	579	3	null	[ "gguf", "Composer", "MosaicML", "llm-foundry", "dataset:the_pile_books3", "arxiv:2108.12409", "arxiv:2205.14135", "arxiv:2302.06675", "license:apache-2.0", "region:us" ]	null	2023-10-17T20:18:26Z	--- license: apache-2.0 tags: - Composer - MosaicML - llm-foundry datasets: - the_pile_books3 inference: false --- [![banner](https://maddes8cht.github.io/assets/buttons/Huggingface-banner.jpg)]() I'm constantly enhancing these model descriptions to provide you with the most relevant and comprehensive information # mpt-7b-storywriter - GGUF - Model creator: [mosaicml](https://huggingface.co/mosaicml) - Original model: [mpt-7b-storywriter](https://huggingface.co/mosaicml/mpt-7b-storywriter) MPT-7b and MPT-30B are part of the family of Mosaic Pretrained Transformer (MPT) models, which use a modified transformer architecture optimized for efficient training and inference. --- # Brief MPT-7B Storywriter is a Model based on MPT-7b, designed to read and write fictional stories with super long context lengths. --- # About GGUF format `gguf` is the current file format used by the [`ggml`](https://github.com/ggerganov/ggml) library. A growing list of Software is using it and can therefore use this model. The core project making use of the ggml library is the [llama.cpp](https://github.com/ggerganov/llama.cpp) project by Georgi Gerganov # Quantization variants There is a bunch of quantized files available to cater to your specific needs. Here's how to choose the best option for you: # Legacy quants Q4_0, Q4_1, Q5_0, Q5_1 and Q8 are `legacy` quantization types. Nevertheless, they are fully supported, as there are several circumstances that cause certain model not to be compatible with the modern K-quants. ## Note: Now there's a new option to use K-quants even for previously 'incompatible' models, although this involves some fallback solution that makes them not real K-quants. More details can be found in affected model descriptions. (This mainly refers to Falcon 7b and Starcoder models) # K-quants K-quants are designed with the idea that different levels of quantization in specific parts of the model can optimize performance, file size, and memory load. So, if possible, use K-quants. With a Q6_K, you'll likely find it challenging to discern a quality difference from the original model - ask your model two times the same question and you may encounter bigger quality differences. --- # Original Model Card: # MPT-7B-StoryWriter-65k+ MPT-7B-StoryWriter-65k+ is a model designed to read and write fictional stories with super long context lengths. It was built by finetuning MPT-7B with a context length of 65k tokens on a filtered fiction subset of the [books3 dataset](https://huggingface.co/datasets/the_pile_books3). At inference time, thanks to [ALiBi](https://arxiv.org/abs/2108.12409), MPT-7B-StoryWriter-65k+ can extrapolate even beyond 65k tokens. We demonstrate generations as long as 84k tokens on a single node of 8 A100-80GB GPUs in our [blogpost](https://www.mosaicml.com/blog/mpt-7b). * License: Apache 2.0 This model was trained by [MosaicML](https://www.mosaicml.com) and follows a modified decoder-only transformer architecture. ## Model Date May 5, 2023 ## Model License Apache 2.0 ## Documentation * [Blog post: Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs](https://www.mosaicml.com/blog/mpt-7b) * [Codebase (mosaicml/llm-foundry repo)](https://github.com/mosaicml/llm-foundry/) * Questions: Feel free to contact us via the [MosaicML Community Slack](https://mosaicml.me/slack)! ## 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 that is not yet part of the `transformers` package. It 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-7b-storywriter', trust_remote_code=True ) ``` To use the optimized [triton implementation](https://github.com/openai/triton) of FlashAttention, you can load the model on GPU (`cuda:0`) with `attn_impl='triton'` and with `bfloat16` precision: ```python import torch import transformers name = 'mosaicml/mpt-7b-storywriter' config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True) config.attn_config['attn_impl'] = 'triton' config.init_device = 'cuda:0' # For fast initialization directly on GPU! model = transformers.AutoModelForCausalLM.from_pretrained( name, config=config, torch_dtype=torch.bfloat16, # Load model weights in bfloat16 trust_remote_code=True ) ``` Although the model was trained with a sequence length of 2048 and finetuned with a sequence length of 65536, ALiBi enables users to increase the maximum sequence length during finetuning and/or inference. For example: ```python import transformers name = 'mosaicml/mpt-7b' config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True) config.max_seq_len = 83968 # (input + output) tokens can now be up to 83968 model = transformers.AutoModelForCausalLM.from_pretrained( name, config=config, trust_remote_code=True ) ``` This model was trained with the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. ```python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b") ``` The model can then be used, for example, within a text-generation pipeline. Note: when running Torch modules in lower precision, it is best practice to use the [torch.autocast context manager](https://pytorch.org/docs/stable/amp.html). ```python from transformers import pipeline pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, device='cuda:0') with torch.autocast('cuda', dtype=torch.bfloat16): print( pipe('Here is a recipe for vegan banana bread:\n', max_new_tokens=100, do_sample=True, use_cache=True)) ``` ## Community-Created Resources These were not created by MosaicML, but you may find them useful. These links are not an endorsement of the creators or their content. - [Oobabooga Running MPT-7B-Storywriter](https://youtu.be/QVVb6Md6huA) - [NEW MPT-7B-StoryWriter CRUSHES GPT-4!](https://www.youtube.com/watch?v=O9Y_ZdsuKWQ&t=649s) - Has a long section on running locally using Oobabooga ## Example Epilogue The full text of the _The Great Gatsby_ (67873 tokens) was fed to the model, followed by the text "EPILOGUE" TheRealFitzgerald233: > ... > > So we beat on, boats against the current, borne back ceaselessly into the past > > EPILOGUE MPT-7B-StoryWriter: >I have lived so long in the shadow of this dream that I may never see the bright sunlight of reality. I may look up some morning and find that I am six feet under, and that Gatsby, who gave me the dream and everything else, will have vanished behind me. > >And so it was. I had hardly crossed the threshold of the old world when my eye caught the headlines of a newspaper, and I knew at once that my dream was over. I knew that Gatsby was dead. > >And I knew that day that I didn't love her. > >That night I took the last train for West Egg, and when I reached home I went directly to my attic and brought down all Gatsby's letters. > >I have sorted them by date and placed them in the order in which they were written. They are very few in number. > >They are dated between April of nineteen eighteen and January of nineteen nineteen, and the last one I have here is the one that told me he was dead. > >There is something in that last letter that frightens me. It is the sense of a finality, of a completeness. I never heard of him after that, and I was glad. > >But a few months ago I read somewhere that he had been seen in New York. He had become a great man. > >And I knew that he had not changed at all. ## Model Description The architecture is a modification of a standard decoder-only transformer. The model has been modified from a standard transformer in the following ways: * It uses [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) * It uses [ALiBi (Attention with Linear Biases)](https://arxiv.org/abs/2108.12409) and does not use positional embeddings * It does not use biases \| Hyperparameter \| Value \| \|----------------\|-------\| \|n_parameters \| 6.7B \| \|n_layers \| 32 \| \| n_heads \| 32 \| \| d_model \| 4096 \| \| vocab size \| 50432 \| \| sequence length \| 65536 \| ## PreTraining Data For more details on the pretraining process, see [MPT-7B](https://huggingface.co/mosaicml/mpt-7b). 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 8 A100-80GBs for about 2 days using the [MosaicML Platform](https://www.mosaicml.com/platform). The model was trained with sharded data parallelism using [FSDP](https://pytorch.org/docs/stable/fsdp.html) and used the [LION](https://arxiv.org/abs/2302.06675) optimizer. ## Limitations and Biases _The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_ MPT-7B-StoryWriter can produce factually incorrect output, and should not be relied on to produce factually accurate information. MPT-7B-StoryWriter was trained on various public datasets. While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs. ## Acknowledgements This model was finetuned by Alex Trott and the MosaicML NLP team ## MosaicML Platform If you're interested in [training](https://www.mosaicml.com/training) and [deploying](https://www.mosaicml.com/inference) your own MPT or LLMs on the MosaicML Platform, [sign up here](https://forms.mosaicml.com/demo?utm_source=huggingface&utm_medium=referral&utm_campaign=mpt-7b). ## 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. ## Citation Please cite this model using the following format: ``` @online{MosaicML2023Introducing, author = {MosaicML NLP Team}, title = {Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs}, year = {2023}, url = {www.mosaicml.com/blog/mpt-7b}, note = {Accessed: 2023-03-28}, % change this date urldate = {2023-03-28} % change this date } ``` *End of original Model File* --- ## Please consider to support my work Coming Soon: I'm in the process of launching a sponsorship/crowdfunding campaign for my work. I'm evaluating Kickstarter, Patreon, or the new GitHub Sponsors platform, and I am hoping for some support and contribution to the continued availability of these kind of models. Your support will enable me to provide even more valuable resources and maintain the models you rely on. Your patience and ongoing support are greatly appreciated as I work to make this page an even more valuable resource for the community. <center> [![GitHub](https://maddes8cht.github.io/assets/buttons/github-io-button.png)](https://maddes8cht.github.io) [![Stack Exchange](https://stackexchange.com/users/flair/26485911.png)](https://stackexchange.com/users/26485911) [![GitHub](https://maddes8cht.github.io/assets/buttons/github-button.png)](https://github.com/maddes8cht) [![HuggingFace](https://maddes8cht.github.io/assets/buttons/huggingface-button.png)](https://huggingface.co/maddes8cht) [![Twitter](https://maddes8cht.github.io/assets/buttons/twitter-button.png)](https://twitter.com/maddes1966) </center>
maddes8cht/mosaicml-mpt-7b-instruct-gguf	maddes8cht	2023-11-01T15:36:54Z	170	1	null	[ "gguf", "Composer", "MosaicML", "llm-foundry", "dataset:mosaicml/dolly_hhrlhf", "arxiv:2205.14135", "arxiv:2108.12409", "arxiv:2010.04245", "license:cc-by-sa-3.0", "region:us" ]	null	2023-10-26T22:12:33Z	--- license: cc-by-sa-3.0 datasets: - mosaicml/dolly_hhrlhf tags: - Composer - MosaicML - llm-foundry inference: false --- [![banner](https://maddes8cht.github.io/assets/buttons/Huggingface-banner.jpg)]() I'm constantly enhancing these model descriptions to provide you with the most relevant and comprehensive information # mpt-7b-instruct - GGUF - Model creator: [mosaicml](https://huggingface.co/mosaicml) - Original model: [mpt-7b-instruct](https://huggingface.co/mosaicml/mpt-7b-instruct) MPT-7b and MPT-30B are part of the family of Mosaic Pretrained Transformer (MPT) models, which use a modified transformer architecture optimized for efficient training and inference. # About GGUF format `gguf` is the current file format used by the [`ggml`](https://github.com/ggerganov/ggml) library. A growing list of Software is using it and can therefore use this model. The core project making use of the ggml library is the [llama.cpp](https://github.com/ggerganov/llama.cpp) project by Georgi Gerganov # Quantization variants There is a bunch of quantized files available to cater to your specific needs. Here's how to choose the best option for you: # Legacy quants Q4_0, Q4_1, Q5_0, Q5_1 and Q8 are `legacy` quantization types. Nevertheless, they are fully supported, as there are several circumstances that cause certain model not to be compatible with the modern K-quants. ## Note: Now there's a new option to use K-quants even for previously 'incompatible' models, although this involves some fallback solution that makes them not real K-quants. More details can be found in affected model descriptions. (This mainly refers to Falcon 7b and Starcoder models) # K-quants K-quants are designed with the idea that different levels of quantization in specific parts of the model can optimize performance, file size, and memory load. So, if possible, use K-quants. With a Q6_K, you'll likely find it challenging to discern a quality difference from the original model - ask your model two times the same question and you may encounter bigger quality differences. --- # Original Model Card: # MPT-7B-Instruct MPT-7B-Instruct is a model for short-form instruction following. It is built by finetuning [MPT-7B](https://huggingface.co/mosaicml/mpt-7b) on a [dataset](https://huggingface.co/datasets/sam-mosaic/dolly_hhrlhf) derived from the [Databricks Dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) and the [Anthropic Helpful and Harmless (HH-RLHF)](https://huggingface.co/datasets/Anthropic/hh-rlhf) datasets. * License: _CC-By-SA-3.0_ * [Demo on Hugging Face Spaces](https://huggingface.co/spaces/mosaicml/mpt-7b-instruct) This model was trained by [MosaicML](https://www.mosaicml.com) and follows a modified decoder-only transformer architecture. ## Model Date May 5, 2023 ## Model License CC-By-SA-3.0 ## Documentation * [Blog post: Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs](https://www.mosaicml.com/blog/mpt-7b) * [Codebase (mosaicml/llm-foundry repo)](https://github.com/mosaicml/llm-foundry/) * Questions: Feel free to contact us via the [MosaicML Community Slack](https://mosaicml.me/slack)! ### Example Question/Instruction Longboi24: > What is a quoll? MPT-7B-Instruct: >A Quoll (pronounced “cool”) is one of Australia’s native carnivorous marsupial mammals, which are also known as macropods or wallabies in other parts around Asia and South America ## 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 that is not yet part of the `transformers` package. It 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-7b-instruct', trust_remote_code=True ) ``` Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method. This is because we use a custom `MPT` model architecture that is not yet part of the Hugging Face `transformers` package. `MPT` includes options for many training efficiency features such as [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi](https://arxiv.org/abs/2108.12409), [QK LayerNorm](https://arxiv.org/abs/2010.04245), and more. To use the optimized [triton implementation](https://github.com/openai/triton) of FlashAttention, you can load the model on GPU (`cuda:0`) with `attn_impl='triton'` and with `bfloat16` precision: ```python import torch import transformers name = 'mosaicml/mpt-7b-instruct' config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True) config.attn_config['attn_impl'] = 'triton' config.init_device = 'cuda:0' # For fast initialization directly on GPU! model = transformers.AutoModelForCausalLM.from_pretrained( name, config=config, torch_dtype=torch.bfloat16, # Load model weights in bfloat16 trust_remote_code=True ) ``` Although the model was trained with a sequence length of 2048, ALiBi enables users to increase the maximum sequence length during finetuning and/or inference. For example: ```python import transformers name = 'mosaicml/mpt-7b-instruct' config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True) config.max_seq_len = 4096 # (input + output) tokens can now be up to 4096 model = transformers.AutoModelForCausalLM.from_pretrained( name, config=config, trust_remote_code=True ) ``` This model was trained with the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. ```python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b") ``` The model can then be used, for example, within a text-generation pipeline. Note: when running Torch modules in lower precision, it is best practice to use the [torch.autocast context manager](https://pytorch.org/docs/stable/amp.html). ```python from transformers import pipeline pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, device='cuda:0') with torch.autocast('cuda', dtype=torch.bfloat16): print( pipe('Here is a recipe for vegan banana bread:\n', max_new_tokens=100, do_sample=True, use_cache=True)) ``` ### Formatting This model was trained on data formatted in the dolly-15k format: ```python INSTRUCTION_KEY = "### Instruction:" RESPONSE_KEY = "### Response:" INTRO_BLURB = "Below is an instruction that describes a task. Write a response that appropriately completes the request." PROMPT_FOR_GENERATION_FORMAT = """{intro} {instruction_key} {instruction} {response_key} """.format( intro=INTRO_BLURB, instruction_key=INSTRUCTION_KEY, instruction="{instruction}", response_key=RESPONSE_KEY, ) example = "James decides to run 3 sprints 3 times a week. He runs 60 meters each sprint. How many total meters does he run a week? Explain before answering." fmt_ex = PROMPT_FOR_GENERATION_FORMAT.format(instruction=example) ``` In the above example, `fmt_ex` is ready to be tokenized and sent through the model. ## Model Description The architecture is a modification of a standard decoder-only transformer. The model has been modified from a standard transformer in the following ways: * It uses [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) * It uses [ALiBi (Attention with Linear Biases)](https://arxiv.org/abs/2108.12409) and does not use positional embeddings * It does not use biases \| Hyperparameter \| Value \| \|----------------\|-------\| \|n_parameters \| 6.7B \| \|n_layers \| 32 \| \| n_heads \| 32 \| \| d_model \| 4096 \| \| vocab size \| 50432 \| \| sequence length \| 2048 \| ## PreTraining Data For more details on the pretraining process, see [MPT-7B](https://huggingface.co/mosaicml/mpt-7b). 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 8 A100-40GBs for about 2.3 hours using the [MosaicML Platform](https://www.mosaicml.com/platform). The model was trained with sharded data parallelism using [FSDP](https://pytorch.org/docs/stable/fsdp.html) and used the AdamW optimizer. ## Limitations and Biases _The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_ MPT-7B-Instruct can produce factually incorrect output, and should not be relied on to produce factually accurate information. MPT-7B-Instruct was trained on various public datasets. While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs. ## Acknowledgements This model was finetuned by Sam Havens and the MosaicML NLP team ## MosaicML Platform If you're interested in [training](https://www.mosaicml.com/training) and [deploying](https://www.mosaicml.com/inference) your own MPT or LLMs on the MosaicML Platform, [sign up here](https://forms.mosaicml.com/demo?utm_source=huggingface&utm_medium=referral&utm_campaign=mpt-7b). ## 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. ## Citation Please cite this model using the following format: ``` @online{MosaicML2023Introducing, author = {MosaicML NLP Team}, title = {Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs}, year = {2023}, url = {www.mosaicml.com/blog/mpt-7b}, note = {Accessed: 2023-03-28}, % change this date urldate = {2023-03-28} % change this date } ``` *End of original Model File* --- ## Please consider to support my work Coming Soon: I'm in the process of launching a sponsorship/crowdfunding campaign for my work. I'm evaluating Kickstarter, Patreon, or the new GitHub Sponsors platform, and I am hoping for some support and contribution to the continued availability of these kind of models. Your support will enable me to provide even more valuable resources and maintain the models you rely on. Your patience and ongoing support are greatly appreciated as I work to make this page an even more valuable resource for the community. <center> [![GitHub](https://maddes8cht.github.io/assets/buttons/github-io-button.png)](https://maddes8cht.github.io) [![Stack Exchange](https://stackexchange.com/users/flair/26485911.png)](https://stackexchange.com/users/26485911) [![GitHub](https://maddes8cht.github.io/assets/buttons/github-button.png)](https://github.com/maddes8cht) [![HuggingFace](https://maddes8cht.github.io/assets/buttons/huggingface-button.png)](https://huggingface.co/maddes8cht) [![Twitter](https://maddes8cht.github.io/assets/buttons/twitter-button.png)](https://twitter.com/maddes1966) </center>
maddes8cht/mosaicml-mpt-7b-8k-instruct-gguf	maddes8cht	2023-11-01T15:36:48Z	71	1	null	[ "gguf", "Composer", "MosaicML", "llm-foundry", "arxiv:2205.14135", "arxiv:2108.12409", "arxiv:2010.04245", "license:cc-by-sa-3.0", "region:us" ]	null	2023-10-28T09:34:05Z	--- license: cc-by-sa-3.0 datasets: - competition_math - conceptofmind/cot_submix_original/cot_gsm8k - knkarthick/dialogsum - mosaicml/dolly_hhrlhf - duorc - tau/scrolls/qasper - emozilla/quality - scrolls/summ_screen_fd - spider tags: - Composer - MosaicML - llm-foundry inference: false --- [![banner](https://maddes8cht.github.io/assets/buttons/Huggingface-banner.jpg)]() I'm constantly enhancing these model descriptions to provide you with the most relevant and comprehensive information # mpt-7b-8k-instruct - GGUF - Model creator: [mosaicml](https://huggingface.co/mosaicml) - Original model: [mpt-7b-8k-instruct](https://huggingface.co/mosaicml/mpt-7b-8k-instruct) MPT-7b and MPT-30B are part of the family of Mosaic Pretrained Transformer (MPT) models, which use a modified transformer architecture optimized for efficient training and inference. # About GGUF format `gguf` is the current file format used by the [`ggml`](https://github.com/ggerganov/ggml) library. A growing list of Software is using it and can therefore use this model. The core project making use of the ggml library is the [llama.cpp](https://github.com/ggerganov/llama.cpp) project by Georgi Gerganov # Quantization variants There is a bunch of quantized files available to cater to your specific needs. Here's how to choose the best option for you: # Legacy quants Q4_0, Q4_1, Q5_0, Q5_1 and Q8 are `legacy` quantization types. Nevertheless, they are fully supported, as there are several circumstances that cause certain model not to be compatible with the modern K-quants. ## Note: Now there's a new option to use K-quants even for previously 'incompatible' models, although this involves some fallback solution that makes them not real K-quants. More details can be found in affected model descriptions. (This mainly refers to Falcon 7b and Starcoder models) # K-quants K-quants are designed with the idea that different levels of quantization in specific parts of the model can optimize performance, file size, and memory load. So, if possible, use K-quants. With a Q6_K, you'll likely find it challenging to discern a quality difference from the original model - ask your model two times the same question and you may encounter bigger quality differences. --- # Original Model Card: # MPT-7B-Instruct-8k MPT-7B-Instruct-8k is a model for long-form instruction following, especially question-answering on and summarization of longer documents. It is built by finetuning [MPT-7B-8k](https://huggingface.co/mosaicml/mpt-7b-8k) on [Dolly HHRLHF](https://huggingface.co/datasets/mosaicml/dolly_hhrlhf) derived from the [Databricks Dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) and the [Anthropic Helpful and Harmless (HH-RLHF)](https://huggingface.co/datasets/Anthropic/hh-rlhf) datasets. It is also trained on [Competition Math](https://huggingface.co/datasets/competition_math), [Duorc](https://huggingface.co/datasets/duorc), [CoT GSM8k](https://huggingface.co/datasets/conceptofmind/cot_submix_original), [Qasper](https://huggingface.co/datasets/allenai/qasper), [Quality](https://huggingface.co/datasets/emozilla/quality), [Summ Screen FD](https://huggingface.co/datasets/tau/scrolls) and [Spider](https://huggingface.co/datasets/spider). This is the same dataset that [MPT-30B-Instruct](https://huggingface.co/mosaicml/mpt-30b-instruct) was trained on. * License: _CC-By-SA-3.0_ This model was trained by [MosaicML](https://www.mosaicml.com) and follows a modified decoder-only transformer architecture. ## Model Date July 18, 2023 ## Model License _CC-By-SA-3.0_ ## Documentation * [Blog post: MPT-7B-8k](https://www.mosaicml.com/blog/long-context-mpt-7b-8k) * [Codebase (mosaicml/llm-foundry repo)](https://github.com/mosaicml/llm-foundry/) * Questions: Feel free to contact us via the [MosaicML Community Slack](https://mosaicml.me/slack)! ## How to Use This model is best used with the MosaicML [llm-foundry repository](https://github.com/mosaicml/llm-foundry) for training and finetuning. ```python import transformers model = transformers.AutoModelForCausalLM.from_pretrained( 'mosaicml/mpt-7b-instruct-8k', trust_remote_code=True ) ``` Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method. This is because we use a custom `MPT` model architecture that is not yet part of the Hugging Face `transformers` package. `MPT` includes options for many training efficiency features such as [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi](https://arxiv.org/abs/2108.12409), [QK LayerNorm](https://arxiv.org/abs/2010.04245), and more. To use the optimized [triton implementation](https://github.com/openai/triton) of FlashAttention, you can load the model on GPU (`cuda:0`) with `attn_impl='triton'` and with `bfloat16` precision: ```python import torch import transformers name = 'mosaicml/mpt-7b-instruct-8k' config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True) config.attn_config['attn_impl'] = 'triton' # change this to use triton-based FlashAttention config.init_device = 'cuda:0' # For fast initialization directly on GPU! model = transformers.AutoModelForCausalLM.from_pretrained( name, config=config, torch_dtype=torch.bfloat16, # Load model weights in bfloat16 trust_remote_code=True ) ``` The model was trained initially with a sequence length of 2048 with an additional pretraining stage for sequence length adapation up to 8192. However, ALiBi enables users to increase the maximum sequence length even further during finetuning and/or inference. For example: ```python import transformers name = 'mosaicml/mpt-7b-instruct-8k' config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True) config.max_seq_len = 16384 # (input + output) tokens can now be up to 16384 model = transformers.AutoModelForCausalLM.from_pretrained( name, config=config, trust_remote_code=True ) ``` This model was trained with the MPT-7B-chat tokenizer which is based on the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer and includes additional ChatML tokens. ```python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('mosaicml/mpt-7b-8k') ``` The model can then be used, for example, within a text-generation pipeline. Note: when running Torch modules in lower precision, it is best practice to use the [torch.autocast context manager](https://pytorch.org/docs/stable/amp.html). ```python from transformers import pipeline with torch.autocast('cuda', dtype=torch.bfloat16): inputs = tokenizer('Here is a recipe for vegan banana bread:\n', return_tensors="pt").to('cuda') outputs = model.generate(*inputs, max_new_tokens=100) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) # or using the HF pipeline pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, device='cuda:0') with torch.autocast('cuda', dtype=torch.bfloat16): print( pipe('Here is a recipe for vegan banana bread:\n', max_new_tokens=100, do_sample=True, use_cache=True)) ``` ## Model Description The architecture is a modification of a standard decoder-only transformer. The model has been modified from a standard transformer in the following ways: It uses [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) * It uses [ALiBi (Attention with Linear Biases)](https://arxiv.org/abs/2108.12409) and does not use positional embeddings * It does not use biases \| Hyperparameter \| Value \| \|----------------\|-------\| \|n_parameters \| 6.7B \| \|n_layers \| 32 \| \| n_heads \| 32 \| \| d_model \| 4096 \| \| vocab size \| 50432 \| \| sequence length \| 2048 \| ## Data Mix The model was trained on the following data mix: \| Data Source \| Number of Tokens in Source \| Proportion \| \|-------------\|----------------------------\|------------\| \| competition_math \| 1.6 M \| 3.66% \| \| cot_gsm8k \| 3.36 M \| 7.67% \| \| dialogsum \| 0.1 M \| 0.23% \| \| dolly_hhrlhf \| 5.89 M \| 13.43% \| \| duorc \| 7.8 M \| 17.80% \| \| qasper \| 8.72 M \| 19.90% \| \| quality \| 11.29 M \| 25.78% \| \| scrolls/summ_screen_fd \| 4.97 M \| 11.33% \| \| spider \| 0.089 M \| 0.20% \| ### Training Configuration This model was trained on 8 80GB A100s for about 6.3 hours using the [MosaicML Platform](https://www.mosaicml.com/platform). The model was trained with sharded data parallelism using [FSDP](https://pytorch.org/docs/stable/fsdp.html) and used the AdamW optimizer. ## Limitations and Biases _The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_ MPT-7B-Instruct-8k can produce factually incorrect output, and should not be relied on to produce factually accurate information. MPT-7B-Instruct-8k was trained on various public datasets. While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs. ## Acknowledgements This model was finetuned by the MosaicML NLP team. ## 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 consult an attorney before using this model for commercial purposes. ## MosaicML Platform If you're interested in [training](https://www.mosaicml.com/training) and [deploying](https://www.mosaicml.com/inference) your own MPT or LLMs on the MosaicML Platform, [sign up here](https://www.mosaicml.com/get-started?utm_source=huggingface&utm_medium=referral&utm_campaign=mpt-7b-8k). ## Citation Please cite this model using the following format: ``` @online{MosaicML2023Introducing, author = {MosaicML NLP Team}, title = {Introducing MPT-30B: Raising the bar for open-source foundation models}, year = {2023}, url = {www.mosaicml.com/blog/mpt-30b}, note = {Accessed: 2023-06-22}, urldate = {2023-06-22} } ``` *End of original Model File* --- ## Please consider to support my work Coming Soon: I'm in the process of launching a sponsorship/crowdfunding campaign for my work. I'm evaluating Kickstarter, Patreon, or the new GitHub Sponsors platform, and I am hoping for some support and contribution to the continued availability of these kind of models. Your support will enable me to provide even more valuable resources and maintain the models you rely on. Your patience and ongoing support are greatly appreciated as I work to make this page an even more valuable resource for the community. <center> [![GitHub](https://maddes8cht.github.io/assets/buttons/github-io-button.png)](https://maddes8cht.github.io) [![Stack Exchange](https://stackexchange.com/users/flair/26485911.png)](https://stackexchange.com/users/26485911) [![GitHub](https://maddes8cht.github.io/assets/buttons/github-button.png)](https://github.com/maddes8cht) [![HuggingFace](https://maddes8cht.github.io/assets/buttons/huggingface-button.png)](https://huggingface.co/maddes8cht) [![Twitter](https://maddes8cht.github.io/assets/buttons/twitter-button.png)](https://twitter.com/maddes1966) </center>
maddes8cht/mosaicml-mpt-30b-instruct-gguf	maddes8cht	2023-11-01T15:36:43Z	60	0	null	[ "gguf", "Composer", "MosaicML", "llm-foundry", "arxiv:2205.14135", "arxiv:2108.12409", "license:cc-by-sa-3.0", "region:us" ]	null	2023-10-27T16:50:33Z	--- license: cc-by-sa-3.0 datasets: - competition_math - conceptofmind/cot_submix_original/cot_gsm8k - knkarthick/dialogsum - mosaicml/dolly_hhrlhf - duorc - tau/scrolls/qasper - emozilla/quality - scrolls/summ_screen_fd - spider tags: - Composer - MosaicML - llm-foundry inference: false --- [![banner](https://maddes8cht.github.io/assets/buttons/Huggingface-banner.jpg)]() I'm constantly enhancing these model descriptions to provide you with the most relevant and comprehensive information # mpt-30b-instruct - GGUF - Model creator: [mosaicml](https://huggingface.co/mosaicml) - Original model: [mpt-30b-instruct](https://huggingface.co/mosaicml/mpt-30b-instruct) MPT-7b and MPT-30B are part of the family of Mosaic Pretrained Transformer (MPT) models, which use a modified transformer architecture optimized for efficient training and inference. # About GGUF format `gguf` is the current file format used by the [`ggml`](https://github.com/ggerganov/ggml) library. A growing list of Software is using it and can therefore use this model. The core project making use of the ggml library is the [llama.cpp](https://github.com/ggerganov/llama.cpp) project by Georgi Gerganov # Quantization variants There is a bunch of quantized files available to cater to your specific needs. Here's how to choose the best option for you: # Legacy quants Q4_0, Q4_1, Q5_0, Q5_1 and Q8 are `legacy` quantization types. Nevertheless, they are fully supported, as there are several circumstances that cause certain model not to be compatible with the modern K-quants. ## Note: Now there's a new option to use K-quants even for previously 'incompatible' models, although this involves some fallback solution that makes them not real K-quants. More details can be found in affected model descriptions. (This mainly refers to Falcon 7b and Starcoder models) # K-quants K-quants are designed with the idea that different levels of quantization in specific parts of the model can optimize performance, file size, and memory load. So, if possible, use K-quants. With a Q6_K, you'll likely find it challenging to discern a quality difference from the original model - ask your model two times the same question and you may encounter bigger quality differences. --- # Original Model Card: # MPT-30B-Instruct MPT-30B-Instruct is a model for short-form instruction following. It is built by finetuning [MPT-30B](https://huggingface.co/mosaicml/mpt-30b) on [Dolly HHRLHF](https://huggingface.co/datasets/mosaicml/dolly_hhrlhf) derived from the [Databricks Dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) and the [Anthropic Helpful and Harmless (HH-RLHF)](https://huggingface.co/datasets/Anthropic/hh-rlhf) datasets. It is also trained on [Competition Math](https://huggingface.co/datasets/competition_math), [Duorc](https://huggingface.co/datasets/duorc), [CoT GSM8k](https://huggingface.co/datasets/conceptofmind/cot_submix_original), [Qasper](https://huggingface.co/datasets/allenai/qasper), [Quality](https://huggingface.co/datasets/emozilla/quality), [Summ Screen FD](https://huggingface.co/datasets/tau/scrolls) and [Spider](https://huggingface.co/datasets/spider). * License: _CC-By-SA-3.0_ This model was trained by [MosaicML](https://www.mosaicml.com) and follows a modified decoder-only transformer architecture. ## Model Date June 22, 2023 ## Model License CC-By-SA-3.0 ## Documentation * [Blog post: Raising the bar for open-source foundation models](https://www.mosaicml.com/blog/mpt-30b) * [Codebase (mosaicml/llm-foundry repo)](https://github.com/mosaicml/llm-foundry/) * Questions: Feel free to contact us via the [MosaicML Community Slack](https://mosaicml.me/slack)! ### Example Question/Instruction Bespokenizer46 > I need to convince my friend, Phyllis, that she should train a custom LLM for her Fortune 500 company using the MosaicML Platform. > Please write an email that explains why MosaicML's emphasis on cutting edge methodology, data privacy, and efficiency are so important. > End the email with a friendly inquiry about Phyllis's family. MPT-30B-Instruct: > Phyllis - > I know you're always looking for ways to improve efficiency at work and I think I've found a solution that you'll be very interested in. > LLMs are only as good as the data they're trained on and the training process can be very time consuming and resource intensive. MosaicML uses cutting edge methodology to efficiently train high quality models without compromising data privacy. > They also provide tools to easily connect to and use the model in your daily workflow. > I think you'd really enjoy speaking with their founder, we can set up a call if you're interested. > Also, I know it's been a tough year for your family, how are things? > Best, > Your Friend ## 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 that is not yet part of the `transformers` package. It 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-30b-instruct', trust_remote_code=True ) ``` To use the optimized [triton implementation](https://github.com/openai/triton) of FlashAttention, you can load the model on GPU (`cuda:0`) with `attn_impl='triton'` and with `bfloat16` precision: ```python import torch import transformers name = 'mosaicml/mpt-30b-instruct' config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True) config.attn_config['attn_impl'] = 'triton' # change this to use triton-based FlashAttention config.init_device = 'cuda:0' # For fast initialization directly on GPU! model = transformers.AutoModelForCausalLM.from_pretrained( name, config=config, torch_dtype=torch.bfloat16, # Load model weights in bfloat16 trust_remote_code=True ) ``` The model was trained initially on a sequence length of 2048. An additional pre-training phase was included for sequence length adaptation to 8192. However, ALiBi further enables users to increase the maximum sequence length during finetuning and/or inference. For example: ```python import transformers name = 'mosaicml/mpt-30b-instruct' config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True) config.max_seq_len = 16384 # (input + output) tokens can now be up to 16384 model = transformers.AutoModelForCausalLM.from_pretrained( name, config=config, trust_remote_code=True ) ``` This model was trained with the MPT-30B tokenizer which is based on the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer and includes additional padding and eos tokens. ```python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('mosaicml/mpt-30b') ``` The model can then be used, for example, within a text-generation pipeline. Note: when running Torch modules in lower precision, it is best practice to use the [torch.autocast context manager](https://pytorch.org/docs/stable/amp.html). ```python from transformers import pipeline with torch.autocast('cuda', dtype=torch.bfloat16): inputs = tokenizer('Here is a recipe for vegan banana bread:\n', return_tensors="pt").to('cuda') outputs = model.generate(*inputs, max_new_tokens=100) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) # or using the HF pipeline pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, device='cuda:0') with torch.autocast('cuda', dtype=torch.bfloat16): print( pipe('Here is a recipe for vegan banana bread:\n', max_new_tokens=100, do_sample=True, use_cache=True)) ``` ### Formatting This model was trained on data formatted as follows: ```python def format_prompt(instruction): 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\n" return template.format(instruction=instruction) example = "Tell me a funny joke.\nDon't make it too funny though." fmt_ex = format_prompt(instruction=example) ``` In the above example, `fmt_ex` is ready to be tokenized and sent through the model. ## Model Description The architecture is a modification of a standard decoder-only transformer. The model has been modified from a standard transformer in the following ways: It uses [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) * It uses [ALiBi (Attention with Linear Biases)](https://arxiv.org/abs/2108.12409) and does not use positional embeddings * It does not use biases \| Hyperparameter \| Value \| \|----------------\|-------\| \|n_parameters \| 29.95B \| \|n_layers \| 48 \| \| n_heads \| 64 \| \| d_model \| 7168 \| \| vocab size \| 50432 \| \| sequence length \| 8192 \| ## Data Mix The model was trained on the following data mix: \| Data Source \| Number of Tokens in Source \| Proportion \| \|-------------\|----------------------------\|------------\| \| competition_math \| 1.6 M \| 3.66% \| \| cot_gsm8k \| 3.36 M \| 7.67% \| \| dialogsum \| 0.1 M \| 0.23% \| \| dolly_hhrlhf \| 5.89 M \| 13.43% \| \| duorc \| 7.8 M \| 17.80% \| \| qasper \| 8.72 M \| 19.90% \| \| quality \| 11.29 M \| 25.78% \| \| scrolls/summ_screen_fd \| 4.97 M \| 11.33% \| \| spider \| 0.089 M \| 0.20% \| ## PreTraining Data For more details on the pretraining process, see [MPT-30B](https://huggingface.co/mosaicml/mpt-30b). 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 72 A100 40GB GPUs for 8 hours using the [MosaicML Platform](https://www.mosaicml.com/platform). The model was trained with sharded data parallelism using [FSDP](https://pytorch.org/docs/stable/fsdp.html) and used the AdamW optimizer. ## Limitations and Biases _The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_ MPT-30B-Instruct can produce factually incorrect output, and should not be relied on to produce factually accurate information. MPT-30B-Instruct was trained on various public datasets. While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs. ## Acknowledgements This model was finetuned by Sam Havens, Alex Trott, and the MosaicML NLP team ## MosaicML Platform If you're interested in [training](https://www.mosaicml.com/training) and [deploying](https://www.mosaicml.com/inference) your own MPT or LLMs on the MosaicML Platform, [sign up here](https://forms.mosaicml.com/demo?utm_source=huggingface&utm_medium=referral&utm_campaign=mpt-30b). ## 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 consult an attorney before using this model for commercial purposes. ## Citation Please cite this model using the following format: ``` @online{MosaicML2023Introducing, author = {MosaicML NLP Team}, title = {Introducing MPT-30B: Raising the bar for open-source foundation models}, year = {2023}, url = {www.mosaicml.com/blog/mpt-30b}, note = {Accessed: 2023-06-22}, urldate = {2023-06-22} } ``` *End of original Model File* --- ## Please consider to support my work Coming Soon: I'm in the process of launching a sponsorship/crowdfunding campaign for my work. I'm evaluating Kickstarter, Patreon, or the new GitHub Sponsors platform, and I am hoping for some support and contribution to the continued availability of these kind of models. Your support will enable me to provide even more valuable resources and maintain the models you rely on. Your patience and ongoing support are greatly appreciated as I work to make this page an even more valuable resource for the community. <center> [![GitHub](https://maddes8cht.github.io/assets/buttons/github-io-button.png)](https://maddes8cht.github.io) [![Stack Exchange](https://stackexchange.com/users/flair/26485911.png)](https://stackexchange.com/users/26485911) [![GitHub](https://maddes8cht.github.io/assets/buttons/github-button.png)](https://github.com/maddes8cht) [![HuggingFace](https://maddes8cht.github.io/assets/buttons/huggingface-button.png)](https://huggingface.co/maddes8cht) [![Twitter](https://maddes8cht.github.io/assets/buttons/twitter-button.png)](https://twitter.com/maddes1966) </center>
PedroSampaio/vit-base-patch16-224-in21k-fruits-360-16-7	PedroSampaio	2023-11-01T15:24:31Z	5	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-11-01T13:30:34Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-base-patch16-224-in21k-fruits-360-16-7 results: [] --- <!-- 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. --> # vit-base-patch16-224-in21k-fruits-360-16-7 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0131 - Accuracy: 0.9992 ## 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: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4608 \| 1.0 \| 1057 \| 0.4181 \| 0.9983 \| \| 0.0699 \| 2.0 \| 2115 \| 0.0649 \| 0.9953 \| \| 0.0313 \| 3.0 \| 3173 \| 0.0243 \| 0.9986 \| \| 0.0143 \| 4.0 \| 4231 \| 0.0131 \| 0.9992 \| \| 0.0121 \| 5.0 \| 5288 \| 0.0103 \| 0.9989 \| \| 0.009 \| 6.0 \| 6346 \| 0.0095 \| 0.9988 \| \| 0.0037 \| 7.0 \| 7399 \| 0.0090 \| 0.9989 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
Mabel465/bert-finetuned-ner	Mabel465	2023-11-01T15:21:21Z	3	0	transformers	[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-11-01T14:53:22Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: [] --- <!-- 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. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1769 - Precision: 0.5799 - Recall: 0.4773 - F1: 0.5236 - Accuracy: 0.9264 ## 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: 8 - eval_batch_size: 8 - 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 \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 425 \| 0.1569 \| 0.5424 \| 0.3828 \| 0.4488 \| 0.9168 \| \| 0.1143 \| 2.0 \| 850 \| 0.1811 \| 0.6304 \| 0.4163 \| 0.5014 \| 0.9219 \| \| 0.046 \| 3.0 \| 1275 \| 0.1769 \| 0.5799 \| 0.4773 \| 0.5236 \| 0.9264 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0 - Datasets 2.14.6 - Tokenizers 0.14.1
ShinDC/marian-finetuned-kde4-en-to-fr	ShinDC	2023-11-01T15:20:44Z	5	0	transformers	[ "transformers", "pytorch", "marian", "text2text-generation", "translation", "generated_from_trainer", "dataset:kde4", "base_model:Helsinki-NLP/opus-mt-en-fr", "base_model:finetune:Helsinki-NLP/opus-mt-en-fr", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-09-28T14:56:30Z	--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-fr tags: - translation - generated_from_trainer datasets: - kde4 metrics: - bleu model-index: - name: marian-finetuned-kde4-en-to-fr results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: kde4 type: kde4 config: en-fr split: train args: en-fr metrics: - name: Bleu type: bleu value: 100.00000000000004 --- <!-- 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. --> # marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 0.8562 - Bleu: 100.0000 ## 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: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
MakAttack/6540dc5ec89e47f9ae652663	MakAttack	2023-11-01T15:15:46Z	3	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2023-11-01T14:12:38Z	--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks dog tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - MakAttack/6540dc5ec89e47f9ae652663 These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
zyoscovits/uplimit-project-3-phi-1.5	zyoscovits	2023-11-01T14:55:42Z	0	0	null	[ "generated_from_trainer", "dataset:scitldr", "base_model:microsoft/phi-1_5", "base_model:finetune:microsoft/phi-1_5", "license:other", "region:us" ]	null	2023-11-01T14:55:40Z	--- license: other base_model: microsoft/phi-1_5 tags: - generated_from_trainer datasets: - scitldr model-index: - name: uplimit-project-3-phi-1.5 results: [] --- <!-- 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. --> # uplimit-project-3-phi-1.5 This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the scitldr dataset. It achieves the following results on the evaluation set: - Loss: 2.5991 ## 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: 0.001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.554 \| 0.1 \| 200 \| 2.6506 \| \| 2.5735 \| 0.2 \| 400 \| 2.6515 \| \| 2.529 \| 0.3 \| 600 \| 2.6370 \| \| 2.5853 \| 0.4 \| 800 \| 2.6223 \| \| 2.5414 \| 0.5 \| 1000 \| 2.6204 \| \| 2.5768 \| 0.6 \| 1200 \| 2.6136 \| \| 2.5488 \| 0.7 \| 1400 \| 2.6095 \| \| 2.5259 \| 0.8 \| 1600 \| 2.6017 \| \| 2.5789 \| 0.9 \| 1800 \| 2.5991 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
bakkos/sjjsjdjswj	bakkos	2023-11-01T14:42:27Z	0	0	null	[ "license:other", "region:us" ]	null	2023-11-01T14:41:31Z	--- license: other license_name: other license_link: LICENSE ---
Skier8402/wav2vec-xls-r	Skier8402	2023-11-01T14:30:31Z	3	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "sw", "dataset:mozilla-foundation/common_voice_11_0", "base_model:facebook/wav2vec2-xls-r-300m", "base_model:finetune:facebook/wav2vec2-xls-r-300m", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-11-01T12:58:35Z	--- language: - sw license: apache-2.0 base_model: facebook/wav2vec2-xls-r-300m tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: wav2vec-xls-r results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: sw split: test args: 'config: sw, split: train+test' metrics: - name: Wer type: wer value: 0.9982181245473462 --- <!-- 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. --> # wav2vec-xls-r This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 2.1585 - Wer: 0.9982 Increase the number of epochs to improve performance or use a bigger model. ## 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: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.0131 \| 1.53 \| 1000 \| 3.0846 \| 1.0 \| \| 2.322 \| 3.07 \| 2000 \| 2.6234 \| 1.0000 \| \| 1.3523 \| 4.6 \| 3000 \| 2.2515 \| 0.9991 \| \| 1.1727 \| 6.13 \| 4000 \| 2.1585 \| 0.9982 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
kyujinpy/Korean-OpenOrca-13B-v2	kyujinpy	2023-11-01T14:13:02Z	87	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "ko", "dataset:kyujinpy/OpenOrca-ko-v2", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-10-30T19:09:11Z	--- language: - ko datasets: - kyujinpy/OpenOrca-ko-v2 library_name: transformers pipeline_tag: text-generation license: cc-by-nc-sa-4.0 --- (주)미디어그룹사람과숲과 (주)마커의 LLM 연구 컨소시엄에서 개발된 모델입니다 The license is `cc-by-nc-sa-4.0`. # 🐳Korean-OpenOrca-13B-v2🐳 ![img](./Korean-OpenOrca.png) ## Model Details Model Developers Kyujin Han (kyujinpy) Model Architecture Korean-OpenOrca-13B is an auto-regressive language model based on the LLaMA2 transformer architecture. Repo Link Github Korean-OpenOrca: [🐳Korean-OpenOrca🐳](https://github.com/Marker-Inc-Korea/Korean-OpenOrca) Base Model [hyunseoki/ko-en-llama2-13b](https://huggingface.co/hyunseoki/ko-en-llama2-13b) Training Dataset I use [OpenOrca-ko-v2](https://huggingface.co/datasets/kyujinpy/OpenOrca-ko-v2). Using DeepL, translate about [OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca). I use A100 GPU 40GB and COLAB, when trianing. # Model comparisons \| Model \| Average \|Ko-ARC \| Ko-HellaSwag \| Ko-MMLU \| Ko-TruthfulQA \| Ko-CommonGen V2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| [Korean-OpenOrca-13B🐳] \| 48.79 \| 43.09 \| 54.13 \| 40.24 \| 45.22 \| 61.28 \| \| Korean-OpenOrca-13B-v2🐳 \| 48.17 \| 43.17 \| 54.51 \| 42.90 \| 41.82 \| 58.44 \| # Implementation Code ```python ### KO-Platypus from transformers import AutoModelForCausalLM, AutoTokenizer import torch repo = "kyujinpy/Korean-OpenOrca-13B-v2" OpenOrca = AutoModelForCausalLM.from_pretrained( repo, return_dict=True, torch_dtype=torch.float16, device_map='auto' ) OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo) ``` ---
am-infoweb/QA_REDACTION_NOV1	am-infoweb	2023-11-01T14:12:54Z	3	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-11-01T13:22:13Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer model-index: - name: QA_REDACTION_NOV1 results: [] --- <!-- 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. --> # QA_REDACTION_NOV1 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0170 ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.9087 \| 1.0 \| 928 \| 0.0382 \| \| 0.0727 \| 2.0 \| 1856 \| 0.0195 \| \| 0.0954 \| 3.0 \| 2784 \| 3.1621 \| \| 0.3541 \| 4.0 \| 3712 \| 0.0192 \| \| 0.0849 \| 5.0 \| 4640 \| 0.0164 \| \| 0.0317 \| 6.0 \| 5568 \| 0.0131 \| \| 0.0327 \| 7.0 \| 6496 \| 0.0151 \| \| 0.0407 \| 8.0 \| 7424 \| 0.0142 \| \| 0.0383 \| 9.0 \| 8352 \| 0.0176 \| \| 0.0182 \| 10.0 \| 9280 \| 0.0170 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.0.1 - Datasets 2.14.6 - Tokenizers 0.14.1
ramdhanfirdaus/falcon-1b-finetuned-aings-adapters-testing	ramdhanfirdaus	2023-11-01T14:02:30Z	1	0	peft	[ "peft", "arxiv:1910.09700", "base_model:tiiuae/falcon-rw-1b", "base_model:adapter:tiiuae/falcon-rw-1b", "region:us" ]	null	2023-11-01T14:02:25Z	--- library_name: peft base_model: tiiuae/falcon-rw-1b --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure ### Framework versions - PEFT 0.6.0.dev0 ## Training procedure ### Framework versions - PEFT 0.6.0.dev0
T-Brockhouse/first_model_more_data	T-Brockhouse	2023-11-01T14:01:43Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-01T13:52:07Z	--- license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: first_model_more_data results: [] --- <!-- 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. --> # first_model_more_data This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. ## 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: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
cnrhs/sen-sim-es	cnrhs	2023-11-01T13:55:25Z	7	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "es", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-11-01T13:17:48Z	--- pipeline_tag: sentence-similarity language: - es tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers license: apache-2.0 --- # hiiamsid/sentence_similarity_spanish_es This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ['Mi nombre es Siddhartha', 'Mis amigos me llamaron por mi nombre Siddhartha'] model = SentenceTransformer('hiiamsid/sentence_similarity_spanish_es') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['Mi nombre es Siddhartha', 'Mis amigos me llamaron por mi nombre Siddhartha'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('hiiamsid/sentence_similarity_spanish_es') model = AutoModel.from_pretrained('hiiamsid/sentence_similarity_spanish_es') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results ``` cosine_pearson : 0.8280372842978689 cosine_spearman : 0.8232689765056079 euclidean_pearson : 0.81021993884437 euclidean_spearman : 0.8087904592393836 manhattan_pearson : 0.809645390126291 manhattan_spearman : 0.8077035464970413 dot_pearson : 0.7803662255836028 dot_spearman : 0.7699607641618339 ``` For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=hiiamsid/sentence_similarity_spanish_es) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 360 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "callback": null, "epochs": 4, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 144, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors - Datasets : [stsb_multi_mt](https://huggingface.co/datasets/stsb_multi_mt) - Model : [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) - Sentence Transformers [Semantic Textual Similarity](https://www.sbert.net/examples/training/sts/README.html)
PedroSampaio/fruits-360-16-7	PedroSampaio	2023-11-01T13:12:22Z	9	0	transformers	[ "transformers", "pytorch", "swin", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-11-01T04:47:57Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_trainer metrics: - accuracy model-index: - name: fruits-360-16-7 results: [] --- <!-- 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. --> # fruits-360-16-7 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0026 - Accuracy: 0.9992 ## 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: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0718 \| 1.0 \| 1057 \| 0.0188 \| 0.9976 \| \| 0.0135 \| 2.0 \| 2115 \| 0.0055 \| 0.9992 \| \| 0.0236 \| 3.0 \| 3173 \| 0.0077 \| 0.9976 \| \| 0.0082 \| 4.0 \| 4231 \| 0.0026 \| 0.9992 \| \| 0.004 \| 5.0 \| 5288 \| 0.0036 \| 0.9988 \| \| 0.0067 \| 6.0 \| 6346 \| 0.0024 \| 0.9991 \| \| 0.0005 \| 7.0 \| 7399 \| 0.0022 \| 0.9992 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
tkay264/data-tk	tkay264	2023-11-01T13:06:21Z	4	0	peft	[ "peft", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "region:us" ]	null	2023-11-01T13:05:39Z	--- library_name: peft base_model: meta-llama/Llama-2-7b-chat-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0
LoneStriker/Skywork-13B-Spicyboros-3.1-5.0bpw-h6-exl2	LoneStriker	2023-11-01T13:04:20Z	5	0	transformers	[ "transformers", "pytorch", "skywork", "text-generation", "custom_code", "dataset:jondurbin/airoboros-3.1", "arxiv:2310.19341", "arxiv:2310.16713", "license:other", "autotrain_compatible", "region:us" ]	text-generation	2023-11-01T13:03:56Z	--- license: other license_name: license license_link: >- https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20Community%20License.pdf datasets: - jondurbin/airoboros-3.1 --- <!-- <div align="center"> <h1> ✨Skywork </h1> </div> --> <div align="center"><img src="misc/skywork_logo.jpeg" width="550"/></div> <p align="center"> 👨‍💻 <a href="https://github.com/SkyworkAI/Skywork" target="_blank">Github</a> • 🤗 <a href="https://huggingface.co/Skywork" target="_blank">Hugging Face</a>• 🤖 <a href="https://modelscope.cn/organization/Skywork" target="_blank">ModelScope</a> • 💬 <a href="https://github.com/SkyworkAI/Skywork/blob/main/misc/wechat.png?raw=true" target="_blank">WeChat</a>• 📜<a href="http://arxiv.org/abs/2310.19341" target="_blank">Tech Report</a> </p> <div align="center"> [🎉天工在线对话平台已正式向公众开放](https://sso.tiangong.cn/?redirect=https://model-platform.tiangong.cn/overview&client_id=200005) </div> <div align="center"> [![GitHub Stars](https://img.shields.io/github/stars/SkyworkAI/Skywork)](https://github.com/SkyworkAI/Skywork/stargazers) [![GitHub Forks](https://img.shields.io/github/forks/SkyworkAI/Skywork)](https://github.com/SkyworkAI/Skywork/fork) </div> # 模型介绍（Introduction） Skywork-13B-Base模型在高质量清洗过滤的3.2万亿个多语言（主要是中文和英文）和代码数据上进行预训练，它在多种评测和各种基准测试上都展现了同等规模模型的最佳效果。 Skywork-13B-Base: The model was trained on a high-quality cleaned dataset consisting of 3.2 trillion multilingual data (mainly Chinese and English) and code. It has demonstrated the best performance among models of similar scale in various evaluations and benchmark tests. 如果您希望了解更多的信息，如训练方案，评估方法，请参考我们的[技术报告](http://arxiv.org/abs/2310.19341)，[Skymath](https://arxiv.org/abs/2310.16713)论文，[SkyworkMM](https://github.com/will-singularity/Skywork-MM/blob/main/skywork_mm.pdf)论文。 If you are interested in more training and evaluation details, please refer to our [technical report](http://arxiv.org/abs/2310.19341), [Skymath]((https://arxiv.org/skywork-tech-report)) paper and [SkyworkMM](https://github.com/will-singularity/Skywork-MM/blob/main/skywork_mm.pdf) paper. ## 训练数据（Training Data）我们精心搭建了数据清洗流程对文本中的低质量数据、有害信息、敏感信息进行清洗过滤。我们的Skywork-13B-Base模型是在清洗后的3.2TB高质量中、英、代码数据上进行训练，其中英文占比52.2%，中文占比39.6%，代码占比8%，在兼顾中文和英文上的表现的同时，代码能力也能有保证。 We have developed a data cleaning pipeline with great care to effectively clean and filter low-quality data and eliminate harmful information from text data. Our Skywork-13B-Base model is trained on a dataset with 3.2TB tokens that consists of high-quality Chinese, English, and code data, all of which have been thoroughly cleaned. The English data comprises 52.2% of the dataset, the Chinese data accounts for 39.6%, and the code data makes up 8%. This comprehensive approach ensures optimal performance for both Chinese and English while also maintaining the ability to handle code. \| \| Category \| Percentage \| \|-------------\|------------------\|------------\| \| English \| Webpages \| 39.8% \| \| \| Books \| 3.6% \| \| \| Academic Papers \| 3.0% \| \| \| Encyclopedia \| 0.5% \| \| \| Miscellany \| 2.9% \| \| Chinese \| Webpages \| 30.4% \| \| \| Social Media \| 5.5% \| \| \| Encyclopedia \| 0.8% \| \| \| Miscellany \| 3.1% \| \| Other Lang. \| Encyclopedia \| 2.4% \| \| Code \| Github \| 8.0% \| ## 模型结构（Model Structure）与Llama-2-13B模型对比，天工Skywork-13B模型采用相对更加瘦长的网络结构，层数为52层，同时将FFN Dim和Hidden Dim缩小到12288和4608，从而保证模型参数量和原始Llama-2-13B模型相当。根据我们前期实验对比，相对瘦长的网络结构在大Batch Size训练下可以取得更好的泛化效果。Skywork-13B和Llama-2-13B模型的对比如下： Compared to the Llama2-13B model, the Skywork-13B model adopts a relatively thinner and deeper network structure with 52 layers. At the same time, the FFN Dim and Hidden Dim are reduced to 12288 and 4608, respectively, to ensure that the model has a similar number of parameters as the original Llama-13B model. Based on our preliminary experimental results, a relatively thinner and deeper network structure can achieve better generalization performance under large batch size training. The detailed comparison between the Skywork-13B and Llama-2-13B models is as follows: \| Model Structure \| Llama2-13B \| Skywork-13B \| \|----------------------\|:----:\|:-----------:\| \| Vocab. Size \| 32,000 \| 65,536 \| \| Hidden Dim. \| 5,120 \| 4,608 \| \| FFN Dim. \| 13,696 \| 12,288 \| \| Head Dim. \| 128 \| 128 \| \| Num. Heads \| 40 \| 36 \| \| Num. Layers \| 40 \| 52 \| \| Seq. Len. \| 4,096 \| 4,096 \| \| Positional Embedding \| RoPE \| RoPE \| ## 分词器（Tokenizer）我们使用Byte-Pair Encoding（BPE）对数据进行分词，词表大小为65536，其中拉丁字符和子词为32000个，汉字和Unicode符号8000个，汉语词语25519个，剩下的17个为保留字。 We use Byte-Pair Encoding (BPE) to tokenize the data, with a vocabulary size of 65536. Among them, there are 32000 Latin characters and subwords, 8000 Chinese characters and Unicode symbols, 25519 Chinese words, and the remaining 17 are reserved words. \| Category \| Size \| \|---------------------------------\|--------\| \| Latin based words & subwords \| 32000 \| \| Chinese characters & Unicode symbols \| 8000 \| \| Chinese words \| 25519 \| \| Reserved symbols \| 17 \| \| Total \| 65536 \| # 模型评估（Evaluation） ## 领域数据困惑度评估（Perplexity Evaluaiton）语言模型训练的本质上是让预测下一个词更准确。基于这个认知，我们认为评估基础大模型一个重要的方式是评估在各大领域上语言模型生成文章的概率。在模型训练中预测下一个词的概率一般使用Cross Entropy损失函数，整体的损失函数为每个位置预测真实词损失的平均，则有： $$loss = \sum^{n}_{i=1} log(p_i) / n = log( \prod_{i=1}^n p_i) / n$$ 其中$n$是文档的长度，即token数，$p_i$是位置i上真实词的概率，我们知道文档中每一个位置上真实词的概率的联乘则为生成该文档的概率，如此我们就将loss和生成文章的概率联系在了一起。而不同模型因为使用的分词器不同，具有不同的token数，因此对损失函数乘以token数目$n$，这样就仅考虑生成文章的概率部分，不同模型也可以进行比较。我们将标准化后loss取指数转换成perplexity，使得模型的差异更加可读。为了阅读方便后续提到的loss和ppl为模型标准化后的loss和perplexity。基于上述分析，我们对对多个领域筛选出2023年9月份新发布的几百到上千篇高质量文章，并人工进行了核对。保证所有的测试数据不在天工模型以及其他所有模型的训练集中，并且测试数据的来源也足够广泛，质量也高。我们可以选取当前最新的文章评测不同模型的ppl，模型很难作弊。下图列出了不同开源模型，天工Skywork-13B-Base取得最优效果，证明了我们的Base模型的基础能力处于国内开源模型中文最强水平。 We have chosen several hundred to thousands of high-quality articles that were published after September 1, 2023 across various fields. We have manually verified these articles to ensure their quality. It is important to note that none of the test data used in evaluating the Skywork model or any other models is included in their training set. Furthermore, the test data is diverse and of high quality, making it challenging for the models to gain an unfair advantage. The figure below displays the performance of different open source models. Skywork-13B-Base achieves the best results. \| \| Tech \| Movie \| Gov. \| Game \| Finance \| General \| Average \| \|------------------\|-------\|-------\|-------\|-------\|---------\|---------\|---------\| \| MOSS-7B \| 20.83 \| 39.66 \| 11.08 \| 31.24 \| 10.59 \| 13.25 \| 18.50 \| \| InternLM-7B \| 13.43 \| 24.90 \| 5.88 \| 19.78 \| 6.17 \| 8.10 \| 11.17 \| \| Qwen-7B \| 13.39 \| 25.16 \| 5.55 \| 19.26 \| 5.76 \| 7.78 \| 10.83 \| \| Baichuan2-7B \| 12.89 \| 23.26 \| 5.34 \| 18.36 \| 5.68 \| 7.62 \| 10.41 \| \| LLaMA2-13B \| 23.26 \| 50.66 \| 18.09 \| 32.52 \| 14.85 \| 16.55 \| 23.54 \| \| Xverse-13B \| 12.55 \| 23.49 \| 5.20 \| 17.69 \| 5.54 \| 7.46 \| 10.19 \| \| Baichuan-13B \| 12.38 \| 22.46 \| 5.21 \| 17.59 \| 5.42 \| 7.37 \| 10.03 \| \| Baichuan2-13B \| 12.14 \| 21.85 \| 5.05 \| 17.15 \| 5.35 \| 7.24 \| 9.81 \| \| Qwen-14B \| 11.90 \| 22.43 \| 4.89 \| 16.94 \| 5.24 \| 7.03 \| 9.67 \| \| InternLM-20B \| 12.34 \| 22.06 \| 5.75 \| 17.45 \| 5.73 \| 7.78 \| 10.34 \| \| Aquila2-34B \| 14.62 \| 29.09 \| 5.72 \| 21.78 \| 5.83 \| 8.45 \| 11.73 \| \| Skywork-13B-Base \| 11.58 \| 21.84 \| 4.76 \| 17.28 \| 4.92 \| 6.82 \| 9.42 \| ### 评测数据和评测脚本（Loss Evaluation）我们将评测数据和评测脚本也进行了开源，下载github上的代码运行下面命令则可以复现我们的结果。 We have also open-sourced the data and evaluation scripts. You can reproduce our results by running the following command. ``` bash bash_scripts/skywork_eval_loss.sh ``` ## Benchmark评估（Benchmark Results）我们评估了各大权威评测benchmark上的结果作为参考，包括C-Eval，MMLU，CMMLU，GSM8K。遵循之前的评估流程，C-Eval、MMLU、CMMLU测试5-shot结果，GSM8K测试8-shot结果。可以看到Skywork-13B-Base模型在中文开源模型中处于前列，在同等参数规模下为最优水平。 We evaluated Skywork-13B-Base on several popular benchmarks, including C-Eval, MMLU, CMMLU, and GSM8K. Following the previous evaluation process, we tested the 5-shot results of C-Eval, MMLU, and CMMLU, and the 8-shot results of GSM8K. It can be seen that the Skywork-13B-Base model is among the top models in the Chinese open source model community, performing at an optimal level with the same parameter scale. \| Model \| C-Eval \| CMMLU \| MMLU \| GSM8K \| \|-------------------------\|:-----:\|:---------------:\|:----------:\|:-------:\| \| LLaMA-1-13B-Base \| 35.5 \| 31.2 \| 46.9 \| 17.8 \| \| Open-LLaMA-13B \| 27.1 \| 26.7 \| 42.7 \| 12.4 \| \| LLaMA-2-13B-Base \| 36.5 \| 36.6 \| 54.8 \| 28.7 \| \| InternLM-20B \| 58.8 \| - \| 62.0 \| 52.6 \| \| Qwen-14B-Base \| 72.1 \| 71.0 \| 66.3 \| 61.3 \| \| Aquila2-34B-Base \| 63.1 \| 71.4 \| 64.2 \| 58.4 \| \| XVERSE-13B-Base \| 54.7 \| - \| 55.1 \| - \| \| Baichuan-13B-Base \| 52.4 \| 55.3 \| 51.6 \| 26.6 \| \| Baichuan-2-13B-Base \| 58.1 \| 62.0 \| 59.2 \| 52.3 \| \| Skywork-13B-Base (ours) \| 60.6 \| 61.8 \| 62.1 \| 55.8 \| ## Benchmark评估详细结果我们给出Skywork-13B-Base模型在C-Eval，CMMLU，MMLU上模型的详细结果。 We provide detailed results of the Skywork-13B-Base model on C-EVAL, CMMLU, and MMLU. \| Benchmark \| STEM \| Humanities \| Social Science \| Other \| China Specific \| Hard \| Average \| \|:-----:\|:---------:\|:--------:\|:-------------:\|:--------:\|:--------:\|:--------:\|:--------:\| \| C-EVAL \| 51.2 \| 67.8 \| 74.6 \| 57.5 \| - \| 39.4 \| 60.6 \| \| CMMLU \| 49.5 \| 69.3 \| 65.9 \| 63.3 \| 64.2 \| - \| 61.8 \| \| MMLU \| 51.6 \| 58.0 \| 72.5 \| 68.8 \| - \| - \| 62.1 \| # 快速开始（Quickstart）我们将模型参数、配置文件、tokenizer等在huggingface和modelscope上进行了开源。 We have open-sourced the model parameters, configuration files, tokenizer, and more on Huggingface and Modelscope. ## 依赖安装（Requirements） - Python 3.8及以上版本 - Pytorch 2.0及以上版本 - CUDA建议使用11.4以上版本。 Skywork-13B-Base模型，Skywork-13B-Chat模型和Skywork-13B-Math模型运行下面的脚本进行Python依赖安装。 - Python 3.8 and above - Pytorch 2.0 and above - CUDA 11.4 and above are recommended. Skywork-13B-Base model, Skywork-13B-Chat model, and Skywork-13B-Math model run the following script for Python dependency installation: ```shell pip install -r requirements.txt ``` ## Huggingface模型测试（Demonstration） ### Base 模型推理（Base Model Inference） ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer >>> from transformers.generation import GenerationConfig >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("SkyworkAI/Skywork-13B-Base", trust_remote_code=True) >>> model = AutoModelForCausalLM.from_pretrained("SkyworkAI/Skywork-13B-Base", device_map="auto", trust_remote_code=True).eval() >>> inputs = tokenizer('陕西的省会是西安', return_tensors='pt').to(model.device) >>> response = model.generate(inputs.input_ids, max_length=128) >>> print(tokenizer.decode(response.cpu()[0], skip_special_tokens=True)) 陕西的省会是西安，西安是我国著名的古都，在历史上有十三个朝代在此建都，所以西安又被称为“十三朝古都”。西安是我国著名的旅游城市，每年都有大量的游客来到西安旅游，西安的旅游资源非常丰富，有很多著名的旅游景点，比如秦始皇兵马俑、大雁塔、华清池、大唐芙蓉园、西安城墙、大明宫国家遗址公园、西安碑林博物馆、西安钟楼、西安鼓楼、西安半坡博物馆、西安大兴善寺、西安小雁塔 >>> inputs = tokenizer('陕西的省会是西安，甘肃的省会是兰州，河南的省会是郑州', return_tensors='pt').to(model.device) >>> response = model.generate(inputs.input_ids, max_length=128) >>> print(tokenizer.decode(response.cpu()[0], skip_special_tokens=True)) 陕西的省会是西安，甘肃的省会是兰州，河南的省会是郑州，湖北的省会是武汉，湖南的省会是长沙，江西的省会是南昌，安徽的省会是合肥，江苏的省会是南京，浙江的省会是杭州，福建的省会是福州，广东的省会是广州，广西的省会是南宁，海南的省会是海口，四川的省会是成都，贵州的省会是贵阳，云南的省会是昆明，西藏的省会是拉萨，青海的省会是西宁，宁夏的省会是银川，新疆的省会是乌鲁木齐。 ``` # 模型微调（Fine-tuning） ## 全量微调（Full-parameter Fine-tuning）使用Skywork-13B-Base模型进行预训练微调 ```bash ## preprocess continue pretraining data ## Because pre-training data is usually large, we use a script to process the training data separately. python train/pt_data_preprocess.py \ -t $MODEL_PATH \ -i data/pt_train.jsonl \ -o data_cache/pt_train_demo ## launch training export WANDB_API_KEY=YOUR_WANDB_KEY export WANDB_ENTITY=skywork export WANDB_PROJECT=skywork-13b-opensource export MODEL_PATH=skywork-13b-models/skywork-13b-base export DATA_CACHE_DIR=data_cache/pt_train_demo/pt_train bash bash_scripts/skywork_13b_pt.sh ``` 使用Skywork-13B-Base模型进行有监督微调（SFT, Supevise Fine-tuning） ```bash ## preprocess data and launch training export WANDB_API_KEY=YOUR_WANDB_KEY export WANDB_ENTITY=skywork export WANDB_PROJECT=skywork-13b-opensource export SFT_DATA_DIR=data/sft_data export DATA_CACHE_DIR=data_cache/sft_train_demo bash bash_scripts/skywork_13b_sft.sh ``` ## LoRA微调（PEFT）使用Skywork-13B-Base模型以及LoRA进行预训练微调 ```bash ## preprocess continue pretraining data ## Because pre-training data is usually large, we use a script to process the training data separately. python train/pt_data_preprocess.py \ -t $MODEL_PATH \ -i data/pt_train.jsonl \ -o data_cache/pt_train_demo export WANDB_API_KEY=YOUR_WANDB_KEY export WANDB_ENTITY=skywork export WANDB_PROJECT=skywork-13b-opensource export MODEL_PATH=skywork-13b-models/skywork-13b-base export DATA_CACHE_DIR=data_cache/pt_train_demo/pt_train bash bash_scripts/skywork_13b_pt_lora.sh ``` 使用Skywork-13B-Base模型以及LoRA进行有监督微调（SFT, Supevise Fine-tuning） ```bash export WANDB_API_KEY=YOUR_WANDB_KEY export WANDB_ENTITY=skywork export WANDB_PROJECT=skywork-13b-opensource export SFT_DATA_DIR=data/sft_data export DATA_CACHE_DIR=data_cache/sft_train_demo bash bash_scripts/skywork_13b_sft_lora.sh ``` # 声明和协议（Declaration and License Agreement） ## 声明（Declaration）我们在此声明，不要利用Skywork模型进行任何危害国家社会安全或违法的活动。另外，我们也要求使用者不要将 Skywork 模型用于未经适当安全审查和备案的互联网服务。我们希望所有的使用者都能遵守这个原则，确保科技的发展能在规范和合法的环境下进行。我们已经尽我们所能，来确保模型训练过程中使用的数据的合规性。然而，尽管我们已经做出了巨大的努力，但由于模型和数据的复杂性，仍有可能存在一些无法预见的问题。因此，如果由于使用skywork开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。 We hereby declare that the Skywork model should not be used for any activities that pose a threat to national or societal security or engage in unlawful actions. Additionally, we request users not to deploy the Skywork model for internet services without appropriate security reviews and records. We hope that all users will adhere to this principle to ensure that technological advancements occur in a regulated and lawful environment. We have done our utmost to ensure the compliance of the data used during the model's training process. However, despite our extensive efforts, due to the complexity of the model and data, there may still be unpredictable risks and issues. Therefore, if any problems arise as a result of using the Skywork open-source model, including but not limited to data security issues, public opinion risks, or any risks and problems arising from the model being misled, abused, disseminated, or improperly utilized, we will not assume any responsibility. ## 协议（License Agreement）社区使用Skywork模型需要遵循[《Skywork 模型社区许可协议》](https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20模型社区许可协议.pdf)。Skywork模型支持商业用途，如果您计划将Skywork模型或其衍生品用于商业目的，无需再次申请，但请您仔细阅读[《Skywork 模型社区许可协议》](https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20模型社区许可协议.pdf)并严格遵守相关条款。 The community usage of Skywork model requires [Skywork Community License](https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20Community%20License.pdf). The Skywork model supports commercial use. If you plan to use the Skywork model or its derivatives for commercial purposes, you must abide by terms and conditions within [Skywork Community License](https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20Community%20License.pdf). [《Skywork 模型社区许可协议》》]:https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20模型社区许可协议.pdf [[email protected]]: mailto:[email protected] # 引用和联系我们（Contact Us and Citation）如果您觉得我们的工作对您有帮助，欢迎引用我们的论文~ If you find our work helpful, please feel free to cite our paper~ ``` @misc{wei2023skywork, title={Skywork: A More Open Bilingual Foundation Model}, author={Tianwen Wei and Liang Zhao and Lichang Zhang and Bo Zhu and Lijie Wang and Haihua Yang and Biye Li and Cheng Cheng and Weiwei Lü and Rui Hu and Chenxia Li and Liu Yang and Xilin Luo and Xuejie Wu and Lunan Liu and Wenjun Cheng and Peng Cheng and Jianhao Zhang and Xiaoyu Zhang and Lei Lin and Xiaokun Wang and Yutuan Ma and Chuanhai Dong and Yanqi Sun and Yifu Chen and Yongyi Peng and Xiaojuan Liang and Shuicheng Yan and Han Fang and Yahui Zhou}, year={2023}, eprint={2310.19341}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ``` @article{skyworkmath, title={SkyMath: Technical Report}, author={Liu Yang, Haihua Yang, Wenjun Cheng, Lei Lin, Chenxia Li, Yifu Chen, Lunan Liu, Jianfei Pan, Tianwen Wei, Biye Li, Liang Zhao, Lijie Wang, Bo Zhu, Guoliang Li, Xuejie Wu, Xilin Luo, Rui Hu}, journal={arXiv preprint arXiv: 2310.16713}, url={https://arxiv.org/abs/2310.16713}, year={2023} } ``` ``` @article{Skywork_Multi-Modal_Group_Empirical_Study_Towards_2023, author = {Skywork Multi-Modal Group}, month = sep, title = {{Empirical Study Towards Building An Effective Multi-Modal Large Language Model}}, year = {2023} } ```
hugging-ecaf/ppo-LunarLander-v2	hugging-ecaf	2023-11-01T12:57:48Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-11-01T12:57:27Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 265.15 +/- 17.94 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Lakoc/TED_CLM_gpt2_tedlium_bigger_lr	Lakoc	2023-11-01T12:57:37Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-01T03:55:44Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: TED_CLM_gpt2_tedlium_bigger_lr results: [] --- <!-- 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. --> # TED_CLM_gpt2_tedlium_bigger_lr This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8755 - Accuracy: 0.5540 ## 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: 0.004 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 512 - total_eval_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 20000 - num_epochs: 15.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 2.0351 \| 0.62 \| 3000 \| 2.2280 \| 0.4798 \| \| 1.9186 \| 1.24 \| 6000 \| 2.0994 \| 0.5074 \| \| 1.88 \| 1.86 \| 9000 \| 2.0577 \| 0.5142 \| \| 1.8505 \| 2.49 \| 12000 \| 2.0113 \| 0.5223 \| \| 1.8284 \| 3.11 \| 15000 \| 1.9957 \| 0.5279 \| \| 1.8182 \| 3.73 \| 18000 \| 1.9891 \| 0.5305 \| \| 1.8061 \| 4.35 \| 21000 \| 1.9617 \| 0.5371 \| \| 1.7969 \| 4.97 \| 24000 \| 1.9413 \| 0.5369 \| \| 2.0383 \| 5.59 \| 27000 \| 2.1697 \| 0.4894 \| \| 1.7668 \| 6.22 \| 30000 \| 1.9366 \| 0.5397 \| \| 1.7556 \| 6.84 \| 33000 \| 1.9303 \| 0.5402 \| \| 1.7492 \| 7.46 \| 36000 \| 1.9140 \| 0.5432 \| \| 1.7409 \| 8.08 \| 39000 \| 1.9088 \| 0.5445 \| \| 1.7317 \| 8.7 \| 42000 \| 1.9030 \| 0.5455 \| \| 1.7218 \| 9.32 \| 45000 \| 1.9040 \| 0.5496 \| \| 1.7261 \| 9.94 \| 48000 \| 1.8952 \| 0.5506 \| \| 1.7175 \| 10.57 \| 51000 \| 1.8959 \| 0.5498 \| \| 1.708 \| 11.19 \| 54000 \| 1.8909 \| 0.5510 \| \| 1.7056 \| 11.81 \| 57000 \| 1.8917 \| 0.5518 \| \| 1.6971 \| 12.43 \| 60000 \| 1.8879 \| 0.5523 \| \| 1.6986 \| 13.05 \| 63000 \| 1.8790 \| 0.5532 \| \| 1.6972 \| 13.67 \| 66000 \| 1.8799 \| 0.5526 \| \| 1.6858 \| 14.29 \| 69000 \| 1.8782 \| 0.5543 \| \| 1.6875 \| 14.92 \| 72000 \| 1.8755 \| 0.5540 \| ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.13.1 - Tokenizers 0.13.3
Yukang/LongAlpaca-70B	Yukang	2023-11-01T12:57:26Z	1,455	20	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "arxiv:2309.12307", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-10-09T02:53:36Z	# LongLoRA and LongAlpaca for Long-context LLMs [![Huggingface Models](https://img.shields.io/badge/Models-Huggingface%20Models-bron)](https://huggingface.co/Yukang) [![Github](https://img.shields.io/badge/Github-Repo-cyan)](https://github.com/dvlab-research/LongLoRA) [![Data](https://img.shields.io/badge/Data-LongAlpaca%2012k-light)](https://huggingface.co/datasets/Yukang/LongAlpaca-12k) [![Paper](https://img.shields.io/badge/Paper-Arvix-blue)](https://arxiv.org/abs/2309.12307) [![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-yellow.svg)](https://github.com/dvlab-research/LongLoRA/blob/main/LICENSE) [![Data License](https://img.shields.io/badge/Data%20License-CC%20By%20NC%204.0-orange.svg)](https://github.com/dvlab-research/LongLoRA/blob/main/DATA_LICENSE) [![Weight License](https://img.shields.io/badge/Weight%20License-CC%20By%20NC%204.0-red)](https://github.com/dvlab-research/LongLoRA/blob/main/WEIGHT_LICENSE) For detailed usage and codes, please visit the [Github project](https://github.com/dvlab-research/LongLoRA). ## TABLE OF CONTENTS 1. [News](#news) 2. [Examples](#examples) 3. [Highlights](#highlights) 4. [How to contribute](#how-to-contribute) 5. [Requirements](#usage-requirements) 6. [Installation and quick guide](#installation-and-quick-guide) 7. [LongAlpaca Data](#longalpaca-data) 8. [Models](#models) 9. [Training](#training) 10. [Evaluation](#evaluation) 11. [Demo](#demo) 12. [Data Generation via Pdf2Text](#data-generation-via-pdf2text) 13. [Citation](#citation) 14. [Acknowledgement](#acknowledgement) 15. [License](#license) ## News - [x] [2023.10.8] We release the long instruction-following dataset, [LongAlpaca-12k](https://huggingface.co/datasets/Yukang/LongAlpaca-12k) and the corresponding models, [LongAlpaca-7B](https://huggingface.co/Yukang/LongAlpaca-7B), [LongAlpaca-13B](https://huggingface.co/Yukang/LongAlpaca-13B), and [LongAlpaca-70B](https://huggingface.co/Yukang/LongAlpaca-70B). - (The previous sft models, [Llama-2-13b-chat-longlora-32k-sft](https://huggingface.co/Yukang/Llama-2-13b-chat-longlora-32k-sft) and [Llama-2-70b-chat-longlora-32k-sft](https://huggingface.co/Yukang/Llama-2-70b-chat-longlora-32k-sft), have been depreciated.) - [x] [2023.10.3] We add support GPTNeoX models. Please refer to this [PR](https://github.com/dvlab-research/LongLoRA/pull/32) for usage. Thanks for @naubull2 for this contribution. - [x] [2023.9.22] We release all our fine-tuned [models](https://huggingface.co/Yukang), including 70B-32k models, [LLaMA2-LongLoRA-70B-32k](https://huggingface.co/Yukang/Llama-2-70b-longlora-32k), [LLaMA2-LongLoRA-7B-100k](https://huggingface.co/Yukang/Llama-2-7b-longlora-100k-ft). Welcome to check them out! - [x] [2023.9.22] We release [Paper](http://arxiv.org/abs/2309.12307) and this GitHub repo, including training and evaluation code. LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models [[Paper](http://arxiv.org/abs/2309.12307)] <br /> [Yukang Chen](https://scholar.google.com/citations?user=6p0ygKUAAAAJ&hl=en), [Shengju Qian](https://scholar.google.com/citations?user=QNnWmasAAAAJ), [Haotian Tang](https://scholar.google.com/citations?user=WxL13BAAAAAJ&hl), [Xin Lai](https://scholar.google.com/citations?user=tqNDPA4AAAAJ&hl=zh-CN), [Zhijian Liu](https://scholar.google.com/citations?user=3coYSTUAAAAJ&hl=en), [Song Han](https://scholar.google.com/citations?user=E0iCaa4AAAAJ&hl=zh-CN), [Jiaya Jia](https://scholar.google.com/citations?user=XPAkzTEAAAAJ&hl=en)<br /> ## Highlights 1. In LongLoRA approach, The proposed shifted short attention is easy to implement, compatible with Flash-Attention, and is not required during inference. 2. We released all our models, including models from 7B to 70B, context length from 8k to 100k, including [LLaMA2-LongLoRA-7B-100k](https://huggingface.co/Yukang/Llama-2-7b-longlora-100k-ft), [LLaMA2-LongLoRA-13B-64k](https://huggingface.co/Yukang/Llama-2-13b-longlora-64k), and [LLaMA2-LongLoRA-70B-32k](https://huggingface.co/Yukang/Llama-2-70b-longlora-32k). 3. We built up a long-context instruction-following dataset, [LongAlpaca-12k](#longalpaca-data). We released the corresponding [LongAlpaca-7B](https://huggingface.co/Yukang/LongAlpaca-7B), [LongAlpaca-13B](https://huggingface.co/Yukang/LongAlpaca-13B) and [LongAlpaca-70B](https://huggingface.co/Yukang/LongAlpaca-70B) models. To our best knowledge, this is the first open-sourced long-context 70B model. ## How to Contribute - Make sure to have git installed. - Create your own [fork](https://github.com/dvlab-research/LongLoRA/fork) of the project. - Clone the repository on your local machine, using git clone and pasting the url of this project. - Read both the `Requirements` and `Installation and Quick Guide` sections below. - Commit and push your changes. - Make a pull request when finished modifying the project. ## Usage Requirements To download and use the [pre-trained weights](#pre-trained-weights) you will need: 1. Hugging Face (HF) account with valid email. Note, the email used for HF must alse be used for the license agreement. 2. Accept the Meta [license and acceptable use policy](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) ## Installation and Quick Guide To install and run the application: 1. [Fork this repo](https://github.com/dvlab-research/LongLoRA/fork) on github 2. Clone the repository on your local machine, using git clone and pasting the url of this project. 3. Run the following code: ``` pip install -r requirements.txt pip install flash-attn --no-build-isolation ``` 4. Use either a [Released model](#released-models) or [Fine tune](#fine-tuning) a model to fit your preferences. 5. Test your model by chat. 6. Deploy your own demo. ## LongAlpaca Data LongAlpaca-12k contains 9k long QA data that we collected and 3k short QA sampled from the original [Alpaca data](https://github.com/tatsu-lab/stanford_alpaca/blob/main/alpaca_data.json). This is to avoid the case that the model might degrade at short instruction following. The data we collect contains various types and amounts as the following figure. \| Data \| Short QA \| Long QA \| Total \| Download \| \|:---------------\|----------\|----------\|----------\|----------\| \| LongAlpaca-12k \| 3k \| 9k \| 12k \| [Link](https://huggingface.co/datasets/Yukang/LongAlpaca-12k) \| Following the original Alpaca format, our Long QA data uses the following prompts for fine-tuning: - `instruction`: `str`, describes the task the model should perform. For example, to answer a question after reading a book section or paper. We vary the contents and questions to make instructions diverse. - `output`: `str`, the answer to the instruction. We did not use the `input` format in the Alpaca format for simplicity. ## Models ### Models with supervised fine-tuning \| Model \| Size \| Context \| Train \| Link \| \|:---------------\|------\|---------\|---------\|-----------------------------------------------------------------------------------------------------------------------\| \| LongAlpaca-7B \| 7B \| 32768 \| Full FT \| [Model](https://huggingface.co/Yukang/LongAlpaca-7B) \| \| LongAlpaca-13B \| 13B \| 32768 \| Full FT \| [Model](https://huggingface.co/Yukang/LongAlpaca-13B) \| \| LongAlpaca-70B \| 70B \| 32768 \| LoRA+ \| [Model](https://huggingface.co/Yukang/LongAlpaca-70B) [(LoRA-weight)](https://huggingface.co/Yukang/LongAlpaca-70B-lora) \| ### Models with context extension via fully fine-tuning \| Model \| Size \| Context \| Train \| Link \| \|:----------------------------\|------\|---------\|-------\|-------------------------------------------------------------------\| \| Llama-2-7b-longlora-8k-ft \| 7B \| 8192 \| Full FT \| [Model](https://huggingface.co/Yukang/Llama-2-7b-longlora-8k-ft) \| \| Llama-2-7b-longlora-16k-ft \| 7B \| 16384 \| Full FT \| [Model](https://huggingface.co/Yukang/Llama-2-7b-longlora-16k-ft) \| \| Llama-2-7b-longlora-32k-ft \| 7B \| 32768 \| Full FT \| [Model](https://huggingface.co/Yukang/Llama-2-7b-longlora-32k-ft) \| \| Llama-2-7b-longlora-100k-ft \| 7B \| 100000 \| Full FT \| [Model](https://huggingface.co/Yukang/Llama-2-7b-longlora-100k-ft) \| \| Llama-2-13b-longlora-8k-ft \| 13B \| 8192 \| Full FT \| [Model](https://huggingface.co/Yukang/Llama-2-13b-longlora-8k-ft) \| \| Llama-2-13b-longlora-16k-ft \| 13B \| 16384 \| Full FT \| [Model](https://huggingface.co/Yukang/Llama-2-13b-longlora-16k-ft) \| \| Llama-2-13b-longlora-32k-ft \| 13B \| 32768 \| Full FT \| [Model](https://huggingface.co/Yukang/Llama-2-13b-longlora-32k-ft) \| ### Models with context extension via improved LoRA fine-tuning \| Model \| Size \| Context \| Train \| Link \| \|:----------------------------\|------\|---------\|-------\|---------------------------------------------------------------------\| \| Llama-2-7b-longlora-8k \| 7B \| 8192 \| LoRA+ \| [LoRA-weight](https://huggingface.co/Yukang/Llama-2-7b-longlora-8k) \| \| Llama-2-7b-longlora-16k \| 7B \| 16384 \| LoRA+ \| [LoRA-weight](https://huggingface.co/Yukang/Llama-2-7b-longlora-16k) \| \| Llama-2-7b-longlora-32k \| 7B \| 32768 \| LoRA+ \| [LoRA-weight](https://huggingface.co/Yukang/Llama-2-7b-longlora-32k) \| \| Llama-2-13b-longlora-8k \| 13B \| 8192 \| LoRA+ \| [LoRA-weight](https://huggingface.co/Yukang/Llama-2-13b-longlora-8k) \| \| Llama-2-13b-longlora-16k \| 13B \| 16384 \| LoRA+ \| [LoRA-weight](https://huggingface.co/Yukang/Llama-2-13b-longlora-16k) \| \| Llama-2-13b-longlora-32k \| 13B \| 32768 \| LoRA+ \| [LoRA-weight](https://huggingface.co/Yukang/Llama-2-13b-longlora-32k) \| \| Llama-2-13b-longlora-64k \| 13B \| 65536 \| LoRA+ \| [LoRA-weight](https://huggingface.co/Yukang/Llama-2-13b-longlora-64k) \| \| Llama-2-70b-longlora-32k \| 70B \| 32768 \| LoRA+ \| [LoRA-weight](https://huggingface.co/Yukang/Llama-2-70b-longlora-32k) \| \| Llama-2-70b-chat-longlora-32k \| 70B \| 32768 \| LoRA+ \| [LoRA-weight](https://huggingface.co/Yukang/Llama-2-70b-chat-longlora-32k) \| ## Training ### Pre-trained weights We use LLaMA2 models as the pre-trained weights and fine-tune them to long context window sizes. Download based on your choices. \| Pre-trained weights \| \|:-------------------------------------------------------------------------------------\| \| [Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) \| \|[Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) \| \| [Llama-2-70b-hf](https://huggingface.co/meta-llama/Llama-2-70b-hf) \| \| [Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) \| \| [Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) \| \| [Llama-2-70b-chat-hf](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) \| This project also supports GPTNeoX models as the base model architecture. Some candidate pre-trained weights may include [GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b), [Polyglot-ko-12.8B](https://huggingface.co/EleutherAI/polyglot-ko-12.8b) and other variants. ### Fine-tuning ``` torchrun --nproc_per_node=8 fine-tune.py \ --model_name_or_path path_to/Llama-2-7b-hf \ --bf16 True \ --output_dir path_to_saving_checkpoints \ --cache_dir path_to_cache \ --model_max_length 8192 \ --use_flash_attn True \ --low_rank_training False \ --num_train_epochs 1 \ --per_device_train_batch_size 1 \ --per_device_eval_batch_size 2 \ --gradient_accumulation_steps 8 \ --evaluation_strategy "no" \ --save_strategy "steps" \ --save_steps 1000 \ --save_total_limit 2 \ --learning_rate 2e-5 \ --weight_decay 0.0 \ --warmup_steps 20 \ --lr_scheduler_type "constant_with_warmup" \ --logging_steps 1 \ --deepspeed "ds_configs/stage2.json" \ --tf32 True \ --max_steps 1000 ``` - Please remember to change `path_to/Llama-2-7b-hf`, `path_to_saving_checkpoints`, `path_to_cache` to your own directory. - Note that you can change `model_max_length` to other values. - You could change `ds_configs/stage2.json` to `ds_configs/stage3.json` if you want. - Please set `use_flash_attn` as `False` if you use V100 machines or do not install flash attention. - You can set `low_rank_training` as `False` if you want to use fully fine-tuning. It will cost more GPU memory and slower, but the performance will be a bit better. - When training is finished, to get the full model weight: ``` cd path_to_saving_checkpoints && python zero_to_fp32.py . pytorch_model.bin ``` ### Supervised Fine-tuning ``` torchrun --nproc_per_node=8 supervised-fine-tune.py \ --model_name_or_path path_to_Llama2_chat_models \ --bf16 True \ --output_dir path_to_saving_checkpoints \ --model_max_length 32768 \ --use_flash_attn True \ --data_path LongAlpaca-12k.json \ --low_rank_training True \ --num_train_epochs 3 \ --per_device_train_batch_size 1 \ --per_device_eval_batch_size 2 \ --gradient_accumulation_steps 1 \ --evaluation_strategy "no" \ --save_strategy "steps" \ --save_steps 1000 \ --save_total_limit 2 \ --learning_rate 2e-5 \ --weight_decay 0.0 \ --warmup_steps 20 \ --lr_scheduler_type "constant_with_warmup" \ --logging_steps 1 \ --deepspeed "ds_configs/stage2.json" \ --tf32 True ``` - There is no need to make supervised fine-tuning upon the fine-tuned context extended models. It is all right to directly use base model as Llama2-chat models, as the amount of long instruction following data is enough for SFT. - Our long instruction following data can be found in [LongAlpaca-12k.json](https://huggingface.co/datasets/Yukang/LongAlpaca-12k). ### Get trainable weights in low-rank training In low-rank training, we set embedding and normalization layers as trainable. Please use the following line to extract the trainable weights `trainable_params.bin` from `pytorch_model.bin` ``` python3 get_trainable_weights.py --checkpoint_path path_to_saving_checkpoints --trainable_params "embed,norm" ``` ### Merge LoRA Weight Merge the LoRA weights of `pytorch_model.bin` and trainable parameters `trainable_params.bin`, save the resulting model into your desired path in the Hugging Face format: ``` python3 merge_lora_weights_and_save_hf_model.py \ --base_model path_to/Llama-2-7b-hf \ --peft_model path_to_saving_checkpoints \ --context_size 8192 \ --save_path path_to_saving_merged_model ``` For example, ``` python3 merge_lora_weights_and_save_hf_model.py \ --base_model /dataset/pretrained-models/Llama-2-7b-hf \ --peft_model /dataset/yukangchen/hf_models/lora-models/Llama-2-7b-longlora-8k \ --context_size 8192 \ --save_path /dataset/yukangchen/models/Llama-2-7b-longlora-8k-merged ``` ## Evaluation ### Perplexity Validation To evaluate a model that is trained in the low-rank setting, please set both `base_model` and `peft_model`. `base_model` is the pre-trained weight. `peft_model` is the path to the saved checkpoint, which should contain `trainable_params.bin`, `adapter_model.bin` and `adapter_config.json`. For example, ``` python3 eval.py --seq_len 8192 --context_size 8192 --batch_size 1 --base_model path_to/Llama-2-7b-hf --peft_model path_to_saving_checkpoints --data_path pg19/test.bin ``` To evaluate a model that is fully fine-tuned, you only need to set `base_model` as the path to the saved checkpoint, which should contain `pytorch_model.bin` and `config.json`. `peft_model` should be ignored. ``` python3 eval.py --seq_len 8192 --context_size 8192 --batch_size 1 --base_model path_to_saving_checkpoints --data_path pg19/test.bin ``` - Note that `--seq_len` is to set the sequence length for evaluation. `--context_size` is to set the context length of the model during fine-tuning. `--seq_len` should not be larger than `--context_size`. - We have already tokenized the validation and test splits of PG19 and proof-pile dataset into `pg19/validation.bin`, `pg19/test.bin`, and `proof-pile/test_sampled_data.bin`, with the tokenizer of LLaMA. `proof-pile/test_sampled_data.bin` contains 128 documents that are randomly sampled from the total proof-pile test split. For each document, it has at least 32768 tokens. We also release the sampled ids in [proof-pile/test_sampled_ids.bin](https://drive.google.com/file/d/1cnzWODLRQYAd7HeugzLCIhaqzaLZv7J5/view?usp=share_link). You can download them from the links below. \| Dataset \| Split \| Link \| \|:-----------\|------------\|--------------------------------------------------------------------------------------------------------------\| \| PG19 \| validation \| [pg19/validation.bin](https://drive.google.com/file/d/1rbJvb0qRIf2mQoN2ON7S93TbTzMnlrN6/view?usp=share_link) \| \| PG19 \| test \| [pg19/test.bin](https://drive.google.com/file/d/1QANDMdctpacPAYgS04adDXqByGEq-Ret/view?usp=share_link) \| \| Proof-pile \| test \| [proof-pile/test_sampled_data.bin](https://drive.google.com/file/d/1bUI5lPDvrqzY_XXJJ2sSuvZx0Y9AZClE/view?usp=share_link) \| ### Passkey Retrieval We provide a manner to test the passkey retrieval accuracy. For example, ``` python3 passkey_retrivial.py \ --context_size 32768 \ --base_model path_to/Llama-2-7b-longlora-32k \ --max_tokens 32768 \ --interval 1000 ``` - Note that the `context_size` is the context length during fine-tuning. - `max_tokens` is maximum length for the document in passkey retrieval evaluation. - `interval` is the interval during the document length increasing. It is a rough number because the document increases by sentences. ## Demo ### Local Inference To chat with [Llama-2-13b-chat-longlora-32k-sft](https://huggingface.co/Yukang/Llama-2-13b-chat-longlora-32k-sft) or [Llama-2-70b-chat-longlora-32k-sft](https://huggingface.co/Yukang/Llama-2-70b-chat-longlora-32k-sft), you need to run `merge_lora_weights_and_save_hf_model.py` first, and then: ``` python3 inference.py \ --base_model path_to_model \ --question $question \ --context_size $context_length \ --max_gen_len $max_gen_len \ --flash_attn True \ --material $material_content \ --material_type $material_type \ --material_title $material_title ``` To ask a question related to a book: ``` python3 inference.py \ --base_model /data/models/Llama-2-13b-chat-longlora-32k-sft \ --question "Why doesn't Professor Snape seem to like Harry?" \ --context_size 32768 \ --max_gen_len 512 \ --flash_attn True \ --material "materials/Harry Potter and the Philosophers Stone_section2.txt" \ --material_type "book" \ --material_title "Harry Potter and the Philosophers Stone" ``` Note that you can ignore `material_type` or `material_title`. To ask a question related to a paper: ``` python3 inference.py \ --base_model /data/models/Llama-2-13b-chat-longlora-32k-sft \ --question "What are the main contributions and novelties of this work?" \ --context_size 32768 \ --max_gen_len 512 \ --flash_attn True \ --material "materials/paper1.txt" \ --material_type "paper" ``` ### Online Demo To deploy your own demo run ``` python3 demo.py \ --base_model path_to_model \ --context_size $context_size \ --max_gen_len $max_gen_len \ --flash_attn True ``` Example ``` python3 demo.py \ --base_model /data/models/Llama-2-13b-chat-longlora-32k-sft \ --context_size 32768 \ --max_gen_len 512 \ --flash_attn True ``` - Note that `flash_attn=True` will make the generation slow but save much GPU memory. ## Data Generation via Pdf2text During our dataset collection, we convert paper and books from pdf to text. The conversion quality has a large influence on the final model quality. We think that this step is non-trivial. We release the tool for the pdf2txt conversion, in the folder `pdf2txt`. It is built upon `pdf2image`, `easyocr`, `ditod` and `detectron2`. Please refer to the [README.md](pdf2txt/README.md) in `pdf2txt` for more details. ## Citation If you find this project useful in your research, please consider citing: ``` @article{longlora, title={LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models}, author={Yukang Chen and Shengju Qian and Haotian Tang and Xin Lai and Zhijian Liu and Song Han and Jiaya Jia}, journal={arXiv:2309.12307}, year={2023} } ``` ``` @misc{long-alpaca, author = {Yukang Chen and Shaozuo Yu and Shengju Qian and Haotian Tang and Xin Lai and Zhijian Liu and Song Han and Jiaya Jia}, title = {Long Alpaca: Long-context Instruction-following models}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/dvlab-research/LongLoRA}}, } ``` ## Acknowledgement - This work is built upon the [LLaMA2](https://ai.meta.com/llama) as the pre-trained models. - This work can also be built upon the [GPTNeoX-HF](https://huggingface.co/docs/transformers/model_doc/gpt_neox) which is based upon [EleutherAI/GPTNeoX](https://github.com/EleutherAI/gpt-neox) as the pre-trained model architecture. - This work is based on [DeepSpeed](https://github.com/microsoft/DeepSpeed), [peft](https://github.com/huggingface/peft), and [Flash-Attention2](https://github.com/Dao-AILab/flash-attention) for acceleration. - Some evaluation code is modified upon [Landmark Attention](https://github.com/epfml/landmark-attention). - We use [LongChat](https://github.com/DachengLi1/LongChat) for the retrieval evaluation. ## License - LongLoRA is licensed under the Apache License 2.0. This means that it requires the preservation of copyright and license notices. - Data and weights are under CC-BY-NC 4.0 License. They are licensed for research use only, and allowed only non-commercial. Models trained using the dataset should not be used outside of research purposes.
onarganogun/videomae-base-cctv-brawl_extended	onarganogun	2023-11-01T12:53:57Z	1	0	transformers	[ "transformers", "pytorch", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "base_model:finetune:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2023-10-31T22:22:02Z	--- license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-cctv-brawl_extended results: [] --- <!-- 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. --> # videomae-base-cctv-brawl_extended This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6457 - Accuracy: 0.5962 ## 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: 1e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 5022 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.232 \| 0.17 \| 838 \| 0.5134 \| 0.7813 \| \| 0.1099 \| 1.17 \| 1676 \| 0.7192 \| 0.7824 \| \| 0.2178 \| 2.17 \| 2514 \| 0.4419 \| 0.8299 \| \| 0.0697 \| 3.17 \| 3352 \| 0.6157 \| 0.8448 \| \| 0.2116 \| 4.17 \| 4190 \| 0.4998 \| 0.8644 \| \| 0.1655 \| 5.17 \| 5022 \| 0.5615 \| 0.8542 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
LoneStriker/Skywork-13B-Spicyboros-3.1-4.0bpw-h6-exl2	LoneStriker	2023-11-01T12:53:53Z	16	1	transformers	[ "transformers", "pytorch", "skywork", "text-generation", "custom_code", "dataset:jondurbin/airoboros-3.1", "arxiv:2310.19341", "arxiv:2310.16713", "license:other", "autotrain_compatible", "region:us" ]	text-generation	2023-11-01T12:53:32Z	--- license: other license_name: license license_link: >- https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20Community%20License.pdf datasets: - jondurbin/airoboros-3.1 --- <!-- <div align="center"> <h1> ✨Skywork </h1> </div> --> <div align="center"><img src="misc/skywork_logo.jpeg" width="550"/></div> <p align="center"> 👨‍💻 <a href="https://github.com/SkyworkAI/Skywork" target="_blank">Github</a> • 🤗 <a href="https://huggingface.co/Skywork" target="_blank">Hugging Face</a>• 🤖 <a href="https://modelscope.cn/organization/Skywork" target="_blank">ModelScope</a> • 💬 <a href="https://github.com/SkyworkAI/Skywork/blob/main/misc/wechat.png?raw=true" target="_blank">WeChat</a>• 📜<a href="http://arxiv.org/abs/2310.19341" target="_blank">Tech Report</a> </p> <div align="center"> [🎉天工在线对话平台已正式向公众开放](https://sso.tiangong.cn/?redirect=https://model-platform.tiangong.cn/overview&client_id=200005) </div> <div align="center"> [![GitHub Stars](https://img.shields.io/github/stars/SkyworkAI/Skywork)](https://github.com/SkyworkAI/Skywork/stargazers) [![GitHub Forks](https://img.shields.io/github/forks/SkyworkAI/Skywork)](https://github.com/SkyworkAI/Skywork/fork) </div> # 模型介绍（Introduction） Skywork-13B-Base模型在高质量清洗过滤的3.2万亿个多语言（主要是中文和英文）和代码数据上进行预训练，它在多种评测和各种基准测试上都展现了同等规模模型的最佳效果。 Skywork-13B-Base: The model was trained on a high-quality cleaned dataset consisting of 3.2 trillion multilingual data (mainly Chinese and English) and code. It has demonstrated the best performance among models of similar scale in various evaluations and benchmark tests. 如果您希望了解更多的信息，如训练方案，评估方法，请参考我们的[技术报告](http://arxiv.org/abs/2310.19341)，[Skymath](https://arxiv.org/abs/2310.16713)论文，[SkyworkMM](https://github.com/will-singularity/Skywork-MM/blob/main/skywork_mm.pdf)论文。 If you are interested in more training and evaluation details, please refer to our [technical report](http://arxiv.org/abs/2310.19341), [Skymath]((https://arxiv.org/skywork-tech-report)) paper and [SkyworkMM](https://github.com/will-singularity/Skywork-MM/blob/main/skywork_mm.pdf) paper. ## 训练数据（Training Data）我们精心搭建了数据清洗流程对文本中的低质量数据、有害信息、敏感信息进行清洗过滤。我们的Skywork-13B-Base模型是在清洗后的3.2TB高质量中、英、代码数据上进行训练，其中英文占比52.2%，中文占比39.6%，代码占比8%，在兼顾中文和英文上的表现的同时，代码能力也能有保证。 We have developed a data cleaning pipeline with great care to effectively clean and filter low-quality data and eliminate harmful information from text data. Our Skywork-13B-Base model is trained on a dataset with 3.2TB tokens that consists of high-quality Chinese, English, and code data, all of which have been thoroughly cleaned. The English data comprises 52.2% of the dataset, the Chinese data accounts for 39.6%, and the code data makes up 8%. This comprehensive approach ensures optimal performance for both Chinese and English while also maintaining the ability to handle code. \| \| Category \| Percentage \| \|-------------\|------------------\|------------\| \| English \| Webpages \| 39.8% \| \| \| Books \| 3.6% \| \| \| Academic Papers \| 3.0% \| \| \| Encyclopedia \| 0.5% \| \| \| Miscellany \| 2.9% \| \| Chinese \| Webpages \| 30.4% \| \| \| Social Media \| 5.5% \| \| \| Encyclopedia \| 0.8% \| \| \| Miscellany \| 3.1% \| \| Other Lang. \| Encyclopedia \| 2.4% \| \| Code \| Github \| 8.0% \| ## 模型结构（Model Structure）与Llama-2-13B模型对比，天工Skywork-13B模型采用相对更加瘦长的网络结构，层数为52层，同时将FFN Dim和Hidden Dim缩小到12288和4608，从而保证模型参数量和原始Llama-2-13B模型相当。根据我们前期实验对比，相对瘦长的网络结构在大Batch Size训练下可以取得更好的泛化效果。Skywork-13B和Llama-2-13B模型的对比如下： Compared to the Llama2-13B model, the Skywork-13B model adopts a relatively thinner and deeper network structure with 52 layers. At the same time, the FFN Dim and Hidden Dim are reduced to 12288 and 4608, respectively, to ensure that the model has a similar number of parameters as the original Llama-13B model. Based on our preliminary experimental results, a relatively thinner and deeper network structure can achieve better generalization performance under large batch size training. The detailed comparison between the Skywork-13B and Llama-2-13B models is as follows: \| Model Structure \| Llama2-13B \| Skywork-13B \| \|----------------------\|:----:\|:-----------:\| \| Vocab. Size \| 32,000 \| 65,536 \| \| Hidden Dim. \| 5,120 \| 4,608 \| \| FFN Dim. \| 13,696 \| 12,288 \| \| Head Dim. \| 128 \| 128 \| \| Num. Heads \| 40 \| 36 \| \| Num. Layers \| 40 \| 52 \| \| Seq. Len. \| 4,096 \| 4,096 \| \| Positional Embedding \| RoPE \| RoPE \| ## 分词器（Tokenizer）我们使用Byte-Pair Encoding（BPE）对数据进行分词，词表大小为65536，其中拉丁字符和子词为32000个，汉字和Unicode符号8000个，汉语词语25519个，剩下的17个为保留字。 We use Byte-Pair Encoding (BPE) to tokenize the data, with a vocabulary size of 65536. Among them, there are 32000 Latin characters and subwords, 8000 Chinese characters and Unicode symbols, 25519 Chinese words, and the remaining 17 are reserved words. \| Category \| Size \| \|---------------------------------\|--------\| \| Latin based words & subwords \| 32000 \| \| Chinese characters & Unicode symbols \| 8000 \| \| Chinese words \| 25519 \| \| Reserved symbols \| 17 \| \| Total \| 65536 \| # 模型评估（Evaluation） ## 领域数据困惑度评估（Perplexity Evaluaiton）语言模型训练的本质上是让预测下一个词更准确。基于这个认知，我们认为评估基础大模型一个重要的方式是评估在各大领域上语言模型生成文章的概率。在模型训练中预测下一个词的概率一般使用Cross Entropy损失函数，整体的损失函数为每个位置预测真实词损失的平均，则有： $$loss = \sum^{n}_{i=1} log(p_i) / n = log( \prod_{i=1}^n p_i) / n$$ 其中$n$是文档的长度，即token数，$p_i$是位置i上真实词的概率，我们知道文档中每一个位置上真实词的概率的联乘则为生成该文档的概率，如此我们就将loss和生成文章的概率联系在了一起。而不同模型因为使用的分词器不同，具有不同的token数，因此对损失函数乘以token数目$n$，这样就仅考虑生成文章的概率部分，不同模型也可以进行比较。我们将标准化后loss取指数转换成perplexity，使得模型的差异更加可读。为了阅读方便后续提到的loss和ppl为模型标准化后的loss和perplexity。基于上述分析，我们对对多个领域筛选出2023年9月份新发布的几百到上千篇高质量文章，并人工进行了核对。保证所有的测试数据不在天工模型以及其他所有模型的训练集中，并且测试数据的来源也足够广泛，质量也高。我们可以选取当前最新的文章评测不同模型的ppl，模型很难作弊。下图列出了不同开源模型，天工Skywork-13B-Base取得最优效果，证明了我们的Base模型的基础能力处于国内开源模型中文最强水平。 We have chosen several hundred to thousands of high-quality articles that were published after September 1, 2023 across various fields. We have manually verified these articles to ensure their quality. It is important to note that none of the test data used in evaluating the Skywork model or any other models is included in their training set. Furthermore, the test data is diverse and of high quality, making it challenging for the models to gain an unfair advantage. The figure below displays the performance of different open source models. Skywork-13B-Base achieves the best results. \| \| Tech \| Movie \| Gov. \| Game \| Finance \| General \| Average \| \|------------------\|-------\|-------\|-------\|-------\|---------\|---------\|---------\| \| MOSS-7B \| 20.83 \| 39.66 \| 11.08 \| 31.24 \| 10.59 \| 13.25 \| 18.50 \| \| InternLM-7B \| 13.43 \| 24.90 \| 5.88 \| 19.78 \| 6.17 \| 8.10 \| 11.17 \| \| Qwen-7B \| 13.39 \| 25.16 \| 5.55 \| 19.26 \| 5.76 \| 7.78 \| 10.83 \| \| Baichuan2-7B \| 12.89 \| 23.26 \| 5.34 \| 18.36 \| 5.68 \| 7.62 \| 10.41 \| \| LLaMA2-13B \| 23.26 \| 50.66 \| 18.09 \| 32.52 \| 14.85 \| 16.55 \| 23.54 \| \| Xverse-13B \| 12.55 \| 23.49 \| 5.20 \| 17.69 \| 5.54 \| 7.46 \| 10.19 \| \| Baichuan-13B \| 12.38 \| 22.46 \| 5.21 \| 17.59 \| 5.42 \| 7.37 \| 10.03 \| \| Baichuan2-13B \| 12.14 \| 21.85 \| 5.05 \| 17.15 \| 5.35 \| 7.24 \| 9.81 \| \| Qwen-14B \| 11.90 \| 22.43 \| 4.89 \| 16.94 \| 5.24 \| 7.03 \| 9.67 \| \| InternLM-20B \| 12.34 \| 22.06 \| 5.75 \| 17.45 \| 5.73 \| 7.78 \| 10.34 \| \| Aquila2-34B \| 14.62 \| 29.09 \| 5.72 \| 21.78 \| 5.83 \| 8.45 \| 11.73 \| \| Skywork-13B-Base \| 11.58 \| 21.84 \| 4.76 \| 17.28 \| 4.92 \| 6.82 \| 9.42 \| ### 评测数据和评测脚本（Loss Evaluation）我们将评测数据和评测脚本也进行了开源，下载github上的代码运行下面命令则可以复现我们的结果。 We have also open-sourced the data and evaluation scripts. You can reproduce our results by running the following command. ``` bash bash_scripts/skywork_eval_loss.sh ``` ## Benchmark评估（Benchmark Results）我们评估了各大权威评测benchmark上的结果作为参考，包括C-Eval，MMLU，CMMLU，GSM8K。遵循之前的评估流程，C-Eval、MMLU、CMMLU测试5-shot结果，GSM8K测试8-shot结果。可以看到Skywork-13B-Base模型在中文开源模型中处于前列，在同等参数规模下为最优水平。 We evaluated Skywork-13B-Base on several popular benchmarks, including C-Eval, MMLU, CMMLU, and GSM8K. Following the previous evaluation process, we tested the 5-shot results of C-Eval, MMLU, and CMMLU, and the 8-shot results of GSM8K. It can be seen that the Skywork-13B-Base model is among the top models in the Chinese open source model community, performing at an optimal level with the same parameter scale. \| Model \| C-Eval \| CMMLU \| MMLU \| GSM8K \| \|-------------------------\|:-----:\|:---------------:\|:----------:\|:-------:\| \| LLaMA-1-13B-Base \| 35.5 \| 31.2 \| 46.9 \| 17.8 \| \| Open-LLaMA-13B \| 27.1 \| 26.7 \| 42.7 \| 12.4 \| \| LLaMA-2-13B-Base \| 36.5 \| 36.6 \| 54.8 \| 28.7 \| \| InternLM-20B \| 58.8 \| - \| 62.0 \| 52.6 \| \| Qwen-14B-Base \| 72.1 \| 71.0 \| 66.3 \| 61.3 \| \| Aquila2-34B-Base \| 63.1 \| 71.4 \| 64.2 \| 58.4 \| \| XVERSE-13B-Base \| 54.7 \| - \| 55.1 \| - \| \| Baichuan-13B-Base \| 52.4 \| 55.3 \| 51.6 \| 26.6 \| \| Baichuan-2-13B-Base \| 58.1 \| 62.0 \| 59.2 \| 52.3 \| \| Skywork-13B-Base (ours) \| 60.6 \| 61.8 \| 62.1 \| 55.8 \| ## Benchmark评估详细结果我们给出Skywork-13B-Base模型在C-Eval，CMMLU，MMLU上模型的详细结果。 We provide detailed results of the Skywork-13B-Base model on C-EVAL, CMMLU, and MMLU. \| Benchmark \| STEM \| Humanities \| Social Science \| Other \| China Specific \| Hard \| Average \| \|:-----:\|:---------:\|:--------:\|:-------------:\|:--------:\|:--------:\|:--------:\|:--------:\| \| C-EVAL \| 51.2 \| 67.8 \| 74.6 \| 57.5 \| - \| 39.4 \| 60.6 \| \| CMMLU \| 49.5 \| 69.3 \| 65.9 \| 63.3 \| 64.2 \| - \| 61.8 \| \| MMLU \| 51.6 \| 58.0 \| 72.5 \| 68.8 \| - \| - \| 62.1 \| # 快速开始（Quickstart）我们将模型参数、配置文件、tokenizer等在huggingface和modelscope上进行了开源。 We have open-sourced the model parameters, configuration files, tokenizer, and more on Huggingface and Modelscope. ## 依赖安装（Requirements） - Python 3.8及以上版本 - Pytorch 2.0及以上版本 - CUDA建议使用11.4以上版本。 Skywork-13B-Base模型，Skywork-13B-Chat模型和Skywork-13B-Math模型运行下面的脚本进行Python依赖安装。 - Python 3.8 and above - Pytorch 2.0 and above - CUDA 11.4 and above are recommended. Skywork-13B-Base model, Skywork-13B-Chat model, and Skywork-13B-Math model run the following script for Python dependency installation: ```shell pip install -r requirements.txt ``` ## Huggingface模型测试（Demonstration） ### Base 模型推理（Base Model Inference） ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer >>> from transformers.generation import GenerationConfig >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("SkyworkAI/Skywork-13B-Base", trust_remote_code=True) >>> model = AutoModelForCausalLM.from_pretrained("SkyworkAI/Skywork-13B-Base", device_map="auto", trust_remote_code=True).eval() >>> inputs = tokenizer('陕西的省会是西安', return_tensors='pt').to(model.device) >>> response = model.generate(inputs.input_ids, max_length=128) >>> print(tokenizer.decode(response.cpu()[0], skip_special_tokens=True)) 陕西的省会是西安，西安是我国著名的古都，在历史上有十三个朝代在此建都，所以西安又被称为“十三朝古都”。西安是我国著名的旅游城市，每年都有大量的游客来到西安旅游，西安的旅游资源非常丰富，有很多著名的旅游景点，比如秦始皇兵马俑、大雁塔、华清池、大唐芙蓉园、西安城墙、大明宫国家遗址公园、西安碑林博物馆、西安钟楼、西安鼓楼、西安半坡博物馆、西安大兴善寺、西安小雁塔 >>> inputs = tokenizer('陕西的省会是西安，甘肃的省会是兰州，河南的省会是郑州', return_tensors='pt').to(model.device) >>> response = model.generate(inputs.input_ids, max_length=128) >>> print(tokenizer.decode(response.cpu()[0], skip_special_tokens=True)) 陕西的省会是西安，甘肃的省会是兰州，河南的省会是郑州，湖北的省会是武汉，湖南的省会是长沙，江西的省会是南昌，安徽的省会是合肥，江苏的省会是南京，浙江的省会是杭州，福建的省会是福州，广东的省会是广州，广西的省会是南宁，海南的省会是海口，四川的省会是成都，贵州的省会是贵阳，云南的省会是昆明，西藏的省会是拉萨，青海的省会是西宁，宁夏的省会是银川，新疆的省会是乌鲁木齐。 ``` # 模型微调（Fine-tuning） ## 全量微调（Full-parameter Fine-tuning）使用Skywork-13B-Base模型进行预训练微调 ```bash ## preprocess continue pretraining data ## Because pre-training data is usually large, we use a script to process the training data separately. python train/pt_data_preprocess.py \ -t $MODEL_PATH \ -i data/pt_train.jsonl \ -o data_cache/pt_train_demo ## launch training export WANDB_API_KEY=YOUR_WANDB_KEY export WANDB_ENTITY=skywork export WANDB_PROJECT=skywork-13b-opensource export MODEL_PATH=skywork-13b-models/skywork-13b-base export DATA_CACHE_DIR=data_cache/pt_train_demo/pt_train bash bash_scripts/skywork_13b_pt.sh ``` 使用Skywork-13B-Base模型进行有监督微调（SFT, Supevise Fine-tuning） ```bash ## preprocess data and launch training export WANDB_API_KEY=YOUR_WANDB_KEY export WANDB_ENTITY=skywork export WANDB_PROJECT=skywork-13b-opensource export SFT_DATA_DIR=data/sft_data export DATA_CACHE_DIR=data_cache/sft_train_demo bash bash_scripts/skywork_13b_sft.sh ``` ## LoRA微调（PEFT）使用Skywork-13B-Base模型以及LoRA进行预训练微调 ```bash ## preprocess continue pretraining data ## Because pre-training data is usually large, we use a script to process the training data separately. python train/pt_data_preprocess.py \ -t $MODEL_PATH \ -i data/pt_train.jsonl \ -o data_cache/pt_train_demo export WANDB_API_KEY=YOUR_WANDB_KEY export WANDB_ENTITY=skywork export WANDB_PROJECT=skywork-13b-opensource export MODEL_PATH=skywork-13b-models/skywork-13b-base export DATA_CACHE_DIR=data_cache/pt_train_demo/pt_train bash bash_scripts/skywork_13b_pt_lora.sh ``` 使用Skywork-13B-Base模型以及LoRA进行有监督微调（SFT, Supevise Fine-tuning） ```bash export WANDB_API_KEY=YOUR_WANDB_KEY export WANDB_ENTITY=skywork export WANDB_PROJECT=skywork-13b-opensource export SFT_DATA_DIR=data/sft_data export DATA_CACHE_DIR=data_cache/sft_train_demo bash bash_scripts/skywork_13b_sft_lora.sh ``` # 声明和协议（Declaration and License Agreement） ## 声明（Declaration）我们在此声明，不要利用Skywork模型进行任何危害国家社会安全或违法的活动。另外，我们也要求使用者不要将 Skywork 模型用于未经适当安全审查和备案的互联网服务。我们希望所有的使用者都能遵守这个原则，确保科技的发展能在规范和合法的环境下进行。我们已经尽我们所能，来确保模型训练过程中使用的数据的合规性。然而，尽管我们已经做出了巨大的努力，但由于模型和数据的复杂性，仍有可能存在一些无法预见的问题。因此，如果由于使用skywork开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。 We hereby declare that the Skywork model should not be used for any activities that pose a threat to national or societal security or engage in unlawful actions. Additionally, we request users not to deploy the Skywork model for internet services without appropriate security reviews and records. We hope that all users will adhere to this principle to ensure that technological advancements occur in a regulated and lawful environment. We have done our utmost to ensure the compliance of the data used during the model's training process. However, despite our extensive efforts, due to the complexity of the model and data, there may still be unpredictable risks and issues. Therefore, if any problems arise as a result of using the Skywork open-source model, including but not limited to data security issues, public opinion risks, or any risks and problems arising from the model being misled, abused, disseminated, or improperly utilized, we will not assume any responsibility. ## 协议（License Agreement）社区使用Skywork模型需要遵循[《Skywork 模型社区许可协议》](https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20模型社区许可协议.pdf)。Skywork模型支持商业用途，如果您计划将Skywork模型或其衍生品用于商业目的，无需再次申请，但请您仔细阅读[《Skywork 模型社区许可协议》](https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20模型社区许可协议.pdf)并严格遵守相关条款。 The community usage of Skywork model requires [Skywork Community License](https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20Community%20License.pdf). The Skywork model supports commercial use. If you plan to use the Skywork model or its derivatives for commercial purposes, you must abide by terms and conditions within [Skywork Community License](https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20Community%20License.pdf). [《Skywork 模型社区许可协议》》]:https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20模型社区许可协议.pdf [[email protected]]: mailto:[email protected] # 引用和联系我们（Contact Us and Citation）如果您觉得我们的工作对您有帮助，欢迎引用我们的论文~ If you find our work helpful, please feel free to cite our paper~ ``` @misc{wei2023skywork, title={Skywork: A More Open Bilingual Foundation Model}, author={Tianwen Wei and Liang Zhao and Lichang Zhang and Bo Zhu and Lijie Wang and Haihua Yang and Biye Li and Cheng Cheng and Weiwei Lü and Rui Hu and Chenxia Li and Liu Yang and Xilin Luo and Xuejie Wu and Lunan Liu and Wenjun Cheng and Peng Cheng and Jianhao Zhang and Xiaoyu Zhang and Lei Lin and Xiaokun Wang and Yutuan Ma and Chuanhai Dong and Yanqi Sun and Yifu Chen and Yongyi Peng and Xiaojuan Liang and Shuicheng Yan and Han Fang and Yahui Zhou}, year={2023}, eprint={2310.19341}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ``` @article{skyworkmath, title={SkyMath: Technical Report}, author={Liu Yang, Haihua Yang, Wenjun Cheng, Lei Lin, Chenxia Li, Yifu Chen, Lunan Liu, Jianfei Pan, Tianwen Wei, Biye Li, Liang Zhao, Lijie Wang, Bo Zhu, Guoliang Li, Xuejie Wu, Xilin Luo, Rui Hu}, journal={arXiv preprint arXiv: 2310.16713}, url={https://arxiv.org/abs/2310.16713}, year={2023} } ``` ``` @article{Skywork_Multi-Modal_Group_Empirical_Study_Towards_2023, author = {Skywork Multi-Modal Group}, month = sep, title = {{Empirical Study Towards Building An Effective Multi-Modal Large Language Model}}, year = {2023} } ```
minoosh/AST-finetuned-on-shEMO_speech	minoosh	2023-11-01T12:50:15Z	5	0	transformers	[ "transformers", "pytorch", "audio-spectrogram-transformer", "audio-classification", "generated_from_trainer", "base_model:MIT/ast-finetuned-audioset-10-10-0.4593", "base_model:finetune:MIT/ast-finetuned-audioset-10-10-0.4593", "endpoints_compatible", "region:us" ]	audio-classification	2023-11-01T10:57:42Z	--- base_model: MIT/ast-finetuned-audioset-10-10-0.4593 tags: - generated_from_trainer metrics: - accuracy model-index: - name: AST-finetuned-on-shEMO_speech results: [] --- <!-- 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. --> # AST-finetuned-on-shEMO_speech This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6988 - Accuracy: 0.7967 ## 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: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.8657 \| 1.0 \| 75 \| 0.7066 \| 0.7867 \| \| 0.6951 \| 2.0 \| 150 \| 0.6622 \| 0.7567 \| \| 0.3368 \| 3.0 \| 225 \| 0.5851 \| 0.8433 \| \| 0.1414 \| 4.0 \| 300 \| 0.7233 \| 0.79 \| \| 0.1011 \| 5.0 \| 375 \| 0.8763 \| 0.7967 \| \| 0.0438 \| 6.0 \| 450 \| 0.9009 \| 0.8067 \| \| 0.0108 \| 7.0 \| 525 \| 1.0540 \| 0.83 \| \| 0.0033 \| 8.0 \| 600 \| 1.0177 \| 0.81 \| \| 0.0003 \| 9.0 \| 675 \| 1.1074 \| 0.84 \| \| 0.0113 \| 10.0 \| 750 \| 1.1107 \| 0.8433 \| \| 0.0002 \| 11.0 \| 825 \| 1.1273 \| 0.8367 \| \| 0.0001 \| 12.0 \| 900 \| 1.1634 \| 0.8333 \| \| 0.0001 \| 13.0 \| 975 \| 1.1502 \| 0.84 \| \| 0.0045 \| 14.0 \| 1050 \| 1.1541 \| 0.84 \| \| 0.0039 \| 15.0 \| 1125 \| 1.1550 \| 0.84 \| ### Framework versions - Transformers 4.34.1 - Pytorch 1.12.0+cu116 - Datasets 2.14.6 - Tokenizers 0.14.1
ubermenchh/sd-class-butterflies-32	ubermenchh	2023-11-01T12:43:51Z	4	1	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2023-11-01T12:43:04Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('ubermenchh/sd-class-butterflies-32') image = pipeline().images[0] image ```
rakeshpardeshi25/distilbert-base-uncased-finetuned-emotion	rakeshpardeshi25	2023-11-01T12:35:51Z	3	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-10-31T09:16:13Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.642 - name: F1 type: f1 value: 0.5564294349778702 --- <!-- 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. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 1.0079 - Accuracy: 0.642 - F1: 0.5564 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 1.462 \| 1.0 \| 63 \| 1.1656 \| 0.579 \| 0.4533 \| \| 1.0694 \| 2.0 \| 126 \| 1.0079 \| 0.642 \| 0.5564 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cpu - Datasets 2.14.6 - Tokenizers 0.14.1
roystonsanctis/kp_model	roystonsanctis	2023-11-01T12:35:25Z	2	0	diffusers	[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	text-to-image	2023-11-01T12:35:21Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of a sks keerthan prabhu tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Text encoder was not trained.
thrunlab/t5-large_cola_sp0_ar0_test	thrunlab	2023-11-01T12:26:08Z	7	0	transformers	[ "transformers", "pytorch", "t5", "text-classification", "generated_from_trainer", "dataset:glue", "base_model:google-t5/t5-large", "base_model:finetune:google-t5/t5-large", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2023-11-01T01:26:25Z	--- license: apache-2.0 base_model: t5-large tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: t5-large_cola_sp0_ar0_test results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: cola split: validation args: cola metrics: - name: Accuracy type: accuracy value: 0.712890625 --- <!-- 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. --> # t5-large_cola_sp0_ar0_test This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 79.0284 - Accuracy: 0.7129 ## 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: 5e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 1 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 20 - training_steps: 10 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.11.6
sanak/llm_seller	sanak	2023-11-01T12:25:45Z	4	1	peft	[ "peft", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2023-11-01T12:25:40Z	--- library_name: peft base_model: meta-llama/Llama-2-7b-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0 ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0
Sapare/lion-running	Sapare	2023-11-01T12:22:27Z	0	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-11-01T12:15:47Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### lion-running Dreambooth model trained by Sapare following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: CVR-66 Sample pictures of this concept: ![0](https://huggingface.co/Sapare/lion-running/resolve/main/sample_images/lio(5).jpg) ![1](https://huggingface.co/Sapare/lion-running/resolve/main/sample_images/lio(2).jpg) ![2](https://huggingface.co/Sapare/lion-running/resolve/main/sample_images/lio(3).jpg) ![3](https://huggingface.co/Sapare/lion-running/resolve/main/sample_images/lio(4).jpg) ![4](https://huggingface.co/Sapare/lion-running/resolve/main/sample_images/lio(1).jpg)
legacy107/flan-t5-large-ia3-bioasq-paraphrase	legacy107	2023-11-01T12:13:02Z	15	0	peft	[ "peft", "dataset:legacy107/bioasq10b-factoid", "base_model:google/flan-t5-large", "base_model:adapter:google/flan-t5-large", "region:us" ]	null	2023-09-16T05:07:03Z	--- library_name: peft base_model: google/flan-t5-large datasets: - legacy107/bioasq10b-factoid --- ## Training procedure ### Framework versions - PEFT 0.5.0 - PEFT 0.5.0 - PEFT 0.5.0 - PEFT 0.5.0 - PEFT 0.5.0 - PEFT 0.5.0
legacy107/flan-t5-large-ia3-covidqa	legacy107	2023-11-01T12:12:00Z	0	0	peft	[ "peft", "dataset:minh21/COVID-QA-Chunk-64-question-answering-biencoder-data-90_10", "region:us" ]	null	2023-10-10T00:42:46Z	--- library_name: peft datasets: - minh21/COVID-QA-Chunk-64-question-answering-biencoder-data-90_10 --- ## Training procedure ### Framework versions - PEFT 0.5.0 - PEFT 0.5.0 - PEFT 0.5.0
KyriaAnnwyn/lora-trained-plu4-xl	KyriaAnnwyn	2023-11-01T12:10:22Z	1	1	diffusers	[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2023-11-01T11:46:10Z	--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of plutosha cat tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - KyriaAnnwyn/lora-trained-plu4-xl These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of plutosha cat using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
legacy107/flan-t5-large-ia3-cpgQA	legacy107	2023-11-01T12:09:28Z	4	0	peft	[ "peft", "pytorch", "tensorboard", "t5", "dataset:minh21/cpgQA-v1.0-unique-context-test-10-percent-validation-10-percent", "region:us" ]	null	2023-08-31T23:53:16Z	--- library_name: peft datasets: - minh21/cpgQA-v1.0-unique-context-test-10-percent-validation-10-percent --- ## Training procedure ### Framework versions - PEFT 0.5.0 - PEFT 0.5.0 - PEFT 0.5.0 - PEFT 0.5.0 - PEFT 0.5.0 - PEFT 0.5.0
crystalkur/textual_inversion_cat	crystalkur	2023-11-01T12:06:14Z	10	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-11-01T06:12:34Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - crystalkur/textual_inversion_cat These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
lmarchyok/new_results-100	lmarchyok	2023-11-01T11:59:42Z	3	0	transformers	[ "transformers", "tf", "bert", "fill-mask", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-11-01T11:53:29Z	--- tags: - generated_from_keras_callback model-index: - name: lmarchyok/new_results-100 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # lmarchyok/new_results-100 This model is a fine-tuned version of [pretrained_models/ClinicalBERT_1a/pytorch_model.bin](https://huggingface.co/pretrained_models/ClinicalBERT_1a/pytorch_model.bin) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.4308 - Validation Loss: 4.2847 - Epoch: 8 ## 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -876, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 10.5498 \| 8.9095 \| 0 \| \| 8.3823 \| 7.9120 \| 1 \| \| 7.5092 \| 6.9472 \| 2 \| \| 6.6263 \| 5.8327 \| 3 \| \| 5.7999 \| 5.2736 \| 4 \| \| 5.2952 \| 5.0061 \| 5 \| \| 4.9444 \| 4.5906 \| 6 \| \| 4.6362 \| 4.2785 \| 7 \| \| 4.4308 \| 4.2847 \| 8 \| ### Framework versions - Transformers 4.26.1 - TensorFlow 2.11.0 - Datasets 2.13.1 - Tokenizers 0.13.2
LoneStriker/Skywork-13B-Spicyboros-3.1-GGUF	LoneStriker	2023-11-01T11:59:26Z	4	6	null	[ "gguf", "dataset:jondurbin/airoboros-3.1", "arxiv:2310.19341", "arxiv:2310.16713", "license:other", "endpoints_compatible", "region:us" ]	null	2023-11-01T11:50:41Z	--- license: other license_name: license license_link: >- https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20Community%20License.pdf datasets: - jondurbin/airoboros-3.1 --- <!-- <div align="center"> <h1> ✨Skywork </h1> </div> --> <div align="center"><img src="misc/skywork_logo.jpeg" width="550"/></div> <p align="center"> 👨‍💻 <a href="https://github.com/SkyworkAI/Skywork" target="_blank">Github</a> • 🤗 <a href="https://huggingface.co/Skywork" target="_blank">Hugging Face</a>• 🤖 <a href="https://modelscope.cn/organization/Skywork" target="_blank">ModelScope</a> • 💬 <a href="https://github.com/SkyworkAI/Skywork/blob/main/misc/wechat.png?raw=true" target="_blank">WeChat</a>• 📜<a href="http://arxiv.org/abs/2310.19341" target="_blank">Tech Report</a> </p> <div align="center"> [🎉天工在线对话平台已正式向公众开放](https://sso.tiangong.cn/?redirect=https://model-platform.tiangong.cn/overview&client_id=200005) </div> <div align="center"> [![GitHub Stars](https://img.shields.io/github/stars/SkyworkAI/Skywork)](https://github.com/SkyworkAI/Skywork/stargazers) [![GitHub Forks](https://img.shields.io/github/forks/SkyworkAI/Skywork)](https://github.com/SkyworkAI/Skywork/fork) </div> # 模型介绍（Introduction） Skywork-13B-Base模型在高质量清洗过滤的3.2万亿个多语言（主要是中文和英文）和代码数据上进行预训练，它在多种评测和各种基准测试上都展现了同等规模模型的最佳效果。 Skywork-13B-Base: The model was trained on a high-quality cleaned dataset consisting of 3.2 trillion multilingual data (mainly Chinese and English) and code. It has demonstrated the best performance among models of similar scale in various evaluations and benchmark tests. 如果您希望了解更多的信息，如训练方案，评估方法，请参考我们的[技术报告](http://arxiv.org/abs/2310.19341)，[Skymath](https://arxiv.org/abs/2310.16713)论文，[SkyworkMM](https://github.com/will-singularity/Skywork-MM/blob/main/skywork_mm.pdf)论文。 If you are interested in more training and evaluation details, please refer to our [technical report](http://arxiv.org/abs/2310.19341), [Skymath]((https://arxiv.org/skywork-tech-report)) paper and [SkyworkMM](https://github.com/will-singularity/Skywork-MM/blob/main/skywork_mm.pdf) paper. ## 训练数据（Training Data）我们精心搭建了数据清洗流程对文本中的低质量数据、有害信息、敏感信息进行清洗过滤。我们的Skywork-13B-Base模型是在清洗后的3.2TB高质量中、英、代码数据上进行训练，其中英文占比52.2%，中文占比39.6%，代码占比8%，在兼顾中文和英文上的表现的同时，代码能力也能有保证。 We have developed a data cleaning pipeline with great care to effectively clean and filter low-quality data and eliminate harmful information from text data. Our Skywork-13B-Base model is trained on a dataset with 3.2TB tokens that consists of high-quality Chinese, English, and code data, all of which have been thoroughly cleaned. The English data comprises 52.2% of the dataset, the Chinese data accounts for 39.6%, and the code data makes up 8%. This comprehensive approach ensures optimal performance for both Chinese and English while also maintaining the ability to handle code. \| \| Category \| Percentage \| \|-------------\|------------------\|------------\| \| English \| Webpages \| 39.8% \| \| \| Books \| 3.6% \| \| \| Academic Papers \| 3.0% \| \| \| Encyclopedia \| 0.5% \| \| \| Miscellany \| 2.9% \| \| Chinese \| Webpages \| 30.4% \| \| \| Social Media \| 5.5% \| \| \| Encyclopedia \| 0.8% \| \| \| Miscellany \| 3.1% \| \| Other Lang. \| Encyclopedia \| 2.4% \| \| Code \| Github \| 8.0% \| ## 模型结构（Model Structure）与Llama-2-13B模型对比，天工Skywork-13B模型采用相对更加瘦长的网络结构，层数为52层，同时将FFN Dim和Hidden Dim缩小到12288和4608，从而保证模型参数量和原始Llama-2-13B模型相当。根据我们前期实验对比，相对瘦长的网络结构在大Batch Size训练下可以取得更好的泛化效果。Skywork-13B和Llama-2-13B模型的对比如下： Compared to the Llama2-13B model, the Skywork-13B model adopts a relatively thinner and deeper network structure with 52 layers. At the same time, the FFN Dim and Hidden Dim are reduced to 12288 and 4608, respectively, to ensure that the model has a similar number of parameters as the original Llama-13B model. Based on our preliminary experimental results, a relatively thinner and deeper network structure can achieve better generalization performance under large batch size training. The detailed comparison between the Skywork-13B and Llama-2-13B models is as follows: \| Model Structure \| Llama2-13B \| Skywork-13B \| \|----------------------\|:----:\|:-----------:\| \| Vocab. Size \| 32,000 \| 65,536 \| \| Hidden Dim. \| 5,120 \| 4,608 \| \| FFN Dim. \| 13,696 \| 12,288 \| \| Head Dim. \| 128 \| 128 \| \| Num. Heads \| 40 \| 36 \| \| Num. Layers \| 40 \| 52 \| \| Seq. Len. \| 4,096 \| 4,096 \| \| Positional Embedding \| RoPE \| RoPE \| ## 分词器（Tokenizer）我们使用Byte-Pair Encoding（BPE）对数据进行分词，词表大小为65536，其中拉丁字符和子词为32000个，汉字和Unicode符号8000个，汉语词语25519个，剩下的17个为保留字。 We use Byte-Pair Encoding (BPE) to tokenize the data, with a vocabulary size of 65536. Among them, there are 32000 Latin characters and subwords, 8000 Chinese characters and Unicode symbols, 25519 Chinese words, and the remaining 17 are reserved words. \| Category \| Size \| \|---------------------------------\|--------\| \| Latin based words & subwords \| 32000 \| \| Chinese characters & Unicode symbols \| 8000 \| \| Chinese words \| 25519 \| \| Reserved symbols \| 17 \| \| Total \| 65536 \| # 模型评估（Evaluation） ## 领域数据困惑度评估（Perplexity Evaluaiton）语言模型训练的本质上是让预测下一个词更准确。基于这个认知，我们认为评估基础大模型一个重要的方式是评估在各大领域上语言模型生成文章的概率。在模型训练中预测下一个词的概率一般使用Cross Entropy损失函数，整体的损失函数为每个位置预测真实词损失的平均，则有： $$loss = \sum^{n}_{i=1} log(p_i) / n = log( \prod_{i=1}^n p_i) / n$$ 其中$n$是文档的长度，即token数，$p_i$是位置i上真实词的概率，我们知道文档中每一个位置上真实词的概率的联乘则为生成该文档的概率，如此我们就将loss和生成文章的概率联系在了一起。而不同模型因为使用的分词器不同，具有不同的token数，因此对损失函数乘以token数目$n$，这样就仅考虑生成文章的概率部分，不同模型也可以进行比较。我们将标准化后loss取指数转换成perplexity，使得模型的差异更加可读。为了阅读方便后续提到的loss和ppl为模型标准化后的loss和perplexity。基于上述分析，我们对对多个领域筛选出2023年9月份新发布的几百到上千篇高质量文章，并人工进行了核对。保证所有的测试数据不在天工模型以及其他所有模型的训练集中，并且测试数据的来源也足够广泛，质量也高。我们可以选取当前最新的文章评测不同模型的ppl，模型很难作弊。下图列出了不同开源模型，天工Skywork-13B-Base取得最优效果，证明了我们的Base模型的基础能力处于国内开源模型中文最强水平。 We have chosen several hundred to thousands of high-quality articles that were published after September 1, 2023 across various fields. We have manually verified these articles to ensure their quality. It is important to note that none of the test data used in evaluating the Skywork model or any other models is included in their training set. Furthermore, the test data is diverse and of high quality, making it challenging for the models to gain an unfair advantage. The figure below displays the performance of different open source models. Skywork-13B-Base achieves the best results. \| \| Tech \| Movie \| Gov. \| Game \| Finance \| General \| Average \| \|------------------\|-------\|-------\|-------\|-------\|---------\|---------\|---------\| \| MOSS-7B \| 20.83 \| 39.66 \| 11.08 \| 31.24 \| 10.59 \| 13.25 \| 18.50 \| \| InternLM-7B \| 13.43 \| 24.90 \| 5.88 \| 19.78 \| 6.17 \| 8.10 \| 11.17 \| \| Qwen-7B \| 13.39 \| 25.16 \| 5.55 \| 19.26 \| 5.76 \| 7.78 \| 10.83 \| \| Baichuan2-7B \| 12.89 \| 23.26 \| 5.34 \| 18.36 \| 5.68 \| 7.62 \| 10.41 \| \| LLaMA2-13B \| 23.26 \| 50.66 \| 18.09 \| 32.52 \| 14.85 \| 16.55 \| 23.54 \| \| Xverse-13B \| 12.55 \| 23.49 \| 5.20 \| 17.69 \| 5.54 \| 7.46 \| 10.19 \| \| Baichuan-13B \| 12.38 \| 22.46 \| 5.21 \| 17.59 \| 5.42 \| 7.37 \| 10.03 \| \| Baichuan2-13B \| 12.14 \| 21.85 \| 5.05 \| 17.15 \| 5.35 \| 7.24 \| 9.81 \| \| Qwen-14B \| 11.90 \| 22.43 \| 4.89 \| 16.94 \| 5.24 \| 7.03 \| 9.67 \| \| InternLM-20B \| 12.34 \| 22.06 \| 5.75 \| 17.45 \| 5.73 \| 7.78 \| 10.34 \| \| Aquila2-34B \| 14.62 \| 29.09 \| 5.72 \| 21.78 \| 5.83 \| 8.45 \| 11.73 \| \| Skywork-13B-Base \| 11.58 \| 21.84 \| 4.76 \| 17.28 \| 4.92 \| 6.82 \| 9.42 \| ### 评测数据和评测脚本（Loss Evaluation）我们将评测数据和评测脚本也进行了开源，下载github上的代码运行下面命令则可以复现我们的结果。 We have also open-sourced the data and evaluation scripts. You can reproduce our results by running the following command. ``` bash bash_scripts/skywork_eval_loss.sh ``` ## Benchmark评估（Benchmark Results）我们评估了各大权威评测benchmark上的结果作为参考，包括C-Eval，MMLU，CMMLU，GSM8K。遵循之前的评估流程，C-Eval、MMLU、CMMLU测试5-shot结果，GSM8K测试8-shot结果。可以看到Skywork-13B-Base模型在中文开源模型中处于前列，在同等参数规模下为最优水平。 We evaluated Skywork-13B-Base on several popular benchmarks, including C-Eval, MMLU, CMMLU, and GSM8K. Following the previous evaluation process, we tested the 5-shot results of C-Eval, MMLU, and CMMLU, and the 8-shot results of GSM8K. It can be seen that the Skywork-13B-Base model is among the top models in the Chinese open source model community, performing at an optimal level with the same parameter scale. \| Model \| C-Eval \| CMMLU \| MMLU \| GSM8K \| \|-------------------------\|:-----:\|:---------------:\|:----------:\|:-------:\| \| LLaMA-1-13B-Base \| 35.5 \| 31.2 \| 46.9 \| 17.8 \| \| Open-LLaMA-13B \| 27.1 \| 26.7 \| 42.7 \| 12.4 \| \| LLaMA-2-13B-Base \| 36.5 \| 36.6 \| 54.8 \| 28.7 \| \| InternLM-20B \| 58.8 \| - \| 62.0 \| 52.6 \| \| Qwen-14B-Base \| 72.1 \| 71.0 \| 66.3 \| 61.3 \| \| Aquila2-34B-Base \| 63.1 \| 71.4 \| 64.2 \| 58.4 \| \| XVERSE-13B-Base \| 54.7 \| - \| 55.1 \| - \| \| Baichuan-13B-Base \| 52.4 \| 55.3 \| 51.6 \| 26.6 \| \| Baichuan-2-13B-Base \| 58.1 \| 62.0 \| 59.2 \| 52.3 \| \| Skywork-13B-Base (ours) \| 60.6 \| 61.8 \| 62.1 \| 55.8 \| ## Benchmark评估详细结果我们给出Skywork-13B-Base模型在C-Eval，CMMLU，MMLU上模型的详细结果。 We provide detailed results of the Skywork-13B-Base model on C-EVAL, CMMLU, and MMLU. \| Benchmark \| STEM \| Humanities \| Social Science \| Other \| China Specific \| Hard \| Average \| \|:-----:\|:---------:\|:--------:\|:-------------:\|:--------:\|:--------:\|:--------:\|:--------:\| \| C-EVAL \| 51.2 \| 67.8 \| 74.6 \| 57.5 \| - \| 39.4 \| 60.6 \| \| CMMLU \| 49.5 \| 69.3 \| 65.9 \| 63.3 \| 64.2 \| - \| 61.8 \| \| MMLU \| 51.6 \| 58.0 \| 72.5 \| 68.8 \| - \| - \| 62.1 \| # 快速开始（Quickstart）我们将模型参数、配置文件、tokenizer等在huggingface和modelscope上进行了开源。 We have open-sourced the model parameters, configuration files, tokenizer, and more on Huggingface and Modelscope. ## 依赖安装（Requirements） - Python 3.8及以上版本 - Pytorch 2.0及以上版本 - CUDA建议使用11.4以上版本。 Skywork-13B-Base模型，Skywork-13B-Chat模型和Skywork-13B-Math模型运行下面的脚本进行Python依赖安装。 - Python 3.8 and above - Pytorch 2.0 and above - CUDA 11.4 and above are recommended. Skywork-13B-Base model, Skywork-13B-Chat model, and Skywork-13B-Math model run the following script for Python dependency installation: ```shell pip install -r requirements.txt ``` ## Huggingface模型测试（Demonstration） ### Base 模型推理（Base Model Inference） ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer >>> from transformers.generation import GenerationConfig >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("SkyworkAI/Skywork-13B-Base", trust_remote_code=True) >>> model = AutoModelForCausalLM.from_pretrained("SkyworkAI/Skywork-13B-Base", device_map="auto", trust_remote_code=True).eval() >>> inputs = tokenizer('陕西的省会是西安', return_tensors='pt').to(model.device) >>> response = model.generate(inputs.input_ids, max_length=128) >>> print(tokenizer.decode(response.cpu()[0], skip_special_tokens=True)) 陕西的省会是西安，西安是我国著名的古都，在历史上有十三个朝代在此建都，所以西安又被称为“十三朝古都”。西安是我国著名的旅游城市，每年都有大量的游客来到西安旅游，西安的旅游资源非常丰富，有很多著名的旅游景点，比如秦始皇兵马俑、大雁塔、华清池、大唐芙蓉园、西安城墙、大明宫国家遗址公园、西安碑林博物馆、西安钟楼、西安鼓楼、西安半坡博物馆、西安大兴善寺、西安小雁塔 >>> inputs = tokenizer('陕西的省会是西安，甘肃的省会是兰州，河南的省会是郑州', return_tensors='pt').to(model.device) >>> response = model.generate(inputs.input_ids, max_length=128) >>> print(tokenizer.decode(response.cpu()[0], skip_special_tokens=True)) 陕西的省会是西安，甘肃的省会是兰州，河南的省会是郑州，湖北的省会是武汉，湖南的省会是长沙，江西的省会是南昌，安徽的省会是合肥，江苏的省会是南京，浙江的省会是杭州，福建的省会是福州，广东的省会是广州，广西的省会是南宁，海南的省会是海口，四川的省会是成都，贵州的省会是贵阳，云南的省会是昆明，西藏的省会是拉萨，青海的省会是西宁，宁夏的省会是银川，新疆的省会是乌鲁木齐。 ``` # 模型微调（Fine-tuning） ## 全量微调（Full-parameter Fine-tuning）使用Skywork-13B-Base模型进行预训练微调 ```bash ## preprocess continue pretraining data ## Because pre-training data is usually large, we use a script to process the training data separately. python train/pt_data_preprocess.py \ -t $MODEL_PATH \ -i data/pt_train.jsonl \ -o data_cache/pt_train_demo ## launch training export WANDB_API_KEY=YOUR_WANDB_KEY export WANDB_ENTITY=skywork export WANDB_PROJECT=skywork-13b-opensource export MODEL_PATH=skywork-13b-models/skywork-13b-base export DATA_CACHE_DIR=data_cache/pt_train_demo/pt_train bash bash_scripts/skywork_13b_pt.sh ``` 使用Skywork-13B-Base模型进行有监督微调（SFT, Supevise Fine-tuning） ```bash ## preprocess data and launch training export WANDB_API_KEY=YOUR_WANDB_KEY export WANDB_ENTITY=skywork export WANDB_PROJECT=skywork-13b-opensource export SFT_DATA_DIR=data/sft_data export DATA_CACHE_DIR=data_cache/sft_train_demo bash bash_scripts/skywork_13b_sft.sh ``` ## LoRA微调（PEFT）使用Skywork-13B-Base模型以及LoRA进行预训练微调 ```bash ## preprocess continue pretraining data ## Because pre-training data is usually large, we use a script to process the training data separately. python train/pt_data_preprocess.py \ -t $MODEL_PATH \ -i data/pt_train.jsonl \ -o data_cache/pt_train_demo export WANDB_API_KEY=YOUR_WANDB_KEY export WANDB_ENTITY=skywork export WANDB_PROJECT=skywork-13b-opensource export MODEL_PATH=skywork-13b-models/skywork-13b-base export DATA_CACHE_DIR=data_cache/pt_train_demo/pt_train bash bash_scripts/skywork_13b_pt_lora.sh ``` 使用Skywork-13B-Base模型以及LoRA进行有监督微调（SFT, Supevise Fine-tuning） ```bash export WANDB_API_KEY=YOUR_WANDB_KEY export WANDB_ENTITY=skywork export WANDB_PROJECT=skywork-13b-opensource export SFT_DATA_DIR=data/sft_data export DATA_CACHE_DIR=data_cache/sft_train_demo bash bash_scripts/skywork_13b_sft_lora.sh ``` # 声明和协议（Declaration and License Agreement） ## 声明（Declaration）我们在此声明，不要利用Skywork模型进行任何危害国家社会安全或违法的活动。另外，我们也要求使用者不要将 Skywork 模型用于未经适当安全审查和备案的互联网服务。我们希望所有的使用者都能遵守这个原则，确保科技的发展能在规范和合法的环境下进行。我们已经尽我们所能，来确保模型训练过程中使用的数据的合规性。然而，尽管我们已经做出了巨大的努力，但由于模型和数据的复杂性，仍有可能存在一些无法预见的问题。因此，如果由于使用skywork开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。 We hereby declare that the Skywork model should not be used for any activities that pose a threat to national or societal security or engage in unlawful actions. Additionally, we request users not to deploy the Skywork model for internet services without appropriate security reviews and records. We hope that all users will adhere to this principle to ensure that technological advancements occur in a regulated and lawful environment. We have done our utmost to ensure the compliance of the data used during the model's training process. However, despite our extensive efforts, due to the complexity of the model and data, there may still be unpredictable risks and issues. Therefore, if any problems arise as a result of using the Skywork open-source model, including but not limited to data security issues, public opinion risks, or any risks and problems arising from the model being misled, abused, disseminated, or improperly utilized, we will not assume any responsibility. ## 协议（License Agreement）社区使用Skywork模型需要遵循[《Skywork 模型社区许可协议》](https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20模型社区许可协议.pdf)。Skywork模型支持商业用途，如果您计划将Skywork模型或其衍生品用于商业目的，无需再次申请，但请您仔细阅读[《Skywork 模型社区许可协议》](https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20模型社区许可协议.pdf)并严格遵守相关条款。 The community usage of Skywork model requires [Skywork Community License](https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20Community%20License.pdf). The Skywork model supports commercial use. If you plan to use the Skywork model or its derivatives for commercial purposes, you must abide by terms and conditions within [Skywork Community License](https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20Community%20License.pdf). [《Skywork 模型社区许可协议》》]:https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20模型社区许可协议.pdf [[email protected]]: mailto:[email protected] # 引用和联系我们（Contact Us and Citation）如果您觉得我们的工作对您有帮助，欢迎引用我们的论文~ If you find our work helpful, please feel free to cite our paper~ ``` @misc{wei2023skywork, title={Skywork: A More Open Bilingual Foundation Model}, author={Tianwen Wei and Liang Zhao and Lichang Zhang and Bo Zhu and Lijie Wang and Haihua Yang and Biye Li and Cheng Cheng and Weiwei Lü and Rui Hu and Chenxia Li and Liu Yang and Xilin Luo and Xuejie Wu and Lunan Liu and Wenjun Cheng and Peng Cheng and Jianhao Zhang and Xiaoyu Zhang and Lei Lin and Xiaokun Wang and Yutuan Ma and Chuanhai Dong and Yanqi Sun and Yifu Chen and Yongyi Peng and Xiaojuan Liang and Shuicheng Yan and Han Fang and Yahui Zhou}, year={2023}, eprint={2310.19341}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ``` @article{skyworkmath, title={SkyMath: Technical Report}, author={Liu Yang, Haihua Yang, Wenjun Cheng, Lei Lin, Chenxia Li, Yifu Chen, Lunan Liu, Jianfei Pan, Tianwen Wei, Biye Li, Liang Zhao, Lijie Wang, Bo Zhu, Guoliang Li, Xuejie Wu, Xilin Luo, Rui Hu}, journal={arXiv preprint arXiv: 2310.16713}, url={https://arxiv.org/abs/2310.16713}, year={2023} } ``` ``` @article{Skywork_Multi-Modal_Group_Empirical_Study_Towards_2023, author = {Skywork Multi-Modal Group}, month = sep, title = {{Empirical Study Towards Building An Effective Multi-Modal Large Language Model}}, year = {2023} } ```
shubhamgantayat/bigscience-bloom-560m-brief-history-of-time-model	shubhamgantayat	2023-11-01T11:50:42Z	12	0	transformers	[ "transformers", "pytorch", "bloom", "text-generation", "generated_from_trainer", "base_model:bigscience/bloom-560m", "base_model:finetune:bigscience/bloom-560m", "license:bigscience-bloom-rail-1.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-01T10:44:53Z	--- license: bigscience-bloom-rail-1.0 base_model: bigscience/bloom-560m tags: - generated_from_trainer model-index: - name: bigscience-bloom-560m-brief-history-of-time-model results: [] --- <!-- 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. --> # bigscience-bloom-560m-brief-history-of-time-model This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) on the None dataset. ## 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: 0.0005 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 100 ### Training results ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
omarelsayeed/tsdae_unsup	omarelsayeed	2023-11-01T11:48:07Z	1	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-11-01T11:46:29Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 38790 with parameters: ``` {'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.DenoisingAutoEncoderLoss.DenoisingAutoEncoderLoss` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 3e-05 }, "scheduler": "constantlr", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
lmarchyok/new_results-50	lmarchyok	2023-11-01T11:48:04Z	3	0	transformers	[ "transformers", "tf", "bert", "fill-mask", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-11-01T11:41:49Z	--- tags: - generated_from_keras_callback model-index: - name: lmarchyok/new_results-50 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # lmarchyok/new_results-50 This model is a fine-tuned version of [pretrained_models/ClinicalBERT_1a/pytorch_model.bin](https://huggingface.co/pretrained_models/ClinicalBERT_1a/pytorch_model.bin) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.6160 - Validation Loss: 4.4496 - Epoch: 8 ## 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -876, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 10.5875 \| 8.8027 \| 0 \| \| 8.3830 \| 7.7917 \| 1 \| \| 7.5113 \| 6.9286 \| 2 \| \| 6.7266 \| 5.9542 \| 3 \| \| 5.9461 \| 5.4455 \| 4 \| \| 5.4186 \| 5.2021 \| 5 \| \| 5.0915 \| 4.7773 \| 6 \| \| 4.8382 \| 4.5644 \| 7 \| \| 4.6160 \| 4.4496 \| 8 \| ### Framework versions - Transformers 4.26.1 - TensorFlow 2.11.0 - Datasets 2.13.1 - Tokenizers 0.13.2
Juniplayground/Mistral_orca-7B-512_v1	Juniplayground	2023-11-01T11:45:59Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Open-Orca/Mistral-7B-OpenOrca", "base_model:adapter:Open-Orca/Mistral-7B-OpenOrca", "region:us" ]	null	2023-11-01T11:45:51Z	--- library_name: peft base_model: Open-Orca/Mistral-7B-OpenOrca --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0.dev0
bellagio-ai/WalterNgo-face-vn-pictures-dreambooth-512-2k	bellagio-ai	2023-11-01T11:44:38Z	1	0	diffusers	[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:bellagio-ai/t2i-vietnam-pictures-2k", "base_model:adapter:bellagio-ai/t2i-vietnam-pictures-2k", "license:openrail++", "region:us" ]	text-to-image	2023-11-01T10:14:58Z	--- license: openrail++ base_model: bellagio-ai/t2i-vietnam-pictures-2k instance_prompt: a portrait photo of happy WalterNgo person's face tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - bellagio-ai/WalterNgo-face-vn-pictures-dreambooth-512-2k These are LoRA adaption weights for bellagio-ai/t2i-vietnam-pictures-2k. The weights were trained on a portrait photo of happy WalterNgo person's face using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
linuscarey123/out	linuscarey123	2023-11-01T11:38:11Z	0	0	null	[ "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:finetune:meta-llama/Llama-2-7b-chat-hf", "region:us" ]	null	2023-11-01T11:37:55Z	--- base_model: meta-llama/Llama-2-7b-chat-hf tags: - generated_from_trainer model-index: - name: out results: [] --- <!-- 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. --> # out This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5419 ## 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: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.0573 \| 1.0 \| 250 \| 0.5403 \| \| 0.0432 \| 2.0 \| 500 \| 0.5419 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
kanishka/smolm-autoreg-bpe-babylm-aann-counterfactual-anan-1e-3	kanishka	2023-11-01T11:12:47Z	5	0	transformers	[ "transformers", "pytorch", "opt", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-10-31T03:56:50Z	--- base_model: models/smolm-autoreg-bpe-babylm-aann-counterfactual-anan-1e-3/config.json tags: - generated_from_trainer metrics: - accuracy model-index: - name: smolm-autoreg-bpe-babylm-aann-counterfactual-anan-1e-3 results: [] --- <!-- 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. --> # smolm-autoreg-bpe-babylm-aann-counterfactual-anan-1e-3 This model is a fine-tuned version of [models/smolm-autoreg-bpe-babylm-aann-counterfactual-anan-1e-3/config.json](https://huggingface.co/models/smolm-autoreg-bpe-babylm-aann-counterfactual-anan-1e-3/config.json) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.1635 - Accuracy: 0.4321 ## 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: 0.001 - train_batch_size: 32 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 32000 - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\| \| 3.4374 \| 1.0 \| 18353 \| 3.5466 \| 0.3796 \| \| 3.2099 \| 2.0 \| 36706 \| 3.3491 \| 0.3995 \| \| 3.0711 \| 3.0 \| 55059 \| 3.2446 \| 0.4134 \| \| 2.9939 \| 4.0 \| 73412 \| 3.1977 \| 0.4186 \| \| 2.933 \| 5.0 \| 91765 \| 3.1660 \| 0.4224 \| \| 2.8889 \| 6.0 \| 110118 \| 3.1475 \| 0.4254 \| \| 2.8508 \| 7.0 \| 128471 \| 3.1383 \| 0.4273 \| \| 2.8168 \| 8.0 \| 146824 \| 3.1297 \| 0.4288 \| \| 2.7896 \| 9.0 \| 165177 \| 3.1278 \| 0.4295 \| \| 2.7611 \| 10.0 \| 183530 \| 3.1289 \| 0.4303 \| \| 2.7357 \| 11.0 \| 201883 \| 3.1282 \| 0.4309 \| \| 2.7211 \| 12.0 \| 220236 \| 3.1289 \| 0.4312 \| \| 2.695 \| 13.0 \| 238589 \| 3.1367 \| 0.4315 \| \| 2.6675 \| 14.0 \| 256942 \| 3.1350 \| 0.4323 \| \| 2.6523 \| 15.0 \| 275295 \| 3.1402 \| 0.4318 \| \| 2.6323 \| 16.0 \| 293648 \| 3.1416 \| 0.4322 \| \| 2.6102 \| 17.0 \| 312001 \| 3.1500 \| 0.4322 \| \| 2.5871 \| 18.0 \| 330354 \| 3.1509 \| 0.4325 \| \| 2.5708 \| 19.0 \| 348707 \| 3.1577 \| 0.4323 \| \| 2.549 \| 20.0 \| 367060 \| 3.1635 \| 0.4321 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.14.1
lmarchyok/new_results-1	lmarchyok	2023-11-01T11:12:29Z	3	0	transformers	[ "transformers", "tf", "bert", "fill-mask", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-11-01T11:06:11Z	--- tags: - generated_from_keras_callback model-index: - name: lmarchyok/new_results-1 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # lmarchyok/new_results-1 This model is a fine-tuned version of [pretrained_models/ClinicalBERT_1a/pytorch_model.bin](https://huggingface.co/pretrained_models/ClinicalBERT_1a/pytorch_model.bin) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.5011 - Validation Loss: 4.3356 - Epoch: 8 ## 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -876, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 10.5550 \| 8.8075 \| 0 \| \| 8.3737 \| 7.9108 \| 1 \| \| 7.5230 \| 6.9752 \| 2 \| \| 6.6432 \| 5.9450 \| 3 \| \| 5.7863 \| 5.3979 \| 4 \| \| 5.3367 \| 5.0730 \| 5 \| \| 4.9932 \| 4.7959 \| 6 \| \| 4.6619 \| 4.3309 \| 7 \| \| 4.5011 \| 4.3356 \| 8 \| ### Framework versions - Transformers 4.26.1 - TensorFlow 2.11.0 - Datasets 2.13.1 - Tokenizers 0.13.2
rmiaouh/zephyr-support-chatbot	rmiaouh	2023-11-01T11:09:51Z	0	0	null	[ "generated_from_trainer", "base_model:TheBloke/zephyr-7B-alpha-GPTQ", "base_model:finetune:TheBloke/zephyr-7B-alpha-GPTQ", "license:mit", "region:us" ]	null	2023-11-01T10:29:01Z	--- license: mit base_model: TheBloke/zephyr-7B-alpha-GPTQ tags: - generated_from_trainer model-index: - name: zephyr-support-chatbot results: [] --- <!-- 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. --> # zephyr-support-chatbot This model is a fine-tuned version of [TheBloke/zephyr-7B-alpha-GPTQ](https://huggingface.co/TheBloke/zephyr-7B-alpha-GPTQ) on the None dataset. ## 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: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 250 ### Training results ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
briannlongzhao/simpsons	briannlongzhao	2023-11-01T11:00:32Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:stabilityai/stable-diffusion-2-1", "base_model:finetune:stabilityai/stable-diffusion-2-1", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-11-01T09:27:18Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1 instance_prompt: a cartoon of sks tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - briannlongzhao/simpsons This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a cartoon of sks using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: True.

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