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hllj/sft-mistral-v1-clean-valid
hllj
2023-11-29T20:23:05Z
0
0
null
[ "transformers", "mistral", "text-generation", "generated_from_trainer", "base_model:hllj/mistral-vi-math", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2023-11-29T20:23:05Z
2023-11-29T18:03:25.000Z
null
null
--- base_model: hllj/mistral-vi-math tags: - generated_from_trainer model-index: - name: sft-mistral-v1-clean-valid 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. --> # sft-mistral-v1-clean-valid This model is a fine-tuned version of [hllj/mistral-vi-math](https://huggingface.co/hllj/mistral-vi-math) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4106 ## 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: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.2982 | 1.02 | 1000 | 0.4478 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0 - Datasets 2.15.0 - Tokenizers 0.15.0
null
transformers
text-generation
null
null
null
null
null
null
null
null
null
hllj/sft-mistral-v1-clean-valid
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shait7999/hanayokoizumi
shait7999
2023-11-29T18:06:28Z
0
0
null
[ "region:us" ]
2023-11-29T18:06:28Z
2023-11-29T18:03:43.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
shait7999/hanayokoizumi
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sd-concepts-library/babies-poster
sd-concepts-library
2023-11-29T18:04:14Z
0
0
null
[ "base_model:stabilityai/stable-diffusion-2", "license:mit", "region:us" ]
2023-11-29T18:04:14Z
2023-11-29T18:04:13.000Z
null
null
--- license: mit base_model: stabilityai/stable-diffusion-2 --- ### babies-poster on Stable Diffusion This is the `<babies-poster>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<babies-poster> 0](https://huggingface.co/sd-concepts-library/babies-poster/resolve/main/concept_images/baby2.jpeg) ![<babies-poster> 1](https://huggingface.co/sd-concepts-library/babies-poster/resolve/main/concept_images/baby4.jpeg) ![<babies-poster> 2](https://huggingface.co/sd-concepts-library/babies-poster/resolve/main/concept_images/baby3.jpeg) ![<babies-poster> 3](https://huggingface.co/sd-concepts-library/babies-poster/resolve/main/concept_images/baby1.jpeg) ![<babies-poster> 4](https://huggingface.co/sd-concepts-library/babies-poster/resolve/main/concept_images/baby7.jpeg) ![<babies-poster> 5](https://huggingface.co/sd-concepts-library/babies-poster/resolve/main/concept_images/baby5.jpeg) ![<babies-poster> 6](https://huggingface.co/sd-concepts-library/babies-poster/resolve/main/concept_images/baby6.jpeg)
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sd-concepts-library/babies-poster
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kyle0518/HW3_rank256
kyle0518
2023-11-29T23:25:17Z
0
0
null
[ "region:us" ]
2023-11-29T23:25:17Z
2023-11-29T18:05:12.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
kyle0518/HW3_rank256
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nisargm/pegasus-large-NewsSummarization
nisargm
2023-11-29T18:08:42Z
0
0
null
[ "region:us" ]
2023-11-29T18:08:42Z
2023-11-29T18:08:42.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
nisargm/pegasus-large-NewsSummarization
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zhaoxudong01/Yuan2.0-51B
zhaoxudong01
2023-11-29T18:09:52Z
0
0
null
[ "license:apache-2.0", "region:us" ]
2023-11-29T18:09:52Z
2023-11-29T18:09:52.000Z
null
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--- license: apache-2.0 ---
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zhaoxudong01/Yuan2.0-51B
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GiuseppeECardinale/DEsando
GiuseppeECardinale
2023-11-29T18:12:17Z
0
0
null
[ "license:openrail", "region:us" ]
2023-11-29T18:12:17Z
2023-11-29T18:10:18.000Z
null
null
--- license: openrail ---
null
null
null
null
null
null
null
null
null
null
null
null
GiuseppeECardinale/DEsando
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow10
FounderOfHuggingface
2023-11-29T18:10:27Z
0
0
null
[ "peft", "arxiv:1910.09700", "base_model:gpt2", "region:us" ]
2023-11-29T18:10:27Z
2023-11-29T18:10:23.000Z
null
null
--- library_name: peft base_model: gpt2 --- # 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] - **Funded by [optional]:** [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 Dataset 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 Dataset 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 0.6.2
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peft
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow10
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snombler/schmeat
snombler
2023-11-29T18:44:21Z
0
0
null
[ "gguf", "region:us" ]
2023-11-29T18:44:21Z
2023-11-29T18:12:38.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
snombler/schmeat
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CLMBR/preemption_testing-full-transformer-30
CLMBR
2023-11-30T00:17:13Z
0
0
null
[ "transformers", "pytorch", "opt", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2023-11-30T00:17:13Z
2023-11-29T18:13:55.000Z
null
null
Entry not found
null
transformers
text-generation
null
null
null
null
null
null
null
null
null
CLMBR/preemption_testing-full-transformer-30
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yizhilll/git-base-pokemon
yizhilll
2023-11-29T18:30:43Z
0
0
null
[ "transformers", "safetensors", "git", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2023-11-29T18:30:43Z
2023-11-29T18:13:59.000Z
null
null
Entry not found
null
transformers
text-generation
null
null
null
null
null
null
null
null
null
yizhilll/git-base-pokemon
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giangduong/adapter-multinews-trainv4
giangduong
2023-11-29T18:15:49Z
0
0
null
[ "region:us" ]
2023-11-29T18:15:49Z
2023-11-29T18:14:56.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
giangduong/adapter-multinews-trainv4
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K-kiron/distilbert-lr-cosine-scheduler
K-kiron
2023-11-29T18:19:39Z
0
0
null
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2023-11-29T18:19:39Z
2023-11-29T18:16:23.000Z
null
null
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-lr-cosine-scheduler 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. --> # distilbert-lr-cosine-scheduler 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: - Loss: 0.2986 - Accuracy: 0.8934 - F1: 0.8932 ## 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: cosine - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.1+cu121 - Datasets 2.12.0 - Tokenizers 0.13.3
null
transformers
text-classification
null
null
null
null
null
null
null
null
null
K-kiron/distilbert-lr-cosine-scheduler
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haryoaw/scenario-non-kd-from-scratch-data-tyqiangz-multilingual-sentiments-malay-model-x
haryoaw
2023-11-29T18:18:00Z
0
0
null
[ "transformers", "pytorch", "xlm-roberta", "text-classification", "generated_from_trainer", "dataset:multilingual-sentiments", "base_model:xlm-roberta-base", "license:mit", "model-index", "endpoints_compatible", "region:us" ]
2023-11-29T18:18:00Z
2023-11-29T18:17:07.000Z
null
null
--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - multilingual-sentiments metrics: - accuracy - f1 model-index: - name: scenario-non-kd-from-scratch-data-tyqiangz-multilingual-sentiments-malay-model-x results: - task: name: Text Classification type: text-classification dataset: name: multilingual-sentiments type: multilingual-sentiments config: malay split: validation args: malay metrics: - name: Accuracy type: accuracy value: 0.6238805970149254 - name: F1 type: f1 value: 0.5868956366785256 --- <!-- 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. --> # scenario-non-kd-from-scratch-data-tyqiangz-multilingual-sentiments-malay-model-x This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the multilingual-sentiments dataset. It achieves the following results on the evaluation set: - Loss: 3.0493 - Accuracy: 0.6239 - F1: 0.5869 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6969 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 0.68 | 100 | 0.8916 | 0.4915 | 0.3639 | | No log | 1.36 | 200 | 0.8477 | 0.5771 | 0.4958 | | No log | 2.04 | 300 | 0.9598 | 0.5771 | 0.4267 | | No log | 2.72 | 400 | 0.8090 | 0.6010 | 0.5801 | | 0.7834 | 3.4 | 500 | 0.8969 | 0.6189 | 0.5971 | | 0.7834 | 4.08 | 600 | 1.0623 | 0.6368 | 0.6102 | | 0.7834 | 4.76 | 700 | 1.3065 | 0.6060 | 0.5366 | | 0.7834 | 5.44 | 800 | 1.6245 | 0.6179 | 0.5417 | | 0.7834 | 6.12 | 900 | 1.9429 | 0.5940 | 0.4724 | | 0.2823 | 6.8 | 1000 | 1.5144 | 0.6159 | 0.5910 | | 0.2823 | 7.48 | 1100 | 1.6368 | 0.6458 | 0.6231 | | 0.2823 | 8.16 | 1200 | 2.0009 | 0.5831 | 0.5738 | | 0.2823 | 8.84 | 1300 | 2.0191 | 0.6100 | 0.5634 | | 0.2823 | 9.52 | 1400 | 2.1917 | 0.6388 | 0.5793 | | 0.1213 | 10.2 | 1500 | 2.0430 | 0.6348 | 0.5901 | | 0.1213 | 10.88 | 1600 | 2.6876 | 0.5980 | 0.5471 | | 0.1213 | 11.56 | 1700 | 2.6026 | 0.6358 | 0.5932 | | 0.1213 | 12.24 | 1800 | 2.8122 | 0.6129 | 0.5579 | | 0.1213 | 12.93 | 1900 | 2.6769 | 0.6189 | 0.5731 | | 0.0555 | 13.61 | 2000 | 2.6787 | 0.6418 | 0.5997 | | 0.0555 | 14.29 | 2100 | 3.0463 | 0.6100 | 0.5937 | | 0.0555 | 14.97 | 2200 | 2.8793 | 0.6428 | 0.6061 | | 0.0555 | 15.65 | 2300 | 3.0057 | 0.6259 | 0.5970 | | 0.0555 | 16.33 | 2400 | 3.0933 | 0.6468 | 0.6181 | | 0.0363 | 17.01 | 2500 | 3.0729 | 0.6428 | 0.6067 | | 0.0363 | 17.69 | 2600 | 3.0493 | 0.6239 | 0.5869 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1 - Datasets 2.14.5 - Tokenizers 0.13.3
null
transformers
text-classification
null
null
null
null
null
null
null
null
null
haryoaw/scenario-non-kd-from-scratch-data-tyqiangz-multilingual-sentiments-malay-model-x
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lvPoellhuber/ift6758-hw6
lvPoellhuber
2023-11-29T18:18:06Z
0
0
null
[ "region:us" ]
2023-11-29T18:18:06Z
2023-11-29T18:18:06.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
lvPoellhuber/ift6758-hw6
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javismiles/javilora5
javismiles
2023-11-29T20:42:53Z
0
0
null
[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]
2023-11-29T20:42:53Z
2023-11-29T18:18:16.000Z
null
null
--- 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 - javismiles/javilora5 These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the javismiles/lora5 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)
null
diffusers
text-to-image
null
null
null
null
null
null
null
null
null
javismiles/javilora5
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Realgon/roberta_sst5_padding40model
Realgon
2023-11-29T19:12:30Z
0
0
null
[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "base_model:roberta-base", "license:mit", "endpoints_compatible", "region:us" ]
2023-11-29T19:12:30Z
2023-11-29T18:18:33.000Z
null
null
--- license: mit base_model: roberta-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: roberta_sst5_padding40model 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. --> # roberta_sst5_padding40model This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.7057 - Accuracy: 0.5620 ## 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: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.2915 | 1.0 | 534 | 1.3392 | 0.4199 | | 1.0328 | 2.0 | 1068 | 1.0073 | 0.5679 | | 0.8737 | 3.0 | 1602 | 1.0071 | 0.5855 | | 0.7544 | 4.0 | 2136 | 1.1617 | 0.5552 | | 0.6062 | 5.0 | 2670 | 1.2311 | 0.5588 | | 0.4991 | 6.0 | 3204 | 1.4888 | 0.5516 | | 0.4032 | 7.0 | 3738 | 1.5889 | 0.5498 | | 0.3291 | 8.0 | 4272 | 1.6908 | 0.5597 | | 0.2557 | 9.0 | 4806 | 1.9047 | 0.5593 | | 0.2262 | 10.0 | 5340 | 2.1743 | 0.5502 | | 0.1997 | 11.0 | 5874 | 2.4720 | 0.5534 | | 0.1719 | 12.0 | 6408 | 2.7418 | 0.5633 | | 0.1584 | 13.0 | 6942 | 2.9747 | 0.5665 | | 0.1423 | 14.0 | 7476 | 3.2228 | 0.5543 | | 0.1016 | 15.0 | 8010 | 3.4270 | 0.5475 | | 0.0865 | 16.0 | 8544 | 3.4458 | 0.5611 | | 0.0655 | 17.0 | 9078 | 3.6376 | 0.5552 | | 0.0486 | 18.0 | 9612 | 3.6493 | 0.5579 | | 0.0446 | 19.0 | 10146 | 3.7012 | 0.5611 | | 0.0395 | 20.0 | 10680 | 3.7057 | 0.5620 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.1 - Datasets 2.12.0 - Tokenizers 0.13.3
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transformers
text-classification
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null
null
null
null
null
null
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Realgon/roberta_sst5_padding40model
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TheBloke/deepseek-llm-7B-chat-GGUF
TheBloke
2023-11-29T23:43:11Z
0
2
null
[ "transformers", "gguf", "deepseek", "base_model:deepseek-ai/deepseek-llm-7b-chat", "license:other", "has_space", "region:us" ]
2023-11-29T23:43:11Z
2023-11-29T18:18:39.000Z
null
null
--- base_model: deepseek-ai/deepseek-llm-7b-chat inference: false license: other license_link: LICENSE license_name: deepseek model_creator: DeepSeek model_name: Deepseek LLM 7B Chat model_type: deepseek prompt_template: 'User: {prompt} Assistant: ' 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 --> # Deepseek LLM 7B Chat - GGUF - Model creator: [DeepSeek](https://huggingface.co/deepseek-ai) - Original model: [Deepseek LLM 7B Chat](https://huggingface.co/deepseek-ai/deepseek-llm-7b-chat) <!-- description start --> ## Description This repo contains GGUF format model files for [DeepSeek's Deepseek LLM 7B Chat](https://huggingface.co/deepseek-ai/deepseek-llm-7b-chat). 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 incomplete 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. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [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. * [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. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GGUF) * [DeepSeek's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/deepseek-ai/deepseek-llm-7b-chat) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: DeepSeek-LLM ``` User: {prompt} Assistant: ``` <!-- prompt-template 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 | | ---- | ---- | ---- | ---- | ---- | ----- | | [deepseek-llm-7b-chat.Q2_K.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GGUF/blob/main/deepseek-llm-7b-chat.Q2_K.gguf) | Q2_K | 2 | 2.99 GB| 5.49 GB | smallest, significant quality loss - not recommended for most purposes | | [deepseek-llm-7b-chat.Q3_K_S.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GGUF/blob/main/deepseek-llm-7b-chat.Q3_K_S.gguf) | Q3_K_S | 3 | 3.14 GB| 5.64 GB | very small, high quality loss | | [deepseek-llm-7b-chat.Q3_K_M.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GGUF/blob/main/deepseek-llm-7b-chat.Q3_K_M.gguf) | Q3_K_M | 3 | 3.47 GB| 5.97 GB | very small, high quality loss | | [deepseek-llm-7b-chat.Q3_K_L.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GGUF/blob/main/deepseek-llm-7b-chat.Q3_K_L.gguf) | Q3_K_L | 3 | 3.75 GB| 6.25 GB | small, substantial quality loss | | [deepseek-llm-7b-chat.Q4_0.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GGUF/blob/main/deepseek-llm-7b-chat.Q4_0.gguf) | Q4_0 | 4 | 4.00 GB| 6.50 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [deepseek-llm-7b-chat.Q4_K_S.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GGUF/blob/main/deepseek-llm-7b-chat.Q4_K_S.gguf) | Q4_K_S | 4 | 4.03 GB| 6.53 GB | small, greater quality loss | | [deepseek-llm-7b-chat.Q4_K_M.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GGUF/blob/main/deepseek-llm-7b-chat.Q4_K_M.gguf) | Q4_K_M | 4 | 4.22 GB| 6.72 GB | medium, balanced quality - recommended | | [deepseek-llm-7b-chat.Q5_0.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GGUF/blob/main/deepseek-llm-7b-chat.Q5_0.gguf) | Q5_0 | 5 | 4.81 GB| 7.31 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [deepseek-llm-7b-chat.Q5_K_S.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GGUF/blob/main/deepseek-llm-7b-chat.Q5_K_S.gguf) | Q5_K_S | 5 | 4.81 GB| 7.31 GB | large, low quality loss - recommended | | [deepseek-llm-7b-chat.Q5_K_M.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GGUF/blob/main/deepseek-llm-7b-chat.Q5_K_M.gguf) | Q5_K_M | 5 | 4.93 GB| 7.43 GB | large, very low quality loss - recommended | | [deepseek-llm-7b-chat.Q6_K.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GGUF/blob/main/deepseek-llm-7b-chat.Q6_K.gguf) | Q6_K | 6 | 5.67 GB| 8.17 GB | very large, extremely low quality loss | | [deepseek-llm-7b-chat.Q8_0.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GGUF/blob/main/deepseek-llm-7b-chat.Q8_0.gguf) | Q8_0 | 8 | 7.35 GB| 9.85 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/deepseek-llm-7B-chat-GGUF and below it, a specific filename to download, such as: deepseek-llm-7b-chat.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/deepseek-llm-7B-chat-GGUF deepseek-llm-7b-chat.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/deepseek-llm-7B-chat-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` 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/deepseek-llm-7B-chat-GGUF deepseek-llm-7b-chat.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 35 -m deepseek-llm-7b-chat.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "User: {prompt}\n\nAssistant:" ``` 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. Note that longer sequence lengths require much more resources, so you may need to reduce this value. 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 can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## 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. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # 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 = Llama( model_path="./deepseek-llm-7b-chat.Q4_K_M.gguf", # Download the model file first n_ctx=4096, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "User: {prompt}\n\nAssistant:", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./deepseek-llm-7b-chat.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## 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: DeepSeek's Deepseek LLM 7B Chat <p align="center"> <img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true"> </p> <p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/qr.jpeg">[Wechat(微信)]</a> </p> <hr> ### 1. Introduction of Deepseek LLM Introducing DeepSeek LLM, an advanced language model comprising 7 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. In order to foster research, we have made DeepSeek LLM 7B/67B Base and DeepSeek LLM 7B/67B Chat open source for the research community. ### 2. Model Summary `deepseek-llm-7b-chat` is a 7B parameter model initialized from `deepseek-llm-7b-base` and fine-tuned on extra instruction data. - **Home Page:** [DeepSeek](https://deepseek.com/) - **Repository:** [deepseek-ai/deepseek-LLM](https://github.com/deepseek-ai/deepseek-LLM) - **Chat With DeepSeek LLM:** [DeepSeek-LLM](https://chat.deepseek.com/) ### 3. How to Use Here give some examples of how to use our model. #### Chat Completion ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig model_name = "deepseek-ai/deepseek-llm-7b-chat" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto") model.generation_config = GenerationConfig.from_pretrained(model_name) model.generation_config.pad_token_id = model.generation_config.eos_token_id messages = [ {"role": "user", "content": "Who are you?"} ] input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt") outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100) result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True) print(result) ``` Avoiding the use of the provided function `apply_chat_template`, you can also interact with our model following the sample template. Note that `messages` should be replaced by your input. ``` User: {messages[0]['content']} Assistant: {messages[1]['content']}<|end▁of▁sentence|>User: {messages[2]['content']} Assistant: ``` **Note:** By default (`add_special_tokens=True`), our tokenizer automatically adds a `bos_token` (`<|begin▁of▁sentence|>`) before the input text. Additionally, since the system prompt is not compatible with this version of our models, we DO NOT RECOMMEND including the system prompt in your input. ### 4. License This code repository is licensed under the MIT License. The use of DeepSeek LLM models is subject to the Model License. DeepSeek LLM supports commercial use. See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-LLM/blob/main/LICENSE-MODEL) for more details. ### 5. Contact If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com). <!-- original-model-card end -->
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TheBloke/deepseek-llm-7B-chat-GGUF
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TheBloke/deepseek-llm-7B-chat-AWQ
TheBloke
2023-11-29T23:43:32Z
0
0
null
[ "transformers", "safetensors", "llama", "text-generation", "base_model:deepseek-ai/deepseek-llm-7b-chat", "license:other", "autotrain_compatible", "text-generation-inference", "4-bit", "region:us" ]
2023-11-29T23:43:32Z
2023-11-29T18:18:39.000Z
null
null
--- base_model: deepseek-ai/deepseek-llm-7b-chat inference: false license: other license_link: LICENSE license_name: deepseek model_creator: DeepSeek model_name: Deepseek LLM 7B Chat model_type: deepseek prompt_template: 'User: {prompt} Assistant: ' 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 --> # Deepseek LLM 7B Chat - AWQ - Model creator: [DeepSeek](https://huggingface.co/deepseek-ai) - Original model: [Deepseek LLM 7B Chat](https://huggingface.co/deepseek-ai/deepseek-llm-7b-chat) <!-- description start --> ## Description This repo contains AWQ model files for [DeepSeek's Deepseek LLM 7B Chat](https://huggingface.co/deepseek-ai/deepseek-llm-7b-chat). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. It is supported by: - [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ - [vLLM](https://github.com/vllm-project/vllm) - Llama and Mistral models only - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GGUF) * [DeepSeek's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/deepseek-ai/deepseek-llm-7b-chat) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: DeepSeek-LLM ``` User: {prompt} Assistant: ``` <!-- prompt-template end --> <!-- README_AWQ.md-provided-files start --> ## Provided files, and AWQ parameters I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered. Models are released as sharded safetensors files. | Branch | Bits | GS | AWQ Dataset | Seq Len | Size | | ------ | ---- | -- | ----------- | ------- | ---- | | [main](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.83 GB <!-- README_AWQ.md-provided-files end --> <!-- README_AWQ.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/deepseek-llm-7B-chat-AWQ`. 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: `deepseek-llm-7B-chat-AWQ` 7. Select **Loader: AutoAWQ**. 8. Click Load, and the model will load and is now ready for use. 9. 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. 10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started! <!-- README_AWQ.md-text-generation-webui end --> <!-- README_AWQ.md-use-from-vllm start --> ## Multi-user inference server: vLLM Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/). - Please ensure you are using vLLM version 0.2 or later. - When using vLLM as a server, pass the `--quantization awq` parameter. For example: ```shell python3 -m vllm.entrypoints.api_server --model TheBloke/deepseek-llm-7B-chat-AWQ --quantization awq --dtype auto ``` - When using vLLM from Python code, again set `quantization=awq`. For example: ```python from vllm import LLM, SamplingParams prompts = [ "Tell me about AI", "Write a story about llamas", "What is 291 - 150?", "How much wood would a woodchuck chuck if a woodchuck could chuck wood?", ] prompt_template=f'''User: {prompt} Assistant: ''' prompts = [prompt_template.format(prompt=prompt) for prompt in prompts] sampling_params = SamplingParams(temperature=0.8, top_p=0.95) llm = LLM(model="TheBloke/deepseek-llm-7B-chat-AWQ", quantization="awq", dtype="auto") outputs = llm.generate(prompts, sampling_params) # Print the outputs. for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") ``` <!-- README_AWQ.md-use-from-vllm start --> <!-- README_AWQ.md-use-from-tgi start --> ## Multi-user inference server: Hugging Face Text Generation Inference (TGI) 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/deepseek-llm-7B-chat-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/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'''User: {prompt} Assistant: ''' 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_AWQ.md-use-from-tgi end --> <!-- README_AWQ.md-use-from-python start --> ## Inference from Python code using Transformers ### Install the necessary packages - Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later. - Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later. ```shell pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0" ``` Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0. If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command: ```shell pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl ``` If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y autoawq git clone https://github.com/casper-hansen/AutoAWQ cd AutoAWQ pip3 install . ``` ### Transformers example code (requires Transformers 4.35.0 and later) ```python from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer model_name_or_path = "TheBloke/deepseek-llm-7B-chat-AWQ" tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) model = AutoModelForCausalLM.from_pretrained( model_name_or_path, low_cpu_mem_usage=True, device_map="cuda:0" ) # Using the text streamer to stream output one token at a time streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) prompt = "Tell me about AI" prompt_template=f'''User: {prompt} Assistant: ''' # Convert prompt to tokens tokens = tokenizer( prompt_template, return_tensors='pt' ).input_ids.cuda() generation_params = { "do_sample": True, "temperature": 0.7, "top_p": 0.95, "top_k": 40, "max_new_tokens": 512, "repetition_penalty": 1.1 } # Generate streamed output, visible one token at a time generation_output = model.generate( tokens, streamer=streamer, **generation_params ) # Generation without a streamer, which will include the prompt in the output generation_output = model.generate( tokens, **generation_params ) # Get the tokens from the output, decode them, print them token_output = generation_output[0] text_output = tokenizer.decode(token_output) print("model.generate output: ", text_output) # Inference is also possible via Transformers' pipeline from transformers import pipeline pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, **generation_params ) pipe_output = pipe(prompt_template)[0]['generated_text'] print("pipeline output: ", pipe_output) ``` <!-- README_AWQ.md-use-from-python end --> <!-- README_AWQ.md-compatibility start --> ## Compatibility The files provided are tested to work with: - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`. - [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later. - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later. - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later. <!-- README_AWQ.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: DeepSeek's Deepseek LLM 7B Chat <p align="center"> <img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true"> </p> <p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/qr.jpeg">[Wechat(微信)]</a> </p> <hr> ### 1. Introduction of Deepseek LLM Introducing DeepSeek LLM, an advanced language model comprising 7 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. In order to foster research, we have made DeepSeek LLM 7B/67B Base and DeepSeek LLM 7B/67B Chat open source for the research community. ### 2. Model Summary `deepseek-llm-7b-chat` is a 7B parameter model initialized from `deepseek-llm-7b-base` and fine-tuned on extra instruction data. - **Home Page:** [DeepSeek](https://deepseek.com/) - **Repository:** [deepseek-ai/deepseek-LLM](https://github.com/deepseek-ai/deepseek-LLM) - **Chat With DeepSeek LLM:** [DeepSeek-LLM](https://chat.deepseek.com/) ### 3. How to Use Here give some examples of how to use our model. #### Chat Completion ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig model_name = "deepseek-ai/deepseek-llm-7b-chat" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto") model.generation_config = GenerationConfig.from_pretrained(model_name) model.generation_config.pad_token_id = model.generation_config.eos_token_id messages = [ {"role": "user", "content": "Who are you?"} ] input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt") outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100) result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True) print(result) ``` Avoiding the use of the provided function `apply_chat_template`, you can also interact with our model following the sample template. Note that `messages` should be replaced by your input. ``` User: {messages[0]['content']} Assistant: {messages[1]['content']}<|end▁of▁sentence|>User: {messages[2]['content']} Assistant: ``` **Note:** By default (`add_special_tokens=True`), our tokenizer automatically adds a `bos_token` (`<|begin▁of▁sentence|>`) before the input text. Additionally, since the system prompt is not compatible with this version of our models, we DO NOT RECOMMEND including the system prompt in your input. ### 4. License This code repository is licensed under the MIT License. The use of DeepSeek LLM models is subject to the Model License. DeepSeek LLM supports commercial use. See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-LLM/blob/main/LICENSE-MODEL) for more details. ### 5. Contact If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).
null
transformers
text-generation
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null
null
null
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null
TheBloke/deepseek-llm-7B-chat-AWQ
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow11
FounderOfHuggingface
2023-11-29T18:18:52Z
0
0
null
[ "peft", "arxiv:1910.09700", "base_model:gpt2", "region:us" ]
2023-11-29T18:18:52Z
2023-11-29T18:18:48.000Z
null
null
--- library_name: peft base_model: gpt2 --- # 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] - **Funded by [optional]:** [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 Dataset 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 Dataset 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 0.6.2
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peft
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow11
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khanhnto/ilytoto
khanhnto
2023-11-29T18:29:54Z
0
0
null
[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2023-11-29T18:29:54Z
2023-11-29T18:19:19.000Z
null
null
Entry not found
null
transformers
text-generation
null
null
null
null
null
null
null
null
null
khanhnto/ilytoto
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K-kiron/distilbert-batch-32-epoch-5
K-kiron
2023-11-29T18:28:29Z
0
0
null
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2023-11-29T18:28:29Z
2023-11-29T18:23:16.000Z
null
null
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-batch-32-epoch-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. --> # distilbert-batch-32-epoch-5 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: - Loss: 0.5839 - Accuracy: 0.8913 - F1: 0.8908 ## 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 - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.1+cu121 - Datasets 2.12.0 - Tokenizers 0.13.3
null
transformers
text-classification
null
null
null
null
null
null
null
null
null
K-kiron/distilbert-batch-32-epoch-5
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OpenNMT/nllb-200-onmt
OpenNMT
2023-11-29T18:24:15Z
0
0
null
[ "region:us" ]
2023-11-29T18:24:15Z
2023-11-29T18:24:15.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
OpenNMT/nllb-200-onmt
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tinupicky/t55_flan-t5-base-jssssnnn
tinupicky
2023-11-29T18:25:41Z
0
0
null
[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2023-11-29T18:25:41Z
2023-11-29T18:24:43.000Z
null
null
--- license: apache-2.0 base_model: google/flan-t5-base tags: - generated_from_trainer model-index: - name: t55_flan-t5-base-jssssnnn 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. --> # t55_flan-t5-base-jssssnnn This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown 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: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
null
transformers
text2text-generation
null
null
null
null
null
null
null
null
null
tinupicky/t55_flan-t5-base-jssssnnn
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A-Issa-1999/whisper-small
A-Issa-1999
2023-11-29T18:24:46Z
0
0
null
[ "region:us" ]
2023-11-29T18:24:46Z
2023-11-29T18:24:46.000Z
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A-Issa-1999/whisper-small
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lafual/Uzoth
lafual
2023-11-29T18:26:03Z
0
0
null
[ "license:openrail", "region:us" ]
2023-11-29T18:26:03Z
2023-11-29T18:25:38.000Z
null
null
--- license: openrail ---
null
null
null
null
null
null
null
null
null
null
null
null
lafual/Uzoth
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sordonia/test-library
sordonia
2023-11-29T20:04:02Z
0
0
null
[ "region:us" ]
2023-11-29T20:04:02Z
2023-11-29T18:26:02.000Z
null
null
Number of experts present in the library: 1 | Expert Name | Base Model | Trained on | Adapter Type | | --- | --- | --- | --- | | abstract_algebra | phi-2 | sordonia/qa-flat-mmlu/abstract_algebra | lora | Last updated on: 2023-11-29T18:30:48.000Z
null
null
null
null
null
null
null
null
null
null
null
null
sordonia/test-library
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TotoLefo/RAY350EPOCHSRVC2
TotoLefo
2023-11-29T18:27:23Z
0
0
null
[ "license:openrail", "region:us" ]
2023-11-29T18:27:23Z
2023-11-29T18:26:04.000Z
null
null
--- license: openrail ---
null
null
null
null
null
null
null
null
null
null
null
null
TotoLefo/RAY350EPOCHSRVC2
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xiangliu1123/ADIDA_Model
xiangliu1123
2023-11-30T01:27:46Z
0
0
null
[ "region:us" ]
2023-11-30T01:27:46Z
2023-11-29T18:26:06.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
xiangliu1123/ADIDA_Model
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JohnDoe70/ic
JohnDoe70
2023-11-29T18:29:52Z
0
0
null
[ "transformers", "onnx", "safetensors", "bert", "text-classification", "endpoints_compatible", "region:us" ]
2023-11-29T18:29:52Z
2023-11-29T18:26:39.000Z
null
null
Entry not found
null
transformers
text-classification
null
null
null
null
null
null
null
null
null
JohnDoe70/ic
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow12
FounderOfHuggingface
2023-11-29T18:27:16Z
0
0
null
[ "peft", "arxiv:1910.09700", "base_model:gpt2", "region:us" ]
2023-11-29T18:27:16Z
2023-11-29T18:27:13.000Z
null
null
--- library_name: peft base_model: gpt2 --- # 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] - **Funded by [optional]:** [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 Dataset 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 Dataset 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 0.6.2
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow12
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tomaarsen/setfit-paraphrase-mpnet-base-v2-sst2-8-shot
tomaarsen
2023-11-29T18:43:57Z
0
0
null
[ "setfit", "pytorch", "mpnet", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "en", "dataset:sst2", "arxiv:2209.11055", "base_model:sentence-transformers/paraphrase-mpnet-base-v2", "license:apache-2.0", "model-index", "co2_eq_emissions", "region:us" ]
2023-11-29T18:43:57Z
2023-11-29T18:28:59.000Z
null
null
--- language: - en license: apache-2.0 library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer datasets: - sst2 metrics: - precision - recall - f1 widget: - text: 'this is a story of two misfits who do n''t stand a chance alone , but together they are magnificent . ' - text: 'it does n''t believe in itself , it has no sense of humor ... it ''s just plain bored . ' - text: 'the band ''s courage in the face of official repression is inspiring , especially for aging hippies ( this one included ) . ' - text: 'a fast , funny , highly enjoyable movie . ' - text: 'the movie achieves as great an impact by keeping these thoughts hidden as ... ( quills ) did by showing them . ' pipeline_tag: text-classification co2_eq_emissions: emissions: 2.5933709269110308 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 0.027 hardware_used: 1 x NVIDIA GeForce RTX 3090 base_model: sentence-transformers/paraphrase-mpnet-base-v2 model-index: - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2 on sst2 results: - task: type: text-classification name: Text Classification dataset: name: sst2 type: sst2 split: test metrics: - type: accuracy value: 0.8588082901554405 name: Accuracy --- # SetFit with sentence-transformers/paraphrase-mpnet-base-v2 on sst2 This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [sst2](https://huggingface.co/datasets/sst2) dataset that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. For classification, it uses a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance. - **Maximum Sequence Length:** 512 tokens - **Number of Classes:** 2 classes - **Training Dataset:** [sst2](https://huggingface.co/datasets/sst2) - **Language:** en - **License:** apache-2.0 ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels | Label | Examples | |:---------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | negative | <ul><li>'stale and uninspired . '</li><li>"the film 's considered approach to its subject matter is too calm and thoughtful for agitprop , and the thinness of its characterizations makes it a failure as straight drama . ' "</li><li>"that their charm does n't do a load of good "</li></ul> | | positive | <ul><li>"broomfield is energized by volletta wallace 's maternal fury , her fearlessness "</li><li>'flawless '</li><li>'insightfully written , delicately performed '</li></ul> | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.8588 | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from 🤗 Hub model = SetFitModel.from_pretrained("tomaarsen/setfit-paraphrase-mpnet-base-v2-sst2-8-shot") # Run inference preds = model("a fast , funny , highly enjoyable movie . ") ``` <!-- ### Downstream Use *List how someone could finetune this model on their own dataset.* --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 2 | 11.4375 | 33 | | Label | Training Sample Count | |:---------|:----------------------| | negative | 8 | | positive | 8 | ### Training Hyperparameters - batch_size: (16, 16) - num_epochs: (10, 10) - max_steps: -1 - sampling_strategy: oversampling - body_learning_rate: (2e-05, 1e-05) - head_learning_rate: 0.01 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - load_best_model_at_end: True ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:----------:|:------:|:-------------:|:---------------:| | 0.1111 | 1 | 0.2126 | - | | 1.1111 | 10 | 0.1604 | - | | **2.2222** | **20** | **0.0224** | **0.1761** | | 3.3333 | 30 | 0.0039 | - | | 4.4444 | 40 | 0.0029 | 0.1935 | | 5.5556 | 50 | 0.0026 | - | | 6.6667 | 60 | 0.0008 | 0.1944 | | 7.7778 | 70 | 0.0009 | - | | 8.8889 | 80 | 0.0027 | 0.1941 | | 10.0 | 90 | 0.0004 | - | * The bold row denotes the saved checkpoint. ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Carbon Emitted**: 0.003 kg of CO2 - **Hours Used**: 0.027 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.9.16 - SetFit: 1.0.0.dev0 - Sentence Transformers: 2.2.2 - Transformers: 4.29.0 - PyTorch: 1.13.1+cu117 - Datasets: 2.15.0 - Tokenizers: 0.13.3 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
null
setfit
text-classification
null
null
null
null
null
null
null
null
null
tomaarsen/setfit-paraphrase-mpnet-base-v2-sst2-8-shot
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imdatta0/llama_2_13b_oproj_alpaca_r8_lr0.0001_bg8
imdatta0
2023-11-29T18:29:33Z
0
0
null
[ "generated_from_trainer", "base_model:meta-llama/Llama-2-13b-hf", "region:us" ]
2023-11-29T18:29:33Z
2023-11-29T18:29:31.000Z
null
null
--- base_model: meta-llama/Llama-2-13b-hf tags: - generated_from_trainer model-index: - name: llama_2_13b_oproj_alpaca_r8_lr0.0001_bg8 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. --> # llama_2_13b_oproj_alpaca_r8_lr0.0001_bg8 This model is a fine-tuned version of [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7571 ## 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: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 0.02 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.4715 | 0.01 | 33 | 0.9762 | | 0.8875 | 0.02 | 66 | 0.8186 | | 0.8287 | 0.03 | 99 | 0.7859 | | 0.8234 | 0.04 | 132 | 0.7796 | | 0.7748 | 0.05 | 165 | 0.7750 | | 0.7995 | 0.06 | 198 | 0.7732 | | 0.8039 | 0.07 | 231 | 0.7723 | | 0.7779 | 0.08 | 264 | 0.7702 | | 0.7943 | 0.09 | 297 | 0.7699 | | 0.7912 | 0.1 | 330 | 0.7691 | | 0.7833 | 0.11 | 363 | 0.7681 | | 0.7672 | 0.12 | 396 | 0.7672 | | 0.7746 | 0.13 | 429 | 0.7662 | | 0.7933 | 0.14 | 462 | 0.7664 | | 0.7881 | 0.15 | 495 | 0.7659 | | 0.7813 | 0.16 | 528 | 0.7650 | | 0.7919 | 0.17 | 561 | 0.7650 | | 0.7909 | 0.18 | 594 | 0.7650 | | 0.7912 | 0.19 | 627 | 0.7641 | | 0.7609 | 0.21 | 660 | 0.7641 | | 0.7983 | 0.22 | 693 | 0.7641 | | 0.7724 | 0.23 | 726 | 0.7627 | | 0.7825 | 0.24 | 759 | 0.7637 | | 0.7706 | 0.25 | 792 | 0.7634 | | 0.7615 | 0.26 | 825 | 0.7629 | | 0.7721 | 0.27 | 858 | 0.7616 | | 0.7679 | 0.28 | 891 | 0.7623 | | 0.7691 | 0.29 | 924 | 0.7620 | | 0.7696 | 0.3 | 957 | 0.7618 | | 0.7749 | 0.31 | 990 | 0.7619 | | 0.7997 | 0.32 | 1023 | 0.7615 | | 0.7615 | 0.33 | 1056 | 0.7619 | | 0.783 | 0.34 | 1089 | 0.7619 | | 0.7879 | 0.35 | 1122 | 0.7614 | | 0.7829 | 0.36 | 1155 | 0.7624 | | 0.7682 | 0.37 | 1188 | 0.7621 | | 0.7786 | 0.38 | 1221 | 0.7628 | | 0.7682 | 0.39 | 1254 | 0.7611 | | 0.7717 | 0.4 | 1287 | 0.7609 | | 0.7829 | 0.41 | 1320 | 0.7606 | | 0.7856 | 0.42 | 1353 | 0.7605 | | 0.7676 | 0.43 | 1386 | 0.7610 | | 0.7873 | 0.44 | 1419 | 0.7604 | | 0.7743 | 0.45 | 1452 | 0.7605 | | 0.7538 | 0.46 | 1485 | 0.7590 | | 0.8077 | 0.47 | 1518 | 0.7599 | | 0.7851 | 0.48 | 1551 | 0.7594 | | 0.7737 | 0.49 | 1584 | 0.7601 | | 0.7822 | 0.5 | 1617 | 0.7602 | | 0.7904 | 0.51 | 1650 | 0.7596 | | 0.7566 | 0.52 | 1683 | 0.7586 | | 0.7833 | 0.53 | 1716 | 0.7591 | | 0.7621 | 0.54 | 1749 | 0.7592 | | 0.7787 | 0.55 | 1782 | 0.7586 | | 0.7746 | 0.56 | 1815 | 0.7586 | | 0.774 | 0.57 | 1848 | 0.7586 | | 0.782 | 0.58 | 1881 | 0.7586 | | 0.8033 | 0.59 | 1914 | 0.7594 | | 0.784 | 0.6 | 1947 | 0.7581 | | 0.778 | 0.62 | 1980 | 0.7582 | | 0.7655 | 0.63 | 2013 | 0.7582 | | 0.7864 | 0.64 | 2046 | 0.7580 | | 0.784 | 0.65 | 2079 | 0.7576 | | 0.7724 | 0.66 | 2112 | 0.7583 | | 0.7767 | 0.67 | 2145 | 0.7577 | | 0.7754 | 0.68 | 2178 | 0.7574 | | 0.7813 | 0.69 | 2211 | 0.7580 | | 0.7606 | 0.7 | 2244 | 0.7581 | | 0.7912 | 0.71 | 2277 | 0.7582 | | 0.7632 | 0.72 | 2310 | 0.7574 | | 0.7779 | 0.73 | 2343 | 0.7576 | | 0.7654 | 0.74 | 2376 | 0.7576 | | 0.7788 | 0.75 | 2409 | 0.7578 | | 0.7959 | 0.76 | 2442 | 0.7577 | | 0.7997 | 0.77 | 2475 | 0.7573 | | 0.7763 | 0.78 | 2508 | 0.7573 | | 0.7883 | 0.79 | 2541 | 0.7571 | | 0.7902 | 0.8 | 2574 | 0.7572 | | 0.7775 | 0.81 | 2607 | 0.7571 | | 0.7977 | 0.82 | 2640 | 0.7573 | | 0.7847 | 0.83 | 2673 | 0.7573 | | 0.7845 | 0.84 | 2706 | 0.7574 | | 0.7774 | 0.85 | 2739 | 0.7572 | | 0.7865 | 0.86 | 2772 | 0.7571 | | 0.7681 | 0.87 | 2805 | 0.7574 | | 0.7634 | 0.88 | 2838 | 0.7571 | | 0.7791 | 0.89 | 2871 | 0.7572 | | 0.7779 | 0.9 | 2904 | 0.7570 | | 0.7599 | 0.91 | 2937 | 0.7572 | | 0.7732 | 0.92 | 2970 | 0.7570 | | 0.7979 | 0.93 | 3003 | 0.7571 | | 0.7662 | 0.94 | 3036 | 0.7571 | | 0.7847 | 0.95 | 3069 | 0.7572 | | 0.77 | 0.96 | 3102 | 0.7571 | | 0.7752 | 0.97 | 3135 | 0.7572 | | 0.7886 | 0.98 | 3168 | 0.7572 | | 0.7663 | 0.99 | 3201 | 0.7571 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.5.2 - Tokenizers 0.14.1
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imdatta0/llama_2_13b_oproj_alpaca_r8_lr0.0001_bg8
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GilbertClaus/Dummy
GilbertClaus
2023-11-29T20:44:22Z
0
0
null
[ "region:us" ]
2023-11-29T20:44:22Z
2023-11-29T18:30:28.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
GilbertClaus/Dummy
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Broomva/llama-2-7b-spa-guc
Broomva
2023-11-29T18:38:06Z
0
0
null
[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2023-11-29T18:38:06Z
2023-11-29T18:35:25.000Z
null
null
Entry not found
null
transformers
text-generation
null
null
null
null
null
null
null
null
null
Broomva/llama-2-7b-spa-guc
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow13
FounderOfHuggingface
2023-11-29T18:35:41Z
0
0
null
[ "peft", "arxiv:1910.09700", "base_model:gpt2", "region:us" ]
2023-11-29T18:35:41Z
2023-11-29T18:35:37.000Z
null
null
--- library_name: peft base_model: gpt2 --- # 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] - **Funded by [optional]:** [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 Dataset 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 Dataset 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 0.6.2
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peft
null
null
null
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow13
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imspidey/mCybersushi
imspidey
2023-11-29T18:37:44Z
0
0
null
[ "region:us" ]
2023-11-29T18:37:44Z
2023-11-29T18:35:51.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
imspidey/mCybersushi
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A-Issa-1999/whisper-small-1
A-Issa-1999
2023-11-29T18:36:26Z
0
0
null
[ "region:us" ]
2023-11-29T18:36:26Z
2023-11-29T18:36:26.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
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null
A-Issa-1999/whisper-small-1
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Homiebear/Ryth
Homiebear
2023-11-29T19:06:48Z
0
0
null
[ "license:openrail", "region:us" ]
2023-11-29T19:06:48Z
2023-11-29T18:36:59.000Z
null
null
--- license: openrail ---
null
null
null
null
null
null
null
null
null
null
null
null
Homiebear/Ryth
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briefai/LongShort-Llama-2-13B
briefai
2023-11-29T19:06:11Z
0
0
null
[ "peft", "region:us" ]
2023-11-29T19:06:11Z
2023-11-29T18:37:28.000Z
null
null
--- license: apache-2.0 datasets: - briefai/LongShort-Dataset language: - en pipeline_tag: text-generation tags: - pytorch - llama-2 - Gen-AI - Finance - KPI Extraction --- # LongShort-Llama-2-13B ### Model Description LongShort-Llama-2-13B is a large language model fine-tuned on earnings call documents to extract financial KPIs from the earnings call documents. It is based on the Llama-2-7B Architecture. - Model creator: [Brief AI](https://huggingface.co/briefai) - Original model: [Llama 2 13B Chat](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) ### Dataset Description - Data Source: Factiva - Data Description: 28K+ Earnings Call Documents - Data Scope: 1K+ public companies - Fine Tuning Data: Collection of 60K+ samples. ## Prompt template: LongShort-Llama-2-13B ``` [INST]Given the context, answer the question. ### Question: Extract all the finance-based performance indicators and evaluation metrics. ### Context: {context} ### Answer: [/INST] ``` ## Basics *This section provides information about the model type, version, license, funders, release date, developers, and contact information.* *It is useful for anyone who wants to reference the model.* **Developed by:** [Brief AI Team](https://huggingface.co/briefai) **Model Type:** Transformer-based Large Language Model **Version:** 1.0.0 **Languages:** English **License:** Apache 2.0 **Release Date Estimate:** Wednesday, 29.November.2023 **Send Questions to:** vishalparameswaran96@gmail.com **Cite as:** Brief AI LongShort Language Model **Funded by:** UChicago Data Science Institute **Mentored by:** Nick Kadochnikov ## Technical Specifications *This section includes details about the model objective and architecture, and the compute infrastructure.* *It is useful for people interested in model development.* Please see [the LongShort training README](https://github.com/brief-ai-uchicago/LongShort-Dataset) for full details on replicating training. ### Model Architecture and Objective * Modified from Llama-2-13B **Objective:** Financial KPI extraction from earnings call documents. ### Hardware and Software - Compute Infrastructure * 4 NVIDIA L4 GPUs & 48 vCPUs * Environment: PyTorch (pytorch-2.0 w/ CUDA-11.8; see [Github link](https://github.com/pytorch/pytorch)) * CPU: GCP G2 Standard 48 (Platform: Intel Cascade Lake) (Accelerator Optimized) * CPU memory: 192GB RAM * GPU memory: 30GB per GPU ## Training *This section provides information about the training.* *It is useful for people who want to learn more about the model inputs and training footprint.* The following bits and bytes 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 0.4.0 ### Training Data *This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.* Details for the dataset can be found in [LongShort Dataset](https://github.com/brief-ai-uchicago/LongShort-Dataset) Training data includes: - 5000 Earnings Call Documents ## How to use This model can be easily used and deployed using HuggingFace's ecosystem. This needs `transformers` and `accelerate` installed. The model can be downloaded as follows: [LongShort-Llama-2-13B](https://huggingface.co/briefai/LongShort-Llama-2-13B) ## Intended Use This model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pre-trained base model that can be further fine-tuned for specific tasks. The use cases below are not exhaustive. ### Direct Use - Text generation - Exploring characteristics of language generated by a language model - Examples: Cloze tests, counterfactuals, generations with reframings ### Downstream Use - Tasks that leverage language models include: Information Extraction, Question Answering, Summarization #### Out-of-scope Uses Using the model in [high-stakes](#high-stakes) settings is out of scope for this model. The model is not designed for [critical decisions](#critical-decisions) nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but may not be correct. Out-of-scope Uses Include: - Usage for evaluating or scoring individuals, such as for employment, education, or credit - Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct #### Misuse Intentionally using the model for harm, violating [human rights](#human-rights), or other kinds of malicious activities, is a misuse of this model. This includes: - Spam generation - Disinformation and influence operations - Disparagement and defamation - Harassment and abuse - [Deception](#deception) - Unconsented impersonation and imitation - Unconsented surveillance - Generating content without attribution to the model, as specified in the [RAIL License, Use Restrictions](https://huggingface.co/spaces/bigscience/license) ## Intended Users ### Direct Users - General Public - Researchers - Students - Educators - Engineers/developers - Non-commercial entities - Financial Industry # Risks and Limitations *This section identifies foreseeable harms and misunderstandings.* Model may: - Overrepresent some viewpoints and underrepresent others - Contain stereotypes - Contain [personal information](#personal-data-and-information) - Generate: - Hateful, abusive, or violent language - Discriminatory or prejudicial language - Content that may not be appropriate for all settings, including sexual content - Make errors, including producing incorrect information as if it were factual - Generate irrelevant or repetitive outputs - Induce users into attributing human traits to it, such as sentience or consciousness # Evaluation *This section describes the evaluation protocols and provides the results.* Result: LongShort-Llama-2-13B gives 43.4% accuracy on a validation set of 10% of the original training dataset. **Train-time Evaluation:** Final checkpoint after 700 epochs: - Training Loss: 1.187 # Recommendations *This section provides information on warnings and potential mitigations.* - Indirect users should be made aware when the content they're working with is created by the LLM. - Users should be aware of [Risks and Limitations](#risks-and-limitations), and include an appropriate age disclaimer or blocking interface as necessary. - Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments. # Model Card Authors Vishal Parameshwaran, Garima Sohi, Jose Gerala, Sanchit Narayan Kumar
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briefai/LongShort-Llama-2-13B
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briefai/LongShort-Dolly-2-7B
briefai
2023-11-29T20:09:06Z
0
0
null
[ "pytorch", "dolly", "Gen-AI", "Finance", "KPI Extraction", "text-generation", "en", "dataset:briefai/LongShort-Dataset", "license:apache-2.0", "region:us" ]
2023-11-29T20:09:06Z
2023-11-29T18:39:12.000Z
null
null
--- license: apache-2.0 datasets: - briefai/LongShort-Dataset language: - en pipeline_tag: text-generation tags: - pytorch - dolly - Gen-AI - Finance - KPI Extraction --- # LongShort-Dolly-2-7B ### Model Description LongShort-Dolly-2-7B is a large language model fine-tuned on earnings call documents to extract financial KPIs from the earnings call documents. It is based on the Dolly-2-7B Architecture. - Model creator: [Brief AI](https://huggingface.co/briefai) - Original model: [Dolly-2-7B](https://huggingface.co/databricks/dolly-v2-7b) ### Dataset Description - Data Source: Factiva - Data Description: 28K+ Earnings Call Documents - Data Scope: 1K+ public companies - Fine Tuning Data: Collection of 60K+ samples. ## Prompt template: LongShort-Dolly-2-7B ``` [INST]Given the context, answer the question. ### Question: Extract all the finance-based performance indicators and evaluation metrics. ### Context: {context} ### Answer: [/INST] ``` ## Basics *This section provides information about the model type, version, license, funders, release date, developers, and contact information.* *It is useful for anyone who wants to reference the model.* **Developed by:** [Brief AI Team](https://huggingface.co/briefai) **Model Type:** Transformer-based Large Language Model **Version:** 1.0.0 **Languages:** English **License:** Apache 2.0 **Release Date Estimate:** Wednesday, 29.November.2023 **Send Questions to:** vishalparameswaran96@gmail.com **Cite as:** Brief AI LongShort Language Model **Funded by:** UChicago Data Science Institute **Mentored by:** Nick Kadochnikov ## Technical Specifications *This section includes details about the model objective and architecture, and the compute infrastructure.* *It is useful for people interested in model development.* Please see [the LongShort training README](https://github.com/brief-ai-uchicago/LongShort-Dataset) for full details on replicating training. ### Model Architecture and Objective * Modified from Dolly-2-7B **Objective:** Financial KPI extraction from earnings call documents. ### Hardware and Software - Compute Infrastructure * 4 NVIDIA L4 GPUs & 48 vCPUs * Environment: PyTorch (pytorch-2.0 w/ CUDA-11.8; see [Github link](https://github.com/pytorch/pytorch)) * CPU: GCP G2 Standard 48 (Platform: Intel Cascade Lake) (Accelerator Optimized) * CPU memory: 192GB RAM * GPU memory: 30GB per GPU ## Training *This section provides information about the training.* *It is useful for people who want to learn more about the model inputs and training footprint.* The following bits and bytes 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 0.4.0 ### Training Data *This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.* Details for the dataset can be found in [LongShort Dataset](https://github.com/brief-ai-uchicago/LongShort-Dataset) Training data includes: - 5000 Earnings Call Documents ## How to use This model can be easily used and deployed using HuggingFace's ecosystem. This needs `transformers` and `accelerate` installed. The model can be downloaded as follows: [LongShort-Dolly-2-7B](https://huggingface.co/briefai/LongShort-Dolly-2-7B) ## Intended Use This model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pre-trained base model that can be further fine-tuned for specific tasks. The use cases below are not exhaustive. ### Direct Use - Text generation - Exploring characteristics of language generated by a language model - Examples: Cloze tests, counterfactuals, generations with reframings ### Downstream Use - Tasks that leverage language models include: Information Extraction, Question Answering, Summarization #### Out-of-scope Uses Using the model in [high-stakes](#high-stakes) settings is out of scope for this model. The model is not designed for [critical decisions](#critical-decisions) nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but may not be correct. Out-of-scope Uses Include: - Usage for evaluating or scoring individuals, such as for employment, education, or credit - Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct #### Misuse Intentionally using the model for harm, violating [human rights](#human-rights), or other kinds of malicious activities, is a misuse of this model. This includes: - Spam generation - Disinformation and influence operations - Disparagement and defamation - Harassment and abuse - [Deception](#deception) - Unconsented impersonation and imitation - Unconsented surveillance - Generating content without attribution to the model, as specified in the [RAIL License, Use Restrictions](https://huggingface.co/spaces/bigscience/license) ## Intended Users ### Direct Users - General Public - Researchers - Students - Educators - Engineers/developers - Non-commercial entities - Financial Industry # Risks and Limitations *This section identifies foreseeable harms and misunderstandings.* Model may: - Overrepresent some viewpoints and underrepresent others - Contain stereotypes - Contain [personal information](#personal-data-and-information) - Generate: - Hateful, abusive, or violent language - Discriminatory or prejudicial language - Content that may not be appropriate for all settings, including sexual content - Make errors, including producing incorrect information as if it were factual - Generate irrelevant or repetitive outputs - Induce users into attributing human traits to it, such as sentience or consciousness # Evaluation *This section describes the evaluation protocols and provides the results.* Result: LongShort-Falcon-7B gives 45.4% accuracy on a validation set of 10% of the original training dataset. **Train-time Evaluation:** Final checkpoint after 700 epochs: - Training Loss: 1.645 # Recommendations *This section provides information on warnings and potential mitigations.* - Indirect users should be made aware when the content they're working with is created by the LLM. - Users should be aware of [Risks and Limitations](#risks-and-limitations), and include an appropriate age disclaimer or blocking interface as necessary. - Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments. # Model Card Authors Vishal Parameshwaran, Garima Sohi, Jose Gerala, Sanchit Narayan Kumar
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text-generation
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briefai/LongShort-Dolly-2-7B
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SuperTyler101/SpongeBob
SuperTyler101
2023-11-29T19:08:43Z
0
0
null
[ "region:us" ]
2023-11-29T19:08:43Z
2023-11-29T18:39:44.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
SuperTyler101/SpongeBob
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himitery/trainer_dict
himitery
2023-11-29T18:39:49Z
0
0
null
[ "region:us" ]
2023-11-29T18:39:49Z
2023-11-29T18:39:49.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
himitery/trainer_dict
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briefai/LongShort-Falcon-7B
briefai
2023-11-30T00:48:25Z
0
0
null
[ "pytorch", "falcon", "Gen-AI", "Finance", "KPI Extraction", "text-generation", "en", "dataset:briefai/LongShort-Dataset", "license:apache-2.0", "region:us" ]
2023-11-30T00:48:25Z
2023-11-29T18:40:25.000Z
null
null
--- license: apache-2.0 datasets: - briefai/LongShort-Dataset language: - en pipeline_tag: text-generation tags: - pytorch - falcon - Gen-AI - Finance - KPI Extraction --- # LongShort-Falcon-7B ### Model Description LongShort-Falcon-7B is a large language model fine-tuned on earnings call documents to extract financial KPIs from the earnings call documents. It is based on the Falcon-7B Instruct Architecture. - Model creator: [Brief AI](https://huggingface.co/briefai) - Original model: [Falcon-7B-Instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) ### Dataset Description - Data Source: Factiva - Data Description: 28K+ Earnings Call Documents - Data Scope: 1K+ public companies - Fine Tuning Data: Collection of 60K+ samples. ## Prompt template: LongShort-Falcon-7B ``` [INST]Given the context, answer the question. ### Question: Extract all the finance-based performance indicators and evaluation metrics. ### Context: {context} ### Answer: [/INST] ``` ## Basics *This section provides information about the model type, version, license, funders, release date, developers, and contact information.* *It is useful for anyone who wants to reference the model.* **Developed by:** [Brief AI Team](https://huggingface.co/briefai) **Model Type:** Transformer-based Large Language Model **Version:** 1.0.0 **Languages:** English **License:** Apache 2.0 **Release Date Estimate:** Wednesday, 29.November.2023 **Send Questions to:** vishalparameswaran96@gmail.com **Cite as:** Brief AI LongShort Language Model **Funded by:** UChicago Data Science Institute **Mentored by:** Nick Kadochnikov ## Technical Specifications *This section includes details about the model objective and architecture, and the compute infrastructure.* *It is useful for people interested in model development.* Please see [the LongShort training README](https://github.com/brief-ai-uchicago/LongShort-Dataset) for full details on replicating training. ### Model Architecture and Objective * Modified from Falcon-7B-Instruct **Objective:** Financial KPI extraction from earnings call documents. ### Hardware and Software - Compute Infrastructure * 4 NVIDIA L4 GPUs & 48 vCPUs * Environment: PyTorch (pytorch-2.0 w/ CUDA-11.8; see [Github link](https://github.com/pytorch/pytorch)) * CPU: GCP G2 Standard 48 (Platform: Intel Cascade Lake) (Accelerator Optimized) * CPU memory: 192GB RAM * GPU memory: 30GB per GPU ## Training *This section provides information about the training.* *It is useful for people who want to learn more about the model inputs and training footprint.* The following bits and bytes 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 0.4.0 ### Training Data *This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.* Details for the dataset can be found in [LongShort Dataset](https://github.com/brief-ai-uchicago/LongShort-Dataset) Training data includes: - 5000 Earnings Call Documents ## How to use This model can be easily used and deployed using HuggingFace's ecosystem. This needs `transformers` and `accelerate` installed. The model can be downloaded as follows: [LongShort-Falcon-7B](https://huggingface.co/briefai/LongShort-Falcon-7B) ## Intended Use This model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pre-trained base model that can be further fine-tuned for specific tasks. The use cases below are not exhaustive. ### Direct Use - Text generation - Exploring characteristics of language generated by a language model - Examples: Cloze tests, counterfactuals, generations with reframings ### Downstream Use - Tasks that leverage language models include: Information Extraction, Question Answering, Summarization #### Out-of-scope Uses Using the model in [high-stakes](#high-stakes) settings is out of scope for this model. The model is not designed for [critical decisions](#critical-decisions) nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but may not be correct. Out-of-scope Uses Include: - Usage for evaluating or scoring individuals, such as for employment, education, or credit - Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct #### Misuse Intentionally using the model for harm, violating [human rights](#human-rights), or other kinds of malicious activities, is a misuse of this model. This includes: - Spam generation - Disinformation and influence operations - Disparagement and defamation - Harassment and abuse - [Deception](#deception) - Unconsented impersonation and imitation - Unconsented surveillance - Generating content without attribution to the model, as specified in the [RAIL License, Use Restrictions](https://huggingface.co/spaces/bigscience/license) ## Intended Users ### Direct Users - General Public - Researchers - Students - Educators - Engineers/developers - Non-commercial entities - Financial Industry # Risks and Limitations *This section identifies foreseeable harms and misunderstandings.* Model may: - Overrepresent some viewpoints and underrepresent others - Contain stereotypes - Contain [personal information](#personal-data-and-information) - Generate: - Hateful, abusive, or violent language - Discriminatory or prejudicial language - Content that may not be appropriate for all settings, including sexual content - Make errors, including producing incorrect information as if it were factual - Generate irrelevant or repetitive outputs - Induce users into attributing human traits to it, such as sentience or consciousness # Evaluation *This section describes the evaluation protocols and provides the results.* Result: LongShort-Falcon-7B gives 45.4% accuracy on a validation set of 10% of the original training dataset. **Train-time Evaluation:** Final checkpoint after 700 epochs: - Training Loss: 1.479 # Recommendations *This section provides information on warnings and potential mitigations.* - Indirect users should be made aware when the content they're working with is created by the LLM. - Users should be aware of [Risks and Limitations](#risks-and-limitations), and include an appropriate age disclaimer or blocking interface as necessary. - Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments. # Model Card Authors Vishal Parameshwaran, Garima Sohi, Jose Gerala, Sanchit Narayan Kumar
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text-generation
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briefai/LongShort-Falcon-7B
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liambyrne/distilbert-stackoverflow
liambyrne
2023-11-29T18:41:41Z
0
0
null
[ "region:us" ]
2023-11-29T18:41:41Z
2023-11-29T18:41:41.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
liambyrne/distilbert-stackoverflow
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affecto/Tess34-fans
affecto
2023-11-29T23:17:15Z
0
0
null
[ "generated_from_trainer", "base_model:TheBloke/Tess-M-v1.3-GPTQ", "license:other", "region:us" ]
2023-11-29T23:17:15Z
2023-11-29T18:42:04.000Z
null
null
--- license: other base_model: TheBloke/Tess-M-v1.3-GPTQ tags: - generated_from_trainer model-index: - name: Tess34-fans 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. --> # Tess34-fans This model is a fine-tuned version of [TheBloke/Tess-M-v1.3-GPTQ](https://huggingface.co/TheBloke/Tess-M-v1.3-GPTQ) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7360 ## 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: 0.0004 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.2094 | 0.02 | 50 | 0.9702 | | 0.9633 | 0.04 | 100 | 0.9093 | | 0.91 | 0.06 | 150 | 0.8754 | | 0.8928 | 0.08 | 200 | 0.8588 | | 0.8768 | 0.1 | 250 | 0.8428 | | 0.8344 | 0.12 | 300 | 0.8235 | | 0.8345 | 0.14 | 350 | 0.8160 | | 0.812 | 0.16 | 400 | 0.8125 | | 0.8605 | 0.18 | 450 | 0.8079 | | 0.8263 | 0.21 | 500 | 0.7884 | | 0.7863 | 0.23 | 550 | 0.7849 | | 0.7751 | 0.25 | 600 | 0.7798 | | 0.7976 | 0.27 | 650 | 0.7695 | | 0.7584 | 0.29 | 700 | 0.7631 | | 0.8019 | 0.31 | 750 | 0.7552 | | 0.7626 | 0.33 | 800 | 0.7501 | | 0.7566 | 0.35 | 850 | 0.7472 | | 0.7261 | 0.37 | 900 | 0.7424 | | 0.7613 | 0.39 | 950 | 0.7379 | | 0.7274 | 0.41 | 1000 | 0.7360 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
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affecto/Tess34-fans
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briefai/LongShort-Mistral-7B
briefai
2023-11-30T00:36:38Z
0
0
null
[ "peft", "region:us" ]
2023-11-30T00:36:38Z
2023-11-29T18:42:40.000Z
null
null
--- license: apache-2.0 datasets: - briefai/LongShort-Dataset language: - en pipeline_tag: text-generation tags: - pytorch - mistral - Gen-AI - Finance - KPI Extraction --- # LongShort-Mistral-7B 🤗 [Huggingface Model Card](https://huggingface.co/briefai/LongShort-Mistral-7B) ### Model Description LongShort-Mistral-7B is a large language model fine-tuned on earnings call documents to extract financial KPIs from the earnings call documents. It is based on the Mistral-7B Instruct Architecture. - Model creator: [Brief AI](https://huggingface.co/briefai) - Original model: [Mistral-7B-Instruct](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) ### Dataset Description - Data Source: Factiva - Data Description: 28K+ Earnings Call Documents - Data Scope: 1K+ public companies - Fine Tuning Data: Collection of 60K+ samples. ## Prompt template: LongShort-Mistral-7B ``` [INST]Given the context, answer the question. ### Question: Extract all the finance-based performance indicators and evaluation metrics. ### Context: {context} ### Answer: [/INST] ``` ## Basics *This section provides information about the model type, version, license, funders, release date, developers, and contact information.* *It is useful for anyone who wants to reference the model.* **Developed by:** [Brief AI Team](https://huggingface.co/briefai) **Model Type:** Transformer-based Large Language Model **Version:** 1.0.0 **Languages:** English **License:** Apache 2.0 **Release Date Estimate:** Wednesday, 29.November.2023 **Send Questions to:** vishalparameswaran96@gmail.com **Cite as:** Brief AI LongShort Language Model **Funded by:** UChicago Data Science Institute **Mentored by:** Nick Kadochnikov ## Technical Specifications *This section includes details about the model objective and architecture, and the compute infrastructure.* *It is useful for people interested in model development.* Please see [the LongShort training README](https://github.com/brief-ai-uchicago/LongShort-Dataset) for full details on replicating training. ### Model Architecture and Objective * Modified from Mistral-7B-Instruct **Objective:** Financial KPI extraction from earnings call documents. ### Hardware and Software - Compute Infrastructure * 4 NVIDIA L4 GPUs & 48 vCPUs * Environment: PyTorch (pytorch-2.0 w/ CUDA-11.8; see [Github link](https://github.com/pytorch/pytorch)) * CPU: GCP G2 Standard 48 (Platform: Intel Cascade Lake) (Accelerator Optimized) * CPU memory: 192GB RAM * GPU memory: 30GB per GPU ## Training *This section provides information about the training.* *It is useful for people who want to learn more about the model inputs and training footprint.* The following bits and bytes 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 0.4.0 ### Training Data *This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.* Details for the dataset can be found in [LongShort Dataset](https://github.com/brief-ai-uchicago/LongShort-Dataset) Training data includes: - 5000 Earnings Call Documents ## How to use This model can be easily used and deployed using HuggingFace's ecosystem. This needs `transformers` and `accelerate` installed. The model can be downloaded as follows: [LongShort-Mistral-7B](https://huggingface.co/briefai/LongShort-Mistral-7B) ## Intended Use This model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pre-trained base model that can be further fine-tuned for specific tasks. The use cases below are not exhaustive. ### Direct Use - Text generation - Exploring characteristics of language generated by a language model - Examples: Cloze tests, counterfactuals, generations with reframings ### Downstream Use - Tasks that leverage language models include: Information Extraction, Question Answering, Summarization #### Out-of-scope Uses Using the model in [high-stakes](#high-stakes) settings is out of scope for this model. The model is not designed for [critical decisions](#critical-decisions) nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but may not be correct. Out-of-scope Uses Include: - Usage for evaluating or scoring individuals, such as for employment, education, or credit - Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct #### Misuse Intentionally using the model for harm, violating [human rights](#human-rights), or other kinds of malicious activities, is a misuse of this model. This includes: - Spam generation - Disinformation and influence operations - Disparagement and defamation - Harassment and abuse - [Deception](#deception) - Unconsented impersonation and imitation - Unconsented surveillance - Generating content without attribution to the model, as specified in the [RAIL License, Use Restrictions](https://huggingface.co/spaces/bigscience/license) ## Intended Users ### Direct Users - General Public - Researchers - Students - Educators - Engineers/developers - Non-commercial entities - Financial Industry # Risks and Limitations *This section identifies foreseeable harms and misunderstandings.* Model may: - Overrepresent some viewpoints and underrepresent others - Contain stereotypes - Contain [personal information](#personal-data-and-information) - Generate: - Hateful, abusive, or violent language - Discriminatory or prejudicial language - Content that may not be appropriate for all settings, including sexual content - Make errors, including producing incorrect information as if it were factual - Generate irrelevant or repetitive outputs - Induce users into attributing human traits to it, such as sentience or consciousness # Evaluation *This section describes the evaluation protocols and provides the results.* Result: LongShort-Llama-2-13B gives 43.4% accuracy on a validation set of 10% of the original training dataset. **Train-time Evaluation:** Final checkpoint after 300 epochs: - Training Loss: 1.228 # Recommendations *This section provides information on warnings and potential mitigations.* - Indirect users should be made aware when the content they're working with is created by the LLM. - Users should be aware of [Risks and Limitations](#risks-and-limitations), and include an appropriate age disclaimer or blocking interface as necessary. - Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments. # Model Card Authors Vishal Parameshwaran, Garima Sohi, Jose Gerala, Sanchit Narayan Kumar
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briefai/LongShort-Mistral-7B
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow14
FounderOfHuggingface
2023-11-29T18:44:06Z
0
0
null
[ "peft", "arxiv:1910.09700", "base_model:gpt2", "region:us" ]
2023-11-29T18:44:06Z
2023-11-29T18:44:01.000Z
null
null
--- library_name: peft base_model: gpt2 --- # 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] - **Funded by [optional]:** [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 Dataset 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 Dataset 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 0.6.2
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peft
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow14
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K-kiron/distilbert-lr-cosine-epoch-5
K-kiron
2023-11-29T18:52:59Z
0
0
null
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2023-11-29T18:52:59Z
2023-11-29T18:46:03.000Z
null
null
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-lr-cosine-epoch-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. --> # distilbert-lr-cosine-epoch-5 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: - Loss: 0.8566 - Accuracy: 0.8920 - F1: 0.8919 ## 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: cosine - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.1+cu121 - Datasets 2.12.0 - Tokenizers 0.13.3
null
transformers
text-classification
null
null
null
null
null
null
null
null
null
K-kiron/distilbert-lr-cosine-epoch-5
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Maxo300/ddpm-butterflies-128
Maxo300
2023-11-29T18:46:32Z
0
0
null
[ "license:mit", "region:us" ]
2023-11-29T18:46:32Z
2023-11-29T18:46:32.000Z
null
null
--- license: mit ---
null
null
null
null
null
null
null
null
null
null
null
null
Maxo300/ddpm-butterflies-128
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JohnDoe70/embed
JohnDoe70
2023-11-29T18:49:03Z
0
0
null
[ "transformers.js", "onnx", "mpnet", "fill-mask", "sentence-similarity", "region:us" ]
2023-11-29T18:49:03Z
2023-11-29T18:47:51.000Z
null
null
--- library_name: transformers.js pipeline_tag: sentence-similarity --- https://huggingface.co/sentence-transformers/all-mpnet-base-v2 with ONNX weights to be compatible with Transformers.js.
null
transformers.js
sentence-similarity
null
null
null
null
null
null
null
null
null
JohnDoe70/embed
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stuWORK/my_awesome_model2
stuWORK
2023-11-29T20:48:25Z
0
0
null
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "endpoints_compatible", "region:us" ]
2023-11-29T20:48:25Z
2023-11-29T18:49:48.000Z
null
null
Entry not found
null
transformers
text-classification
null
null
null
null
null
null
null
null
null
stuWORK/my_awesome_model2
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Ortografe/LektorPL
Ortografe
2023-11-29T18:51:44Z
0
0
null
[ "pl", "dataset:HuggingFaceH4/no_robots", "arxiv:1910.09700", "license:unknown", "region:us" ]
2023-11-29T18:51:44Z
2023-11-29T18:49:59.000Z
null
null
--- license: unknown datasets: - HuggingFaceH4/no_robots language: - pl --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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 Dataset 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 Dataset 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]
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Ortografe/LektorPL
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Vyach22222/Senko_RVC2
Vyach22222
2023-11-29T18:51:21Z
0
0
null
[ "license:other", "region:us" ]
2023-11-29T18:51:21Z
2023-11-29T18:50:14.000Z
null
null
--- license: other license_name: iacat-descord license_link: LICENSE ---
null
null
null
null
null
null
null
null
null
null
null
null
Vyach22222/Senko_RVC2
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MaryRead/my_awesome_model
MaryRead
2023-11-29T18:50:59Z
0
0
null
[ "region:us" ]
2023-11-29T18:50:59Z
2023-11-29T18:50:59.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
MaryRead/my_awesome_model
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TheBloke/LLaMA2-13B-Psyfighter2-GGUF
TheBloke
2023-11-30T00:51:57Z
0
0
null
[ "transformers", "gguf", "llama", "base_model:KoboldAI/LLaMA2-13B-Psyfighter2", "license:llama2", "text-generation-inference", "region:us" ]
2023-11-30T00:51:57Z
2023-11-29T18:51:04.000Z
null
null
--- base_model: KoboldAI/LLaMA2-13B-Psyfighter2 inference: false license: llama2 model_creator: KoboldAI model_name: Llama2 13B Psyfighter2 model_type: llama prompt_template: "### Instruction: \n{prompt}\n### Response:\n" 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 --> # Llama2 13B Psyfighter2 - GGUF - Model creator: [KoboldAI](https://huggingface.co/KoboldAI) - Original model: [Llama2 13B Psyfighter2](https://huggingface.co/KoboldAI/LLaMA2-13B-Psyfighter2) <!-- description start --> ## Description This repo contains GGUF format model files for [KoboldAI's Llama2 13B Psyfighter2](https://huggingface.co/KoboldAI/LLaMA2-13B-Psyfighter2). 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 incomplete 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. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [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. * [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. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/LLaMA2-13B-Psyfighter2-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/LLaMA2-13B-Psyfighter2-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/LLaMA2-13B-Psyfighter2-GGUF) * [KoboldAI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/KoboldAI/LLaMA2-13B-Psyfighter2) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Alpaca-Tiefighter ``` ### Instruction: {prompt} ### Response: ``` <!-- prompt-template 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 | | ---- | ---- | ---- | ---- | ---- | ----- | | [llama2-13b-psyfighter2.Q2_K.gguf](https://huggingface.co/TheBloke/LLaMA2-13B-Psyfighter2-GGUF/blob/main/llama2-13b-psyfighter2.Q2_K.gguf) | Q2_K | 2 | 5.43 GB| 7.93 GB | smallest, significant quality loss - not recommended for most purposes | | [llama2-13b-psyfighter2.Q3_K_S.gguf](https://huggingface.co/TheBloke/LLaMA2-13B-Psyfighter2-GGUF/blob/main/llama2-13b-psyfighter2.Q3_K_S.gguf) | Q3_K_S | 3 | 5.66 GB| 8.16 GB | very small, high quality loss | | [llama2-13b-psyfighter2.Q3_K_M.gguf](https://huggingface.co/TheBloke/LLaMA2-13B-Psyfighter2-GGUF/blob/main/llama2-13b-psyfighter2.Q3_K_M.gguf) | Q3_K_M | 3 | 6.34 GB| 8.84 GB | very small, high quality loss | | [llama2-13b-psyfighter2.Q3_K_L.gguf](https://huggingface.co/TheBloke/LLaMA2-13B-Psyfighter2-GGUF/blob/main/llama2-13b-psyfighter2.Q3_K_L.gguf) | Q3_K_L | 3 | 6.93 GB| 9.43 GB | small, substantial quality loss | | [llama2-13b-psyfighter2.Q4_0.gguf](https://huggingface.co/TheBloke/LLaMA2-13B-Psyfighter2-GGUF/blob/main/llama2-13b-psyfighter2.Q4_0.gguf) | Q4_0 | 4 | 7.37 GB| 9.87 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [llama2-13b-psyfighter2.Q4_K_S.gguf](https://huggingface.co/TheBloke/LLaMA2-13B-Psyfighter2-GGUF/blob/main/llama2-13b-psyfighter2.Q4_K_S.gguf) | Q4_K_S | 4 | 7.41 GB| 9.91 GB | small, greater quality loss | | [llama2-13b-psyfighter2.Q4_K_M.gguf](https://huggingface.co/TheBloke/LLaMA2-13B-Psyfighter2-GGUF/blob/main/llama2-13b-psyfighter2.Q4_K_M.gguf) | Q4_K_M | 4 | 7.87 GB| 10.37 GB | medium, balanced quality - recommended | | [llama2-13b-psyfighter2.Q5_0.gguf](https://huggingface.co/TheBloke/LLaMA2-13B-Psyfighter2-GGUF/blob/main/llama2-13b-psyfighter2.Q5_0.gguf) | Q5_0 | 5 | 8.97 GB| 11.47 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [llama2-13b-psyfighter2.Q5_K_S.gguf](https://huggingface.co/TheBloke/LLaMA2-13B-Psyfighter2-GGUF/blob/main/llama2-13b-psyfighter2.Q5_K_S.gguf) | Q5_K_S | 5 | 8.97 GB| 11.47 GB | large, low quality loss - recommended | | [llama2-13b-psyfighter2.Q5_K_M.gguf](https://huggingface.co/TheBloke/LLaMA2-13B-Psyfighter2-GGUF/blob/main/llama2-13b-psyfighter2.Q5_K_M.gguf) | Q5_K_M | 5 | 9.23 GB| 11.73 GB | large, very low quality loss - recommended | | [llama2-13b-psyfighter2.Q6_K.gguf](https://huggingface.co/TheBloke/LLaMA2-13B-Psyfighter2-GGUF/blob/main/llama2-13b-psyfighter2.Q6_K.gguf) | Q6_K | 6 | 10.68 GB| 13.18 GB | very large, extremely low quality loss | | [llama2-13b-psyfighter2.Q8_0.gguf](https://huggingface.co/TheBloke/LLaMA2-13B-Psyfighter2-GGUF/blob/main/llama2-13b-psyfighter2.Q8_0.gguf) | Q8_0 | 8 | 13.83 GB| 16.33 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/LLaMA2-13B-Psyfighter2-GGUF and below it, a specific filename to download, such as: llama2-13b-psyfighter2.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/LLaMA2-13B-Psyfighter2-GGUF llama2-13b-psyfighter2.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/LLaMA2-13B-Psyfighter2-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` 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/LLaMA2-13B-Psyfighter2-GGUF llama2-13b-psyfighter2.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 35 -m llama2-13b-psyfighter2.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: \n{prompt}\n### Response:" ``` 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. Note that longer sequence lengths require much more resources, so you may need to reduce this value. 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 can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## 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. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # 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 = Llama( model_path="./llama2-13b-psyfighter2.Q4_K_M.gguf", # Download the model file first n_ctx=4096, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "### Instruction: \n{prompt}\n### Response:", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./llama2-13b-psyfighter2.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## 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: KoboldAI's Llama2 13B Psyfighter2 # LLAMA2-13B-Psyfighter2 Psyfighter is a merged model created by the KoboldAI community members Jeb Carter and TwistedShadows and was made possible thanks to the KoboldAI merge request service. The intent was to add medical data to supplement the models fictional ability with more details on anatomy and mental states. Due to the low ratio's of medical data and the high ratio's of fiction this model should not be used for medical advice or therapy because of its high chance of pulling in fictional data. The following mergekit recipe was used: ``` merge_method: task_arithmetic base_model: TheBloke/Llama-2-13B-fp16 models: - model: TheBloke/Llama-2-13B-fp16 - model: KoboldAI/LLaMA2-13B-Tiefighter parameters: weight: 1.0 - model: Doctor-Shotgun/cat-v1.0-13b parameters: weight: 0.01 - model: Doctor-Shotgun/llama-2-13b-chat-limarp-v2-merged parameters: weight: 0.02 dtype: float16 ``` *V1 of this model was published under the account of the creator of the merge This model contains the following ingredients from their upstream models for as far as we can track them: - KoboldAI/LLaMA2-13B-Tiefighter - Undi95/Xwin-MLewd-13B-V0.2 - - Undi95/ReMM-S-Light - Undi95/CreativeEngine - Brouz/Slerpeno - - elinas/chronos-13b-v2 - jondurbin/airoboros-l2-13b-2.1 - NousResearch/Nous-Hermes-Llama2-13b+nRuaif/Kimiko-v2 - CalderaAI/13B-Legerdemain-L2+lemonilia/limarp-llama2-v2 - - KoboldAI/LLAMA2-13B-Holodeck-1 - NousResearch/Nous-Hermes-13b - OpenAssistant/llama2-13b-orca-8k-3319 - ehartford/WizardLM-1.0-Uncensored-Llama2-13b - Henk717/spring-dragon - The-Face-Of-Goonery/Huginn-v3-13b (Contains undisclosed model versions, those we assumed where possible) - - SuperCOT (Undisclosed version) - elinas/chronos-13b-v2 (Version assumed) - NousResearch/Nous-Hermes-Llama2-13b - stabilityai/StableBeluga-13B (Version assumed) - zattio770/120-Days-of-LORA-v2-13B - PygmalionAI/pygmalion-2-13b - Undi95/Storytelling-v1-13B-lora - TokenBender/sakhi_13B_roleplayer_NSFW_chat_adapter - nRuaif/Kimiko-v2-13B - The-Face-Of-Goonery/Huginn-13b-FP16 - - "a lot of different models, like hermes, beluga, airoboros, chronos.. limarp" - lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT - Xwin-LM/Xwin-LM-13B-V0.2 - PocketDoc/Dans-RetroRodeo-13b - Blackroot/Llama-2-13B-Storywriter-LORA - Doctor-Shotgun/cat-v1.0-13b - Doctor-Shotgun/llama-2-13b-chat-limarp-v2-merged - meta-llama/Llama-2-13b-chat-hf - lemonilia/limarp-llama2-v2 While we could possibly not credit every single lora or model involved in this merged model, we'd like to thank all involved creators upstream for making this awesome model possible! Thanks to you the AI ecosystem is thriving, and without your dedicated tuning efforts models such as this one would not be possible. # Usage This model is meant to be creative, If you let it improvise you get better results than if you drown it in details. ## Story Writing Regular story writing in the traditional way is supported, simply copy paste your story and continue writing. Optionally use an instruction in memory or an authors note to guide the direction of your story. ### Generate a story on demand To generate stories on demand you can use an instruction (tested in the Alpaca format) such as "Write a novel about X, use chapters and dialogue" this will generate a story. The format can vary between generations depending on how the model chooses to begin, either write what you want as shown in the earlier example or write the beginning of the story yourself so the model can follow your style. A few retries can also help if the model gets it wrong. ## Chatbots and persona's This model has been tested with various forms of chatting, testers have found that typically less is more and the model is good at improvising. Don't drown the model in paragraphs of detailed information, instead keep it simple first and see how far you can lean on the models own ability to figure out your character. Copy pasting paragraphs of background information is not suitable for a 13B model such as this one, code formatted characters or an instruction prompt describing who you wish to talk to goes much further. For example, you can put this in memory in regular chat mode: ``` ### Instruction: Generate a conversation between Alice and Jeb where they discuss language models. In this conversation Henk is excited to teach Alice about Psyfighter. ### Response: ``` Because the model is a merge of a variety of models, it should support a broad range of instruct formats, or plain chat mode. If you have a particular favourite try it, otherwise we recommend to either use the regular chat mode or Alpaca's format. ## Instruct Prompting This model features various instruct models on a variety of instruction styles, when testing the model we have used Alpaca for our own tests. If you prefer a different format chances are it can work. During instructions we have observed that in some cases the adventure data can leak, it may also be worth experimenting using > as the prefix for a user command to remedy this. But this may result in a stronger fiction bias. Keep in mind that while this model can be used as a factual instruct model, the focus was on fiction. Information provided by the model can be made up. ## Adventuring and Adventure Games This model contains a lora that was trained on the same adventure dataset as the KoboldAI Skein model. Adventuring is best done using an small introduction to the world and your objective while using the > prefix for a user command (KoboldAI's adventure mode). It is possible that the model does not immediately pick up on what you wish to do and does not engage in its Adventure mode behaviour right away. Simply manually correct the output to trim excess dialogue or other undesirable behaviour and continue to submit your actions using the appropriate mode. The model should pick up on this style quickly and will correctly follow this format within 3 turns. ## Discovered something cool and want to engage with us? Join our community at https://koboldai.org/discord ! We can also provide assistance in making your own merges. <!-- original-model-card end -->
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TheBloke/LLaMA2-13B-Psyfighter2-GGUF
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hariknall/sample
hariknall
2023-11-29T18:52:04Z
0
0
null
[ "license:mit", "region:us" ]
2023-11-29T18:52:04Z
2023-11-29T18:52:04.000Z
null
null
--- license: mit ---
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hariknall/sample
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow15
FounderOfHuggingface
2023-11-29T18:52:31Z
0
0
null
[ "peft", "arxiv:1910.09700", "base_model:gpt2", "region:us" ]
2023-11-29T18:52:31Z
2023-11-29T18:52:27.000Z
null
null
--- library_name: peft base_model: gpt2 --- # 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] - **Funded by [optional]:** [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 Dataset 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 Dataset 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 0.6.2
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peft
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow15
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nicholasjg/ddpm-butterflies-128
nicholasjg
2023-11-29T18:52:28Z
0
0
null
[ "region:us" ]
2023-11-29T18:52:28Z
2023-11-29T18:52:28.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
nicholasjg/ddpm-butterflies-128
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joyfine/vicuna-7b-fine-tuning_twitter_20_16
joyfine
2023-11-29T19:02:13Z
0
0
null
[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2023-11-29T19:02:13Z
2023-11-29T18:55:59.000Z
null
null
Entry not found
null
transformers
text-generation
null
null
null
null
null
null
null
null
null
joyfine/vicuna-7b-fine-tuning_twitter_20_16
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bqKuhli/distilbert-base-uncased-unlearned-sun
bqKuhli
2023-11-29T19:56:07Z
0
0
null
[ "transformers", "tensorboard", "safetensors", "distilbert", "fill-mask", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2023-11-29T19:56:07Z
2023-11-29T18:59:00.000Z
null
null
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-unlearned-sun 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. --> # distilbert-base-uncased-unlearned-sun 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: - Loss: nan ## 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: 2 - eval_batch_size: 2 - 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 | |:-------------:|:-----:|:----:|:---------------:| | 2.9064 | 1.0 | 5 | nan | | 4.6425 | 2.0 | 10 | nan | | 0.3447 | 3.0 | 15 | nan | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cpu - Datasets 2.15.0 - Tokenizers 0.15.0
null
transformers
fill-mask
null
null
null
null
null
null
null
null
null
bqKuhli/distilbert-base-uncased-unlearned-sun
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LoewolfAI/L-GPT_Loewolf_AI
LoewolfAI
2023-11-29T19:01:08Z
0
0
null
[ "license:mit", "region:us" ]
2023-11-29T19:01:08Z
2023-11-29T18:59:48.000Z
null
null
--- license: mit --- <h1>KI Modell für Löwolf AI website!</h1> <a href="https://aichatbotlowolf.netlify.app/">Löwolf AI</a>
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null
LoewolfAI/L-GPT_Loewolf_AI
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sordonia/library-phi-2
sordonia
2023-11-29T22:41:03Z
0
0
null
[ "region:us" ]
2023-11-29T22:41:03Z
2023-11-29T19:00:08.000Z
null
null
Number of experts present in the library: 420 | Expert Name | Base Model | Trained on | Adapter Type | | --- | --- | --- | --- | | kilt_tasks_hotpotqa_final_exam | phi-2 | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_final_exam | lora | | social_i_qa_Check_if_a_random_answer_is_valid_or_not | phi-2 | sordonia/flan-10k-flat/social_i_qa_Check_if_a_random_answer_is_valid_or_not | lora | | duorc_SelfRC_answer_question | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_answer_question | lora | | wiqa_effect_with_string_answer | phi-2 | sordonia/flan-10k-flat/wiqa_effect_with_string_answer | lora | | adversarial_qa_droberta_generate_question | phi-2 | sordonia/flan-10k-flat/adversarial_qa_droberta_generate_question | lora | | clinical_knowledge | phi-2 | sordonia/qa-flat-mmlu/clinical_knowledge | lora | | ultrachat_4 | phi-2 | sordonia/adauni-v1-flat/ultrachat_4 | lora | | ultrachat_23 | phi-2 | sordonia/adauni-v1-flat/ultrachat_23 | lora | | ultrachat_20 | phi-2 | sordonia/adauni-v1-flat/ultrachat_20 | lora | | ropes_plain_bottom_hint | phi-2 | sordonia/flan-10k-flat/ropes_plain_bottom_hint | lora | | adversarial_qa_dbert_generate_question | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbert_generate_question | lora | | qasc_qa_with_separated_facts_1 | phi-2 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_1 | lora | | college_biology | phi-2 | sordonia/qa-flat-mmlu/college_biology | lora | | jurisprudence | phi-2 | sordonia/qa-flat-mmlu/jurisprudence | lora | | wiqa_what_is_the_missing_first_step | phi-2 | sordonia/flan-10k-flat/wiqa_what_is_the_missing_first_step | lora | | adversarial_qa_dbert_based_on | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbert_based_on | lora | | niv2_sentence_compression | phi-2 | sordonia/adauni-v1-flat/niv2_sentence_compression | lora | | adversarial_qa_droberta_answer_the_following_q | phi-2 | sordonia/flan-10k-flat/adversarial_qa_droberta_answer_the_following_q | lora | | glue_mnli_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_mnli_2_0_0 | lora | | lambada_1_0_0 | phi-2 | sordonia/flan-10k-flat/lambada_1_0_0 | lora | | niv2_entity_relation_classification | phi-2 | sordonia/adauni-v1-flat/niv2_entity_relation_classification | lora | | wiki_qa_Is_This_True_ | phi-2 | sordonia/flan-10k-flat/wiki_qa_Is_This_True_ | lora | | niv2_ethics_classification | phi-2 | sordonia/adauni-v1-flat/niv2_ethics_classification | lora | | ultrachat_13 | phi-2 | sordonia/adauni-v1-flat/ultrachat_13 | lora | | niv2_negotiation_strategy_detection | phi-2 | sordonia/adauni-v1-flat/niv2_negotiation_strategy_detection | lora | | quartz_paragraph_question_plain_concat | phi-2 | sordonia/flan-10k-flat/quartz_paragraph_question_plain_concat | lora | | high_school_psychology | phi-2 | sordonia/qa-flat-mmlu/high_school_psychology | lora | | niv2_commonsense_classification | phi-2 | sordonia/adauni-v1-flat/niv2_commonsense_classification | lora | | ropes_read_background_situation | phi-2 | sordonia/flan-10k-flat/ropes_read_background_situation | lora | | ultrachat_26 | phi-2 | sordonia/adauni-v1-flat/ultrachat_26 | lora | | niv2_named_entity_recognition | phi-2 | sordonia/adauni-v1-flat/niv2_named_entity_recognition | lora | | para_crawl_enes | phi-2 | sordonia/flan-10k-flat/para_crawl_enes | lora | | piqa_1_0_0 | phi-2 | sordonia/flan-10k-flat/piqa_1_0_0 | lora | | quarel_testing_students | phi-2 | sordonia/flan-10k-flat/quarel_testing_students | lora | | wmt14_translate_fr_en_1_0_0 | phi-2 | sordonia/flan-10k-flat/wmt14_translate_fr_en_1_0_0 | lora | | scibench | phi-2 | sordonia/adauni-v1-flat/scibench | lora | | duorc_SelfRC_title_generation | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_title_generation | lora | | niv2_dialogue_act_recognition | phi-2 | sordonia/adauni-v1-flat/niv2_dialogue_act_recognition | lora | | wiqa_what_might_be_the_last_step_of_the_process | phi-2 | sordonia/flan-10k-flat/wiqa_what_might_be_the_last_step_of_the_process | lora | | nutrition | phi-2 | sordonia/qa-flat-mmlu/nutrition | lora | | adversarial_qa_dbidaf_answer_the_following_q | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_answer_the_following_q | lora | | duorc_SelfRC_decide_worth_it | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_decide_worth_it | lora | | quoref_Guess_Title_For_Context | phi-2 | sordonia/flan-10k-flat/quoref_Guess_Title_For_Context | lora | | electrical_engineering | phi-2 | sordonia/qa-flat-mmlu/electrical_engineering | lora | | guanaco | phi-2 | sordonia/adauni-v1-flat/guanaco | lora | | ultrachat_31 | phi-2 | sordonia/adauni-v1-flat/ultrachat_31 | lora | | ropes_background_situation_middle | phi-2 | sordonia/flan-10k-flat/ropes_background_situation_middle | lora | | wmt16_translate_fi_en_1_0_0 | phi-2 | sordonia/flan-10k-flat/wmt16_translate_fi_en_1_0_0 | lora | | ultrachat_3 | phi-2 | sordonia/adauni-v1-flat/ultrachat_3 | lora | | wiki_bio_what_content | phi-2 | sordonia/flan-10k-flat/wiki_bio_what_content | lora | | marketing | phi-2 | sordonia/qa-flat-mmlu/marketing | lora | | ultrachat_6 | phi-2 | sordonia/adauni-v1-flat/ultrachat_6 | lora | | sociology | phi-2 | sordonia/qa-flat-mmlu/sociology | lora | | leetcode_ne | phi-2 | sordonia/adauni-v1-flat/leetcode_ne | lora | | moral_scenarios | phi-2 | sordonia/qa-flat-mmlu/moral_scenarios | lora | | race_middle_Select_the_best_answer_generate_span_ | phi-2 | sordonia/flan-10k-flat/race_middle_Select_the_best_answer_generate_span_ | lora | | quartz_use_info_from_paragraph_question | phi-2 | sordonia/flan-10k-flat/quartz_use_info_from_paragraph_question | lora | | niv2_word_semantics | phi-2 | sordonia/adauni-v1-flat/niv2_word_semantics | lora | | kilt_tasks_hotpotqa_straighforward_qa | phi-2 | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_straighforward_qa | lora | | cot_ecqa | phi-2 | sordonia/cot:flan-10k-flat/cot_ecqa | lora | | ropes_prompt_bottom_hint_beginning | phi-2 | sordonia/flan-10k-flat/ropes_prompt_bottom_hint_beginning | lora | | gem_dart_1_1_0 | phi-2 | sordonia/flan-10k-flat/gem_dart_1_1_0 | lora | | niv2_number_conversion | phi-2 | sordonia/adauni-v1-flat/niv2_number_conversion | lora | | niv2_translation | phi-2 | sordonia/adauni-v1-flat/niv2_translation | lora | | airoboros | phi-2 | sordonia/adauni-v1-flat/airoboros | lora | | super_glue_wic_1_0_2 | phi-2 | sordonia/flan-10k-flat/super_glue_wic_1_0_2 | lora | | quartz_read_passage_below_choose | phi-2 | sordonia/flan-10k-flat/quartz_read_passage_below_choose | lora | | ultrachat_18 | phi-2 | sordonia/adauni-v1-flat/ultrachat_18 | lora | | adversarial_qa_droberta_question_context_answer | phi-2 | sordonia/flan-10k-flat/adversarial_qa_droberta_question_context_answer | lora | | ultrachat_0 | phi-2 | sordonia/adauni-v1-flat/ultrachat_0 | lora | | cot_sensemaking_ii | phi-2 | sordonia/cot:flan-10k-flat/cot_sensemaking_ii | lora | | niv2_grammar_error_correction | phi-2 | sordonia/adauni-v1-flat/niv2_grammar_error_correction | lora | | niv2_irony_detection | phi-2 | sordonia/adauni-v1-flat/niv2_irony_detection | lora | | econometrics | phi-2 | sordonia/qa-flat-mmlu/econometrics | lora | | niv2_code_to_text | phi-2 | sordonia/adauni-v1-flat/niv2_code_to_text | lora | | college_chemistry | phi-2 | sordonia/qa-flat-mmlu/college_chemistry | lora | | niv2_style_transfer | phi-2 | sordonia/adauni-v1-flat/niv2_style_transfer | lora | | quoref_Find_Answer | phi-2 | sordonia/flan-10k-flat/quoref_Find_Answer | lora | | niv2_paraphrasing | phi-2 | sordonia/adauni-v1-flat/niv2_paraphrasing | lora | | tigerbot-kaggle | phi-2 | sordonia/adauni-v1-flat/tigerbot-kaggle | lora | | ultrachat_2 | phi-2 | sordonia/adauni-v1-flat/ultrachat_2 | lora | | niv2_title_generation | phi-2 | sordonia/adauni-v1-flat/niv2_title_generation | lora | | cot_creak_ii | phi-2 | sordonia/cot:flan-10k-flat/cot_creak_ii | lora | | gigaword_1_2_0 | phi-2 | sordonia/flan-10k-flat/gigaword_1_2_0 | lora | | ultrachat_19 | phi-2 | sordonia/adauni-v1-flat/ultrachat_19 | lora | | high_school_computer_science | phi-2 | sordonia/qa-flat-mmlu/high_school_computer_science | lora | | professional_medicine | phi-2 | sordonia/qa-flat-mmlu/professional_medicine | lora | | dbpedia_14_given_a_choice_of_categories_ | phi-2 | sordonia/flan-10k-flat/dbpedia_14_given_a_choice_of_categories_ | lora | | niv2_word_analogy | phi-2 | sordonia/adauni-v1-flat/niv2_word_analogy | lora | | quoref_Found_Context_Online | phi-2 | sordonia/flan-10k-flat/quoref_Found_Context_Online | lora | | quail_description_context_question_answer_id | phi-2 | sordonia/flan-10k-flat/quail_description_context_question_answer_id | lora | | cot_strategyqa_ii | phi-2 | sordonia/cot:flan-10k-flat/cot_strategyqa_ii | lora | | wiki_hop_original_choose_best_object_affirmative_3 | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_3 | lora | | quac_1_0_0 | phi-2 | sordonia/flan-10k-flat/quac_1_0_0 | lora | | niv2_fill_in_the_blank | phi-2 | sordonia/adauni-v1-flat/niv2_fill_in_the_blank | lora | | philosophy | phi-2 | sordonia/qa-flat-mmlu/philosophy | lora | | super_glue_cb_1_0_2 | phi-2 | sordonia/flan-10k-flat/super_glue_cb_1_0_2 | lora | | race_high_Select_the_best_answer_no_instructions_ | phi-2 | sordonia/flan-10k-flat/race_high_Select_the_best_answer_no_instructions_ | lora | | niv2_program_execution | phi-2 | sordonia/adauni-v1-flat/niv2_program_execution | lora | | ultrachat_16 | phi-2 | sordonia/adauni-v1-flat/ultrachat_16 | lora | | race_high_Select_the_best_answer_generate_span_ | phi-2 | sordonia/flan-10k-flat/race_high_Select_the_best_answer_generate_span_ | lora | | high_school_european_history | phi-2 | sordonia/qa-flat-mmlu/high_school_european_history | lora | | niv2_question_understanding | phi-2 | sordonia/adauni-v1-flat/niv2_question_understanding | lora | | wiki_hop_original_explain_relation | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_explain_relation | lora | | prehistory | phi-2 | sordonia/qa-flat-mmlu/prehistory | lora | | moral_disputes | phi-2 | sordonia/qa-flat-mmlu/moral_disputes | lora | | niv2_sentence_expansion | phi-2 | sordonia/adauni-v1-flat/niv2_sentence_expansion | lora | | super_glue_rte_1_0_2 | phi-2 | sordonia/flan-10k-flat/super_glue_rte_1_0_2 | lora | | wiki_qa_automatic_system | phi-2 | sordonia/flan-10k-flat/wiki_qa_automatic_system | lora | | niv2_text_quality_evaluation | phi-2 | sordonia/adauni-v1-flat/niv2_text_quality_evaluation | lora | | hellaswag_1_1_0 | phi-2 | sordonia/flan-10k-flat/hellaswag_1_1_0 | lora | | wiki_hop_original_generate_object | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_generate_object | lora | | ropes_prompt_beginning | phi-2 | sordonia/flan-10k-flat/ropes_prompt_beginning | lora | | us_foreign_policy | phi-2 | sordonia/qa-flat-mmlu/us_foreign_policy | lora | | snli_1_1_0 | phi-2 | sordonia/flan-10k-flat/snli_1_1_0 | lora | | college_medicine | phi-2 | sordonia/qa-flat-mmlu/college_medicine | lora | | professional_accounting | phi-2 | sordonia/qa-flat-mmlu/professional_accounting | lora | | adversarial_qa_dbert_tell_what_it_is | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbert_tell_what_it_is | lora | | yelp_polarity_reviews_0_2_0 | phi-2 | sordonia/flan-10k-flat/yelp_polarity_reviews_0_2_0 | lora | | quartz_answer_question_based_on | phi-2 | sordonia/flan-10k-flat/quartz_answer_question_based_on | lora | | quarel_heres_a_story | phi-2 | sordonia/flan-10k-flat/quarel_heres_a_story | lora | | cot_qasc | phi-2 | sordonia/cot:flan-10k-flat/cot_qasc | lora | | niv2_text_completion | phi-2 | sordonia/adauni-v1-flat/niv2_text_completion | lora | | adversarial_qa_dbert_answer_the_following_q | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbert_answer_the_following_q | lora | | niv2_information_extraction | phi-2 | sordonia/adauni-v1-flat/niv2_information_extraction | lora | | super_glue_copa_1_0_2 | phi-2 | sordonia/flan-10k-flat/super_glue_copa_1_0_2 | lora | | niv2_text_matching | phi-2 | sordonia/adauni-v1-flat/niv2_text_matching | lora | | wiki_qa_Decide_good_answer | phi-2 | sordonia/flan-10k-flat/wiki_qa_Decide_good_answer | lora | | professional_law | phi-2 | sordonia/qa-flat-mmlu/professional_law | lora | | quail_context_description_question_text | phi-2 | sordonia/flan-10k-flat/quail_context_description_question_text | lora | | quartz_answer_question_below | phi-2 | sordonia/flan-10k-flat/quartz_answer_question_below | lora | | niv2_cause_effect_classification | phi-2 | sordonia/adauni-v1-flat/niv2_cause_effect_classification | lora | | social_i_qa_Generate_answer | phi-2 | sordonia/flan-10k-flat/social_i_qa_Generate_answer | lora | | virology | phi-2 | sordonia/qa-flat-mmlu/virology | lora | | cos_e_v1_11_question_option_description_id | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_question_option_description_id | lora | | theoremqa | phi-2 | sordonia/adauni-v1-flat/theoremqa | lora | | ultrachat_10 | phi-2 | sordonia/adauni-v1-flat/ultrachat_10 | lora | | ultrachat_21 | phi-2 | sordonia/adauni-v1-flat/ultrachat_21 | lora | | wiki_qa_found_on_google | phi-2 | sordonia/flan-10k-flat/wiki_qa_found_on_google | lora | | paws_wiki_1_1_0 | phi-2 | sordonia/flan-10k-flat/paws_wiki_1_1_0 | lora | | quartz_having_read_above_passage | phi-2 | sordonia/flan-10k-flat/quartz_having_read_above_passage | lora | | glue_stsb_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_stsb_2_0_0 | lora | | imdb_reviews_plain_text_1_0_0 | phi-2 | sordonia/flan-10k-flat/imdb_reviews_plain_text_1_0_0 | lora | | math_dataset_algebra__linear_1d_1_0_0 | phi-2 | sordonia/flan-10k-flat/math_dataset_algebra__linear_1d_1_0_0 | lora | | international_law | phi-2 | sordonia/qa-flat-mmlu/international_law | lora | | race_middle_Taking_a_test | phi-2 | sordonia/flan-10k-flat/race_middle_Taking_a_test | lora | | dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to | phi-2 | sordonia/flan-10k-flat/dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to | lora | | wiki_qa_Jeopardy_style | phi-2 | sordonia/flan-10k-flat/wiki_qa_Jeopardy_style | lora | | ultrachat_28 | phi-2 | sordonia/adauni-v1-flat/ultrachat_28 | lora | | abstract_algebra | phi-2 | sordonia/qa-flat-mmlu/abstract_algebra | lora | | cosmos_qa_1_0_0 | phi-2 | sordonia/flan-10k-flat/cosmos_qa_1_0_0 | lora | | race_high_Write_a_multi_choice_question_options_given_ | phi-2 | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_options_given_ | lora | | cos_e_v1_11_description_question_option_text | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_description_question_option_text | lora | | niv2_dialogue_state_tracking | phi-2 | sordonia/adauni-v1-flat/niv2_dialogue_state_tracking | lora | | duorc_ParaphraseRC_question_answering | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_question_answering | lora | | quail_context_question_answer_description_id | phi-2 | sordonia/flan-10k-flat/quail_context_question_answer_description_id | lora | | quail_context_question_description_answer_id | phi-2 | sordonia/flan-10k-flat/quail_context_question_description_answer_id | lora | | quarel_logic_test | phi-2 | sordonia/flan-10k-flat/quarel_logic_test | lora | | scienceqa | phi-2 | sordonia/adauni-v1-flat/scienceqa | lora | | quail_no_prompt_text | phi-2 | sordonia/flan-10k-flat/quail_no_prompt_text | lora | | gem_e2e_nlg_1_1_0 | phi-2 | sordonia/flan-10k-flat/gem_e2e_nlg_1_1_0 | lora | | social_i_qa_Generate_the_question_from_the_answer | phi-2 | sordonia/flan-10k-flat/social_i_qa_Generate_the_question_from_the_answer | lora | | quail_description_context_question_text | phi-2 | sordonia/flan-10k-flat/quail_description_context_question_text | lora | | ropes_prompt_bottom_no_hint | phi-2 | sordonia/flan-10k-flat/ropes_prompt_bottom_no_hint | lora | | ultrachat_24 | phi-2 | sordonia/adauni-v1-flat/ultrachat_24 | lora | | niv2_entity_generation | phi-2 | sordonia/adauni-v1-flat/niv2_entity_generation | lora | | wiqa_effect_with_label_answer | phi-2 | sordonia/flan-10k-flat/wiqa_effect_with_label_answer | lora | | logical_fallacies | phi-2 | sordonia/qa-flat-mmlu/logical_fallacies | lora | | formal_logic | phi-2 | sordonia/qa-flat-mmlu/formal_logic | lora | | qasc_qa_with_separated_facts_4 | phi-2 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_4 | lora | | high_school_chemistry | phi-2 | sordonia/qa-flat-mmlu/high_school_chemistry | lora | | niv2_story_composition | phi-2 | sordonia/adauni-v1-flat/niv2_story_composition | lora | | public_relations | phi-2 | sordonia/qa-flat-mmlu/public_relations | lora | | niv2_summarization | phi-2 | sordonia/adauni-v1-flat/niv2_summarization | lora | | conceptual_physics | phi-2 | sordonia/qa-flat-mmlu/conceptual_physics | lora | | niv2_question_answering | phi-2 | sordonia/adauni-v1-flat/niv2_question_answering | lora | | professional_psychology | phi-2 | sordonia/qa-flat-mmlu/professional_psychology | lora | | cos_e_v1_11_aligned_with_common_sense | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_aligned_with_common_sense | lora | | ultrachat_30 | phi-2 | sordonia/adauni-v1-flat/ultrachat_30 | lora | | quail_no_prompt_id | phi-2 | sordonia/flan-10k-flat/quail_no_prompt_id | lora | | niv2_discourse_connective_identification | phi-2 | sordonia/adauni-v1-flat/niv2_discourse_connective_identification | lora | | ag_news_subset_1_0_0 | phi-2 | sordonia/flan-10k-flat/ag_news_subset_1_0_0 | lora | | social_i_qa_I_was_wondering | phi-2 | sordonia/flan-10k-flat/social_i_qa_I_was_wondering | lora | | quartz_given_the_fact_answer_the_q | phi-2 | sordonia/flan-10k-flat/quartz_given_the_fact_answer_the_q | lora | | cot_esnli | phi-2 | sordonia/cot:flan-10k-flat/cot_esnli | lora | | ultrachat_17 | phi-2 | sordonia/adauni-v1-flat/ultrachat_17 | lora | | niv2_poem_generation | phi-2 | sordonia/adauni-v1-flat/niv2_poem_generation | lora | | quail_context_question_description_answer_text | phi-2 | sordonia/flan-10k-flat/quail_context_question_description_answer_text | lora | | ultrachat_27 | phi-2 | sordonia/adauni-v1-flat/ultrachat_27 | lora | | niv2_word_relation_classification | phi-2 | sordonia/adauni-v1-flat/niv2_word_relation_classification | lora | | niv2_answer_verification | phi-2 | sordonia/adauni-v1-flat/niv2_answer_verification | lora | | glue_mrpc_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_mrpc_2_0_0 | lora | | cos_e_v1_11_i_think | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_i_think | lora | | human_aging | phi-2 | sordonia/qa-flat-mmlu/human_aging | lora | | anatomy | phi-2 | sordonia/qa-flat-mmlu/anatomy | lora | | squad_v1_1_3_0_0 | phi-2 | sordonia/flan-10k-flat/squad_v1_1_3_0_0 | lora | | quoref_Answer_Question_Given_Context | phi-2 | sordonia/flan-10k-flat/quoref_Answer_Question_Given_Context | lora | | ultrachat_12 | phi-2 | sordonia/adauni-v1-flat/ultrachat_12 | lora | | sciq_Direct_Question_Closed_Book_ | phi-2 | sordonia/flan-10k-flat/sciq_Direct_Question_Closed_Book_ | lora | | niv2_discourse_relation_classification | phi-2 | sordonia/adauni-v1-flat/niv2_discourse_relation_classification | lora | | huggingface_xsum | phi-2 | sordonia/flan-10k-flat/huggingface_xsum | lora | | wiki_bio_who | phi-2 | sordonia/flan-10k-flat/wiki_bio_who | lora | | niv2_misc | phi-2 | sordonia/adauni-v1-flat/niv2_misc. | lora | | squad_v2_0_3_0_0 | phi-2 | sordonia/flan-10k-flat/squad_v2_0_3_0_0 | lora | | high_school_us_history | phi-2 | sordonia/qa-flat-mmlu/high_school_us_history | lora | | stream_aqua | phi-2 | sordonia/cot:flan-10k-flat/stream_aqua | lora | | management | phi-2 | sordonia/qa-flat-mmlu/management | lora | | niv2_question_decomposition | phi-2 | sordonia/adauni-v1-flat/niv2_question_decomposition | lora | | niv2_overlap_extraction | phi-2 | sordonia/adauni-v1-flat/niv2_overlap_extraction | lora | | niv2_punctuation_error_detection | phi-2 | sordonia/adauni-v1-flat/niv2_punctuation_error_detection | lora | | cos_e_v1_11_question_description_option_text | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_question_description_option_text | lora | | kilt_tasks_hotpotqa_complex_question | phi-2 | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_complex_question | lora | | multi_news_1_0_0 | phi-2 | sordonia/flan-10k-flat/multi_news_1_0_0 | lora | | quoref_Answer_Test | phi-2 | sordonia/flan-10k-flat/quoref_Answer_Test | lora | | niv2_section_classification | phi-2 | sordonia/adauni-v1-flat/niv2_section_classification | lora | | stream_aqua_ii | phi-2 | sordonia/cot:flan-10k-flat/stream_aqua_ii | lora | | web_questions_potential_correct_answer | phi-2 | sordonia/flan-10k-flat/web_questions_potential_correct_answer | lora | | high_school_statistics | phi-2 | sordonia/qa-flat-mmlu/high_school_statistics | lora | | dream_read_the_following_conversation_and_answer_the_question | phi-2 | sordonia/flan-10k-flat/dream_read_the_following_conversation_and_answer_the_question | lora | | wiqa_what_is_the_final_step_of_the_following_process | phi-2 | sordonia/flan-10k-flat/wiqa_what_is_the_final_step_of_the_following_process | lora | | kilt_tasks_hotpotqa_combining_facts | phi-2 | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_combining_facts | lora | | high_school_biology | phi-2 | sordonia/qa-flat-mmlu/high_school_biology | lora | | niv2_gender_classification | phi-2 | sordonia/adauni-v1-flat/niv2_gender_classification | lora | | app_reviews_convert_to_rating | phi-2 | sordonia/flan-10k-flat/app_reviews_convert_to_rating | lora | | web_questions_short_general_knowledge_q | phi-2 | sordonia/flan-10k-flat/web_questions_short_general_knowledge_q | lora | | definite_pronoun_resolution_1_1_0 | phi-2 | sordonia/flan-10k-flat/definite_pronoun_resolution_1_1_0 | lora | | quail_description_context_question_answer_text | phi-2 | sordonia/flan-10k-flat/quail_description_context_question_answer_text | lora | | ropes_new_situation_background_answer | phi-2 | sordonia/flan-10k-flat/ropes_new_situation_background_answer | lora | | fix_punct | phi-2 | sordonia/flan-10k-flat/fix_punct | lora | | qasc_qa_with_separated_facts_2 | phi-2 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_2 | lora | | niv2_linguistic_probing | phi-2 | sordonia/adauni-v1-flat/niv2_linguistic_probing | lora | | super_glue_record_1_0_2 | phi-2 | sordonia/flan-10k-flat/super_glue_record_1_0_2 | lora | | qasc_is_correct_2 | phi-2 | sordonia/flan-10k-flat/qasc_is_correct_2 | lora | | ultrachat_29 | phi-2 | sordonia/adauni-v1-flat/ultrachat_29 | lora | | duorc_ParaphraseRC_decide_worth_it | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_decide_worth_it | lora | | niv2_coherence_classification | phi-2 | sordonia/adauni-v1-flat/niv2_coherence_classification | lora | | cot_strategyqa | phi-2 | sordonia/cot:flan-10k-flat/cot_strategyqa | lora | | niv2_intent_identification | phi-2 | sordonia/adauni-v1-flat/niv2_intent_identification | lora | | niv2_data_to_text | phi-2 | sordonia/adauni-v1-flat/niv2_data_to_text | lora | | cot_gsm8k | phi-2 | sordonia/cot:flan-10k-flat/cot_gsm8k | lora | | college_computer_science | phi-2 | sordonia/qa-flat-mmlu/college_computer_science | lora | | trivia_qa_rc_1_1_0 | phi-2 | sordonia/flan-10k-flat/trivia_qa_rc_1_1_0 | lora | | duorc_ParaphraseRC_generate_question_by_answer | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_generate_question_by_answer | lora | | college_mathematics | phi-2 | sordonia/qa-flat-mmlu/college_mathematics | lora | | ultrachat_5 | phi-2 | sordonia/adauni-v1-flat/ultrachat_5 | lora | | wmt16_translate_de_en_1_0_0 | phi-2 | sordonia/flan-10k-flat/wmt16_translate_de_en_1_0_0 | lora | | app_reviews_categorize_rating_using_review | phi-2 | sordonia/flan-10k-flat/app_reviews_categorize_rating_using_review | lora | | niv2_text_categorization | phi-2 | sordonia/adauni-v1-flat/niv2_text_categorization | lora | | anli_r1_0_1_0 | phi-2 | sordonia/flan-10k-flat/anli_r1_0_1_0 | lora | | wiqa_which_of_the_following_is_the_supposed_perturbation | phi-2 | sordonia/flan-10k-flat/wiqa_which_of_the_following_is_the_supposed_perturbation | lora | | adversarial_qa_droberta_tell_what_it_is | phi-2 | sordonia/flan-10k-flat/adversarial_qa_droberta_tell_what_it_is | lora | | quoref_Answer_Friend_Question | phi-2 | sordonia/flan-10k-flat/quoref_Answer_Friend_Question | lora | | miscellaneous | phi-2 | sordonia/qa-flat-mmlu/miscellaneous | lora | | gem_web_nlg_en_1_1_0 | phi-2 | sordonia/flan-10k-flat/gem_web_nlg_en_1_1_0 | lora | | dream_baseline | phi-2 | sordonia/flan-10k-flat/dream_baseline | lora | | app_reviews_convert_to_star_rating | phi-2 | sordonia/flan-10k-flat/app_reviews_convert_to_star_rating | lora | | cot_gsm8k_ii | phi-2 | sordonia/cot:flan-10k-flat/cot_gsm8k_ii | lora | | niv2_sentence_composition | phi-2 | sordonia/adauni-v1-flat/niv2_sentence_composition | lora | | quarel_do_not_use | phi-2 | sordonia/flan-10k-flat/quarel_do_not_use | lora | | gem_common_gen_1_1_0 | phi-2 | sordonia/flan-10k-flat/gem_common_gen_1_1_0 | lora | | niv2_question_rewriting | phi-2 | sordonia/adauni-v1-flat/niv2_question_rewriting | lora | | world_religions | phi-2 | sordonia/qa-flat-mmlu/world_religions | lora | | human_sexuality | phi-2 | sordonia/qa-flat-mmlu/human_sexuality | lora | | adversarial_qa_dbidaf_question_context_answer | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_question_context_answer | lora | | cos_e_v1_11_explain_why_human | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_explain_why_human | lora | | ultrachat_9 | phi-2 | sordonia/adauni-v1-flat/ultrachat_9 | lora | | natural_questions_open_1_0_0 | phi-2 | sordonia/flan-10k-flat/natural_questions_open_1_0_0 | lora | | niv2_grammar_error_detection | phi-2 | sordonia/adauni-v1-flat/niv2_grammar_error_detection | lora | | niv2_keyword_tagging | phi-2 | sordonia/adauni-v1-flat/niv2_keyword_tagging | lora | | quail_context_question_description_text | phi-2 | sordonia/flan-10k-flat/quail_context_question_description_text | lora | | race_high_Select_the_best_answer | phi-2 | sordonia/flan-10k-flat/race_high_Select_the_best_answer | lora | | stream_qed | phi-2 | sordonia/cot:flan-10k-flat/stream_qed | lora | | high_school_world_history | phi-2 | sordonia/qa-flat-mmlu/high_school_world_history | lora | | ultrachat_1 | phi-2 | sordonia/adauni-v1-flat/ultrachat_1 | lora | | word_segment | phi-2 | sordonia/flan-10k-flat/word_segment | lora | | niv2_wrong_candidate_generation | phi-2 | sordonia/adauni-v1-flat/niv2_wrong_candidate_generation | lora | | niv2_preposition_prediction | phi-2 | sordonia/adauni-v1-flat/niv2_preposition_prediction | lora | | cos_e_v1_11_description_question_option_id | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_description_question_option_id | lora | | quail_context_description_question_answer_text | phi-2 | sordonia/flan-10k-flat/quail_context_description_question_answer_text | lora | | race_middle_Is_this_the_right_answer | phi-2 | sordonia/flan-10k-flat/race_middle_Is_this_the_right_answer | lora | | ai2_arc_ARC_Challenge_1_0_0 | phi-2 | sordonia/flan-10k-flat/ai2_arc_ARC_Challenge_1_0_0 | lora | | race_high_Is_this_the_right_answer | phi-2 | sordonia/flan-10k-flat/race_high_Is_this_the_right_answer | lora | | wiki_bio_key_content | phi-2 | sordonia/flan-10k-flat/wiki_bio_key_content | lora | | college_physics | phi-2 | sordonia/qa-flat-mmlu/college_physics | lora | | global_facts | phi-2 | sordonia/qa-flat-mmlu/global_facts | lora | | quarel_choose_between | phi-2 | sordonia/flan-10k-flat/quarel_choose_between | lora | | duorc_SelfRC_movie_director | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_movie_director | lora | | high_school_physics | phi-2 | sordonia/qa-flat-mmlu/high_school_physics | lora | | adversarial_qa_dbidaf_generate_question | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbidaf_generate_question | lora | | high_school_mathematics | phi-2 | sordonia/qa-flat-mmlu/high_school_mathematics | lora | | niv2_question_generation | phi-2 | sordonia/adauni-v1-flat/niv2_question_generation | lora | | qasc_qa_with_separated_facts_5 | phi-2 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_5 | lora | | niv2_dialogue_generation | phi-2 | sordonia/adauni-v1-flat/niv2_dialogue_generation | lora | | niv2_explanation | phi-2 | sordonia/adauni-v1-flat/niv2_explanation | lora | | wiki_hop_original_choose_best_object_interrogative_2 | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_interrogative_2 | lora | | race_middle_Write_a_multi_choice_question_options_given_ | phi-2 | sordonia/flan-10k-flat/race_middle_Write_a_multi_choice_question_options_given_ | lora | | high_school_macroeconomics | phi-2 | sordonia/qa-flat-mmlu/high_school_macroeconomics | lora | | ropes_prompt_mix | phi-2 | sordonia/flan-10k-flat/ropes_prompt_mix | lora | | super_glue_multirc_1_0_2 | phi-2 | sordonia/flan-10k-flat/super_glue_multirc_1_0_2 | lora | | glue_qqp_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_qqp_2_0_0 | lora | | cos_e_v1_11_question_option_description_text | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_question_option_description_text | lora | | race_middle_Select_the_best_answer | phi-2 | sordonia/flan-10k-flat/race_middle_Select_the_best_answer | lora | | race_high_Write_a_multi_choice_question_for_the_following_article | phi-2 | sordonia/flan-10k-flat/race_high_Write_a_multi_choice_question_for_the_following_article | lora | | ultrachat_15 | phi-2 | sordonia/adauni-v1-flat/ultrachat_15 | lora | | wiqa_what_might_be_the_first_step_of_the_process | phi-2 | sordonia/flan-10k-flat/wiqa_what_might_be_the_first_step_of_the_process | lora | | niv2_coreference_resolution | phi-2 | sordonia/adauni-v1-flat/niv2_coreference_resolution | lora | | duorc_ParaphraseRC_answer_question | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_answer_question | lora | | wmt16_translate_ro_en_1_0_0 | phi-2 | sordonia/flan-10k-flat/wmt16_translate_ro_en_1_0_0 | lora | | sciq_Multiple_Choice | phi-2 | sordonia/flan-10k-flat/sciq_Multiple_Choice | lora | | high_school_geography | phi-2 | sordonia/qa-flat-mmlu/high_school_geography | lora | | cos_e_v1_11_generate_explanation_given_text | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_generate_explanation_given_text | lora | | adversarial_qa_droberta_based_on | phi-2 | sordonia/flan-10k-flat/adversarial_qa_droberta_based_on | lora | | niv2_text_simplification | phi-2 | sordonia/adauni-v1-flat/niv2_text_simplification | lora | | cot_ecqa_ii | phi-2 | sordonia/cot:flan-10k-flat/cot_ecqa_ii | lora | | cos_e_v1_11_question_description_option_id | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_question_description_option_id | lora | | high_school_microeconomics | phi-2 | sordonia/qa-flat-mmlu/high_school_microeconomics | lora | | wiki_hop_original_choose_best_object_affirmative_2 | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_2 | lora | | trec_1_0_0 | phi-2 | sordonia/flan-10k-flat/trec_1_0_0 | lora | | niv2_text_to_code | phi-2 | sordonia/adauni-v1-flat/niv2_text_to_code | lora | | computer_security | phi-2 | sordonia/qa-flat-mmlu/computer_security | lora | | glue_cola_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_cola_2_0_0 | lora | | glue_sst2_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_sst2_2_0_0 | lora | | machine_learning | phi-2 | sordonia/qa-flat-mmlu/machine_learning | lora | | ropes_plain_background_situation | phi-2 | sordonia/flan-10k-flat/ropes_plain_background_situation | lora | | niv2_sentence_ordering | phi-2 | sordonia/adauni-v1-flat/niv2_sentence_ordering | lora | | quoref_Context_Contains_Answer | phi-2 | sordonia/flan-10k-flat/quoref_Context_Contains_Answer | lora | | kilt_tasks_hotpotqa_formulate | phi-2 | sordonia/flan-10k-flat/kilt_tasks_hotpotqa_formulate | lora | | niv2_stereotype_detection | phi-2 | sordonia/adauni-v1-flat/niv2_stereotype_detection | lora | | wiki_bio_comprehension | phi-2 | sordonia/flan-10k-flat/wiki_bio_comprehension | lora | | web_questions_whats_the_answer | phi-2 | sordonia/flan-10k-flat/web_questions_whats_the_answer | lora | | openbookqa_0_1_0 | phi-2 | sordonia/flan-10k-flat/openbookqa_0_1_0 | lora | | race_middle_Write_a_multi_choice_question_for_the_following_article | phi-2 | sordonia/flan-10k-flat/race_middle_Write_a_multi_choice_question_for_the_following_article | lora | | race_middle_Read_the_article_and_answer_the_question_no_option_ | phi-2 | sordonia/flan-10k-flat/race_middle_Read_the_article_and_answer_the_question_no_option_ | lora | | wiki_qa_Generate_Question_from_Topic | phi-2 | sordonia/flan-10k-flat/wiki_qa_Generate_Question_from_Topic | lora | | web_questions_question_answer | phi-2 | sordonia/flan-10k-flat/web_questions_question_answer | lora | | niv2_toxic_language_detection | phi-2 | sordonia/adauni-v1-flat/niv2_toxic_language_detection | lora | | niv2_spam_classification | phi-2 | sordonia/adauni-v1-flat/niv2_spam_classification | lora | | ai2_arc_ARC_Easy_1_0_0 | phi-2 | sordonia/flan-10k-flat/ai2_arc_ARC_Easy_1_0_0 | lora | | race_high_Taking_a_test | phi-2 | sordonia/flan-10k-flat/race_high_Taking_a_test | lora | | niv2_fact_verification | phi-2 | sordonia/adauni-v1-flat/niv2_fact_verification | lora | | web_questions_get_the_answer | phi-2 | sordonia/flan-10k-flat/web_questions_get_the_answer | lora | | business_ethics | phi-2 | sordonia/qa-flat-mmlu/business_ethics | lora | | duorc_SelfRC_extract_answer | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_extract_answer | lora | | niv2_sentiment_analysis | phi-2 | sordonia/adauni-v1-flat/niv2_sentiment_analysis | lora | | duorc_SelfRC_generate_question | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_generate_question | lora | | stream_qed_ii | phi-2 | sordonia/cot:flan-10k-flat/stream_qed_ii | lora | | qasc_is_correct_1 | phi-2 | sordonia/flan-10k-flat/qasc_is_correct_1 | lora | | duorc_SelfRC_question_answering | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_question_answering | lora | | niv2_textual_entailment | phi-2 | sordonia/adauni-v1-flat/niv2_textual_entailment | lora | | niv2_spelling_error_detection | phi-2 | sordonia/adauni-v1-flat/niv2_spelling_error_detection | lora | | qasc_qa_with_separated_facts_3 | phi-2 | sordonia/flan-10k-flat/qasc_qa_with_separated_facts_3 | lora | | ultrachat_22 | phi-2 | sordonia/adauni-v1-flat/ultrachat_22 | lora | | glue_wnli_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_wnli_2_0_0 | lora | | race_high_Read_the_article_and_answer_the_question_no_option_ | phi-2 | sordonia/flan-10k-flat/race_high_Read_the_article_and_answer_the_question_no_option_ | lora | | niv2_speaker_identification | phi-2 | sordonia/adauni-v1-flat/niv2_speaker_identification | lora | | ropes_background_new_situation_answer | phi-2 | sordonia/flan-10k-flat/ropes_background_new_situation_answer | lora | | niv2_stance_detection | phi-2 | sordonia/adauni-v1-flat/niv2_stance_detection | lora | | wiki_qa_exercise | phi-2 | sordonia/flan-10k-flat/wiki_qa_exercise | lora | | ARB | phi-2 | sordonia/adauni-v1-flat/ARB | lora | | cot_esnli_ii | phi-2 | sordonia/cot:flan-10k-flat/cot_esnli_ii | lora | | niv2_paper_review | phi-2 | sordonia/adauni-v1-flat/niv2_paper_review | lora | | gem_wiki_lingua_english_en_1_1_0 | phi-2 | sordonia/flan-10k-flat/gem_wiki_lingua_english_en_1_1_0 | lora | | astronomy | phi-2 | sordonia/qa-flat-mmlu/astronomy | lora | | wiqa_does_the_supposed_perturbation_have_an_effect | phi-2 | sordonia/flan-10k-flat/wiqa_does_the_supposed_perturbation_have_an_effect | lora | | wmt16_translate_tr_en_1_0_0 | phi-2 | sordonia/flan-10k-flat/wmt16_translate_tr_en_1_0_0 | lora | | cot_sensemaking | phi-2 | sordonia/cot:flan-10k-flat/cot_sensemaking | lora | | adversarial_qa_dbert_question_context_answer | phi-2 | sordonia/flan-10k-flat/adversarial_qa_dbert_question_context_answer | lora | | ultrachat_14 | phi-2 | sordonia/adauni-v1-flat/ultrachat_14 | lora | | sciq_Multiple_Choice_Closed_Book_ | phi-2 | sordonia/flan-10k-flat/sciq_Multiple_Choice_Closed_Book_ | lora | | sciq_Multiple_Choice_Question_First | phi-2 | sordonia/flan-10k-flat/sciq_Multiple_Choice_Question_First | lora | | ultrachat_25 | phi-2 | sordonia/adauni-v1-flat/ultrachat_25 | lora | | social_i_qa_Show_choices_and_generate_answer | phi-2 | sordonia/flan-10k-flat/social_i_qa_Show_choices_and_generate_answer | lora | | quartz_use_info_from_question_paragraph | phi-2 | sordonia/flan-10k-flat/quartz_use_info_from_question_paragraph | lora | | quoref_Given_Context_Answer_Question | phi-2 | sordonia/flan-10k-flat/quoref_Given_Context_Answer_Question | lora | | unified_qa_science_inst | phi-2 | sordonia/flan-10k-flat/unified_qa_science_inst | lora | | dream_generate_last_utterance | phi-2 | sordonia/flan-10k-flat/dream_generate_last_utterance | lora | | niv2_mathematics | phi-2 | sordonia/adauni-v1-flat/niv2_mathematics | lora | | winogrande_1_1_0 | phi-2 | sordonia/flan-10k-flat/winogrande_1_1_0 | lora | | glue_qnli_2_0_0 | phi-2 | sordonia/flan-10k-flat/glue_qnli_2_0_0 | lora | | ropes_given_background_situation | phi-2 | sordonia/flan-10k-flat/ropes_given_background_situation | lora | | race_middle_Select_the_best_answer_no_instructions_ | phi-2 | sordonia/flan-10k-flat/race_middle_Select_the_best_answer_no_instructions_ | lora | | duorc_ParaphraseRC_title_generation | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_title_generation | lora | | quoref_What_Is_The_Answer | phi-2 | sordonia/flan-10k-flat/quoref_What_Is_The_Answer | lora | | duorc_SelfRC_generate_question_by_answer | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_generate_question_by_answer | lora | | super_glue_wsc_fixed_1_0_2 | phi-2 | sordonia/flan-10k-flat/super_glue_wsc_fixed_1_0_2 | lora | | reclor | phi-2 | sordonia/adauni-v1-flat/reclor | lora | | niv2_language_identification | phi-2 | sordonia/adauni-v1-flat/niv2_language_identification | lora | | bool_q_1_0_0 | phi-2 | sordonia/flan-10k-flat/bool_q_1_0_0 | lora | | duorc_ParaphraseRC_movie_director | phi-2 | sordonia/flan-10k-flat/duorc_ParaphraseRC_movie_director | lora | | wiki_bio_guess_person | phi-2 | sordonia/flan-10k-flat/wiki_bio_guess_person | lora | | ultrachat_11 | phi-2 | sordonia/adauni-v1-flat/ultrachat_11 | lora | | security_studies | phi-2 | sordonia/qa-flat-mmlu/security_studies | lora | | cot_creak | phi-2 | sordonia/cot:flan-10k-flat/cot_creak | lora | | quoref_Guess_Answer | phi-2 | sordonia/flan-10k-flat/quoref_Guess_Answer | lora | | wiki_hop_original_choose_best_object_affirmative_1 | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_choose_best_object_affirmative_1 | lora | | dream_generate_first_utterance | phi-2 | sordonia/flan-10k-flat/dream_generate_first_utterance | lora | | wiki_qa_Topic_Prediction_Answer_Only | phi-2 | sordonia/flan-10k-flat/wiki_qa_Topic_Prediction_Answer_Only | lora | | ultrachat_8 | phi-2 | sordonia/adauni-v1-flat/ultrachat_8 | lora | | dbpedia_14_pick_one_category_for_the_following_text | phi-2 | sordonia/flan-10k-flat/dbpedia_14_pick_one_category_for_the_following_text | lora | | medical_genetics | phi-2 | sordonia/qa-flat-mmlu/medical_genetics | lora | | quail_context_question_answer_description_text | phi-2 | sordonia/flan-10k-flat/quail_context_question_answer_description_text | lora | | qasc_qa_with_combined_facts_1 | phi-2 | sordonia/flan-10k-flat/qasc_qa_with_combined_facts_1 | lora | | social_i_qa_Show_choices_and_generate_index | phi-2 | sordonia/flan-10k-flat/social_i_qa_Show_choices_and_generate_index | lora | | duorc_SelfRC_build_story_around_qa | phi-2 | sordonia/flan-10k-flat/duorc_SelfRC_build_story_around_qa | lora | | elementary_mathematics | phi-2 | sordonia/qa-flat-mmlu/elementary_mathematics | lora | | niv2_pos_tagging | phi-2 | sordonia/adauni-v1-flat/niv2_pos_tagging | lora | | sciq_Direct_Question | phi-2 | sordonia/flan-10k-flat/sciq_Direct_Question | lora | | niv2_sentence_perturbation | phi-2 | sordonia/adauni-v1-flat/niv2_sentence_perturbation | lora | | high_school_government_and_politics | phi-2 | sordonia/qa-flat-mmlu/high_school_government_and_politics | lora | | ropes_plain_no_background | phi-2 | sordonia/flan-10k-flat/ropes_plain_no_background | lora | | ultrachat_7 | phi-2 | sordonia/adauni-v1-flat/ultrachat_7 | lora | | niv2_speaker_relation_classification | phi-2 | sordonia/adauni-v1-flat/niv2_speaker_relation_classification | lora | | anli_r3_0_1_0 | phi-2 | sordonia/flan-10k-flat/anli_r3_0_1_0 | lora | | niv2_answerability_classification | phi-2 | sordonia/adauni-v1-flat/niv2_answerability_classification | lora | | wiki_hop_original_generate_subject_and_object | phi-2 | sordonia/flan-10k-flat/wiki_hop_original_generate_subject_and_object | lora | | quoref_Read_And_Extract_ | phi-2 | sordonia/flan-10k-flat/quoref_Read_And_Extract_ | lora | | quail_context_description_question_answer_id | phi-2 | sordonia/flan-10k-flat/quail_context_description_question_answer_id | lora | | wiki_qa_Topic_Prediction_Question_and_Answer_Pair | phi-2 | sordonia/flan-10k-flat/wiki_qa_Topic_Prediction_Question_and_Answer_Pair | lora | | cos_e_v1_11_rationale | phi-2 | sordonia/flan-10k-flat/cos_e_v1_11_rationale | lora | | MATH/PRM-800K | phi-2 | sordonia/adauni-v1-flat/MATH/PRM-800K | lora | Last updated on: 2023-11-30T01:30:49.000Z
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sordonia/library-phi-2
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Alphonsce/metal-lora
Alphonsce
2023-11-29T19:00:24Z
0
0
null
[ "region:us" ]
2023-11-29T19:00:24Z
2023-11-29T19:00:24.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
Alphonsce/metal-lora
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow16
FounderOfHuggingface
2023-11-29T19:00:57Z
0
0
null
[ "peft", "arxiv:1910.09700", "base_model:gpt2", "region:us" ]
2023-11-29T19:00:57Z
2023-11-29T19:00:53.000Z
null
null
--- library_name: peft base_model: gpt2 --- # 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] - **Funded by [optional]:** [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 Dataset 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 Dataset 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 0.6.2
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peft
null
null
null
null
null
null
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow16
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Sarthak7777/distilbert-base-uncased-finetuned-us_patent
Sarthak7777
2023-11-29T19:01:03Z
0
0
null
[ "region:us" ]
2023-11-29T19:01:03Z
2023-11-29T19:01:02.000Z
null
null
Entry not found
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Sarthak7777/distilbert-base-uncased-finetuned-us_patent
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jibblet/furryblendv1-pruned
jibblet
2023-11-29T19:14:00Z
0
0
null
[ "region:us" ]
2023-11-29T19:14:00Z
2023-11-29T19:01:24.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
jibblet/furryblendv1-pruned
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emilstabil/DanSumT5-smallV_47804
emilstabil
2023-11-29T19:01:25Z
0
0
null
[ "region:us" ]
2023-11-29T19:01:25Z
2023-11-29T19:01:25.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
emilstabil/DanSumT5-smallV_47804
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Yaxin1992/llama2-7b-chat-leagues-5000
Yaxin1992
2023-11-29T21:25:38Z
0
0
null
[ "tensorboard", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-chat-hf", "region:us" ]
2023-11-29T21:25:38Z
2023-11-29T19:03:34.000Z
null
null
--- base_model: meta-llama/Llama-2-7b-chat-hf tags: - generated_from_trainer model-index: - name: llama2-7b-chat-leagues-5000 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. --> # llama2-7b-chat-leagues-5000 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. ## 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-06 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
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Yaxin1992/llama2-7b-chat-leagues-5000
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grfdsrgedrf/Niger-Junta-Repeals-Law-Aimed-at-Slowing-Migration-to-Europe-h3-updated
grfdsrgedrf
2023-11-29T19:03:42Z
0
0
null
[ "region:us" ]
2023-11-29T19:03:42Z
2023-11-29T19:03:41.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
grfdsrgedrf/Niger-Junta-Repeals-Law-Aimed-at-Slowing-Migration-to-Europe-h3-updated
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emilstabil/DanSumT5-smallV_11030
emilstabil
2023-11-29T19:04:18Z
0
0
null
[ "region:us" ]
2023-11-29T19:04:18Z
2023-11-29T19:04:18.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
emilstabil/DanSumT5-smallV_11030
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th-nuernberg/gelectra-large-german-counseling-gecco
th-nuernberg
2023-11-29T19:06:19Z
0
0
null
[ "transformers", "safetensors", "electra", "text-classification", "generated_from_trainer", "base_model:deepset/gelectra-large", "license:mit", "endpoints_compatible", "region:us" ]
2023-11-29T19:06:19Z
2023-11-29T19:05:33.000Z
null
null
--- license: mit base_model: deepset/gelectra-large tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: gecco-german-counseling-gelectra-large 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. --> # gecco-german-counseling-gelectra-large This model is a fine-tuned version of [deepset/gelectra-large](https://huggingface.co/deepset/gelectra-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3342 - Accuracy: 0.7290 - F1: 0.4663 ## 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: 16 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 3.6182 | 1.0 | 20 | 3.4739 | 0.1710 | 0.0391 | | 3.3872 | 2.0 | 40 | 3.1759 | 0.2742 | 0.0806 | | 3.1164 | 3.0 | 60 | 2.8804 | 0.3774 | 0.1428 | | 2.8264 | 4.0 | 80 | 2.5862 | 0.5290 | 0.2440 | | 2.5488 | 5.0 | 100 | 2.3090 | 0.5871 | 0.2816 | | 2.2554 | 6.0 | 120 | 2.0804 | 0.6194 | 0.3196 | | 2.0363 | 7.0 | 140 | 1.9051 | 0.6452 | 0.3435 | | 1.8361 | 8.0 | 160 | 1.7684 | 0.6677 | 0.3593 | | 1.5928 | 9.0 | 180 | 1.6380 | 0.7 | 0.4038 | | 1.5584 | 10.0 | 200 | 1.5519 | 0.7097 | 0.4142 | | 1.3548 | 11.0 | 220 | 1.4832 | 0.7194 | 0.4325 | | 1.2585 | 12.0 | 240 | 1.4253 | 0.7226 | 0.4530 | | 1.209 | 13.0 | 260 | 1.3874 | 0.7194 | 0.4512 | | 1.0846 | 14.0 | 280 | 1.3526 | 0.7323 | 0.4667 | | 1.0705 | 15.0 | 300 | 1.3377 | 0.7258 | 0.4643 | | 1.0137 | 16.0 | 320 | 1.3342 | 0.7290 | 0.4663 | ### Framework versions - Transformers 4.35.1 - Pytorch 1.10.1+cu111 - Datasets 2.14.7 - Tokenizers 0.14.1
null
transformers
text-classification
null
null
null
null
null
null
null
null
null
th-nuernberg/gelectra-large-german-counseling-gecco
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emilstabil/DanSumT5-smallV_24384
emilstabil
2023-11-29T19:05:44Z
0
0
null
[ "region:us" ]
2023-11-29T19:05:44Z
2023-11-29T19:05:44.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
emilstabil/DanSumT5-smallV_24384
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grfdsrgedrf/How-AI-is-bringing-new-options-to-mammograms-other-breast-cancer-screenings-hb-updated
grfdsrgedrf
2023-11-29T19:07:48Z
0
0
null
[ "en", "region:us" ]
2023-11-29T19:07:48Z
2023-11-29T19:06:32.000Z
null
null
--- language: - en --- [![Build Status](https://pakobserver.net/wp-content/uploads/2023/08/IMG_1412-1024x683-1.webp)]() read the full article here : https://jsitor.com/ Source : https://jpeg.ly/DCQBt Flash News : https://ibit.ly/-0jWd Biden last Talk : https://pastebin.com/ Russian Ukrain Breaking News : https://jsbin.com/vabalopako/edit?html,output Other Sources : https://jpeg.ly/oecC9 https://jpeg.ly/R0Cxn https://ibit.ly/fgXR5 https://ibit.ly/7iuON https://jpeg.ly/N7edS https://jpeg.ly/WI4Qz https://ibit.ly/4nagr https://ibit.ly/Wg_fW https://ibit.ly/efhSp https://jsbin.com/nodafodepa/edit?html,output https://pastebin.com/ https://jsbin.com/fasebalija/edit?html,output https://jpeg.ly/Hz0h4 https://jpeg.ly/rBJOZ The Suffolk County Police Department has been made aware of an intermittent issue with at least one known cell phone carrier that is causing disruptions to 911 call service within 911 call centers in Suffolk County as of Nov. 27 at 6 p.m. Residents are urged if they call 911 and receive a fast busy signal to hang up and immediately call 631-852-COPS and follow the prompts to be directed to your police jurisdiction. The department is working to identify and rectify the issue as quickly as possible and there has been no disruption to emergency services being dispatched as a result of the issue.....
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grfdsrgedrf/How-AI-is-bringing-new-options-to-mammograms-other-breast-cancer-screenings-hb-updated
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68611-llm-annotation-group/BioLinkBERT-base-NER-NCBI-HA
68611-llm-annotation-group
2023-11-29T19:07:00Z
0
0
null
[ "transformers", "safetensors", "bert", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2023-11-29T19:07:00Z
2023-11-29T19:06:36.000Z
null
null
Entry not found
null
transformers
token-classification
null
null
null
null
null
null
null
null
null
68611-llm-annotation-group/BioLinkBERT-base-NER-NCBI-HA
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grfdsrgedrf/Judge-rejects-Trump-bid-for-US-House-January-6-records-hg-updated
grfdsrgedrf
2023-11-29T19:06:41Z
0
0
null
[ "region:us" ]
2023-11-29T19:06:41Z
2023-11-29T19:06:40.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
grfdsrgedrf/Judge-rejects-Trump-bid-for-US-House-January-6-records-hg-updated
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ash-akjp-ga/finetuning-sentiment-model
ash-akjp-ga
2023-11-29T19:55:08Z
0
0
null
[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "endpoints_compatible", "region:us" ]
2023-11-29T19:55:08Z
2023-11-29T19:08:23.000Z
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transformers
text-classification
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ash-akjp-ga/finetuning-sentiment-model
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LoneStriker/deepseek-llm-67b-chat-2.4bpw-h6-exl2
LoneStriker
2023-11-30T00:08:26Z
0
0
null
[ "transformers", "pytorch", "llama", "text-generation", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2023-11-30T00:08:26Z
2023-11-29T19:09:05.000Z
null
null
--- license: other license_name: deepseek license_link: LICENSE --- <p align="center"> <img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true"> </p> <p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/qr.jpeg">[Wechat(微信)]</a> </p> <hr> ### 1. Introduction of Deepseek LLM Introducing DeepSeek LLM, an advanced language model comprising 67 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. In order to foster research, we have made DeepSeek LLM 7B/67B Base and DeepSeek LLM 7B/67B Chat open source for the research community. ### 2. Model Summary `deepseek-llm-67b-chat` is a 67B parameter model initialized from `deepseek-llm-67b-base` and fine-tuned on extra instruction data. - **Home Page:** [DeepSeek](https://deepseek.com/) - **Repository:** [deepseek-ai/deepseek-LLM](https://github.com/deepseek-ai/deepseek-LLM) - **Chat With DeepSeek LLM:** [DeepSeek-LLM](https://chat.deepseek.com/) ### 3. How to Use Here give some examples of how to use our model. #### Chat Completion ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig model_name = "deepseek-ai/deepseek-llm-67b-chat" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto") model.generation_config = GenerationConfig.from_pretrained(model_name) model.generation_config.pad_token_id = model.generation_config.eos_token_id messages = [ {"role": "user", "content": "Who are you?"} ] input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt") outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100) result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True) print(result) ``` Avoiding the use of the provided function `apply_chat_template`, you can also interact with our model following the sample template. Note that `messages` should be replaced by your input. ``` User: {messages[0]['content']} Assistant: {messages[1]['content']}<|end▁of▁sentence|>User: {messages[2]['content']} Assistant: ``` **Note:** By default (`add_special_tokens=True`), our tokenizer automatically adds a `bos_token` (`<|begin▁of▁sentence|>`) before the input text. Additionally, since the system prompt is not compatible with this version of our models, we DO NOT RECOMMEND including the system prompt in your input. ### 4. License This code repository is licensed under the MIT License. The use of DeepSeek LLM models is subject to the Model License. DeepSeek LLM supports commercial use. See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-LLM/blob/main/LICENSE-MODEL) for more details. ### 5. Contact If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).
null
transformers
text-generation
null
null
null
null
null
null
null
null
null
LoneStriker/deepseek-llm-67b-chat-2.4bpw-h6-exl2
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zichao22/prompt_program_raw_10_epochs
zichao22
2023-11-29T19:10:00Z
0
0
null
[ "generated_from_trainer", "base_model:codellama/CodeLlama-7b-Python-hf", "license:llama2", "region:us" ]
2023-11-29T19:10:00Z
2023-11-29T19:09:09.000Z
null
null
--- license: llama2 base_model: codellama/CodeLlama-7b-Python-hf tags: - generated_from_trainer model-index: - name: prompt_program_raw_10_epochs 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. --> # prompt_program_raw_10_epochs This model is a fine-tuned version of [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf) 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
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zichao22/prompt_program_raw_10_epochs
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grfdsrgedrf/Portland-public-school-classes-resume-after-firstever-teachers-strike-Mix-1027-WCPZ-5h-updated
grfdsrgedrf
2023-11-29T19:10:31Z
0
0
null
[ "en", "region:us" ]
2023-11-29T19:10:31Z
2023-11-29T19:09:14.000Z
null
null
--- language: - en --- [![Build Status](https://bloximages.chicago2.vip.townnews.com/wcfcourier.com/content/tncms/assets/v3/editorial/2/a5/2a5f5118-45c3-11ee-a016-6f331f6599f4/64ecd1ecea855.preview.jpg?crop=1075%2C564%2C0%2C174&resize=438%2C230&order=crop%2Cresize)]() read the full article here : https://jpeg.ly/WixHx Source : https://pastebin.com/ Flash News : https://jpeg.ly/vpnL_ Biden last Talk : https://jsbin.com/qabivebemi/edit?html,output Russian Ukrain Breaking News : https://jpeg.ly/PJQrO Other Sources : https://jpeg.ly/Nwu-x https://ibit.ly/Ey1RF https://ibit.ly/j2YNN https://ibit.ly/rHniu https://ibit.ly/A8gIY https://ibit.ly/PTDTd https://jpeg.ly/FKrXI https://ibit.ly/4yTtL https://jpeg.ly/NIpKW https://jpeg.ly/d-lr3 https://jsbin.com/jaxuturizu/edit?html,output https://pastelink.net/klcl0jy0 https://jsitor.com/ https://jpeg.ly/QVlMd Multiple dogs living at animal shelters in a county just south of Atlanta could be euthanized due to overcrowding if they aren't adopted before Nov. 30, a Georgia animal control group said. Clayton County Animal Control, which houses stray animals at two shelters, said two dozen dogs faced possible euthanasia due to overcrowding at the facilities. In a memorandum posted to social media Thursday, the group said dogs that aren't fostered before Nov. 30 at 6 p.m. faced possible euthanasia. VIRAL POST FROM TEXAS ANIMAL SHELTER HELPS 'ANTISOCIAL' DOG FIND NEW HOME Sarah Ortiz, a Clayton County animal advocate, said several animals were surrendered ahead of the Thanksgiving holiday weekend. WHY THE ADOPTION OF SENIOR PETS CAN BE THE 'PURRFECT' ADDITION TO YOUR LIFE "I don't know what's happening this time of the year people have just lost interest, people they are just dumping their dogs," Ortiz told WANF-TV. Ortiz said rescue groups have been able to take in some of the dogs, but several more still needed homes. In addition to the dogs listed by breed and name, Clayton County Animal Control published a list of staff members on its Facebook page that people interested in adoption can contact.....
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grfdsrgedrf/Portland-public-school-classes-resume-after-firstever-teachers-strike-Mix-1027-WCPZ-5h-updated
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emilstabil/DanSumT5-smallV_32955
emilstabil
2023-11-29T19:09:15Z
0
0
null
[ "region:us" ]
2023-11-29T19:09:15Z
2023-11-29T19:09:15.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
emilstabil/DanSumT5-smallV_32955
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow17
FounderOfHuggingface
2023-11-29T19:09:26Z
0
0
null
[ "peft", "arxiv:1910.09700", "base_model:gpt2", "region:us" ]
2023-11-29T19:09:26Z
2023-11-29T19:09:20.000Z
null
null
--- library_name: peft base_model: gpt2 --- # 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] - **Funded by [optional]:** [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 Dataset 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 Dataset 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 0.6.2
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow17
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emilstabil/DanSumT5-smallV_76792
emilstabil
2023-11-29T19:11:34Z
0
0
null
[ "region:us" ]
2023-11-29T19:11:34Z
2023-11-29T19:11:34.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
emilstabil/DanSumT5-smallV_76792
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grfdsrgedrf/Man-70-and-pet-dog-are-nearly-trampled-to-death-by-herd-of-cows-eh-updated
grfdsrgedrf
2023-11-29T19:12:57Z
0
0
null
[ "en", "region:us" ]
2023-11-29T19:12:57Z
2023-11-29T19:11:38.000Z
null
null
--- language: - en --- [![Build Status](https://k6b8f3f9.stackpathcdn.com/wp-content/uploads/2023/08/PSA-levels-after-treatment-may-not-predict-survival-in-prostate-cancer.jpeg)]() read the full article here : https://rentry.co/wyd8e Source : https://jpeg.ly/4jZKv Flash News : https://jsbin.com/cadepejidu/edit?html,output Biden last Talk : https://jpeg.ly/AY-Df Russian Ukrain Breaking News : https://ibit.ly/1ryLV Other Sources : https://jsbin.com/xanifodeki/edit?html,output https://ibit.ly/aJ7I0 https://jpeg.ly/Rs_RU https://rentry.co/o8wv2 https://rentry.co/mieim https://ibit.ly/KEp0X https://jsitor.com/ https://ibit.ly/JKN_c https://pastebin.com/PCBuetF7 https://ibit.ly/D0aX6 https://jpeg.ly/0H9BM https://jsbin.com/yaqoneqosi/edit?html,output https://jpeg.ly/_fZe3 https://rentry.co/nwp7b A judge has rejected Donald Trump's request to see records from the congressional investigation of the January 6, 2021 attack on the US Capitol, which the former president said may be relevant to his defence against election interference charges. US District Judge Tanya Chutkan on Mondat in Washington found Trump "has not sufficiently justified his requests" for information tied to the Democratic-led House of Representatives probe, which concluded in 2022. Trump sought to subpoena materials that his lawyers said were "missing" from the House investigation, including transcripts and video recordings of interviews with law enforcement officials. The judge concluded that prosecutors had already given Trump written transcripts of those interviews and that his lawyers had not shown how video footage of those same conversations could be relevant to his defence. Trump has pleaded not guilty to charges that he illegally sought to overturn his defeat in the 2020 election. The case, brought by Special Counsel Jack Smith, is one of four criminal cases facing Trump, the frontrunner for the Republican nomination in 2024. Following a separate court ruling, Smith's team on Monday made public new information about the January search warrant issued to X (formerly Twitter), for information on Trump's account. The warrant sought information on all electronic devices that accessed Trump's account between October 2020 and January 2021 as well as the content of direct messages sent and received from the account, according to court filings. Trump's Twitter account was suspended following the 2021 riot at the Capitol by his supporters. The sanction was lifted in November 2022 after the platform was taken over by CEO Elon Musk. Prosecutors convinced a judge to bar Twitter from informing Trump about the warrant, a move the company opposed. Trump is also set to testify for a second time in his ongoing civil fraud trial in New York on December 11, his lawyers said on Monday. In defiant and rambling testimony on November 6, Trump denied the New York attorney-general's allegations that he inflated his net worth to dupe lenders.....
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grfdsrgedrf/Man-70-and-pet-dog-are-nearly-trampled-to-death-by-herd-of-cows-eh-updated
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grfdsrgedrf/Bus-carrying-newly-released-Palestinian-prisoners-arrives-in-West-Bank-city-4a-updated
grfdsrgedrf
2023-11-29T19:12:01Z
0
0
null
[ "region:us" ]
2023-11-29T19:12:01Z
2023-11-29T19:12:01.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
grfdsrgedrf/Bus-carrying-newly-released-Palestinian-prisoners-arrives-in-West-Bank-city-4a-updated
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Realgon/roberta_sst5_padding50model
Realgon
2023-11-29T20:15:59Z
0
0
null
[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "base_model:roberta-base", "license:mit", "endpoints_compatible", "region:us" ]
2023-11-29T20:15:59Z
2023-11-29T19:12:33.000Z
null
null
--- license: mit base_model: roberta-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: roberta_sst5_padding50model 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. --> # roberta_sst5_padding50model This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.9057 - Accuracy: 0.5593 ## 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: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.38 | 1.0 | 534 | 1.2160 | 0.4516 | | 1.0421 | 2.0 | 1068 | 1.0457 | 0.5520 | | 0.8678 | 3.0 | 1602 | 1.0607 | 0.5570 | | 0.7334 | 4.0 | 2136 | 1.0964 | 0.5701 | | 0.5777 | 5.0 | 2670 | 1.2672 | 0.5665 | | 0.4545 | 6.0 | 3204 | 1.4068 | 0.5647 | | 0.3607 | 7.0 | 3738 | 1.6500 | 0.5643 | | 0.2646 | 8.0 | 4272 | 1.8139 | 0.5638 | | 0.2394 | 9.0 | 4806 | 2.0395 | 0.5561 | | 0.1846 | 10.0 | 5340 | 2.4762 | 0.5602 | | 0.1789 | 11.0 | 5874 | 2.8201 | 0.5471 | | 0.1484 | 12.0 | 6408 | 2.9387 | 0.5593 | | 0.1307 | 13.0 | 6942 | 3.2414 | 0.5629 | | 0.1004 | 14.0 | 7476 | 3.3631 | 0.5674 | | 0.0705 | 15.0 | 8010 | 3.5533 | 0.5584 | | 0.0655 | 16.0 | 8544 | 3.5689 | 0.5724 | | 0.0482 | 17.0 | 9078 | 3.7737 | 0.5593 | | 0.0484 | 18.0 | 9612 | 3.8848 | 0.5557 | | 0.0414 | 19.0 | 10146 | 3.8977 | 0.5588 | | 0.0284 | 20.0 | 10680 | 3.9057 | 0.5593 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.1 - Datasets 2.12.0 - Tokenizers 0.13.3
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transformers
text-classification
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Realgon/roberta_sst5_padding50model
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th-nuernberg/gbert-base-german-counseling-gecco
th-nuernberg
2023-11-29T19:13:23Z
0
0
null
[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:deepset/gbert-base", "license:mit", "endpoints_compatible", "region:us" ]
2023-11-29T19:13:23Z
2023-11-29T19:13:01.000Z
null
null
--- license: mit base_model: deepset/gbert-base tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: gecco-german-counseling-gbert-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. --> # gecco-german-counseling-gbert-base This model is a fine-tuned version of [deepset/gbert-base](https://huggingface.co/deepset/gbert-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2480 - Accuracy: 0.7194 - F1: 0.5062 ## 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: 16 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 3.439 | 1.0 | 20 | 3.1181 | 0.2484 | 0.0474 | | 2.9431 | 2.0 | 40 | 2.6841 | 0.3935 | 0.1637 | | 2.5477 | 3.0 | 60 | 2.3120 | 0.5387 | 0.2802 | | 2.1823 | 4.0 | 80 | 2.0526 | 0.5935 | 0.3138 | | 1.8786 | 5.0 | 100 | 1.8242 | 0.6387 | 0.3541 | | 1.6267 | 6.0 | 120 | 1.6720 | 0.6548 | 0.3682 | | 1.4447 | 7.0 | 140 | 1.5538 | 0.6645 | 0.3718 | | 1.2734 | 8.0 | 160 | 1.4655 | 0.6710 | 0.3801 | | 1.1099 | 9.0 | 180 | 1.4040 | 0.6935 | 0.4202 | | 1.0766 | 10.0 | 200 | 1.3541 | 0.6903 | 0.4330 | | 0.913 | 11.0 | 220 | 1.3078 | 0.6968 | 0.4629 | | 0.8557 | 12.0 | 240 | 1.2879 | 0.7161 | 0.5000 | | 0.8477 | 13.0 | 260 | 1.2772 | 0.7097 | 0.4946 | | 0.7412 | 14.0 | 280 | 1.2598 | 0.7161 | 0.5042 | | 0.7341 | 15.0 | 300 | 1.2484 | 0.7194 | 0.5069 | | 0.7029 | 16.0 | 320 | 1.2480 | 0.7194 | 0.5062 | ### Framework versions - Transformers 4.35.1 - Pytorch 1.10.1+cu111 - Datasets 2.14.7 - Tokenizers 0.14.1
null
transformers
text-classification
null
null
null
null
null
null
null
null
null
th-nuernberg/gbert-base-german-counseling-gecco
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A-Issa-1999/whisper-tiny
A-Issa-1999
2023-11-29T19:13:43Z
0
0
null
[ "region:us" ]
2023-11-29T19:13:43Z
2023-11-29T19:13:43.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
A-Issa-1999/whisper-tiny
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grfdsrgedrf/Funds-for-teachers-staff-room-other-building-repairs-a5-updated
grfdsrgedrf
2023-11-29T19:14:46Z
0
0
null
[ "region:us" ]
2023-11-29T19:14:46Z
2023-11-29T19:14:46.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
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null
grfdsrgedrf/Funds-for-teachers-staff-room-other-building-repairs-a5-updated
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grfdsrgedrf/Pentagon-says-Somali-pirates-likely-behind-seizure-of-commercial-ship-near-Yemen-f1-updated
grfdsrgedrf
2023-11-29T19:16:08Z
0
0
null
[ "en", "region:us" ]
2023-11-29T19:16:08Z
2023-11-29T19:14:47.000Z
null
null
--- language: - en --- [![Build Status](https://pakobserver.net/wp-content/uploads/2023/08/Younis.webp)]() read the full article here : https://jpeg.ly/abpG8 Source : https://ibit.ly/QqnX1 Flash News : https://paste.feed-the-beast.com/view/5b548b59 Biden last Talk : https://rentry.co/6c82sz Russian Ukrain Breaking News : https://yamcode.com/china-president-says-wants-update-19850 Other Sources : https://ibit.ly/n1w0A https://yamcode.com/china-president-says-wants-update-19851 https://paste.ee/p/vUUrI https://jsfiddle.net/k4z1jnb3/ https://jpeg.ly/fIywu https://ibit.ly/u8Xgc https://rentry.co/gkdtp https://pasteio.com/xZS2O42T2Cgl https://rentry.co/k34xq https://yamcode.com/ https://jpeg.ly/OQrI- https://ibit.ly/tggrf https://jpeg.ly/lVHC8 https://yamcode.com/biden-says-wants-update-11292023-200752 (NEW YORK) -- More than 40,000 Oregon students returned to classes Monday for the first time this month as Portland teachers ended the longest U.S. teachers strike in 2023. Over the weekend, Portland Public Schools (PPS), the largest school district in the state, reached a tentative agreement to end the more than three-week strike that started on Nov. 1. However, the $175 million contract agreement still needs to be approved by the union as classes resume. "This contract is a watershed moment for Portland students, families, and educators," Portland Association of Teachers (PAT) President Angela Bonilla said in a statement to ABC News. "Educators walked picket lines alongside families, students, and allies -- and because of that, our schools are getting the added investment they need," Bonilla added. Veteran teacher Tiffany Koyama Lane was swarmed by her third grade students and parents when she returned to Sunnyside Environmental School today. "I walked out this morning - as did every other teacher at my school - and like, the families and kids cheered and started crying and like, tackled me in like one giant group hug," Koyama Lane told ABC News. "The magnitude of the community showing up so strongly. That's something that I'm still sitting with and processing - like it was - it was really beautiful." The city's first-ever strike lasted 11 instructional days when factoring in weekends and holidays. After months of negotiations, Portland's nearly 4,000 educators received "key wins" for mental health support, smaller class sizes and cost of living compensation increases, according to the statement. Oregon Education Association (OEA) President Reed Scott-Schwalbach called the tentative agreement "historic." "This is a transformative deal that will improve the lives of students in Portland and have far-reaching positive effects for our students across the rest of the state," Scott-Schwalbach wrote. In recent strikes across the nation, teachers have demanded fair wages and called attention to other grievances included in the negotiations, like guaranteed planning time. Many teachers have said their prep periods were stripped from them due to a massive teaching shortage during the COVID-19 pandemic. Portland's teachers also agreed to a roughly 14% overall pay raise over three years, $20 million dollars for classroom renovations and a first-ever contract article dedicated to special education. Posting to X (formally known as Twitter), Suzanne Bonamici, D-Oregon, said post-pandemic teaching remains a challenge as more students have greater academic and social needs. The proposed agreement "fairly compensates PPS teachers and gives them the resources and support they need to provide their students with a path to success," Bonamici added. Students and teachers will make up the missed class time over winter break and throughout the new year.....
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grfdsrgedrf/Pentagon-says-Somali-pirates-likely-behind-seizure-of-commercial-ship-near-Yemen-f1-updated
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bartowski/deepseek-llm-7b-base-exl2
bartowski
2023-11-29T19:36:59Z
0
0
null
[ "text-generation", "license:other", "region:us" ]
2023-11-29T19:36:59Z
2023-11-29T19:15:13.000Z
null
null
--- license: other license_name: deepseek license_link: LICENSE quantized_by: bartowski pipeline_tag: text-generation --- ## Exllama v2 Quantizations of deepseek-llm-7b-base Using <a href="https://github.com/turboderp/exllamav2/releases/tag/master">turboderp's ExLlamaV2 master</a> for quantization. Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. Conversion was done using Evol-Instruct-Code-80k-v1.parquet as calibration dataset. Default arguments used except when the bits per weight is above 6.0, at that point the lm_head layer is quantized at 8 bits per weight instead of the default 6. Original model: https://huggingface.co/deepseek-ai/deepseek-llm-7b-base ## Download instructions With git: ```shell git clone --single-branch --branch 4_0 https://huggingface.co/bartowski/deepseek-llm-7b-base-exl2 ``` With huggingface hub (credit to TheBloke for instructions): ```shell pip3 install huggingface-hub ``` To download the `main` (only useful if you only care about measurement.json) branch to a folder called `deepseek-llm-7b-base-exl2`: ```shell mkdir deepseek-llm-7b-base-exl2 huggingface-cli download bartowski/deepseek-llm-7b-base-exl2 --local-dir deepseek-llm-7b-base-exl2 --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir deepseek-llm-7b-base-exl2 huggingface-cli download bartowski/deepseek-llm-7b-base-exl2 --revision 4_0 --local-dir deepseek-llm-7b-base-exl2 --local-dir-use-symlinks False ```
null
null
text-generation
null
null
null
null
null
null
null
null
null
bartowski/deepseek-llm-7b-base-exl2
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panopstor/temp
panopstor
2023-11-29T19:15:50Z
0
0
null
[ "region:us" ]
2023-11-29T19:15:50Z
2023-11-29T19:15:50.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
panopstor/temp
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emilstabil/DanSumT5-smallV_89308
emilstabil
2023-11-29T19:17:02Z
0
0
null
[ "region:us" ]
2023-11-29T19:17:02Z
2023-11-29T19:17:02.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
emilstabil/DanSumT5-smallV_89308
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grfdsrgedrf/N-Korea-defends-satellite-launch-at-UN-as-Kim-takes-images-of-White-House-bg-updated
grfdsrgedrf
2023-11-29T19:19:24Z
0
0
null
[ "en", "region:us" ]
2023-11-29T19:19:24Z
2023-11-29T19:17:30.000Z
null
null
--- language: - en --- [![Build Status](https://cdn.abcotvs.com/dip/images/13716595_082923-wls-craig-sox-shooting-update-6p-vid.jpg?w=1600)]() read the full article here : https://jpeg.ly/xe5_Y Source : https://rentry.co/kf3e2 Flash News : https://yamcode.com/kennedy-says-wants-update-1554-20 Biden last Talk : https://yamcode.com/trudeau-says-wants-update-11292023-201034 Russian Ukrain Breaking News : https://rentry.co/xzo2q Other Sources : https://rentry.co/o9x47 https://yamcode.com/raw/hilary-says-wants-update-2422-11292023-201308 https://ibit.ly/GGw_C https://yamcode.com/kennedy-says-wants-update-1414-28 https://yamcode.com/raw/trudeau-says-wants-update-11292023-201109 https://yamcode.com/raw/trudeau-says-wants-update-11292023-201034 https://jpeg.ly/E7xC9 https://ibit.ly/qRH9I https://yamcode.com/holland-says-wants-update-1442-34 https://rentry.co/93uoa8 https://jpeg.ly/Nu_j9 https://jpeg.ly/37lbt https://yamcode.com/china-president-says-wants-update-19853 https://justpaste.me/8Q43 A farmer has been fined after a retiree and his dog were nearly trampled to death by cows on a public footpath. Retired chartered surveyor Patrick Atherton, 70, and his 13-year-old Border Collie 'Lad' were knocked to the ground by seven cows while walking in June last year. They were only saved when someone - believed to be the farmer - waved the cows away, leaving Patrick with cuts and bruises in what could have been a fatal attack. Sadly, Patrick's dog Lad suffered serious injuries in the incident after being repeatedly kicked and stamped on by the cows - which on average weigh nearly 700kg. Patrick, originally from The Wirral, Merseyside, says that he thought he was going to die during the attack at Birdcage Farm, in Ottery Saint Mary, Devon. Patrick thought he was going to die He said: "I've been a trainer of Border Collies for many years. It was ironic that for the first time I had kept Lad on his lead as I had noticed two young calves in the field and thought if he was walking by my side they wouldn't really notice. "Unfortunately, one black cow did and it charged at us, knocking me down by the hedge that bordered the path. I tried to stand up and let Lad off his lead but they kept on knocking me over. There was about seven cows involved, but it was the very aggressive black cow that was trying to kill my dog. I thought we were going to die. I had been powerless to do anything." Patrick said his pet dog was 'never the same' following the attack Throughout the traumatic ordeal, Patrick also was forced to listen to Lad shrieking from pain as he was attacked by the animals. Thankfully the collie survived his injuries - but had to be medicated for the rest of his life, and was never the same. Patrick added: "We were both surrounded by cattle. I could hear Lad shrieking in pain as he was repeatedly kicked and stamped on. He was never the same dog after what happened and he sadly passed away in September this year. "I just want to make other people aware of the risks when out enjoying popular footpaths such as this one." The Health and Safety Executive (HSE) investigation following the incident found that cattle with young calves were being kept in a field with a public right of way across it. Farmer John Hallett, who owns Birdcage Farm was find thousands after the incident Cattle with calves are known to be protective and can be aggressive to walkers, especially to those with dogs. Between 2018 and 2022, more than 30 people were killed by cows in similar incidents - while others have been left with life-changing injuries. Due to these dangers, farmers are encouraged to cull any aggressive animals or avoid putting the animals in fields with public access. Public entrances to fields containing potentially dangerous cattle, stating clearly whether there are cows with calves or bulls in the field. Farmer John Hallett, who owns Birdcage Farm, later plead guilty to breaching Section 3(2) of the Health & Safety at Work etc Act 1974. He was fined £2,000 and ordered to pay costs of £4,500 at Exeter Magistrates' Court. HSE inspector Simon Jones said: "It is fortunate that the injuries sustained by Mr Atherton weren't life-threatening. However given the nature of the attack the end result could have been far worse. Public knowledge - and concern - is increasing about how dangerous cattle can be. On this occasion, Mr Atherton took the right precautions and HSE has taken action against the farmer in question. "Cattle are extremely protective of their calves and even calm cattle can become aggressive if they think the calves may, in any way, be threatened, even by members of the public walking past. Farmers should not place cattle with calves in fields where members of the public have a legal right to walk. Had John Hallett followed this simple rule, then this incident could have been prevented."....
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grfdsrgedrf/N-Korea-defends-satellite-launch-at-UN-as-Kim-takes-images-of-White-House-bg-updated
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langust/brokentest
langust
2023-11-29T19:17:44Z
0
0
null
[ "region:us" ]
2023-11-29T19:17:44Z
2023-11-29T19:17:44.000Z
null
null
Entry not found
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langust/brokentest
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow18
FounderOfHuggingface
2023-11-29T19:17:57Z
0
0
null
[ "peft", "arxiv:1910.09700", "base_model:gpt2", "region:us" ]
2023-11-29T19:17:57Z
2023-11-29T19:17:53.000Z
null
null
--- library_name: peft base_model: gpt2 --- # 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] - **Funded by [optional]:** [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 Dataset 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 Dataset 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 0.6.2
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peft
null
null
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow18
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grfdsrgedrf/Portland-public-school-classes-resume-after-firstever-teachers-strike-2g-updated
grfdsrgedrf
2023-11-29T19:19:51Z
0
0
null
[ "en", "region:us" ]
2023-11-29T19:19:51Z
2023-11-29T19:17:54.000Z
null
null
--- language: - en --- [![Build Status](https://cdn.forumcomm.com/dims4/default/6f3a22f/2147483647/strip/true/crop/5300x3312+0+0/resize/1706x1066!/quality/90/?url=https%3A%2F%2Fforum-communications-production-web.s3.us-west-2.amazonaws.com%2Fbrightspot%2F36%2F0d%2F849391c747688ba3c9718dcc92ca%2F110122.N.FF.EARLYVOTING.2.JPG)]() read the full article here : https://ibit.ly/rn_xu Source : https://yamcode.com/obama-says-wants-update-5311-38 Flash News : https://jpeg.ly/IXs4E Biden last Talk : https://yamcode.com/raw/holland-says-wants-update-1442-34 Russian Ukrain Breaking News : https://yamcode.com/raw/kennedy-says-wants-update-1414-28 Other Sources : https://rentry.co/zxq8i https://etextpad.com/6ptsixxs8z https://ibit.ly/P81EL https://yamcode.com/raw/kennedy-says-wants-update-1554-20 https://yamcode.com/trudeau-says-wants-update-11292023-201109 https://ibit.ly/Sfhiv https://ibit.ly/Yh0yg https://rentry.co/z2o4w https://notes.io/we74n https://yamcode.com/raw/china-president-says-wants-update-19853 https://jpeg.ly/hrDx8 https://yamcode.com/raw/obama-says-wants-update-5311-38 https://ibit.ly/JxYnh https://paste.toolforge.org/view/2e76ae17 TEL AVIV, Israel (AP) -- A Red Cross bus carrying Palestinian prisoners released by Israel has arrived in the West Bank town of Ramallah. It was the fourth such release as part of the cease-fire deal between Israel and Hamas. Earlier, Hamas released eleven Israeli women and children it held hostage. The four-day truce was to come to an end Tuesday, but Qatari mediators said it would be extended for two additional days. The prisoners were greeted by loud cheers from crowds surrounding the bus as it made its way through the streets of the city. THIS IS A BREAKING NEWS UPDATE. AP's earlier story follows below. TEL AVIV, Israel (AP) -- Israel and Hamas agreed to extend their cease-fire for two more days past Monday, raising the prospect of further exchanges of militant-held hostages for Palestinians imprisoned by Israel and a longer halt to their deadliest and most destructive war. The deal for two additional days of cease-fire, announced by Qatar, raised hopes for further extensions, which also allow more aid into Gaza. Conditions there have remained dire for 2.3 million Palestinians, battered by weeks of Israeli bombardment and a ground offensive that have driven three-quarters of the population from their homes. Israel has said it would extend the cease-fire by one day for every 10 additional hostages released. After the announcement by Qatar -- a key mediator in the conflict, along with the United States and Egypt -- Hamas confirmed it had agreed to a two-day extension "under the same terms." But Israel says it remains committed to crushing Hamas' military capabilities and ending its 16-year rule over Gaza after its Oct. 7 attack into southern Israel. That would likely mean expanding a ground offensive from devastated northern Gaza to the south. Monday's releases bring to 50 the number of Israelis freed under the truce, along with 19 hostages of other nationalities. So far, 117 Palestinians have been released from Israeli prisons. After weeks of national trauma over the roughly 240 people abducted by Hamas and other militants, scenes of the women and children reuniting with families have rallied Israelis behind calls to return those who remain in captivity. "We can get all hostages back home. We have to keep pushing," two relatives of Abigail Edan, a 4-year-old girl and dual Israeli-American citizen who was released Sunday, said in a statement. Hamas and other militants could still be holding up to 175 hostages, enough to potentially extend the cease-fire for two and a half weeks. But those include a number of soldiers, and Hamas is likely to make much greater demands for their release. FOURTH RELEASE TEL AVIV, Israel (AP) -- Israel and Hamas agreed to extend their cease-fire for two more days past Monday, raising the prospect of further exchanges of militant-held hostages for Palestinians imprisoned by Israel and a longer halt to their deadliest and most destructive war. Most of the hostages freed so far have appeared to be physically well. But 84-year-old Elma Avraham, released Sunday, was airlifted to Israel's Soroka Medical Center in life-threatening condition because of inadequate care, the hospital said. Avraham's daughter, Tali Amano, said her mother was "hours from death" when she was brought to the hospital. Avraham is currently sedated and has a breathing tube, but Amano said she told her of a new great-grandchild who was born while she was in captivity. Avraham suffered from several chronic conditions that required regular medications but was stable before she was kidnapped, Amano said Monday. So far, 19 people of other nationalities have been freed during the truce, mostly Thai nationals. Many Thais work in Israel, largely as farm laborers. France said three of the hostages released Monday were French-Israeli dual citizens, two 12-year-olds and one 16-year-old. The French government is ''working tirelessly'' to free five other French citizens held hostage, the French Foreign Ministry said in a statement. The Palestinian prisoners released so far have been mostly teenagers accused of throwing stones and firebombs during confrontations with Israeli forces, or of less-serious offenses. Many Palestinians view prisoners held by Israel, including those implicated in attacks, as heroes resisting occupation. The freed hostages have mostly stayed out of the public eye, but details of their captivity have started to trickle out. Merav Raviv, whose three relatives were released Friday, said they had been fed irregularly and lost weight. One reported eating mainly bread and rice and sleeping on a makeshift bed of chairs pushed together. Hostages sometimes had to wait for hours to use the bathroom, she said. In Washington, White House National Security Council spokesman John Kirby welcomed the extension of the truce. "We would, of course, hope to see the pause extended further, and that will depend upon Hamas continuing to release hostages," Kirby told reporters. RESPITE IN GAZA More than 13,300 Palestinians have been killed since the war began, roughly two-thirds of them women and minors, according to the Health Ministry in Hamas-ruled Gaza, which does not differentiate between civilians and combatants. More than 1,200 people have been killed on the Israeli side, mostly civilians killed in the initial attack. At least 77 soldiers have been killed in Israel's ground offensive. The calm from the truce allowed glimpses of the destruction wreaked by weeks of Israeli bombardment that leveled entire neighborhoods. Footage showed a complex of several dozen multistory residential buildings that had been pummeled into a landscape of wreckage in the northern town of Beit Hanoun. Nearly every building was destroyed or severely damaged, some reduced to concrete frames half-slumped over. At a nearby U.N. school, the buildings were intact but partially burned and riddled with holes. The Israeli assault has driven three-quarters of Gaza's population from their homes, and now most of its 2.3 million people are crowded into the south. More than 1 million are living in U.N. shelters. The Israeli military has barred hundreds of thousands of Palestinians who fled south from returning north. Rain and wind added to the hardship of displaced Palestinians sheltering in the compound of Al-Aqsa hospital in central Gaza. Palestinians in coats baked flatbreads over a makeshift fire among tents set up on the muddy grounds. Alaa Mansour said the conditions are simply horrendous. "My clothes are all wet, and I am unable to change them." said Mansour, who is disabled. "I have not drunk water for two days, and there's no bathroom to use." The U.N. says the truce made it possible to scale up the delivery of food, water and medicine to the largest volume since the start of the war. But the 160 to 200 trucks a day is still less than half what Gaza was importing before the fighting, even as humanitarian needs have soared. Long lines formed outside stations distributing cooking fuel, allowed in for the first time. Fuel for generators has been brought for key service providers, including hospitals and water and sanitation facilities, but bakeries have been unable to resume work, the U.N. said. Iyad Ghafary, a vendor in the urban Nuseirat refugee camp in central Gaza, said many families were still unable to retrieve the dead from under the rubble left by Israeli airstrikes, and that local authorities weren't equipped to deal with the level of destruction. Many say the aid is not nearly enough. Amani Taha, a widow and mother of three who fled northern Gaza, said she had only managed to get one canned meal from a U.N. distribution center since the cease-fire began. She said the crowds have overwhelmed local markets and gas stations as people try to stock up on basics. "People were desperate and went out to buy whenever they could," she said. "They are extremely worried that the war will return." Jeffery and Magdy reported from Cairo. Associated Press writer Melanie Lidman in Jerusalem contributed. Full AP coverage at https:\/\/apnews.com\/hub\/israel-hamas-war.....
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grfdsrgedrf/Portland-public-school-classes-resume-after-firstever-teachers-strike-2g-updated
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sheduele/distilbert_ver_1_task_1A
sheduele
2023-11-29T20:34:46Z
0
0
null
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "endpoints_compatible", "region:us" ]
2023-11-29T20:34:46Z
2023-11-29T19:18:04.000Z
null
null
Entry not found
null
transformers
text-classification
null
null
null
null
null
null
null
null
null
sheduele/distilbert_ver_1_task_1A
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Dave12121/chat2Fsentiment
Dave12121
2023-11-29T19:18:57Z
0
0
null
[ "region:us" ]
2023-11-29T19:18:57Z
2023-11-29T19:18:57.000Z
null
null
Entry not found
null
null
null
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Dave12121/chat2Fsentiment
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BackMe/Ise-bak
BackMe
2023-11-29T19:45:30Z
0
0
null
[ "license:openrail", "region:us" ]
2023-11-29T19:45:30Z
2023-11-29T19:20:32.000Z
null
null
--- license: openrail --- >> POOF
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BackMe/Ise-bak
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