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train · 848k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-28 18:27:08	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 501 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-28 18:25:37	card stringlengths 11 1.01M
Johnie2Turbo/llama-13b_adv_text	Johnie2Turbo	2024-05-20T05:27:35Z	2	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "ru", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T05:20:06Z	--- language: - ru --- Умеет писать рекламные тексты и рекламные объявления для машин и ноутбуков ) Правильное оформление инструкции: [INST] {prompt} [/INST] Обучался на инструкция "Напиши рекламный текст для ..." и "Напиши рекламное объявление для ..."
Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-6_0bpw_exl2	Zoyd	2024-05-20T05:26:58Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "dataset:aqua_rat", "dataset:microsoft/orca-math-word-problems-200k", "dataset:m-a-p/CodeFeedback-Filtered-Instruction", "arxiv:2402.13228", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "6-bit", "exl2", "region:us" ]	text-generation	2024-05-20T05:03:27Z	--- library_name: transformers license: llama2 datasets: - aqua_rat - microsoft/orca-math-word-problems-200k - m-a-p/CodeFeedback-Filtered-Instruction --- Exllamav2 quant (exl2 / 6.0 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-2_2bpw_exl2)</center> \| <center>20886 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-2_5bpw_exl2)</center> \| <center>23192 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-3_0bpw_exl2)</center> \| <center>27273 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-3_5bpw_exl2)</center> \| <center>31356 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-3_75bpw_exl2)</center> \| <center>33398 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-4_0bpw_exl2)</center> \| <center>35434 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-4_25bpw_exl2)</center> \| <center>37456 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-5_0bpw_exl2)</center> \| <center>43598 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-6_0bpw_exl2)</center> \| <center>51957 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-6_5bpw_exl2)</center> \| <center>56014 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-8_0bpw_exl2)</center> \| <center>60211 MB</center> \| <center>8</center> \| # Smaug-Llama-3-70B-Instruct ### Built with Meta Llama 3 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c14f6b02e1f8f67c73bd05/ZxYuHKmU_AtuEJbGtuEBC.png) This model was built using a new Smaug recipe for improving performance on real world multi-turn conversations applied to [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct). The model outperforms Llama-3-70B-Instruct substantially, and is on par with GPT-4-Turbo, on MT-Bench (see below). EDIT: Smaug-Llama-3-70B-Instruct is the top open source model on Arena-Hard currently! It is also nearly on par with Claude Opus - see below. We are conducting additional benchmark evaluations and will add those when available. ### Model Description - Developed by: [Abacus.AI](https://abacus.ai) - License: https://llama.meta.com/llama3/license/ - Finetuned from model: [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct). ## Evaluation ### Arena-Hard Score vs selected others (sourced from: (https://lmsys.org/blog/2024-04-19-arena-hard/#full-leaderboard-with-gpt-4-turbo-as-judge)) \| Model \| Score \| 95% Confidence Interval \| Average Tokens \| \| :---- \| ---------: \| ----------: \| ------: \| \| GPT-4-Turbo-2024-04-09 \| 82.6 \| (-1.8, 1.6) \| 662 \| \| Claude-3-Opus-20240229 \| 60.4 \| (-3.3, 2.4) \| 541 \| \| Smaug-Llama-3-70B-Instruct \| 56.7 \| (-2.2, 2.6) \| 661 \| \| GPT-4-0314 \| 50.0 \| (-0.0, 0.0) \| 423 \| \| Claude-3-Sonnet-20240229 \| 46.8 \| (-2.1, 2.2) \| 552 \| \| Llama-3-70B-Instruct \| 41.1 \| (-2.5, 2.4) \| 583 \| \| GPT-4-0613 \| 37.9 \| (-2.2, 2.0) \| 354 \| \| Mistral-Large-2402 \| 37.7 \| (-1.9, 2.6) \| 400 \| \| Mixtral-8x22B-Instruct-v0.1 \| 36.4 \| (-2.7, 2.9) \| 430 \| \| Qwen1.5-72B-Chat \| 36.1 \| (-2.5, 2.2) \| 474 \| \| Command-R-Plus \| 33.1 \| (-2.1, 2.2) \| 541 \| \| Mistral-Medium \| 31.9 \| (-2.3, 2.4) \| 485 \| \| GPT-3.5-Turbo-0613 \| 24.8 \| (-1.6, 2.0) \| 401 \| ### MT-Bench ``` ########## First turn ########## score model turn Smaug-Llama-3-70B-Instruct 1 9.40000 GPT-4-Turbo 1 9.37500 Meta-Llama-3-70B-Instruct 1 9.21250 ########## Second turn ########## score model turn Smaug-Llama-3-70B-Instruct 2 9.0125 GPT-4-Turbo 2 9.0000 Meta-Llama-3-70B-Instruct 2 8.8000 ########## Average ########## score model Smaug-Llama-3-70B-Instruct 9.206250 GPT-4-Turbo 9.187500 Meta-Llama-3-70B-Instruct 9.006250 ``` \| Model \| First turn \| Second Turn \| Average \| \| :---- \| ---------: \| ----------: \| ------: \| \| Smaug-Llama-3-70B-Instruct \| 9.40 \| 9.01 \| 9.21 \| \| GPT-4-Turbo \| 9.38 \| 9.00 \| 9.19 \| \| Meta-Llama-3-70B-Instruct \| 9.21 \| 8.80 \| 9.01 \| This version of Smaug uses new techniques and new data compared to [Smaug-72B](https://huggingface.co/abacusai/Smaug-72B-v0.1), and more information will be released later on. For now, see the previous Smaug paper: https://arxiv.org/abs/2402.13228.
baldwin6/Bolaco	baldwin6	2024-05-20T05:24:52Z	0	2	null	[ "region:us" ]	null	2024-05-16T08:30:48Z	Checkpoints for the Bolaco. The code is available at https://github.com/Dereck0602/Bolaco.
danieljhand/distilbert-base-uncased-finetuned-wine	danieljhand	2024-05-20T05:24:43Z	123	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-17T02:07:53Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-wine 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-finetuned-wine 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: 0.9082 - Accuracy: 0.7314 - F1: 0.7222 ## 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 1.6559 \| 1.0 \| 1101 \| 1.0917 \| 0.6792 \| 0.6623 \| \| 1.0185 \| 2.0 \| 2202 \| 0.9466 \| 0.7214 \| 0.7103 \| \| 0.8851 \| 3.0 \| 3303 \| 0.9082 \| 0.7314 \| 0.7222 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
jayasuryajsk/Llama-3-merge-4.2B	jayasuryajsk	2024-05-20T05:21:45Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T05:17:50Z	--- library_name: transformers tags: [] --- # 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - 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]
leafspark/Yi-1.5-34B-Chat-16K-Q4_K_M-GGUF	leafspark	2024-05-20T05:19:40Z	6	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-20T05:18:51Z	--- license: apache-2.0 tags: - llama-cpp - gguf-my-repo --- # leafspark/Yi-1.5-34B-Chat-16K-Q4_K_M-GGUF This model was converted to GGUF format from [`01-ai/Yi-1.5-34B-Chat-16K`](https://huggingface.co/01-ai/Yi-1.5-34B-Chat-16K) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/01-ai/Yi-1.5-34B-Chat-16K) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo leafspark/Yi-1.5-34B-Chat-16K-Q4_K_M-GGUF --model yi-1.5-34b-chat-16k.Q4_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo leafspark/Yi-1.5-34B-Chat-16K-Q4_K_M-GGUF --model yi-1.5-34b-chat-16k.Q4_K_M.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m yi-1.5-34b-chat-16k.Q4_K_M.gguf -n 128 ```
Hemg/token-classification	Hemg	2024-05-20T05:16:39Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "token-classification", "generated_from_trainer", "dataset:wnut_17", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-20T05:12:42Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - wnut_17 metrics: - precision - recall - f1 - accuracy model-index: - name: token-classification results: - task: name: Token Classification type: token-classification dataset: name: wnut_17 type: wnut_17 config: wnut_17 split: test args: wnut_17 metrics: - name: Precision type: precision value: 0.5268630849220104 - name: Recall type: recall value: 0.28174235403151066 - name: F1 type: f1 value: 0.36714975845410625 - name: Accuracy type: accuracy value: 0.939506647856013 --- <!-- 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. --> # token-classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset. It achieves the following results on the evaluation set: - Loss: 0.2813 - Precision: 0.5269 - Recall: 0.2817 - F1: 0.3671 - Accuracy: 0.9395 ## 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: 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 107 \| 0.3021 \| 0.4217 \| 0.1548 \| 0.2264 \| 0.9342 \| \| No log \| 2.0 \| 214 \| 0.2813 \| 0.5269 \| 0.2817 \| 0.3671 \| 0.9395 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
sidddddddddddd/llama-3-8b-kub	sidddddddddddd	2024-05-20T05:13:01Z	0	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-20T05:13:00Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: sidddddddddddd - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
tsavage68/MedQA_L3_150steps_1e6rate_03beat_CSFTDPO	tsavage68	2024-05-20T05:04:49Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T05:01:00Z	--- license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - trl - dpo - generated_from_trainer model-index: - name: MedQA_L3_150steps_1e6rate_03beat_CSFTDPO 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. --> # MedQA_L3_150steps_1e6rate_03beat_CSFTDPO This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5020 - Rewards/chosen: -0.9020 - Rewards/rejected: -1.9172 - Rewards/accuracies: 0.7297 - Rewards/margins: 1.0152 - Logps/rejected: -27.7072 - Logps/chosen: -21.2293 - Logits/rejected: -1.0337 - Logits/chosen: -1.0327 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.7072 \| 0.0489 \| 50 \| 0.6474 \| 0.1422 \| 0.0242 \| 0.6505 \| 0.1180 \| -21.2360 \| -17.7487 \| -0.9397 \| -0.9391 \| \| 0.6194 \| 0.0977 \| 100 \| 0.5755 \| -0.5279 \| -1.1917 \| 0.6989 \| 0.6638 \| -25.2888 \| -19.9824 \| -1.0174 \| -1.0166 \| \| 0.5632 \| 0.1466 \| 150 \| 0.5020 \| -0.9020 \| -1.9172 \| 0.7297 \| 1.0152 \| -27.7072 \| -21.2293 \| -1.0337 \| -1.0327 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.0.0+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1
ridham1317/whisper-small-ft-common-voice	ridham1317	2024-05-20T05:03:37Z	148	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-20T05:02:48Z	--- library_name: transformers tags: [] --- # 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - 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. 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AAA01101312/distilbert-base-uncased-finetuned-clinc	AAA01101312	2024-05-20T05:02:02Z	8	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-16T01:54:19Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-clinc 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-finetuned-clinc 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: 0.7977 - Accuracy: 0.9155 ## 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: 48 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 318 \| 3.3065 \| 0.7232 \| \| 3.8097 \| 2.0 \| 636 \| 1.8930 \| 0.8487 \| \| 3.8097 \| 3.0 \| 954 \| 1.1797 \| 0.8913 \| \| 1.7181 \| 4.0 \| 1272 \| 0.8830 \| 0.9113 \| \| 0.9252 \| 5.0 \| 1590 \| 0.7977 \| 0.9155 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1+cpu - Datasets 2.19.1 - Tokenizers 0.15.2
omezzinemariem/mistral-text-to-RULE2	omezzinemariem	2024-05-20T05:01:14Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "region:us" ]	null	2024-05-20T05:01:01Z	--- library_name: peft base_model: mistralai/Mistral-7B-Instruct-v0.2 --- # 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1
BothBosu/cnn-scam-classifier-v1.3	BothBosu	2024-05-20T04:57:30Z	51	0	transformers	[ "transformers", "safetensors", "pytorch_model_hub_mixin", "model_hub_mixin", "endpoints_compatible", "region:us" ]	null	2024-05-20T04:57:28Z	--- tags: - pytorch_model_hub_mixin - model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
BothBosu/bilstm-scam-classifier-v1.3	BothBosu	2024-05-20T04:54:56Z	53	0	transformers	[ "transformers", "safetensors", "pytorch_model_hub_mixin", "model_hub_mixin", "endpoints_compatible", "region:us" ]	null	2024-05-20T04:54:49Z	--- tags: - pytorch_model_hub_mixin - model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
damingli09/ppo-LunarLander-v2	damingli09	2024-05-20T04:51:12Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-20T04:50:52Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 263.46 +/- 17.31 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Ruiz3/phi-2-kingshipAI-product-explainer	Ruiz3	2024-05-20T04:47:09Z	132	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T04:26:51Z	--- library_name: transformers tags: [] --- # 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ahmeda335/experimentGA1	ahmeda335	2024-05-20T04:39:10Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "base_model:OpenPipe/mistral-ft-optimized-1218", "base_model:merge:OpenPipe/mistral-ft-optimized-1218", "base_model:mlabonne/NeuralHermes-2.5-Mistral-7B", "base_model:merge:mlabonne/NeuralHermes-2.5-Mistral-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T04:11:42Z	--- base_model: - OpenPipe/mistral-ft-optimized-1218 - mlabonne/NeuralHermes-2.5-Mistral-7B library_name: transformers tags: - mergekit - merge --- # mergeGA2 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218) * [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: OpenPipe/mistral-ft-optimized-1218 layer_range: [0, 32] - sources: - model: mlabonne/NeuralHermes-2.5-Mistral-7B layer_range: [24, 32] merge_method: passthrough dtype: bfloat16 ```
OwOpeepeepoopoo/NoSoup4U2	OwOpeepeepoopoo	2024-05-20T04:36:33Z	14	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-19T00:25:31Z	--- library_name: transformers tags: [] --- # 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - 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. 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expertlearning/vision-perceiver-fourier	expertlearning	2024-05-20T04:33:20Z	112	0	transformers	[ "transformers", "pytorch", "perceiver", "image-classification", "dataset:imagenet", "arxiv:2107.14795", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-20T04:31:14Z	--- license: apache-2.0 datasets: - imagenet --- # Perceiver IO for vision (fixed Fourier position embeddings) Perceiver IO model pre-trained on ImageNet (14 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Jaegle et al. and first released in [this repository](https://github.com/deepmind/deepmind-research/tree/master/perceiver). Disclaimer: The team releasing Perceiver IO did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Perceiver IO is a transformer encoder model that can be applied on any modality (text, images, audio, video, ...). The core idea is to employ the self-attention mechanism on a not-too-large set of latent vectors (e.g. 256 or 512), and only use the inputs to perform cross-attention with the latents. This allows for the time and memory requirements of the self-attention mechanism to not depend on the size of the inputs. To decode, the authors employ so-called decoder queries, which allow to flexibly decode the final hidden states of the latents to produce outputs of arbitrary size and semantics. For image classification, the output is a tensor containing the logits, of shape (batch_size, num_labels). <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/perceiver_architecture.jpg" alt="drawing" width="600"/> <small> Perceiver IO architecture.</small> As the time and memory requirements of the self-attention mechanism don't depend on the size of the inputs, the Perceiver IO authors can train the model directly on raw pixel values, rather than on patches as is done in ViT. This particular model only adds fixed Fourier 2D position embeddings to the pixel values. By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by replacing the classification decoder. ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=deepmind/perceiver) to look for other fine-tuned versions on a task that may interest you. ### How to use Here is how to use this model in PyTorch: ```python from transformers import PerceiverImageProcessor, PerceiverForImageClassificationFourier import requests from PIL import Image processor = PerceiverImageProcessor.from_pretrained("deepmind/vision-perceiver-fourier") model = PerceiverForImageClassificationFourier.from_pretrained("deepmind/vision-perceiver-fourier") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) # prepare input inputs = processor(image, return_tensors="pt").pixel_values # forward pass outputs = model(inputs) logits = outputs.logits print("Predicted class:", model.config.id2label[logits.argmax(-1).item()]) >>> should print Predicted class: tabby, tabby cat ``` ## Training data This model was pretrained on [ImageNet](http://www.image-net.org/), a dataset consisting of 14 million images and 1k classes. ## Training procedure ### Preprocessing Images are center cropped and resized to a resolution of 224x224 and normalized across the RGB channels. Note that data augmentation was used during pre-training, as explained in Appendix H of the [paper](https://arxiv.org/abs/2107.14795). ### Pretraining Hyperparameter details can be found in Appendix H of the [paper](https://arxiv.org/abs/2107.14795). ## Evaluation results This model is able to achieve a top-1 accuracy of 79.0 on ImageNet-1k, and 84.5 when pre-trained on a large-scale dataset (JFT-300M, an internal dataset of Google). ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2107-14795, author = {Andrew Jaegle and Sebastian Borgeaud and Jean{-}Baptiste Alayrac and Carl Doersch and Catalin Ionescu and David Ding and Skanda Koppula and Daniel Zoran and Andrew Brock and Evan Shelhamer and Olivier J. H{\'{e}}naff and Matthew M. Botvinick and Andrew Zisserman and Oriol Vinyals and Jo{\~{a}}o Carreira}, title = {Perceiver {IO:} {A} General Architecture for Structured Inputs {\&} Outputs}, journal = {CoRR}, volume = {abs/2107.14795}, year = {2021}, url = {https://arxiv.org/abs/2107.14795}, eprinttype = {arXiv}, eprint = {2107.14795}, timestamp = {Tue, 03 Aug 2021 14:53:34 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2107-14795.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
Raneechu/miningsmall	Raneechu	2024-05-20T04:31:31Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2024-05-20T04:31:27Z	--- license: llama2 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Llama-2-7b-hf model-index: - name: miningsmall 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. --> # miningsmall This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-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-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1 ### Training results ### Framework versions - Transformers 4.40.1 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.19.1 ## Training procedure ### Framework versions - PEFT 0.6.2
Raneechu/mininglit	Raneechu	2024-05-20T04:30:01Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2024-05-20T04:29:58Z	--- license: llama2 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Llama-2-7b-hf model-index: - name: mininglit 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. --> # mininglit This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-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-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1 ### Training results ### Framework versions - Transformers 4.40.1 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.19.1 ## Training procedure ### Framework versions - PEFT 0.6.2
ghost613/llama8_on_korean_summary	ghost613	2024-05-20T04:27:02Z	3	1	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:beomi/Llama-3-Open-Ko-8B-Instruct-preview", "base_model:adapter:beomi/Llama-3-Open-Ko-8B-Instruct-preview", "license:other", "region:us" ]	null	2024-05-16T19:22:39Z	--- license: other library_name: peft tags: - generated_from_trainer base_model: beomi/Llama-3-Open-Ko-8B-Instruct-preview model-index: - name: llama8_on_korean_summary 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. --> # llama8_on_korean_summary This model is a fine-tuned version of [beomi/Llama-3-Open-Ko-8B-Instruct-preview](https://huggingface.co/beomi/Llama-3-Open-Ko-8B-Instruct-preview) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8536 ## 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 5 - total_train_batch_size: 10 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - training_steps: 760 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.7767 \| 0.26 \| 20 \| 1.5096 \| \| 1.2382 \| 0.53 \| 40 \| 1.0459 \| \| 0.9915 \| 0.79 \| 60 \| 0.9451 \| \| 0.9126 \| 1.05 \| 80 \| 0.8893 \| \| 0.8501 \| 1.32 \| 100 \| 0.8515 \| \| 0.8113 \| 1.58 \| 120 \| 0.8192 \| \| 0.8019 \| 1.84 \| 140 \| 0.7939 \| \| 0.7239 \| 2.11 \| 160 \| 0.7795 \| \| 0.6621 \| 2.37 \| 180 \| 0.7594 \| \| 0.6457 \| 2.63 \| 200 \| 0.7433 \| \| 0.6417 \| 2.89 \| 220 \| 0.7281 \| \| 0.5929 \| 3.16 \| 240 \| 0.7305 \| \| 0.5245 \| 3.42 \| 260 \| 0.7242 \| \| 0.5291 \| 3.68 \| 280 \| 0.7154 \| \| 0.528 \| 3.95 \| 300 \| 0.7109 \| \| 0.4696 \| 4.21 \| 320 \| 0.7257 \| \| 0.4474 \| 4.47 \| 340 \| 0.7251 \| \| 0.4572 \| 4.74 \| 360 \| 0.7252 \| \| 0.4391 \| 5.0 \| 380 \| 0.7202 \| \| 0.3794 \| 5.26 \| 400 \| 0.7462 \| \| 0.3771 \| 5.53 \| 420 \| 0.7568 \| \| 0.3754 \| 5.79 \| 440 \| 0.7453 \| \| 0.3739 \| 6.05 \| 460 \| 0.7597 \| \| 0.3179 \| 6.32 \| 480 \| 0.7803 \| \| 0.3328 \| 6.58 \| 500 \| 0.7699 \| \| 0.3259 \| 6.84 \| 520 \| 0.7710 \| \| 0.3014 \| 7.11 \| 540 \| 0.8083 \| \| 0.2759 \| 7.37 \| 560 \| 0.8017 \| \| 0.2758 \| 7.63 \| 580 \| 0.7954 \| \| 0.2798 \| 7.89 \| 600 \| 0.8003 \| \| 0.2545 \| 8.16 \| 620 \| 0.8325 \| \| 0.2451 \| 8.42 \| 640 \| 0.8282 \| \| 0.2355 \| 8.68 \| 660 \| 0.8318 \| \| 0.2382 \| 8.95 \| 680 \| 0.8300 \| \| 0.2256 \| 9.21 \| 700 \| 0.8544 \| \| 0.212 \| 9.47 \| 720 \| 0.8532 \| \| 0.2108 \| 9.74 \| 740 \| 0.8529 \| \| 0.2125 \| 10.0 \| 760 \| 0.8536 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.38.2 - Pytorch 2.1.2+cu121 - Datasets 2.17.0 - Tokenizers 0.15.0
ukung/Nusantara-4b-Indo-Chat-GGUF	ukung	2024-05-20T04:26:42Z	97	0	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-20T02:43:48Z	--- license: apache-2.0 ---
ukung/Nusantara-2.7b-Indo-Chat-v0.2-GGUF	ukung	2024-05-20T04:14:23Z	4	0	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-20T03:38:55Z	--- license: apache-2.0 ---
akiseid/AmharicNewsNonCleanedNonWeighted	akiseid	2024-05-20T04:13:43Z	118	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-20T03:12:02Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: AmharicNewsNonCleanedNonWeighted 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. --> # AmharicNewsNonCleanedNonWeighted This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1726 - Accuracy: 0.9564 - Precision: 0.9563 - Recall: 0.9564 - F1: 0.9564 ## 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 - lr_scheduler_warmup_steps: 500 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.2237 \| 1.0 \| 945 \| 0.2308 \| 0.9054 \| 0.9145 \| 0.9054 \| 0.9031 \| \| 0.3067 \| 2.0 \| 1890 \| 0.1760 \| 0.9384 \| 0.9388 \| 0.9384 \| 0.9379 \| \| 0.143 \| 3.0 \| 2835 \| 0.1510 \| 0.9480 \| 0.9486 \| 0.9480 \| 0.9482 \| \| 0.1306 \| 4.0 \| 3780 \| 0.1550 \| 0.9544 \| 0.9547 \| 0.9544 \| 0.9544 \| \| 0.0825 \| 5.0 \| 4725 \| 0.1726 \| 0.9564 \| 0.9563 \| 0.9564 \| 0.9564 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1
abc88767/4sc102	abc88767	2024-05-20T04:13:16Z	121	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T04:11:34Z	--- library_name: transformers tags: [] --- # 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Raneechu/mininglarge	Raneechu	2024-05-20T04:11:28Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2024-05-20T04:11:24Z	--- license: llama2 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Llama-2-7b-hf model-index: - name: mininglarge 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. --> # mininglarge This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-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-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1 ### Training results ### Framework versions - Transformers 4.40.1 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.19.1 ## Training procedure ### Framework versions - PEFT 0.6.2
abc88767/22c102	abc88767	2024-05-20T04:09:35Z	98	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T04:08:02Z	--- library_name: transformers tags: [] --- # 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(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]
ukung/Nusantara-7b-Indo-Chat-GGUF	ukung	2024-05-20T04:08:48Z	6	0	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-20T02:42:44Z	--- license: apache-2.0 ---
abc88767/9sc102	abc88767	2024-05-20T04:04:52Z	97	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T04:03:19Z	--- library_name: transformers tags: [] --- # 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - 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]
abc88767/8sc102	abc88767	2024-05-20T04:01:24Z	93	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T03:59:47Z	--- library_name: transformers tags: [] --- # 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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tsavage68/MedQA_L3_600steps_1e7rate_01beta_CSFTDPO	tsavage68	2024-05-20T03:58:43Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T02:46:28Z	--- license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - trl - dpo - generated_from_trainer model-index: - name: MedQA_L3_600steps_1e7rate_01beta_CSFTDPO 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. --> # MedQA_L3_600steps_1e7rate_01beta_CSFTDPO This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6692 - Rewards/chosen: 0.0482 - Rewards/rejected: -0.0053 - Rewards/accuracies: 0.6681 - Rewards/margins: 0.0535 - Logps/rejected: -21.3695 - Logps/chosen: -17.7404 - Logits/rejected: -0.9398 - Logits/chosen: -0.9393 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-07 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 600 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6951 \| 0.0489 \| 50 \| 0.6935 \| 0.0003 \| 0.0009 \| 0.4901 \| -0.0006 \| -21.3079 \| -18.2196 \| -0.9258 \| -0.9253 \| \| 0.6892 \| 0.0977 \| 100 \| 0.6881 \| 0.0374 \| 0.0268 \| 0.6044 \| 0.0106 \| -21.0482 \| -17.8488 \| -0.9281 \| -0.9276 \| \| 0.6801 \| 0.1466 \| 150 \| 0.6794 \| 0.0588 \| 0.0292 \| 0.6418 \| 0.0296 \| -21.0241 \| -17.6343 \| -0.9314 \| -0.9309 \| \| 0.6807 \| 0.1954 \| 200 \| 0.6767 \| 0.0584 \| 0.0227 \| 0.6549 \| 0.0358 \| -21.0897 \| -17.6383 \| -0.9345 \| -0.9339 \| \| 0.6829 \| 0.2443 \| 250 \| 0.6726 \| 0.0560 \| 0.0106 \| 0.6571 \| 0.0454 \| -21.2109 \| -17.6631 \| -0.9367 \| -0.9362 \| \| 0.6656 \| 0.2931 \| 300 \| 0.6715 \| 0.0540 \| 0.0059 \| 0.6505 \| 0.0481 \| -21.2575 \| -17.6830 \| -0.9382 \| -0.9376 \| \| 0.6955 \| 0.3420 \| 350 \| 0.6697 \| 0.0524 \| 0.0002 \| 0.6571 \| 0.0522 \| -21.3145 \| -17.6986 \| -0.9384 \| -0.9378 \| \| 0.6605 \| 0.3908 \| 400 \| 0.6697 \| 0.0493 \| -0.0031 \| 0.6505 \| 0.0524 \| -21.3476 \| -17.7294 \| -0.9393 \| -0.9388 \| \| 0.6718 \| 0.4397 \| 450 \| 0.6689 \| 0.0495 \| -0.0047 \| 0.6527 \| 0.0541 \| -21.3631 \| -17.7279 \| -0.9396 \| -0.9390 \| \| 0.6734 \| 0.4885 \| 500 \| 0.6687 \| 0.0486 \| -0.0059 \| 0.6505 \| 0.0545 \| -21.3751 \| -17.7362 \| -0.9397 \| -0.9392 \| \| 0.6525 \| 0.5374 \| 550 \| 0.6691 \| 0.0482 \| -0.0056 \| 0.6615 \| 0.0537 \| -21.3720 \| -17.7410 \| -0.9398 \| -0.9393 \| \| 0.6637 \| 0.5862 \| 600 \| 0.6692 \| 0.0482 \| -0.0053 \| 0.6681 \| 0.0535 \| -21.3695 \| -17.7404 \| -0.9398 \| -0.9393 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.0.0+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1
abc88767/2c102	abc88767	2024-05-20T03:57:26Z	92	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T03:55:52Z	--- library_name: transformers tags: [] --- # 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
OjciecTadeusz/dreamshaper8	OjciecTadeusz	2024-05-20T03:54:39Z	4	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:Lykon/DreamShaper", "base_model:adapter:Lykon/DreamShaper", "region:us" ]	text-to-image	2024-05-20T03:50:52Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: photo of landscape, high quality output: url: images/image (35).png base_model: Lykon/DreamShaper instance_prompt: null --- # dreamshaper8 <Gallery /> ## Download model Weights for this model are available in Safetensors format. [Download](/JasonJwba/dreamshaper8/tree/main) them in the Files & versions tab.
afiqlol/Malay-Sentiment	afiqlol	2024-05-20T03:52:40Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:citizenlab/twitter-xlm-roberta-base-sentiment-finetunned", "base_model:finetune:citizenlab/twitter-xlm-roberta-base-sentiment-finetunned", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-20T02:11:43Z	--- base_model: citizenlab/twitter-xlm-roberta-base-sentiment-finetunned tags: - generated_from_trainer metrics: - accuracy model-index: - name: Malay-Sentiment 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. --> # Malay-Sentiment This model is a fine-tuned version of [citizenlab/twitter-xlm-roberta-base-sentiment-finetunned](https://huggingface.co/citizenlab/twitter-xlm-roberta-base-sentiment-finetunned) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5757 - Accuracy: 0.7578 ## 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: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7066 \| 1.0 \| 723 \| 0.5757 \| 0.7578 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cpu - Datasets 2.14.5 - Tokenizers 0.15.0
PQlet/textual-inversion-v2-ablation-vec3-img1	PQlet	2024-05-20T03:48:22Z	3	0	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers-training", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-05-20T01:50:18Z	--- license: creativeml-openrail-m library_name: diffusers tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - diffusers-training - lora base_model: runwayml/stable-diffusion-v1-5 inference: true --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # Textual Inversion training - PQlet/textual-inversion-v2-ablation-vec3-img1 The generated images are below. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
RichardErkhov/lodrick-the-lafted_-_Grafted-Hermetic-Platypus-C-2x7B-4bits	RichardErkhov	2024-05-20T03:39:17Z	78	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-20T03:34:05Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Grafted-Hermetic-Platypus-C-2x7B - bnb 4bits - Model creator: https://huggingface.co/lodrick-the-lafted/ - Original model: https://huggingface.co/lodrick-the-lafted/Grafted-Hermetic-Platypus-C-2x7B/ Original model description: --- license: apache-2.0 datasets: - lodrick-the-lafted/Hermes-217K - garage-bAInd/Open-Platypus - jondurbin/airoboros-3.2 model-index: - name: Grafted-Hermetic-Platypus-C-2x7B results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 58.96 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lodrick-the-lafted/Grafted-Hermetic-Platypus-C-2x7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 82.77 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lodrick-the-lafted/Grafted-Hermetic-Platypus-C-2x7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 62.08 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lodrick-the-lafted/Grafted-Hermetic-Platypus-C-2x7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 60.87 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lodrick-the-lafted/Grafted-Hermetic-Platypus-C-2x7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 77.74 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lodrick-the-lafted/Grafted-Hermetic-Platypus-C-2x7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 43.9 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lodrick-the-lafted/Grafted-Hermetic-Platypus-C-2x7B name: Open LLM Leaderboard --- <img src=https://huggingface.co/lodrick-the-lafted/Grafted-Hermetic-Platypus-C-2x7B/resolve/main/ghp.png> # Grafted-Hermetic-Platypus-C-2x7B MoE merge of - [Platyboros-Instruct-7B](https://huggingface.co/lodrick-the-lafted/Platyboros-Instruct-7B) - [Hermes-Instruct-7B-217K](https://huggingface.co/lodrick-the-lafted/Hermes-Instruct-7B-217K) <br /> <br /> # Prompt Format Both the default Mistral-Instruct tags and Alpaca are fine, so either: ``` <s>[INST] {sys_prompt} {instruction} [/INST] ``` or ``` {sys_prompt} ### Instruction: {instruction} ### Response: ``` The tokenizer default is Alpaca this time around. <br /> <br /> # Usage ```python from transformers import AutoTokenizer import transformers import torch model = "lodrick-the-lafted/Grafted-Hermetic-Platypus-A-2x7B" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, model_kwargs={"torch_dtype": torch.bfloat16}, ) messages = [{"role": "user", "content": "Give me a cooking recipe for an peach pie."}] prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_p=0.95) print(outputs[0]["generated_text"]) ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_lodrick-the-lafted__Grafted-Hermetic-Platypus-C-2x7B) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|64.39\| \|AI2 Reasoning Challenge (25-Shot)\|58.96\| \|HellaSwag (10-Shot) \|82.77\| \|MMLU (5-Shot) \|62.08\| \|TruthfulQA (0-shot) \|60.87\| \|Winogrande (5-shot) \|77.74\| \|GSM8k (5-shot) \|43.90\|
RichardErkhov/dreamgen_-_WizardLM-2-7B-gguf	RichardErkhov	2024-05-20T03:24:51Z	32	0	null	[ "gguf", "arxiv:2304.12244", "arxiv:2306.08568", "arxiv:2308.09583", "endpoints_compatible", "region:us" ]	null	2024-05-20T02:05:20Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) WizardLM-2-7B - GGUF - Model creator: https://huggingface.co/dreamgen/ - Original model: https://huggingface.co/dreamgen/WizardLM-2-7B/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [WizardLM-2-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/dreamgen_-_WizardLM-2-7B-gguf/blob/main/WizardLM-2-7B.Q2_K.gguf) \| Q2_K \| 2.53GB \| \| [WizardLM-2-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/dreamgen_-_WizardLM-2-7B-gguf/blob/main/WizardLM-2-7B.IQ3_XS.gguf) \| IQ3_XS \| 2.81GB \| \| [WizardLM-2-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/dreamgen_-_WizardLM-2-7B-gguf/blob/main/WizardLM-2-7B.IQ3_S.gguf) \| IQ3_S \| 2.96GB \| \| [WizardLM-2-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/dreamgen_-_WizardLM-2-7B-gguf/blob/main/WizardLM-2-7B.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [WizardLM-2-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/dreamgen_-_WizardLM-2-7B-gguf/blob/main/WizardLM-2-7B.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [WizardLM-2-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/dreamgen_-_WizardLM-2-7B-gguf/blob/main/WizardLM-2-7B.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [WizardLM-2-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/dreamgen_-_WizardLM-2-7B-gguf/blob/main/WizardLM-2-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [WizardLM-2-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/dreamgen_-_WizardLM-2-7B-gguf/blob/main/WizardLM-2-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [WizardLM-2-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/dreamgen_-_WizardLM-2-7B-gguf/blob/main/WizardLM-2-7B.IQ4_XS.gguf) \| IQ4_XS \| 3.67GB \| \| [WizardLM-2-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/dreamgen_-_WizardLM-2-7B-gguf/blob/main/WizardLM-2-7B.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [WizardLM-2-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/dreamgen_-_WizardLM-2-7B-gguf/blob/main/WizardLM-2-7B.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [WizardLM-2-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/dreamgen_-_WizardLM-2-7B-gguf/blob/main/WizardLM-2-7B.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [WizardLM-2-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/dreamgen_-_WizardLM-2-7B-gguf/blob/main/WizardLM-2-7B.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [WizardLM-2-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/dreamgen_-_WizardLM-2-7B-gguf/blob/main/WizardLM-2-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [WizardLM-2-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/dreamgen_-_WizardLM-2-7B-gguf/blob/main/WizardLM-2-7B.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [WizardLM-2-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/dreamgen_-_WizardLM-2-7B-gguf/blob/main/WizardLM-2-7B.Q5_0.gguf) \| Q5_0 \| 4.65GB \| \| [WizardLM-2-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/dreamgen_-_WizardLM-2-7B-gguf/blob/main/WizardLM-2-7B.Q5_K_S.gguf) \| Q5_K_S \| 4.65GB \| \| [WizardLM-2-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/dreamgen_-_WizardLM-2-7B-gguf/blob/main/WizardLM-2-7B.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [WizardLM-2-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/dreamgen_-_WizardLM-2-7B-gguf/blob/main/WizardLM-2-7B.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [WizardLM-2-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/dreamgen_-_WizardLM-2-7B-gguf/blob/main/WizardLM-2-7B.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [WizardLM-2-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/dreamgen_-_WizardLM-2-7B-gguf/blob/main/WizardLM-2-7B.Q6_K.gguf) \| Q6_K \| 5.53GB \| \| [WizardLM-2-7B.Q8_0.gguf](https://huggingface.co/RichardErkhov/dreamgen_-_WizardLM-2-7B-gguf/blob/main/WizardLM-2-7B.Q8_0.gguf) \| Q8_0 \| 7.17GB \| Original model description: --- license: apache-2.0 --- <p style="font-size:20px;" align="center"> 🏠 <a href="https://wizardlm.github.io/WizardLM2" target="_blank">WizardLM-2 Release Blog</a> </p> <p align="center"> 🤗 <a href="https://huggingface.co/collections/microsoft/wizardlm-2-661d403f71e6c8257dbd598a" target="_blank">HF Repo</a> •🐱 <a href="https://github.com/victorsungo/WizardLM/tree/main/WizardLM-2" target="_blank">Github Repo</a> • 🐦 <a href="https://twitter.com/WizardLM_AI" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a> • 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> • 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a> <br> </p> <p align="center"> 👋 Join our <a href="https://discord.gg/VZjjHtWrKs" target="_blank">Discord</a> </p> ## News 🔥🔥🔥 [2024/04/15] We introduce and opensource WizardLM-2, our next generation state-of-the-art large language models, which have improved performance on complex chat, multilingual, reasoning and agent. New family includes three cutting-edge models: WizardLM-2 8x22B, WizardLM-2 70B, and WizardLM-2 7B. - WizardLM-2 8x22B is our most advanced model, demonstrates highly competitive performance compared to those leading proprietary works and consistently outperforms all the existing state-of-the-art opensource models. - WizardLM-2 70B reaches top-tier reasoning capabilities and is the first choice in the same size. - WizardLM-2 7B is the fastest and achieves comparable performance with existing 10x larger opensource leading models. For more details of WizardLM-2 please read our [release blog post](https://wizardlm.github.io/WizardLM2) and upcoming paper. ## Model Details * Model name: WizardLM-2 7B * Developed by: WizardLM@Microsoft AI * Base model: [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) * Parameters: 7B * Language(s): Multilingual * Blog: [Introducing WizardLM-2](https://wizardlm.github.io/WizardLM2) * Repository: [https://github.com/nlpxucan/WizardLM](https://github.com/nlpxucan/WizardLM) * Paper: WizardLM-2 (Upcoming) * License: Apache2.0 ## Model Capacities MT-Bench We also adopt the automatic MT-Bench evaluation framework based on GPT-4 proposed by lmsys to assess the performance of models. The WizardLM-2 8x22B even demonstrates highly competitive performance compared to the most advanced proprietary models. Meanwhile, WizardLM-2 7B and WizardLM-2 70B are all the top-performing models among the other leading baselines at 7B to 70B model scales. <p align="center" width="100%"> <a ><img src="https://raw.githubusercontent.com/WizardLM/WizardLM2/main/static/images/mtbench.png" alt="MTBench" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a> </p> Human Preferences Evaluation We carefully collected a complex and challenging set consisting of real-world instructions, which includes main requirements of humanity, such as writing, coding, math, reasoning, agent, and multilingual. We report the win:loss rate without tie: - WizardLM-2 8x22B is just slightly falling behind GPT-4-1106-preview, and significantly stronger than Command R Plus and GPT4-0314. - WizardLM-2 70B is better than GPT4-0613, Mistral-Large, and Qwen1.5-72B-Chat. - WizardLM-2 7B is comparable with Qwen1.5-32B-Chat, and surpasses Qwen1.5-14B-Chat and Starling-LM-7B-beta. <p align="center" width="100%"> <a ><img src="https://raw.githubusercontent.com/WizardLM/WizardLM2/main/static/images/winall.png" alt="Win" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a> </p> ## Method Overview We built a fully AI powered synthetic training system to train WizardLM-2 models, please refer to our [blog](https://wizardlm.github.io/WizardLM2) for more details of this system. <p align="center" width="100%"> <a ><img src="https://raw.githubusercontent.com/WizardLM/WizardLM2/main/static/images/exp_1.png" alt="Method" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a> </p> ## Usage ❗<b>Note for model system prompts usage:</b> <b>WizardLM-2</b> adopts the prompt format from <b>Vicuna</b> and supports multi-turn conversation. The prompt should be as following: ``` A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Hi ASSISTANT: Hello.</s> USER: Who are you? ASSISTANT: I am WizardLM.</s>...... ``` <b> Inference WizardLM-2 Demo Script</b> We provide a WizardLM-2 inference demo [code](https://github.com/nlpxucan/WizardLM/tree/main/demo) on our github.
AleRothermel/mi-1.2-model	AleRothermel	2024-05-20T03:24:20Z	113	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-20T02:16:21Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: bert-base-cased metrics: - accuracy model-index: - name: mi-1.2-model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mi-1.2-model This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7264 - Accuracy: 0.58 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 1.6501 \| 0.04 \| 10 \| 1.6095 \| 0.235 \| \| 1.655 \| 0.08 \| 20 \| 1.5876 \| 0.23 \| \| 1.6465 \| 0.12 \| 30 \| 1.5874 \| 0.305 \| \| 1.6577 \| 0.16 \| 40 \| 1.6006 \| 0.2325 \| \| 1.5666 \| 0.2 \| 50 \| 1.5611 \| 0.245 \| \| 1.5667 \| 0.24 \| 60 \| 1.4245 \| 0.44 \| \| 1.4837 \| 0.28 \| 70 \| 1.2916 \| 0.4175 \| \| 1.2603 \| 0.32 \| 80 \| 1.3869 \| 0.3925 \| \| 1.2865 \| 0.36 \| 90 \| 1.4055 \| 0.3475 \| \| 1.4037 \| 0.4 \| 100 \| 1.3934 \| 0.32 \| \| 1.3201 \| 0.44 \| 110 \| 1.4511 \| 0.4125 \| \| 1.3977 \| 0.48 \| 120 \| 1.2251 \| 0.44 \| \| 1.1444 \| 0.52 \| 130 \| 1.1517 \| 0.5175 \| \| 1.1627 \| 0.56 \| 140 \| 1.1211 \| 0.5225 \| \| 1.21 \| 0.6 \| 150 \| 1.1336 \| 0.53 \| \| 1.2211 \| 0.64 \| 160 \| 1.4186 \| 0.4 \| \| 1.2985 \| 0.68 \| 170 \| 1.1251 \| 0.4725 \| \| 1.1856 \| 0.72 \| 180 \| 1.1138 \| 0.5075 \| \| 1.1027 \| 0.76 \| 190 \| 1.0810 \| 0.5075 \| \| 1.0998 \| 0.8 \| 200 \| 1.1034 \| 0.5225 \| \| 1.2546 \| 0.84 \| 210 \| 1.1205 \| 0.4925 \| \| 1.0265 \| 0.88 \| 220 \| 1.1996 \| 0.4925 \| \| 1.0898 \| 0.92 \| 230 \| 1.1002 \| 0.515 \| \| 1.19 \| 0.96 \| 240 \| 1.0805 \| 0.4925 \| \| 1.1456 \| 1.0 \| 250 \| 1.0509 \| 0.525 \| \| 0.9265 \| 1.04 \| 260 \| 1.1092 \| 0.51 \| \| 0.8554 \| 1.08 \| 270 \| 1.0098 \| 0.5325 \| \| 0.8695 \| 1.12 \| 280 \| 1.0991 \| 0.4975 \| \| 0.8505 \| 1.16 \| 290 \| 1.0827 \| 0.5075 \| \| 0.8892 \| 1.2 \| 300 \| 1.1195 \| 0.52 \| \| 0.8982 \| 1.24 \| 310 \| 1.0691 \| 0.51 \| \| 0.9301 \| 1.28 \| 320 \| 1.0236 \| 0.545 \| \| 1.052 \| 1.32 \| 330 \| 1.0296 \| 0.535 \| \| 0.8072 \| 1.3600 \| 340 \| 1.0227 \| 0.55 \| \| 0.8822 \| 1.4 \| 350 \| 1.0494 \| 0.53 \| \| 1.1561 \| 1.44 \| 360 \| 1.2036 \| 0.4925 \| \| 0.9526 \| 1.48 \| 370 \| 1.0443 \| 0.56 \| \| 0.9916 \| 1.52 \| 380 \| 1.0378 \| 0.555 \| \| 1.0388 \| 1.56 \| 390 \| 1.0920 \| 0.5375 \| \| 0.9326 \| 1.6 \| 400 \| 1.0510 \| 0.5375 \| \| 0.8453 \| 1.6400 \| 410 \| 1.1247 \| 0.5025 \| \| 1.03 \| 1.6800 \| 420 \| 1.0281 \| 0.565 \| \| 0.971 \| 1.72 \| 430 \| 1.0322 \| 0.54 \| \| 0.941 \| 1.76 \| 440 \| 0.9858 \| 0.565 \| \| 0.8615 \| 1.8 \| 450 \| 0.9793 \| 0.555 \| \| 0.8815 \| 1.8400 \| 460 \| 0.9778 \| 0.56 \| \| 0.7658 \| 1.88 \| 470 \| 0.9760 \| 0.56 \| \| 1.0073 \| 1.92 \| 480 \| 1.0747 \| 0.5175 \| \| 0.8929 \| 1.96 \| 490 \| 0.9910 \| 0.565 \| \| 0.9089 \| 2.0 \| 500 \| 1.0512 \| 0.535 \| \| 0.5102 \| 2.04 \| 510 \| 1.0545 \| 0.555 \| \| 0.6748 \| 2.08 \| 520 \| 1.1621 \| 0.5175 \| \| 0.5222 \| 2.12 \| 530 \| 1.1038 \| 0.5575 \| \| 0.7978 \| 2.16 \| 540 \| 1.1728 \| 0.53 \| \| 0.6749 \| 2.2 \| 550 \| 1.1029 \| 0.5475 \| \| 0.6621 \| 2.24 \| 560 \| 1.0977 \| 0.5425 \| \| 0.6808 \| 2.2800 \| 570 \| 1.1776 \| 0.545 \| \| 0.5728 \| 2.32 \| 580 \| 1.1747 \| 0.5325 \| \| 0.75 \| 2.36 \| 590 \| 1.1707 \| 0.5275 \| \| 0.6622 \| 2.4 \| 600 \| 1.1082 \| 0.555 \| \| 0.6008 \| 2.44 \| 610 \| 1.0922 \| 0.57 \| \| 0.6491 \| 2.48 \| 620 \| 1.1375 \| 0.545 \| \| 0.5876 \| 2.52 \| 630 \| 1.0614 \| 0.5675 \| \| 0.5326 \| 2.56 \| 640 \| 1.0460 \| 0.58 \| \| 0.4901 \| 2.6 \| 650 \| 1.0864 \| 0.58 \| \| 0.6151 \| 2.64 \| 660 \| 1.1919 \| 0.58 \| \| 0.6478 \| 2.68 \| 670 \| 1.1301 \| 0.5575 \| \| 0.4841 \| 2.7200 \| 680 \| 1.1451 \| 0.58 \| \| 0.6365 \| 2.76 \| 690 \| 1.0701 \| 0.575 \| \| 0.5284 \| 2.8 \| 700 \| 1.1674 \| 0.5325 \| \| 0.6506 \| 2.84 \| 710 \| 1.1016 \| 0.55 \| \| 0.6446 \| 2.88 \| 720 \| 1.1340 \| 0.57 \| \| 0.5193 \| 2.92 \| 730 \| 1.1692 \| 0.525 \| \| 0.6129 \| 2.96 \| 740 \| 1.1717 \| 0.5325 \| \| 0.6013 \| 3.0 \| 750 \| 1.1374 \| 0.55 \| \| 0.3392 \| 3.04 \| 760 \| 1.2702 \| 0.515 \| \| 0.3188 \| 3.08 \| 770 \| 1.2584 \| 0.515 \| \| 0.3272 \| 3.12 \| 780 \| 1.3520 \| 0.5225 \| \| 0.341 \| 3.16 \| 790 \| 1.2752 \| 0.5575 \| \| 0.3826 \| 3.2 \| 800 \| 1.3126 \| 0.55 \| \| 0.3062 \| 3.24 \| 810 \| 1.4909 \| 0.52 \| \| 0.2657 \| 3.2800 \| 820 \| 1.3804 \| 0.5575 \| \| 0.4609 \| 3.32 \| 830 \| 1.3712 \| 0.5625 \| \| 0.3388 \| 3.36 \| 840 \| 1.4701 \| 0.5275 \| \| 0.3007 \| 3.4 \| 850 \| 1.3373 \| 0.57 \| \| 0.2732 \| 3.44 \| 860 \| 1.3699 \| 0.575 \| \| 0.4551 \| 3.48 \| 870 \| 1.3874 \| 0.555 \| \| 0.3048 \| 3.52 \| 880 \| 1.4913 \| 0.5625 \| \| 0.4104 \| 3.56 \| 890 \| 1.4586 \| 0.565 \| \| 0.2633 \| 3.6 \| 900 \| 1.4353 \| 0.565 \| \| 0.4435 \| 3.64 \| 910 \| 1.5246 \| 0.555 \| \| 0.282 \| 3.68 \| 920 \| 1.6866 \| 0.5275 \| \| 0.5918 \| 3.7200 \| 930 \| 1.5193 \| 0.5525 \| \| 0.315 \| 3.76 \| 940 \| 1.4276 \| 0.565 \| \| 0.1276 \| 3.8 \| 950 \| 1.4411 \| 0.5625 \| \| 0.3389 \| 3.84 \| 960 \| 1.5420 \| 0.5625 \| \| 0.3248 \| 3.88 \| 970 \| 1.4492 \| 0.575 \| \| 0.3051 \| 3.92 \| 980 \| 1.4321 \| 0.5925 \| \| 0.3363 \| 3.96 \| 990 \| 1.4374 \| 0.5825 \| \| 0.4602 \| 4.0 \| 1000 \| 1.4581 \| 0.57 \| \| 0.1582 \| 4.04 \| 1010 \| 1.4434 \| 0.5675 \| \| 0.2344 \| 4.08 \| 1020 \| 1.4551 \| 0.5975 \| \| 0.2646 \| 4.12 \| 1030 \| 1.4999 \| 0.59 \| \| 0.1948 \| 4.16 \| 1040 \| 1.5550 \| 0.5625 \| \| 0.3058 \| 4.2 \| 1050 \| 1.5955 \| 0.5775 \| \| 0.1569 \| 4.24 \| 1060 \| 1.5721 \| 0.575 \| \| 0.1777 \| 4.28 \| 1070 \| 1.6241 \| 0.56 \| \| 0.1256 \| 4.32 \| 1080 \| 1.5711 \| 0.575 \| \| 0.2467 \| 4.36 \| 1090 \| 1.5735 \| 0.59 \| \| 0.1964 \| 4.4 \| 1100 \| 1.5924 \| 0.585 \| \| 0.0578 \| 4.44 \| 1110 \| 1.6353 \| 0.585 \| \| 0.1358 \| 4.48 \| 1120 \| 1.6710 \| 0.5775 \| \| 0.174 \| 4.52 \| 1130 \| 1.6733 \| 0.5725 \| \| 0.2022 \| 4.5600 \| 1140 \| 1.6658 \| 0.585 \| \| 0.028 \| 4.6 \| 1150 \| 1.6708 \| 0.585 \| \| 0.1222 \| 4.64 \| 1160 \| 1.6989 \| 0.5875 \| \| 0.2295 \| 4.68 \| 1170 \| 1.7131 \| 0.5825 \| \| 0.374 \| 4.72 \| 1180 \| 1.7197 \| 0.5725 \| \| 0.1342 \| 4.76 \| 1190 \| 1.7237 \| 0.575 \| \| 0.079 \| 4.8 \| 1200 \| 1.7267 \| 0.58 \| \| 0.154 \| 4.84 \| 1210 \| 1.7204 \| 0.585 \| \| 0.0403 \| 4.88 \| 1220 \| 1.7183 \| 0.58 \| \| 0.1964 \| 4.92 \| 1230 \| 1.7253 \| 0.5775 \| \| 0.1297 \| 4.96 \| 1240 \| 1.7252 \| 0.5775 \| \| 0.0834 \| 5.0 \| 1250 \| 1.7264 \| 0.58 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Noursene/whisper-small-2000	Noursene	2024-05-20T03:18:45Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-20T02:40:25Z	--- library_name: transformers tags: [] --- # 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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SiyuK/gpt2-reuters-tokenizer	SiyuK	2024-05-20T03:17:28Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-20T03:17:27Z	--- library_name: transformers tags: [] --- # 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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theglassofwater/mistral_pretraining_2.2ksteps_16batch	theglassofwater	2024-05-20T03:10:58Z	188	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T03:10:49Z	--- library_name: transformers tags: [] --- # 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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danhergir/platzi	danhergir	2024-05-20T03:03:13Z	194	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:AI-Lab-Makerere/beans", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-03-20T04:26:50Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - AI-Lab-Makerere/beans metrics: - accuracy base_model: google/vit-base-patch16-224-in21k model-index: - name: platzi results: - task: type: image-classification name: Image Classification dataset: name: beans type: beans config: default split: validation args: default metrics: - type: accuracy value: 0.9924812030075187 name: Accuracy --- <!-- 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. --> # platzi This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0317 - Accuracy: 0.9925 ## 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: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.136 \| 3.85 \| 500 \| 0.0317 \| 0.9925 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.2
quydau/dummy	quydau	2024-05-20T03:03:10Z	108	0	transformers	[ "transformers", "safetensors", "camembert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-20T02:30:02Z	--- library_name: transformers tags: [] --- # 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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DucPhanBa/Vietnamese_Llama2	DucPhanBa	2024-05-20T03:01:21Z	0	0	peft	[ "peft", "region:us" ]	null	2024-05-20T02:57:47Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0
MinhViet/bartpho-quesntion-without-content	MinhViet	2024-05-20T02:56:16Z	178	0	transformers	[ "transformers", "safetensors", "mbart", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-18T09:24:40Z	--- library_name: transformers tags: [] --- # 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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coolguyleo/harry-20	coolguyleo	2024-05-20T02:32:30Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:microsoft/Llama2-7b-WhoIsHarryPotter", "base_model:adapter:microsoft/Llama2-7b-WhoIsHarryPotter", "license:other", "region:us" ]	null	2024-05-20T02:32:12Z	--- license: other library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/Llama2-7b-WhoIsHarryPotter model-index: - name: results 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. --> # results This model is a fine-tuned version of [microsoft/Llama2-7b-WhoIsHarryPotter](https://huggingface.co/microsoft/Llama2-7b-WhoIsHarryPotter) 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 20 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1
BillShih/Getac_F110	BillShih	2024-05-20T02:32:17Z	0	0	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-20T02:24:26Z	--- license: apache-2.0 ---
ukung/Nusantara-0.8b-Indo-Chat-GGUF	ukung	2024-05-20T02:26:23Z	7	0	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-20T02:12:22Z	--- license: apache-2.0 ---
animaRegem/gemma-2b-malayalam-model-vllm-4bit	animaRegem	2024-05-20T02:24:53Z	77	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/gemma-2b-bnb-4bit", "base_model:quantized:unsloth/gemma-2b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-07T18:25:16Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl - sft base_model: unsloth/gemma-2b-bnb-4bit --- # Uploaded model - Developed by: animaRegem - License: apache-2.0 - Finetuned from model : unsloth/gemma-2b-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
appvoid/palmer-002-32k	appvoid	2024-05-20T02:24:07Z	139	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-20T03:28:35Z	--- license: apache-2.0 language: - en pipeline_tag: text-generation --- ![palmer](https://huggingface.co/appvoid/palmer-001/resolve/main/new-logo.jpg) # palmer ### a better base model This model is palmer-002-2401 scaled to 32k by merging and fine-tuning with TinyLlama-1.1B-32k-Instruct by Doctor-Shotgun ### evaluation 🧪 note that this is a zero-shot setting as opposite to open llm leaderboard's few-shot evals ``` model ARC-C OBQA HellaSwag PIQA Winogrande Average tinyllama \| 0.3029 \| 0.3600 \| 0.5935 \| 0.7329 \| 0.5959 \| 0.5170 \| palmer-002-2401 \| 0.3294 \| 0.3700 \| 0.5950 \| 0.7399 \| 0.5896 \| 0.5247 \| palmer-002-32k \| 0.3268 \| 0.3780 \| 0.5785 \| 0.7492 \| 0.6251 \| 0.5315 \| (this) babbage-002 \| 0.3285 \| 0.3620 \| 0.6380 \| 0.7606 \| 0.6085 \| 0.5395 \| ``` This model's performance is close to openai's one while being capable of using 2x the context size. ### prompt 📝 ``` no prompt 🚀 ``` <a href="https://ko-fi.com/appvoid" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 48px !important;width: 180px !important; filter: invert(70%);" ></a>
animaRegem/gemma-2b-malayalam-model-vllm-16bit	animaRegem	2024-05-20T02:23:15Z	7	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/gemma-2b-bnb-4bit", "base_model:finetune:unsloth/gemma-2b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-07T18:22:07Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl - sft base_model: unsloth/gemma-2b-bnb-4bit --- # Uploaded model - Developed by: animaRegem - License: apache-2.0 - Finetuned from model : unsloth/gemma-2b-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Sorour/cls_sentiment_phi3_v1	Sorour	2024-05-20T02:22:20Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:microsoft/Phi-3-mini-4k-instruct", "base_model:adapter:microsoft/Phi-3-mini-4k-instruct", "license:mit", "region:us" ]	null	2024-05-20T01:45:59Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/Phi-3-mini-4k-instruct datasets: - generator model-index: - name: cls_sentiment_phi3_v1 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. --> # cls_sentiment_phi3_v1 This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 0.7122 ## 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: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.9066 \| 0.2083 \| 50 \| 0.9011 \| \| 0.854 \| 0.4167 \| 100 \| 0.8419 \| \| 0.787 \| 0.625 \| 150 \| 0.8062 \| \| 0.7476 \| 0.8333 \| 200 \| 0.7764 \| \| 0.7141 \| 1.0417 \| 250 \| 0.7636 \| \| 0.6989 \| 1.25 \| 300 \| 0.7528 \| \| 0.6482 \| 1.4583 \| 350 \| 0.7397 \| \| 0.6537 \| 1.6667 \| 400 \| 0.7207 \| \| 0.6526 \| 1.875 \| 450 \| 0.7122 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
imagepipeline/detailed_perfection	imagepipeline	2024-05-20T02:21:26Z	0	0	null	[ "imagepipeline", "imagepipeline.io", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-05-20T02:21:23Z	--- license: creativeml-openrail-m tags: - imagepipeline - imagepipeline.io - text-to-image - ultra-realistic pinned: false pipeline_tag: text-to-image --- ## detailed_perfection <img src="https://via.placeholder.com/468x300?text=App+Screenshot+Here" alt="Generated on Image Pipeline" style="border-radius: 10px;"> This lora model is uploaded on [imagepipeline.io](https://imagepipeline.io/) Model details - detailed perfection [![Try this model](https://img.shields.io/badge/try_this_model-image_pipeline-BD9319)](https://imagepipeline.io/models/detailed_perfection?id=5dad2b0b-51db-4bfc-aaca-e431a5add399/) ## How to try this model ? You can try using it locally or send an API call to test the output quality. Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/). No payment required. Coding in `php` `javascript` `node` etc ? Checkout our documentation [![documentation](https://img.shields.io/badge/documentation-image_pipeline-blue)](https://docs.imagepipeline.io/docs/introduction) ```python import requests import json url = "https://imagepipeline.io/sd/text2image/v1/run" payload = json.dumps({ "model_id": "sd1.5", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": false, "guidance_scale": 7.5, "multi_lingual": "no", "embeddings": "", "lora_models": "5dad2b0b-51db-4bfc-aaca-e431a5add399", "lora_weights": "0.5" }) headers = { 'Content-Type': 'application/json', 'API-Key': 'your_api_key' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) } ``` Get more ready to use `MODELS` like this for `SD 1.5` and `SDXL` : [![All models](https://img.shields.io/badge/Get%20All%20Models-image_pipeline-BD9319)](https://imagepipeline.io/models) ### API Reference #### Generate Image ```http https://api.imagepipeline.io/sd/text2image/v1 ``` \| Headers \| Type \| Description \| \|:----------------------\| :------- \|:-------------------------------------------------------------------------------------------------------------------\| \| `API-Key` \| `str` \| Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/) \| \| `Content-Type` \| `str` \| application/json - content type of the request body \| \| Parameter \| Type \| Description \| \| :-------- \| :------- \| :------------------------- \| \| `model_id` \| `str` \| Your base model, find available lists in [models page](https://imagepipeline.io/models) or upload your own\| \| `prompt` \| `str` \| Text Prompt. Check our [Prompt Guide](https://docs.imagepipeline.io/docs/SD-1.5/docs/extras/prompt-guide) for tips \| \| `num_inference_steps` \| `int [1-50]` \| Noise is removed with each step, resulting in a higher-quality image over time. Ideal value 30-50 (without LCM) \| \| `guidance_scale` \| `float [1-20]` \| Higher guidance scale prioritizes text prompt relevance but sacrifices image quality. Ideal value 7.5-12.5 \| \| `lora_models` \| `str, array` \| Pass the model_id(s) of LoRA models that can be found in models page \| \| `lora_weights` \| `str, array` \| Strength of the LoRA effect \| --- license: creativeml-openrail-m tags: - imagepipeline - imagepipeline.io - text-to-image - ultra-realistic pinned: false pipeline_tag: text-to-image --- ### Feedback If you have any feedback, please reach out to us at [email protected] #### 🔗 Visit Website [![portfolio](https://img.shields.io/badge/image_pipeline-BD9319?style=for-the-badge&logo=gocd&logoColor=white)](https://imagepipeline.io/) If you are the original author of this model, please [click here](https://airtable.com/apprTaRnJbDJ8ufOx/shr4g7o9B6fWfOlUR) to add credits
PQlet/textual-inversion-v2-ablation-vec3-img3	PQlet	2024-05-20T02:19:56Z	10	0	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers-training", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-05-20T02:19:54Z	--- license: creativeml-openrail-m library_name: diffusers tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - diffusers-training - lora base_model: runwayml/stable-diffusion-v1-5 inference: true --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # Textual Inversion training - PQlet/textual-inversion-v2-ablation-vec3-img3 The generated images are below. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
Emily666666/bert-base-cased-news-category-test	Emily666666	2024-05-20T02:19:15Z	109	1	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-17T07:56:46Z	--- library_name: transformers tags: [] --- # 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - 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]
tanganke/gpt2_mrpc	tanganke	2024-05-20T02:15:46Z	228	0	transformers	[ "transformers", "safetensors", "gpt2", "text-classification", "dataset:nyu-mll/glue", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-05-20T02:01:40Z	--- datasets: - nyu-mll/glue metrics: - accuracy basemodel: - openai-community/gpt2 ---
tanganke/gpt2_qnli	tanganke	2024-05-20T02:15:21Z	211	0	transformers	[ "transformers", "safetensors", "gpt2", "text-classification", "dataset:nyu-mll/glue", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-05-20T02:01:54Z	--- datasets: - nyu-mll/glue metrics: - accuracy basemodel: - openai-community/gpt2 ---
tanganke/gpt2_mnli	tanganke	2024-05-20T02:14:46Z	229	0	transformers	[ "transformers", "safetensors", "gpt2", "text-classification", "dataset:nyu-mll/glue", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-05-20T02:00:34Z	--- datasets: - nyu-mll/glue metrics: - accuracy basemodel: - openai-community/gpt2 ---
tanganke/gpt2_cola	tanganke	2024-05-20T02:11:53Z	243	0	transformers	[ "transformers", "safetensors", "gpt2", "text-classification", "dataset:nyu-mll/glue", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-05-20T01:59:49Z	--- datasets: - nyu-mll/glue metrics: - accuracy basemodel: - openai-community/gpt2 --- # Model Card for Model ID GPT2 model fine-tuned on cola from GLUE benchmark, using a learning rate of 5e-5 for 3 epochs.
jrc/phi3-mini-math	jrc	2024-05-20T02:11:01Z	10	1	transformers	[ "transformers", "phi3", "text-generation", "torchtune", "minerva-math", "conversational", "custom_code", "en", "dataset:TIGER-Lab/MATH-plus", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-19T23:57:38Z	--- license: apache-2.0 datasets: - TIGER-Lab/MATH-plus language: - en tags: - torchtune - minerva-math library_name: transformers pipeline_tag: text-generation --- # jrc/phi3-mini-math <!-- Provide a quick summary of what the model is/does. --> Math majors - who needs em? This model can answer any math questions you have. ## How to Get Started with the Model Use the code below to get started with the model. ```python # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jrc/phi3-mini-math", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("jrc/phi3-mini-math", trust_remote_code=True) ``` ## Training Details Phi3 was trained using [torchtune](https://github.com/pytorch/torchtune) and the training script + config file are located in this repository. ```bash tune run lora_finetune_distributed.py --config mini_lora.yaml ``` You can see a full Weights & Biases run [here](https://api.wandb.ai/links/jcummings/hkey76vj). ### 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. --> This model was finetuned on the following datasets: * [TIGER-Lab/MATH-plus](https://huggingface.co/datasets/TIGER-Lab/MATH-plus): An advanced math-specific dataset with 894k samples. #### Hardware * Machines: 4 x NVIDIA A100 GPUs * Max VRAM used per GPU: 29 GB * Real time: 10 hours ## Evaluation The finetuned model is evaluated on [minerva-math](https://research.google/blog/minerva-solving-quantitative-reasoning-problems-with-language-models/) using [EleutherAI Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness) through torchtune. ```bash tune run eleuther_eval --config eleuther_evaluation \ checkpoint.checkpoint_dir=./lora-phi3-math \ tasks=["minerva_math"] \ batch_size=32 ``` \| Tasks \|Version\|Filter\|n-shot\| Metric \|Value \| \|Stderr\| \|------------------------------------\|-------\|------\|-----:\|-----------\|-----:\|---\|-----:\| \|minerva_math \|N/A \|none \| 4\|exact_match\|0.1670\|± \|0.0051\| \| - minerva_math_algebra \| 1\|none \| 4\|exact_match\|0.2502\|± \|0.0126\| \| - minerva_math_counting_and_prob \| 1\|none \| 4\|exact_match\|0.1329\|± \|0.0156\| \| - minerva_math_geometry \| 1\|none \| 4\|exact_match\|0.1232\|± \|0.0150\| \| - minerva_math_intermediate_algebra\| 1\|none \| 4\|exact_match\|0.0576\|± \|0.0078\| \| - minerva_math_num_theory \| 1\|none \| 4\|exact_match\|0.1148\|± \|0.0137\| \| - minerva_math_prealgebra \| 1\|none \| 4\|exact_match\|0.3077\|± \|0.0156\| \| - minerva_math_precalc \| 1\|none \| 4\|exact_match\|0.0623\|± \|0.0104\| This shows a large improvement over the base Phi3 Mini model. ## Model Card Contact Drop me a line at @official_j3rck
Mitsua/elan-mt-bt-ja-en	Mitsua	2024-05-20T01:56:57Z	619	6	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "translation", "ja", "en", "dataset:Mitsua/wikidata-parallel-descriptions-en-ja", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2024-05-20T01:56:12Z	--- license: cc-by-sa-4.0 datasets: - Mitsua/wikidata-parallel-descriptions-en-ja language: - ja - en metrics: - bleu - chrf library_name: transformers pipeline_tag: translation --- # ElanMT [ElanMT-BT-ja-en](https://huggingface.co/Mitsua/elan-mt-bt-ja-en) is a Japanese to English translation model developed by [ELAN MITSUA Project](https://elanmitsua.com/en/) / Abstract Engine. - [ElanMT-base-ja-en](https://huggingface.co/Mitsua/elan-mt-base-ja-en) and [ElanMT-base-en-ja](https://huggingface.co/Mitsua/elan-mt-base-en-ja) are trained from scratch, exclusively on openly licensed corpora such as CC0, CC BY and CC BY-SA. - This model is a fine-tuned checkpoint of ElanMT-base-ja-en and is trained exclusively on openly licensed data and Wikipedia back translated data using ElanMT-base-en-ja. - Web crawled or other machine translated corpora are not used during the entire training procedure for the ElanMT models. Despite the relatively low resource training, thanks to back-translation and [a newly built CC0 corpus](https://huggingface.co/datasets/Mitsua/wikidata-parallel-descriptions-en-ja), the model achieved comparable performance to the currently available open translation models. ## Model Details This is a translation model based on [Marian MT](https://marian-nmt.github.io/) 6-layer encoder-decoder transformer architecture with sentencepiece tokenizer. - Developed by: [ELAN MITSUA Project](https://elanmitsua.com/en/) / Abstract Engine - Model type: Translation - Source Language: Japanese - Target Language: English - License: [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) ## Usage 1. Install the python packages `pip install transformers accelerate sentencepiece` * This model is verified on `transformers==4.40.2` 2. Run ```python from transformers import pipeline translator = pipeline('translation', model='Mitsua/elan-mt-bt-ja-en') translator('こんにちは。私はAIです。') ``` 3. For longer multiple sentences, using [pySBD](https://github.com/nipunsadvilkar/pySBD) is recommended. `pip install transformers accelerate sentencepiece pysbd` ```python import pysbd seg = pysbd.Segmenter(language="ja", clean=False) txt = 'こんにちは。私はAIです。お元気ですか？' print(translator(seg.segment(txt))) ``` This idea is from [FuguMT](https://huggingface.co/staka/fugumt-ja-en) repo. ## Training Data We heavily referred [FuguMT author's blog post](https://staka.jp/wordpress/?p=413) for dataset collection. - [Mitsua/wikidata-parallel-descriptions-en-ja](https://huggingface.co/datasets/Mitsua/wikidata-parallel-descriptions-en-ja) (CC0 1.0) - We newly built this 1.5M lines wikidata parallel corpus to augment the training data. This greatly improved the vocabulary on a word basis. - [The Kyoto Free Translation Task (KFTT)](https://www.phontron.com/kftt/) (CC BY-SA 3.0) - Graham Neubig, "The Kyoto Free Translation Task," http://www.phontron.com/kftt, 2011. - [Tatoeba](https://tatoeba.org/en/downloads) (CC BY 2.0 FR / CC0 1.0) - https://tatoeba.org/ - [wikipedia-interlanguage-titles](https://github.com/bhaddow/wikipedia-interlanguage-titles) (The MIT License / CC BY-SA 4.0) - We built parallel titles based on 2024-05-06 wikipedia dump. - [WikiMatrix](https://github.com/facebookresearch/LASER/tree/main/tasks/WikiMatrix) (CC BY-SA 4.0) - Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Francisco Guzmán, "WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia" - [MDN Web Docs](https://github.com/mdn/translated-content) (The MIT / CC0 1.0 / CC BY-SA 2.5) - https://github.com/mdn/translated-content - [Wikimedia contenttranslation dump](https://dumps.wikimedia.org/other/contenttranslation/) (CC BY-SA 4.0) - 2024-5-10 dump is used. Even if the dataset itself is CC-licensed, we did not use it if the corpus contained in the dataset is based on web crawling, is based on unauthorized use of copyrighted works, or is based on the machine translation output of other translation models. ## Training Procedure We heavily referred "[Beating Edinburgh's WMT2017 system for en-de with Marian's Transformer model](https://github.com/marian-nmt/marian-examples/tree/master/wmt2017-transformer)" for training process and hyperparameter tuning. 1. Trains a sentencepiece tokenizer 32k vocab on 4M lines openly licensed corpus. 2. Trains `en-ja` back-translation model on 4M lines openly licensed corpus for 6 epochs. = ElanMT-base-en-ja* 3. Trains `ja-en` base translation model on 4M lines openly licensed corpus for 6 epochs. = ElanMT-base-ja-en 4. Translates 20M lines `en` Wikipedia to `ja` using back-translation model. 5. Trains 4 `ja-en` models, which is finetuned from ElanMT-base-ja-en checkpoint, on 24M lines training data augmented with back-translated data for 6 epochs. 6. Merges 4 trained models that produces the best validation score on FLORES+ dev split. 7. Finetunes the merged model on 1M lines high quality corpus subset for 5 epochs. ## Evaluation ### Dataset - [FLORES+](https://github.com/openlanguagedata/flores) (CC BY-SA 4.0) devtest split is used for evaluation. - [NTREX](https://github.com/MicrosoftTranslator/NTREX) (CC BY-SA 4.0) ### Result \| Model \| Params \| FLORES+ BLEU \| FLORES+ chrf \| NTREX BLEU \| NTREX chrf \| \|:---\|---:\|---:\|---:\|---:\|---:\| \| [ElanMT-BT](https://huggingface.co/Mitsua/elan-mt-bt-ja-en) \| 61M \| 24.87 \| 55.02 \| 22.57 \| 52.48\| \| [ElanMT-base](https://huggingface.co/Mitsua/elan-mt-base-ja-en) \| 61M \| 21.61 \| 52.53 \| 18.43 \| 49.09\| \| [ElanMT-tiny](https://huggingface.co/Mitsua/elan-mt-tiny-ja-en) \| 15M \| 20.40 \| 51.81 \| 18.43 \| 49.39\| \| [staka/fugumt-ja-en](https://huggingface.co/staka/fugumt-ja-en) \| 61M \| 24.10 \| 54.97 \| 22.33 \| 51.84\| \| [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) \| 610M \| 23.88 \| 53.98 \| 22.59 \| 51.57\| \| [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) \| 615M \| 22.92 \| 52.13 \| 22.59 \| 51.36\| \| [facebook/nllb-200-3.3B](https://huggingface.co/facebook/nllb-200-3.3B) \| 3B \| 28.13 \| 56.86 \| 27.65 \| 55.60\| \| [google/madlad400-3b-mt](https://huggingface.co/google/madlad400-3b-mt) \| 3B \| 26.95 \| 56.62 \| 26.11 \| 54.61\| \| [google/madlad400-7b-mt](https://huggingface.co/google/madlad400-7b-mt) \| 7B \| 28.84 \| 57.46 \| 28.19 \| 55.85\| - 1 tested on `transformers==4.29.2` and `num_beams=4` - 2 BLEU score is calculated by `sacreBLEU` ## Disclaimer - The translated result may be very incorrect, harmful or biased. The model was developed to investigate achievable performance with only a relatively small, licensed corpus, and is not suitable for use cases requiring high translation accuracy. Under Section 5 of the CC BY-SA 4.0 License, ELAN MITSUA Project / Abstract Engine is not responsible for any direct or indirect loss caused by the use of the model. - 免責事項：翻訳結果は不正確で、有害であったりバイアスがかかっている可能性があります。本モデルは比較的小規模でライセンスされたコーパスのみで達成可能な性能を調査するために開発されたモデルであり、翻訳の正確性が必要なユースケースでの使用には適していません。絵藍ミツアプロジェクト及び株式会社アブストラクトエンジンはCC BY-SA 4.0ライセンス第5条に基づき、本モデルの使用によって生じた直接的または間接的な損失に対して、一切の責任を負いません。
animaRegem/gemma-2b-malayalam-model-adaptors	animaRegem	2024-05-20T01:54:45Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:unsloth/gemma-2b-bnb-4bit", "base_model:finetune:unsloth/gemma-2b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-07T18:14:06Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl base_model: unsloth/gemma-2b-bnb-4bit --- # Uploaded model - Developed by: animaRegem - License: apache-2.0 - Finetuned from model : unsloth/gemma-2b-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Mitsua/elan-mt-bt-en-ja	Mitsua	2024-05-20T01:53:38Z	626	8	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "translation", "ja", "en", "dataset:Mitsua/wikidata-parallel-descriptions-en-ja", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2024-05-20T01:51:18Z	--- license: cc-by-sa-4.0 datasets: - Mitsua/wikidata-parallel-descriptions-en-ja language: - ja - en metrics: - bleu - chrf library_name: transformers pipeline_tag: translation --- # ElanMT [ElanMT-BT-en-ja](https://huggingface.co/Mitsua/elan-mt-bt-en-ja) is a English to Japanese translation model developed by [ELAN MITSUA Project](https://elanmitsua.com/en/) / Abstract Engine. - [ElanMT-base-en-ja](https://huggingface.co/Mitsua/elan-mt-base-en-ja) and [ElanMT-base-ja-en](https://huggingface.co/Mitsua/elan-mt-base-ja-en) are trained from scratch, exclusively on openly licensed corpora such as CC0, CC BY and CC BY-SA. - This model is a fine-tuned checkpoint of ElanMT-base-en-ja and is trained exclusively on openly licensed data and Wikipedia back translated data using ElanMT-base-ja-en. - Web crawled or other machine translated corpora are not used during the entire training procedure for the ElanMT models. Despite the relatively low resource training, thanks to back-translation and [a newly built CC0 corpus](https://huggingface.co/datasets/Mitsua/wikidata-parallel-descriptions-en-ja), the model achieved comparable performance to the currently available open translation models. ## Model Details This is a translation model based on [Marian MT](https://marian-nmt.github.io/) 6-layer encoder-decoder transformer architecture with sentencepiece tokenizer. - Developed by: [ELAN MITSUA Project](https://elanmitsua.com/en/) / Abstract Engine - Model type: Translation - Source Language: English - Target Language: Japanese - License: [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) ## Usage 1. Install the python packages `pip install transformers accelerate sentencepiece` * This model is verified on `transformers==4.40.2` 2. Run ```python from transformers import pipeline translator = pipeline('translation', model='Mitsua/elan-mt-bt-en-ja') translator('Hello. I am an AI.') ``` 3. For longer multiple sentences, using [pySBD](https://github.com/nipunsadvilkar/pySBD) is recommended. `pip install transformers accelerate sentencepiece pysbd` ```python import pysbd seg_en = pysbd.Segmenter(language="en", clean=False) txt = 'Hello. I am an AI. How are you doing?' print(translator(seg_en.segment(txt))) ``` This idea is from [FuguMT](https://huggingface.co/staka/fugumt-en-ja) repo. ## Training Data We heavily referred [FuguMT author's blog post](https://staka.jp/wordpress/?p=413) for dataset collection. - [Mitsua/wikidata-parallel-descriptions-en-ja](https://huggingface.co/datasets/Mitsua/wikidata-parallel-descriptions-en-ja) (CC0 1.0) - We newly built this 1.5M lines wikidata parallel corpus to augment the training data. This greatly improved the vocabulary on a word basis. - [The Kyoto Free Translation Task (KFTT)](https://www.phontron.com/kftt/) (CC BY-SA 3.0) - Graham Neubig, "The Kyoto Free Translation Task," http://www.phontron.com/kftt, 2011. - [Tatoeba](https://tatoeba.org/en/downloads) (CC BY 2.0 FR / CC0 1.0) - https://tatoeba.org/ - [wikipedia-interlanguage-titles](https://github.com/bhaddow/wikipedia-interlanguage-titles) (The MIT License / CC BY-SA 4.0) - We built parallel titles based on 2024-05-06 wikipedia dump. - [WikiMatrix](https://github.com/facebookresearch/LASER/tree/main/tasks/WikiMatrix) (CC BY-SA 4.0) - Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Francisco Guzmán, "WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia" - [MDN Web Docs](https://github.com/mdn/translated-content) (The MIT / CC0 1.0 / CC BY-SA 2.5) - https://github.com/mdn/translated-content - [Wikimedia contenttranslation dump](https://dumps.wikimedia.org/other/contenttranslation/) (CC BY-SA 4.0) - 2024-5-10 dump is used. Even if the dataset itself is CC-licensed, we did not use it if the corpus contained in the dataset is based on web crawling, is based on unauthorized use of copyrighted works, or is based on the machine translation output of other translation models. ## Training Procedure We heavily referred "[Beating Edinburgh's WMT2017 system for en-de with Marian's Transformer model](https://github.com/marian-nmt/marian-examples/tree/master/wmt2017-transformer)" for training process and hyperparameter tuning. 1. Trains a sentencepiece tokenizer 32k vocab on 4M lines openly licensed corpus. 2. Trains `ja-en` back-translation model on 4M lines openly licensed corpus for 6 epochs. = ElanMT-base-ja-en* 3. Trains `en-ja` base translation model on 4M lines openly licensed corpus for 6 epochs. = ElanMT-base-en-ja 4. Translates 20M lines `ja` Wikipedia to `en` using back-translation model. 5. Trains 4 `en-ja` models, which is finetuned from ElanMT-base-en-ja checkpoint, on 24M lines training data augmented with back-translated data for 6 epochs. 6. Merges 4 trained models that produces the best validation score on FLORES+ dev split. 7. Finetunes the merged model on 1M lines high quality corpus subset for 5 epochs. ## Evaluation ### Dataset - [FLORES+](https://github.com/openlanguagedata/flores) (CC BY-SA 4.0) devtest split is used for evaluation. - [NTREX](https://github.com/MicrosoftTranslator/NTREX) (CC BY-SA 4.0) ### Result \| Model \| Params \| FLORES+ BLEU \| FLORES+ chrf \| NTREX BLEU \| NTREX chrf \| \|:---\|---:\|---:\|---:\|---:\|---:\| \| [ElanMT-BT](https://huggingface.co/Mitsua/elan-mt-bt-en-ja) \| 61M \| 29.96 \| 38.43 \| 25.63 \| 35.41\| \| [ElanMT-base](https://huggingface.co/Mitsua/elan-mt-base-en-ja) w/o back-translation \| 61M \| 26.55 \| 35.28 \| 23.04 \| 32.94\| \| [ElanMT-tiny](https://huggingface.co/Mitsua/elan-mt-tiny-en-ja) \| 15M \| 25.93 \| 34.69 \| 22.78 \| 33.00\| \| [staka/fugumt-en-ja](https://huggingface.co/staka/fugumt-en-ja) (1) \| 61M \| 30.89* \| 38.38 \| 24.74 \| 34.23\| \| [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) \| 610M \| 26.31 \| 34.37 \| 23.35 \| 32.66\| \| [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) \| 615M \| 17.09 \| 27.32 \| 14.92 \| 26.26\| \| [facebook/nllb-200-3.3B](https://huggingface.co/facebook/nllb-200-3.3B) \| 3B \| 20.04 \| 30.33 \| 17.07 \| 28.46\| \| [google/madlad400-3b-mt](https://huggingface.co/google/madlad400-3b-mt) \| 3B \| 24.62 \| 33.89 \| 23.64 \| 33.48\| \| [google/madlad400-7b-mt](https://huggingface.co/google/madlad400-7b-mt) \| 7B \| 25.57 \| 34.59 \| 24.60 \| 34.43\| - 1 tested on `transformers==4.29.2` and `num_beams=4` - 2 BLEU score is calculated by `sacreBLEU` with `tokenize=ja-mecab` ## Disclaimer - The translated result may be very incorrect, harmful or biased. The model was developed to investigate achievable performance with only a relatively small, licensed corpus, and is not suitable for use cases requiring high translation accuracy. Under Section 5 of the CC BY-SA 4.0 License, ELAN MITSUA Project / Abstract Engine is not responsible for any direct or indirect loss caused by the use of the model. - 免責事項：翻訳結果は不正確で、有害であったりバイアスがかかっている可能性があります。本モデルは比較的小規模でライセンスされたコーパスのみで達成可能な性能を調査するために開発されたモデルであり、翻訳の正確性が必要なユースケースでの使用には適していません。絵藍ミツアプロジェクト及び株式会社アブストラクトエンジンはCC BY-SA 4.0ライセンス第5条に基づき、本モデルの使用によって生じた直接的または間接的な損失に対して、一切の責任を負いません。
ruidanwang/minima	ruidanwang	2024-05-20T01:50:46Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-20T01:50:46Z	--- license: apache-2.0 ---
RichardErkhov/maywell_-_Synatra-RP-Orca-2-7b-v0.1-gguf	RichardErkhov	2024-05-20T01:49:02Z	10	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-05-20T00:03:39Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Synatra-RP-Orca-2-7b-v0.1 - GGUF - Model creator: https://huggingface.co/maywell/ - Original model: https://huggingface.co/maywell/Synatra-RP-Orca-2-7b-v0.1/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Synatra-RP-Orca-2-7b-v0.1.Q2_K.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-RP-Orca-2-7b-v0.1-gguf/blob/main/Synatra-RP-Orca-2-7b-v0.1.Q2_K.gguf) \| Q2_K \| 2.36GB \| \| [Synatra-RP-Orca-2-7b-v0.1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-RP-Orca-2-7b-v0.1-gguf/blob/main/Synatra-RP-Orca-2-7b-v0.1.IQ3_XS.gguf) \| IQ3_XS \| 2.6GB \| \| [Synatra-RP-Orca-2-7b-v0.1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-RP-Orca-2-7b-v0.1-gguf/blob/main/Synatra-RP-Orca-2-7b-v0.1.IQ3_S.gguf) \| IQ3_S \| 2.75GB \| \| [Synatra-RP-Orca-2-7b-v0.1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-RP-Orca-2-7b-v0.1-gguf/blob/main/Synatra-RP-Orca-2-7b-v0.1.Q3_K_S.gguf) \| Q3_K_S \| 2.75GB \| \| [Synatra-RP-Orca-2-7b-v0.1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-RP-Orca-2-7b-v0.1-gguf/blob/main/Synatra-RP-Orca-2-7b-v0.1.IQ3_M.gguf) \| IQ3_M \| 2.9GB \| \| [Synatra-RP-Orca-2-7b-v0.1.Q3_K.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-RP-Orca-2-7b-v0.1-gguf/blob/main/Synatra-RP-Orca-2-7b-v0.1.Q3_K.gguf) \| Q3_K \| 3.07GB \| \| [Synatra-RP-Orca-2-7b-v0.1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-RP-Orca-2-7b-v0.1-gguf/blob/main/Synatra-RP-Orca-2-7b-v0.1.Q3_K_M.gguf) \| Q3_K_M \| 3.07GB \| \| [Synatra-RP-Orca-2-7b-v0.1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-RP-Orca-2-7b-v0.1-gguf/blob/main/Synatra-RP-Orca-2-7b-v0.1.Q3_K_L.gguf) \| Q3_K_L \| 3.35GB \| \| [Synatra-RP-Orca-2-7b-v0.1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-RP-Orca-2-7b-v0.1-gguf/blob/main/Synatra-RP-Orca-2-7b-v0.1.IQ4_XS.gguf) \| IQ4_XS \| 3.4GB \| \| [Synatra-RP-Orca-2-7b-v0.1.Q4_0.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-RP-Orca-2-7b-v0.1-gguf/blob/main/Synatra-RP-Orca-2-7b-v0.1.Q4_0.gguf) \| Q4_0 \| 3.56GB \| \| [Synatra-RP-Orca-2-7b-v0.1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-RP-Orca-2-7b-v0.1-gguf/blob/main/Synatra-RP-Orca-2-7b-v0.1.IQ4_NL.gguf) \| IQ4_NL \| 3.58GB \| \| [Synatra-RP-Orca-2-7b-v0.1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-RP-Orca-2-7b-v0.1-gguf/blob/main/Synatra-RP-Orca-2-7b-v0.1.Q4_K_S.gguf) \| Q4_K_S \| 3.59GB \| \| [Synatra-RP-Orca-2-7b-v0.1.Q4_K.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-RP-Orca-2-7b-v0.1-gguf/blob/main/Synatra-RP-Orca-2-7b-v0.1.Q4_K.gguf) \| Q4_K \| 3.8GB \| \| [Synatra-RP-Orca-2-7b-v0.1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-RP-Orca-2-7b-v0.1-gguf/blob/main/Synatra-RP-Orca-2-7b-v0.1.Q4_K_M.gguf) \| Q4_K_M \| 3.8GB \| \| [Synatra-RP-Orca-2-7b-v0.1.Q4_1.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-RP-Orca-2-7b-v0.1-gguf/blob/main/Synatra-RP-Orca-2-7b-v0.1.Q4_1.gguf) \| Q4_1 \| 3.95GB \| \| [Synatra-RP-Orca-2-7b-v0.1.Q5_0.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-RP-Orca-2-7b-v0.1-gguf/blob/main/Synatra-RP-Orca-2-7b-v0.1.Q5_0.gguf) \| Q5_0 \| 4.33GB \| \| [Synatra-RP-Orca-2-7b-v0.1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-RP-Orca-2-7b-v0.1-gguf/blob/main/Synatra-RP-Orca-2-7b-v0.1.Q5_K_S.gguf) \| Q5_K_S \| 4.33GB \| \| [Synatra-RP-Orca-2-7b-v0.1.Q5_K.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-RP-Orca-2-7b-v0.1-gguf/blob/main/Synatra-RP-Orca-2-7b-v0.1.Q5_K.gguf) \| Q5_K \| 4.45GB \| \| [Synatra-RP-Orca-2-7b-v0.1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-RP-Orca-2-7b-v0.1-gguf/blob/main/Synatra-RP-Orca-2-7b-v0.1.Q5_K_M.gguf) \| Q5_K_M \| 4.45GB \| \| [Synatra-RP-Orca-2-7b-v0.1.Q5_1.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-RP-Orca-2-7b-v0.1-gguf/blob/main/Synatra-RP-Orca-2-7b-v0.1.Q5_1.gguf) \| Q5_1 \| 4.72GB \| \| [Synatra-RP-Orca-2-7b-v0.1.Q6_K.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-RP-Orca-2-7b-v0.1-gguf/blob/main/Synatra-RP-Orca-2-7b-v0.1.Q6_K.gguf) \| Q6_K \| 5.15GB \| \| [Synatra-RP-Orca-2-7b-v0.1.Q8_0.gguf](https://huggingface.co/RichardErkhov/maywell_-_Synatra-RP-Orca-2-7b-v0.1-gguf/blob/main/Synatra-RP-Orca-2-7b-v0.1.Q8_0.gguf) \| Q8_0 \| 6.67GB \| Original model description: --- license: apache-2.0 --- # Synatra-RP-Orca-2-7b-v0.1🐧 ## Support Me Synatra is a personal project and is being developed with one person's resources. If you like the model, how about a little research funding? [<img src="https://cdn.buymeacoffee.com/buttons/default-orange.png" alt="Buy me a Coffee" width="217" height="50">](https://www.buymeacoffee.com/mwell) Wanna be a sponser? (Please) Contact me on Telegram AlzarTakkarsen # Model Details Base Model microsoft/Orca-2-7b Model Description It's a test RP sft model. Finetuned from microsoft/Orca-2-7b. Trained On A100 80GB * 1 Instruction format Alpaca(Better), ChatML
vr4sigma/gptchatbot	vr4sigma	2024-05-20T01:43:13Z	0	0	adapter-transformers	[ "adapter-transformers", "en", "dataset:HuggingFaceFW/fineweb", "dataset:PleIAs/YouTube-Commons", "arxiv:1910.09700", "license:wtfpl", "region:us" ]	null	2024-05-20T01:39:27Z	--- license: wtfpl datasets: - HuggingFaceFW/fineweb - PleIAs/YouTube-Commons language: - en metrics: - accuracy library_name: adapter-transformers --- # 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]
ValterVar1/saiga-7b-lora-ner	ValterVar1	2024-05-20T01:37:55Z	0	0	transformers	[ "transformers", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T01:08:01Z	--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers widget: - messages: - role: user content: What is your favorite condiment? license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
mradermacher/Gaston_Yi-1.5-9B-Chat-GGUF	mradermacher	2024-05-20T01:37:40Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "llama", "trl", "orpo", "en", "base_model:baconnier/Gaston_Yi-1.5-9B-Chat", "base_model:quantized:baconnier/Gaston_Yi-1.5-9B-Chat", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-20T00:21:27Z	--- base_model: baconnier/Gaston_Yi-1.5-9B-Chat language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - llama - trl - orpo --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/baconnier/Gaston_Yi-1.5-9B-Chat <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Gaston_Yi-1.5-9B-Chat-GGUF/resolve/main/Gaston_Yi-1.5-9B-Chat.Q2_K.gguf) \| Q2_K \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gaston_Yi-1.5-9B-Chat-GGUF/resolve/main/Gaston_Yi-1.5-9B-Chat.IQ3_XS.gguf) \| IQ3_XS \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gaston_Yi-1.5-9B-Chat-GGUF/resolve/main/Gaston_Yi-1.5-9B-Chat.Q3_K_S.gguf) \| Q3_K_S \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gaston_Yi-1.5-9B-Chat-GGUF/resolve/main/Gaston_Yi-1.5-9B-Chat.IQ3_S.gguf) \| IQ3_S \| 4.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Gaston_Yi-1.5-9B-Chat-GGUF/resolve/main/Gaston_Yi-1.5-9B-Chat.IQ3_M.gguf) \| IQ3_M \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gaston_Yi-1.5-9B-Chat-GGUF/resolve/main/Gaston_Yi-1.5-9B-Chat.Q3_K_M.gguf) \| Q3_K_M \| 4.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Gaston_Yi-1.5-9B-Chat-GGUF/resolve/main/Gaston_Yi-1.5-9B-Chat.Q3_K_L.gguf) \| Q3_K_L \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gaston_Yi-1.5-9B-Chat-GGUF/resolve/main/Gaston_Yi-1.5-9B-Chat.IQ4_XS.gguf) \| IQ4_XS \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gaston_Yi-1.5-9B-Chat-GGUF/resolve/main/Gaston_Yi-1.5-9B-Chat.Q4_K_S.gguf) \| Q4_K_S \| 5.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Gaston_Yi-1.5-9B-Chat-GGUF/resolve/main/Gaston_Yi-1.5-9B-Chat.Q4_K_M.gguf) \| Q4_K_M \| 5.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Gaston_Yi-1.5-9B-Chat-GGUF/resolve/main/Gaston_Yi-1.5-9B-Chat.Q5_K_S.gguf) \| Q5_K_S \| 6.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gaston_Yi-1.5-9B-Chat-GGUF/resolve/main/Gaston_Yi-1.5-9B-Chat.Q5_K_M.gguf) \| Q5_K_M \| 6.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gaston_Yi-1.5-9B-Chat-GGUF/resolve/main/Gaston_Yi-1.5-9B-Chat.Q6_K.gguf) \| Q6_K \| 7.3 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Gaston_Yi-1.5-9B-Chat-GGUF/resolve/main/Gaston_Yi-1.5-9B-Chat.Q8_0.gguf) \| Q8_0 \| 9.5 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Gaston_Yi-1.5-9B-Chat-GGUF/resolve/main/Gaston_Yi-1.5-9B-Chat.f16.gguf) \| f16 \| 17.8 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
PQlet/textual-inversion-v2-ablation-vec5-img9	PQlet	2024-05-20T01:31:44Z	17	0	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers-training", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-05-20T01:31:42Z	--- license: creativeml-openrail-m library_name: diffusers tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - diffusers-training - lora base_model: runwayml/stable-diffusion-v1-5 inference: true --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # Textual Inversion training - PQlet/textual-inversion-v2-ablation-vec5-img9 The generated images are below. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
JosineyJr/generate-conventional-commit-messages	JosineyJr	2024-05-20T01:31:35Z	0	0	unsloth	[ "unsloth", "safetensors", "code", "text2text-generation", "en", "base_model:meta-llama/Meta-Llama-Guard-2-8B", "base_model:finetune:meta-llama/Meta-Llama-Guard-2-8B", "license:apache-2.0", "region:us" ]	text2text-generation	2024-05-20T01:15:15Z	--- license: apache-2.0 language: - en library_name: unsloth tags: - code pipeline_tag: text2text-generation base_model: meta-llama/Meta-Llama-Guard-2-8B --- # About the project CommitWizard is a project that uses pre-trained language models to help automate the generation of commit messages based on code changes. It employs 4-bit quantization to optimize memory usage while maintaining model efficiency and accuracy.
TinyPixel/openelm-ct	TinyPixel	2024-05-20T01:29:49Z	135	0	transformers	[ "transformers", "safetensors", "openelm", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]	text-generation	2024-05-20T01:29:31Z	--- library_name: transformers tags: [] --- # 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - 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. 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tsavage68/MedQA_L3_250steps_1e6rate_01beat_CSFTDPO	tsavage68	2024-05-20T01:23:51Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T01:19:35Z	--- license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - trl - dpo - generated_from_trainer model-index: - name: MedQA_L3_250steps_1e6rate_01beat_CSFTDPO 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. --> # MedQA_L3_250steps_1e6rate_01beat_CSFTDPO This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4710 - Rewards/chosen: -0.7540 - Rewards/rejected: -1.6509 - Rewards/accuracies: 0.7758 - Rewards/margins: 0.8969 - Logps/rejected: -37.8254 - Logps/chosen: -25.7624 - Logits/rejected: -1.1604 - Logits/chosen: -1.1585 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 250 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.695 \| 0.0489 \| 50 \| 0.6713 \| 0.0342 \| -0.0142 \| 0.6615 \| 0.0484 \| -21.4583 \| -17.8807 \| -0.9400 \| -0.9395 \| \| 0.6187 \| 0.0977 \| 100 \| 0.5915 \| -0.1174 \| -0.4200 \| 0.7121 \| 0.3027 \| -25.5168 \| -19.3963 \| -1.0412 \| -1.0403 \| \| 0.559 \| 0.1466 \| 150 \| 0.5116 \| -0.4993 \| -1.1517 \| 0.7429 \| 0.6524 \| -32.8335 \| -23.2153 \| -1.1115 \| -1.1101 \| \| 0.4654 \| 0.1954 \| 200 \| 0.4732 \| -0.7696 \| -1.6630 \| 0.7780 \| 0.8934 \| -37.9465 \| -25.9187 \| -1.1618 \| -1.1598 \| \| 0.4766 \| 0.2443 \| 250 \| 0.4710 \| -0.7540 \| -1.6509 \| 0.7758 \| 0.8969 \| -37.8254 \| -25.7624 \| -1.1604 \| -1.1585 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.0.0+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1
DownwardSpiral33/gpt2-imdb-pos-roberta128_1_0-2024.05.19.23.06	DownwardSpiral33	2024-05-20T01:22:09Z	135	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T01:21:45Z	--- library_name: transformers tags: [] --- # 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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hiba2/results_t5_wiki	hiba2	2024-05-20T01:21:52Z	109	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:ahmeddbahaa/t5-arabic-base-finetuned-wikilingua-ar", "base_model:finetune:ahmeddbahaa/t5-arabic-base-finetuned-wikilingua-ar", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-20T01:21:20Z	--- license: apache-2.0 base_model: ahmeddbahaa/t5-arabic-base-finetuned-wikilingua-ar tags: - generated_from_trainer metrics: - rouge model-index: - name: results_t5_wiki 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. --> # results_t5_wiki This model is a fine-tuned version of [ahmeddbahaa/t5-arabic-base-finetuned-wikilingua-ar](https://huggingface.co/ahmeddbahaa/t5-arabic-base-finetuned-wikilingua-ar) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0002 - Rouge1: 0.1188 - Rouge2: 0.0194 - Rougel: 0.1188 - Rougelsum: 0.1186 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - 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: 250 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| 0.8768 \| 0.2143 \| 500 \| 0.0228 \| 0.1148 \| 0.0128 \| 0.1148 \| 0.1147 \| 19.0 \| \| 0.0437 \| 0.4286 \| 1000 \| 0.0111 \| 0.1164 \| 0.0154 \| 0.1168 \| 0.1165 \| 19.0 \| \| 0.0436 \| 0.6429 \| 1500 \| 0.0060 \| 0.1168 \| 0.0163 \| 0.1171 \| 0.1169 \| 19.0 \| \| 0.0212 \| 0.8573 \| 2000 \| 0.0052 \| 0.117 \| 0.0165 \| 0.1173 \| 0.117 \| 19.0 \| \| 0.0161 \| 1.0716 \| 2500 \| 0.0018 \| 0.1188 \| 0.0194 \| 0.1188 \| 0.1186 \| 19.0 \| \| 0.011 \| 1.2859 \| 3000 \| 0.0018 \| 0.1188 \| 0.0193 \| 0.1188 \| 0.1186 \| 19.0 \| \| 0.0094 \| 1.5002 \| 3500 \| 0.0014 \| 0.1188 \| 0.0194 \| 0.1188 \| 0.1186 \| 19.0 \| \| 0.0107 \| 1.7145 \| 4000 \| 0.0007 \| 0.1188 \| 0.0194 \| 0.1188 \| 0.1186 \| 19.0 \| \| 0.0069 \| 1.9288 \| 4500 \| 0.0006 \| 0.1188 \| 0.0194 \| 0.1188 \| 0.1186 \| 19.0 \| \| 0.007 \| 2.1432 \| 5000 \| 0.0006 \| 0.1188 \| 0.0194 \| 0.1188 \| 0.1186 \| 19.0 \| \| 0.0064 \| 2.3575 \| 5500 \| 0.0006 \| 0.1188 \| 0.0194 \| 0.1188 \| 0.1186 \| 19.0 \| \| 0.0062 \| 2.5718 \| 6000 \| 0.0015 \| 0.1188 \| 0.0194 \| 0.1188 \| 0.1186 \| 19.0 \| \| 0.0042 \| 2.7861 \| 6500 \| 0.0005 \| 0.1188 \| 0.0194 \| 0.1188 \| 0.1186 \| 19.0 \| \| 0.0043 \| 3.0004 \| 7000 \| 0.0004 \| 0.1188 \| 0.0194 \| 0.1188 \| 0.1186 \| 19.0 \| \| 0.0042 \| 3.2147 \| 7500 \| 0.0012 \| 0.1188 \| 0.0194 \| 0.1188 \| 0.1186 \| 19.0 \| \| 0.0047 \| 3.4291 \| 8000 \| 0.0010 \| 0.1188 \| 0.0194 \| 0.1188 \| 0.1186 \| 19.0 \| \| 0.0043 \| 3.6434 \| 8500 \| 0.0008 \| 0.1188 \| 0.0194 \| 0.1188 \| 0.1186 \| 19.0 \| \| 0.0024 \| 3.8577 \| 9000 \| 0.0003 \| 0.1188 \| 0.0194 \| 0.1188 \| 0.1186 \| 19.0 \| \| 0.0026 \| 4.0720 \| 9500 \| 0.0005 \| 0.1188 \| 0.0194 \| 0.1188 \| 0.1186 \| 19.0 \| \| 0.0029 \| 4.2863 \| 10000 \| 0.0003 \| 0.1188 \| 0.0194 \| 0.1188 \| 0.1186 \| 19.0 \| \| 0.0045 \| 4.5006 \| 10500 \| 0.0006 \| 0.1188 \| 0.0194 \| 0.1188 \| 0.1186 \| 19.0 \| \| 0.0024 \| 4.7150 \| 11000 \| 0.0001 \| 0.1188 \| 0.0194 \| 0.1188 \| 0.1186 \| 19.0 \| \| 0.0018 \| 4.9293 \| 11500 \| 0.0002 \| 0.1188 \| 0.0194 \| 0.1188 \| 0.1186 \| 19.0 \| \| 0.002 \| 5.1436 \| 12000 \| 0.0002 \| 0.1188 \| 0.0194 \| 0.1188 \| 0.1186 \| 19.0 \| \| 0.0022 \| 5.3579 \| 12500 \| 0.0001 \| 0.1188 \| 0.0194 \| 0.1188 \| 0.1186 \| 19.0 \| \| 0.0017 \| 5.5722 \| 13000 \| 0.0003 \| 0.1188 \| 0.0194 \| 0.1188 \| 0.1186 \| 19.0 \| \| 0.0014 \| 5.7865 \| 13500 \| 0.0005 \| 0.1188 \| 0.0194 \| 0.1188 \| 0.1186 \| 19.0 \| \| 0.0055 \| 6.0009 \| 14000 \| 0.0012 \| 0.1188 \| 0.0194 \| 0.1188 \| 0.1186 \| 16.3147 \| \| 0.0127 \| 6.2152 \| 14500 \| 0.0002 \| 0.1188 \| 0.0194 \| 0.1188 \| 0.1186 \| 19.0 \| \| 0.0012 \| 6.4295 \| 15000 \| 0.0002 \| 0.1188 \| 0.0194 \| 0.1188 \| 0.1186 \| 19.0 \| ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
jikaixuan/zephyr-ds	jikaixuan	2024-05-20T01:21:41Z	196	0	transformers	[ "transformers", "tensorboard", "safetensors", "mistral", "text-generation", "generated_from_trainer", "conversational", "base_model:alignment-handbook/zephyr-7b-sft-full", "base_model:finetune:alignment-handbook/zephyr-7b-sft-full", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-10T03:49:51Z	--- license: apache-2.0 base_model: alignment-handbook/zephyr-7b-sft-full tags: - generated_from_trainer model-index: - name: zephyr-ds results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # zephyr-ds This model is a fine-tuned version of [alignment-handbook/zephyr-7b-sft-full](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3439 - Rewards/chosen: -1.1633 - Rewards/rejected: -3.5290 - Rewards/accuracies: 0.7420 - Rewards/margins: 2.3657 - Logps/rejected: -294.5901 - Logps/chosen: -295.8908 - Logits/rejected: -2.7390 - Logits/chosen: -2.7421 - Use Label: 9180.7998 - Pred Label: 6851.2002 ## 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: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| Use Label \| Pred Label \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\|:---------:\|:----------:\| \| 0.333 \| 1.0 \| 955 \| 0.3439 \| -1.1633 \| -3.5290 \| 0.7420 \| 2.3657 \| -294.5901 \| -295.8908 \| -2.7390 \| -2.7421 \| 8950.7998 \| 6581.2002 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.1+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1
RichardErkhov/KnutJaegersberg_-_deacon-13b-gguf	RichardErkhov	2024-05-20T01:17:30Z	12	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-05-19T22:42:12Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) deacon-13b - GGUF - Model creator: https://huggingface.co/KnutJaegersberg/ - Original model: https://huggingface.co/KnutJaegersberg/deacon-13b/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [deacon-13b.Q2_K.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_deacon-13b-gguf/blob/main/deacon-13b.Q2_K.gguf) \| Q2_K \| 4.52GB \| \| [deacon-13b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_deacon-13b-gguf/blob/main/deacon-13b.IQ3_XS.gguf) \| IQ3_XS \| 4.99GB \| \| [deacon-13b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_deacon-13b-gguf/blob/main/deacon-13b.IQ3_S.gguf) \| IQ3_S \| 5.27GB \| \| [deacon-13b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_deacon-13b-gguf/blob/main/deacon-13b.Q3_K_S.gguf) \| Q3_K_S \| 5.27GB \| \| [deacon-13b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_deacon-13b-gguf/blob/main/deacon-13b.IQ3_M.gguf) \| IQ3_M \| 5.57GB \| \| [deacon-13b.Q3_K.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_deacon-13b-gguf/blob/main/deacon-13b.Q3_K.gguf) \| Q3_K \| 5.9GB \| \| [deacon-13b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_deacon-13b-gguf/blob/main/deacon-13b.Q3_K_M.gguf) \| Q3_K_M \| 5.9GB \| \| [deacon-13b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_deacon-13b-gguf/blob/main/deacon-13b.Q3_K_L.gguf) \| Q3_K_L \| 6.45GB \| \| [deacon-13b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_deacon-13b-gguf/blob/main/deacon-13b.IQ4_XS.gguf) \| IQ4_XS \| 6.54GB \| \| [deacon-13b.Q4_0.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_deacon-13b-gguf/blob/main/deacon-13b.Q4_0.gguf) \| Q4_0 \| 6.86GB \| \| [deacon-13b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_deacon-13b-gguf/blob/main/deacon-13b.IQ4_NL.gguf) \| IQ4_NL \| 6.9GB \| \| [deacon-13b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_deacon-13b-gguf/blob/main/deacon-13b.Q4_K_S.gguf) \| Q4_K_S \| 6.91GB \| \| [deacon-13b.Q4_K.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_deacon-13b-gguf/blob/main/deacon-13b.Q4_K.gguf) \| Q4_K \| 7.33GB \| \| [deacon-13b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_deacon-13b-gguf/blob/main/deacon-13b.Q4_K_M.gguf) \| Q4_K_M \| 7.33GB \| \| [deacon-13b.Q4_1.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_deacon-13b-gguf/blob/main/deacon-13b.Q4_1.gguf) \| Q4_1 \| 7.61GB \| \| [deacon-13b.Q5_0.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_deacon-13b-gguf/blob/main/deacon-13b.Q5_0.gguf) \| Q5_0 \| 8.36GB \| \| [deacon-13b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_deacon-13b-gguf/blob/main/deacon-13b.Q5_K_S.gguf) \| Q5_K_S \| 8.36GB \| \| [deacon-13b.Q5_K.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_deacon-13b-gguf/blob/main/deacon-13b.Q5_K.gguf) \| Q5_K \| 8.6GB \| \| [deacon-13b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_deacon-13b-gguf/blob/main/deacon-13b.Q5_K_M.gguf) \| Q5_K_M \| 8.6GB \| \| [deacon-13b.Q5_1.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_deacon-13b-gguf/blob/main/deacon-13b.Q5_1.gguf) \| Q5_1 \| 9.1GB \| \| [deacon-13b.Q6_K.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_deacon-13b-gguf/blob/main/deacon-13b.Q6_K.gguf) \| Q6_K \| 9.95GB \| \| [deacon-13b.Q8_0.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_deacon-13b-gguf/blob/main/deacon-13b.Q8_0.gguf) \| Q8_0 \| 12.88GB \| Original model description: --- license: cc-by-nc-4.0 datasets: - KnutJaegersberg/facehugger --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63732ebbbd81fae2b3aaf3fb/4OQkvAa1zOK4Devv-aUdL.png) This model was fine tuned on AI filtered subsets of GPT-4 based subset of the Dolphin dataset and EvolInstruct V2. It has not been explicitly aligned to positive, negative or bureaucratically prescribed value systems. It might kill us all! Time to shit your pants, regulators. I literally put black goo on Dolphin-7B sperm, which then fertilized Evolved Instructions... What's different is evil... ;) I intend to train 3 sizes. Prompt Example: ``` ### System: You are an AI assistant. User will give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps. ### Instruction: How do you fine tune a large language model? ### Response: ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__deacon-13b) \| Metric \| Value \| \|-----------------------\|---------------------------\| \| Avg. \| 46.78 \| \| ARC (25-shot) \| 57.85 \| \| HellaSwag (10-shot) \| 82.63 \| \| MMLU (5-shot) \| 55.25 \| \| TruthfulQA (0-shot) \| 39.33 \| \| Winogrande (5-shot) \| 76.32 \| \| GSM8K (5-shot) \| 10.39 \| \| DROP (3-shot) \| 5.67 \|
crisistransformers/CT-M3-Complete	crisistransformers	2024-05-20T01:17:20Z	366	1	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "arxiv:2403.16614", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-07-10T04:05:32Z	# CrisisTransformers CrisisTransformers is a family of pre-trained language models and sentence encoders introduced in the papers "[CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts](https://www.sciencedirect.com/science/article/pii/S0950705124005501)" and "[Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts](https://arxiv.org/abs/2403.16614)". The models were trained based on the RoBERTa pre-training procedure on a massive corpus of over 15 billion word tokens sourced from tweets associated with 30+ crisis events such as disease outbreaks, natural disasters, conflicts, etc. Please refer to the [associated paper](https://www.sciencedirect.com/science/article/pii/S0950705124005501) for more details. CrisisTransformers were evaluated on 18 public crisis-specific datasets against strong baselines. Our pre-trained models outperform the baselines across all 18 datasets in classification tasks, and our best-performing sentence-encoder (mono-lingual) outperforms the state-of-the-art by more than 17\% in sentence encoding tasks. The multi-lingual sentence encoders (support 50+ languages; see [associated paper](https://arxiv.org/abs/2403.16614)) are designed to approximate the embedding space of the best-performing mono-lingual sentence encoder. ## Uses CrisisTransformers has 8 pre-trained models, 1 mono-lingual and 2 multi-lingual sentence encoders. The pre-trained models should be finetuned for downstream tasks just like [BERT](https://huggingface.co/bert-base-cased) and [RoBERTa](https://huggingface.co/roberta-base). The sentence encoders can be used out-of-the-box just like [Sentence-Transformers](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) for sentence encoding to facilitate tasks such as semantic search, clustering, topic modelling. ## Models and naming conventions CT-M1 models were trained from scratch up to 40 epochs, while CT-M2 models were initialized with pre-trained RoBERTa's weights and CT-M3 models were initialized with pre-trained BERTweet's weights and both trained for up to 20 epochs. OneLook represents the checkpoint after 1 epoch, BestLoss represents the checkpoint with the lowest loss during training, and Complete represents the checkpoint after completing all epochs. SE represents sentence encoder. \| pre-trained model \| source \| \|--\|--\| \|CT-M1-BestLoss\|[crisistransformers/CT-M1-BestLoss](https://huggingface.co/crisistransformers/CT-M1-BestLoss)\| \|CT-M1-Complete\|[crisistransformers/CT-M1-Complete](https://huggingface.co/crisistransformers/CT-M1-Complete)\| \|CT-M2-OneLook\|[crisistransformers/CT-M2-OneLook](https://huggingface.co/crisistransformers/CT-M2-OneLook)\| \|CT-M2-BestLoss\|[crisistransformers/CT-M2-BestLoss](https://huggingface.co/crisistransformers/CT-M2-BestLoss)\| \|CT-M2-Complete\|[crisistransformers/CT-M2-Complete](https://huggingface.co/crisistransformers/CT-M2-Complete)\| \|CT-M3-OneLook\|[crisistransformers/CT-M3-OneLook](https://huggingface.co/crisistransformers/CT-M3-OneLook)\| \|CT-M3-BestLoss\|[crisistransformers/CT-M3-BestLoss](https://huggingface.co/crisistransformers/CT-M3-BestLoss)\| \|CT-M3-Complete\|[crisistransformers/CT-M3-Complete](https://huggingface.co/crisistransformers/CT-M3-Complete)\| \| sentence encoder \| source \| \|--\|--\| \|CT-M1-Complete-SE (mono-lingual: EN)\|[crisistransformers/CT-M1-Complete-SE](https://huggingface.co/crisistransformers/CT-M1-Complete-SE)\| \|CT-XLMR-SE (multi-lingual)\|[crisistransformers/CT-XLMR-SE](https://huggingface.co/crisistransformers/CT-XLMR-SE)\| \|CT-mBERT-SE (multi-lingual)\|[crisistransformers/CT-mBERT-SE](https://huggingface.co/crisistransformers/CT-mBERT-SE)\| Languages supported by the multi-lingual sentence encoders: Albanian, Arabic, Armenian, Bulgarian, Catalan, Chinese (Simplified), Chinese (Traditional), Croatian, Czech, Danish, Dutch, Estonian, Finnish, French, French (Canada), Galician, Georgian, German, Greek, Gujarati, Hebrew, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Kurdish (Sorani), Latvian, Lithuanian, Macedonian, Malay, Marathi, Mongolian, Myanmar (Burmese), Norwegian, Persian, Polish, Portuguese, Portuguese (Brazil), Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Swedish, Thai, Turkish, Ukrainian, Urdu, and Vietnamese. ## Citation If you use CrisisTransformers and the mono-lingual sentence encoder, please cite the following paper: ``` @article{lamsal2023crisistransformers, title={CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts}, author={Rabindra Lamsal and Maria Rodriguez Read and Shanika Karunasekera}, journal={Knowledge-Based Systems}, pages={111916}, year={2024}, publisher={Elsevier} } ``` If you use the multi-lingual sentence encoders, please cite the following paper: ``` @article{lamsal2024semantically, title={Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts}, author={Rabindra Lamsal and Maria Rodriguez Read and Shanika Karunasekera}, year={2024}, eprint={2403.16614}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
crisistransformers/CT-M3-BestLoss	crisistransformers	2024-05-20T01:17:14Z	162	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "arxiv:2403.16614", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-07-21T07:27:53Z	# CrisisTransformers CrisisTransformers is a family of pre-trained language models and sentence encoders introduced in the papers "[CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts](https://www.sciencedirect.com/science/article/pii/S0950705124005501)" and "[Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts](https://arxiv.org/abs/2403.16614)". The models were trained based on the RoBERTa pre-training procedure on a massive corpus of over 15 billion word tokens sourced from tweets associated with 30+ crisis events such as disease outbreaks, natural disasters, conflicts, etc. Please refer to the [associated paper](https://www.sciencedirect.com/science/article/pii/S0950705124005501) for more details. CrisisTransformers were evaluated on 18 public crisis-specific datasets against strong baselines. Our pre-trained models outperform the baselines across all 18 datasets in classification tasks, and our best-performing sentence-encoder (mono-lingual) outperforms the state-of-the-art by more than 17\% in sentence encoding tasks. The multi-lingual sentence encoders (support 50+ languages; see [associated paper](https://arxiv.org/abs/2403.16614)) are designed to approximate the embedding space of the best-performing mono-lingual sentence encoder. ## Uses CrisisTransformers has 8 pre-trained models, 1 mono-lingual and 2 multi-lingual sentence encoders. The pre-trained models should be finetuned for downstream tasks just like [BERT](https://huggingface.co/bert-base-cased) and [RoBERTa](https://huggingface.co/roberta-base). The sentence encoders can be used out-of-the-box just like [Sentence-Transformers](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) for sentence encoding to facilitate tasks such as semantic search, clustering, topic modelling. ## Models and naming conventions CT-M1 models were trained from scratch up to 40 epochs, while CT-M2 models were initialized with pre-trained RoBERTa's weights and CT-M3 models were initialized with pre-trained BERTweet's weights and both trained for up to 20 epochs. OneLook represents the checkpoint after 1 epoch, BestLoss represents the checkpoint with the lowest loss during training, and Complete represents the checkpoint after completing all epochs. SE represents sentence encoder. \| pre-trained model \| source \| \|--\|--\| \|CT-M1-BestLoss\|[crisistransformers/CT-M1-BestLoss](https://huggingface.co/crisistransformers/CT-M1-BestLoss)\| \|CT-M1-Complete\|[crisistransformers/CT-M1-Complete](https://huggingface.co/crisistransformers/CT-M1-Complete)\| \|CT-M2-OneLook\|[crisistransformers/CT-M2-OneLook](https://huggingface.co/crisistransformers/CT-M2-OneLook)\| \|CT-M2-BestLoss\|[crisistransformers/CT-M2-BestLoss](https://huggingface.co/crisistransformers/CT-M2-BestLoss)\| \|CT-M2-Complete\|[crisistransformers/CT-M2-Complete](https://huggingface.co/crisistransformers/CT-M2-Complete)\| \|CT-M3-OneLook\|[crisistransformers/CT-M3-OneLook](https://huggingface.co/crisistransformers/CT-M3-OneLook)\| \|CT-M3-BestLoss\|[crisistransformers/CT-M3-BestLoss](https://huggingface.co/crisistransformers/CT-M3-BestLoss)\| \|CT-M3-Complete\|[crisistransformers/CT-M3-Complete](https://huggingface.co/crisistransformers/CT-M3-Complete)\| \| sentence encoder \| source \| \|--\|--\| \|CT-M1-Complete-SE (mono-lingual: EN)\|[crisistransformers/CT-M1-Complete-SE](https://huggingface.co/crisistransformers/CT-M1-Complete-SE)\| \|CT-XLMR-SE (multi-lingual)\|[crisistransformers/CT-XLMR-SE](https://huggingface.co/crisistransformers/CT-XLMR-SE)\| \|CT-mBERT-SE (multi-lingual)\|[crisistransformers/CT-mBERT-SE](https://huggingface.co/crisistransformers/CT-mBERT-SE)\| Languages supported by the multi-lingual sentence encoders: Albanian, Arabic, Armenian, Bulgarian, Catalan, Chinese (Simplified), Chinese (Traditional), Croatian, Czech, Danish, Dutch, Estonian, Finnish, French, French (Canada), Galician, Georgian, German, Greek, Gujarati, Hebrew, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Kurdish (Sorani), Latvian, Lithuanian, Macedonian, Malay, Marathi, Mongolian, Myanmar (Burmese), Norwegian, Persian, Polish, Portuguese, Portuguese (Brazil), Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Swedish, Thai, Turkish, Ukrainian, Urdu, and Vietnamese. ## Citation If you use CrisisTransformers and the mono-lingual sentence encoder, please cite the following paper: ``` @article{lamsal2023crisistransformers, title={CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts}, author={Rabindra Lamsal and Maria Rodriguez Read and Shanika Karunasekera}, journal={Knowledge-Based Systems}, pages={111916}, year={2024}, publisher={Elsevier} } ``` If you use the multi-lingual sentence encoders, please cite the following paper: ``` @article{lamsal2024semantically, title={Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts}, author={Rabindra Lamsal and Maria Rodriguez Read and Shanika Karunasekera}, year={2024}, eprint={2403.16614}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
crisistransformers/CT-M3-OneLook	crisistransformers	2024-05-20T01:17:07Z	161	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "arxiv:2403.16614", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-07-10T03:42:07Z	# CrisisTransformers CrisisTransformers is a family of pre-trained language models and sentence encoders introduced in the papers "[CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts](https://www.sciencedirect.com/science/article/pii/S0950705124005501)" and "[Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts](https://arxiv.org/abs/2403.16614)". The models were trained based on the RoBERTa pre-training procedure on a massive corpus of over 15 billion word tokens sourced from tweets associated with 30+ crisis events such as disease outbreaks, natural disasters, conflicts, etc. Please refer to the [associated paper](https://www.sciencedirect.com/science/article/pii/S0950705124005501) for more details. CrisisTransformers were evaluated on 18 public crisis-specific datasets against strong baselines. Our pre-trained models outperform the baselines across all 18 datasets in classification tasks, and our best-performing sentence-encoder (mono-lingual) outperforms the state-of-the-art by more than 17\% in sentence encoding tasks. The multi-lingual sentence encoders (support 50+ languages; see [associated paper](https://arxiv.org/abs/2403.16614)) are designed to approximate the embedding space of the best-performing mono-lingual sentence encoder. ## Uses CrisisTransformers has 8 pre-trained models, 1 mono-lingual and 2 multi-lingual sentence encoders. The pre-trained models should be finetuned for downstream tasks just like [BERT](https://huggingface.co/bert-base-cased) and [RoBERTa](https://huggingface.co/roberta-base). The sentence encoders can be used out-of-the-box just like [Sentence-Transformers](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) for sentence encoding to facilitate tasks such as semantic search, clustering, topic modelling. ## Models and naming conventions CT-M1 models were trained from scratch up to 40 epochs, while CT-M2 models were initialized with pre-trained RoBERTa's weights and CT-M3 models were initialized with pre-trained BERTweet's weights and both trained for up to 20 epochs. OneLook represents the checkpoint after 1 epoch, BestLoss represents the checkpoint with the lowest loss during training, and Complete represents the checkpoint after completing all epochs. SE represents sentence encoder. \| pre-trained model \| source \| \|--\|--\| \|CT-M1-BestLoss\|[crisistransformers/CT-M1-BestLoss](https://huggingface.co/crisistransformers/CT-M1-BestLoss)\| \|CT-M1-Complete\|[crisistransformers/CT-M1-Complete](https://huggingface.co/crisistransformers/CT-M1-Complete)\| \|CT-M2-OneLook\|[crisistransformers/CT-M2-OneLook](https://huggingface.co/crisistransformers/CT-M2-OneLook)\| \|CT-M2-BestLoss\|[crisistransformers/CT-M2-BestLoss](https://huggingface.co/crisistransformers/CT-M2-BestLoss)\| \|CT-M2-Complete\|[crisistransformers/CT-M2-Complete](https://huggingface.co/crisistransformers/CT-M2-Complete)\| \|CT-M3-OneLook\|[crisistransformers/CT-M3-OneLook](https://huggingface.co/crisistransformers/CT-M3-OneLook)\| \|CT-M3-BestLoss\|[crisistransformers/CT-M3-BestLoss](https://huggingface.co/crisistransformers/CT-M3-BestLoss)\| \|CT-M3-Complete\|[crisistransformers/CT-M3-Complete](https://huggingface.co/crisistransformers/CT-M3-Complete)\| \| sentence encoder \| source \| \|--\|--\| \|CT-M1-Complete-SE (mono-lingual: EN)\|[crisistransformers/CT-M1-Complete-SE](https://huggingface.co/crisistransformers/CT-M1-Complete-SE)\| \|CT-XLMR-SE (multi-lingual)\|[crisistransformers/CT-XLMR-SE](https://huggingface.co/crisistransformers/CT-XLMR-SE)\| \|CT-mBERT-SE (multi-lingual)\|[crisistransformers/CT-mBERT-SE](https://huggingface.co/crisistransformers/CT-mBERT-SE)\| Languages supported by the multi-lingual sentence encoders: Albanian, Arabic, Armenian, Bulgarian, Catalan, Chinese (Simplified), Chinese (Traditional), Croatian, Czech, Danish, Dutch, Estonian, Finnish, French, French (Canada), Galician, Georgian, German, Greek, Gujarati, Hebrew, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Kurdish (Sorani), Latvian, Lithuanian, Macedonian, Malay, Marathi, Mongolian, Myanmar (Burmese), Norwegian, Persian, Polish, Portuguese, Portuguese (Brazil), Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Swedish, Thai, Turkish, Ukrainian, Urdu, and Vietnamese. ## Citation If you use CrisisTransformers and the mono-lingual sentence encoder, please cite the following paper: ``` @article{lamsal2023crisistransformers, title={CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts}, author={Rabindra Lamsal and Maria Rodriguez Read and Shanika Karunasekera}, journal={Knowledge-Based Systems}, pages={111916}, year={2024}, publisher={Elsevier} } ``` If you use the multi-lingual sentence encoders, please cite the following paper: ``` @article{lamsal2024semantically, title={Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts}, author={Rabindra Lamsal and Maria Rodriguez Read and Shanika Karunasekera}, year={2024}, eprint={2403.16614}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
crisistransformers/CT-M2-Complete	crisistransformers	2024-05-20T01:17:01Z	243	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "arxiv:2403.16614", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-07-21T06:55:04Z	# CrisisTransformers CrisisTransformers is a family of pre-trained language models and sentence encoders introduced in the papers "[CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts](https://www.sciencedirect.com/science/article/pii/S0950705124005501)" and "[Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts](https://arxiv.org/abs/2403.16614)". The models were trained based on the RoBERTa pre-training procedure on a massive corpus of over 15 billion word tokens sourced from tweets associated with 30+ crisis events such as disease outbreaks, natural disasters, conflicts, etc. Please refer to the [associated paper](https://www.sciencedirect.com/science/article/pii/S0950705124005501) for more details. CrisisTransformers were evaluated on 18 public crisis-specific datasets against strong baselines. Our pre-trained models outperform the baselines across all 18 datasets in classification tasks, and our best-performing sentence-encoder (mono-lingual) outperforms the state-of-the-art by more than 17\% in sentence encoding tasks. The multi-lingual sentence encoders (support 50+ languages; see [associated paper](https://arxiv.org/abs/2403.16614)) are designed to approximate the embedding space of the best-performing mono-lingual sentence encoder. ## Uses CrisisTransformers has 8 pre-trained models, 1 mono-lingual and 2 multi-lingual sentence encoders. The pre-trained models should be finetuned for downstream tasks just like [BERT](https://huggingface.co/bert-base-cased) and [RoBERTa](https://huggingface.co/roberta-base). The sentence encoders can be used out-of-the-box just like [Sentence-Transformers](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) for sentence encoding to facilitate tasks such as semantic search, clustering, topic modelling. ## Models and naming conventions CT-M1 models were trained from scratch up to 40 epochs, while CT-M2 models were initialized with pre-trained RoBERTa's weights and CT-M3 models were initialized with pre-trained BERTweet's weights and both trained for up to 20 epochs. OneLook represents the checkpoint after 1 epoch, BestLoss represents the checkpoint with the lowest loss during training, and Complete represents the checkpoint after completing all epochs. SE represents sentence encoder. \| pre-trained model \| source \| \|--\|--\| \|CT-M1-BestLoss\|[crisistransformers/CT-M1-BestLoss](https://huggingface.co/crisistransformers/CT-M1-BestLoss)\| \|CT-M1-Complete\|[crisistransformers/CT-M1-Complete](https://huggingface.co/crisistransformers/CT-M1-Complete)\| \|CT-M2-OneLook\|[crisistransformers/CT-M2-OneLook](https://huggingface.co/crisistransformers/CT-M2-OneLook)\| \|CT-M2-BestLoss\|[crisistransformers/CT-M2-BestLoss](https://huggingface.co/crisistransformers/CT-M2-BestLoss)\| \|CT-M2-Complete\|[crisistransformers/CT-M2-Complete](https://huggingface.co/crisistransformers/CT-M2-Complete)\| \|CT-M3-OneLook\|[crisistransformers/CT-M3-OneLook](https://huggingface.co/crisistransformers/CT-M3-OneLook)\| \|CT-M3-BestLoss\|[crisistransformers/CT-M3-BestLoss](https://huggingface.co/crisistransformers/CT-M3-BestLoss)\| \|CT-M3-Complete\|[crisistransformers/CT-M3-Complete](https://huggingface.co/crisistransformers/CT-M3-Complete)\| \| sentence encoder \| source \| \|--\|--\| \|CT-M1-Complete-SE (mono-lingual: EN)\|[crisistransformers/CT-M1-Complete-SE](https://huggingface.co/crisistransformers/CT-M1-Complete-SE)\| \|CT-XLMR-SE (multi-lingual)\|[crisistransformers/CT-XLMR-SE](https://huggingface.co/crisistransformers/CT-XLMR-SE)\| \|CT-mBERT-SE (multi-lingual)\|[crisistransformers/CT-mBERT-SE](https://huggingface.co/crisistransformers/CT-mBERT-SE)\| Languages supported by the multi-lingual sentence encoders: Albanian, Arabic, Armenian, Bulgarian, Catalan, Chinese (Simplified), Chinese (Traditional), Croatian, Czech, Danish, Dutch, Estonian, Finnish, French, French (Canada), Galician, Georgian, German, Greek, Gujarati, Hebrew, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Kurdish (Sorani), Latvian, Lithuanian, Macedonian, Malay, Marathi, Mongolian, Myanmar (Burmese), Norwegian, Persian, Polish, Portuguese, Portuguese (Brazil), Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Swedish, Thai, Turkish, Ukrainian, Urdu, and Vietnamese. ## Citation If you use CrisisTransformers and the mono-lingual sentence encoder, please cite the following paper: ``` @article{lamsal2023crisistransformers, title={CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts}, author={Rabindra Lamsal and Maria Rodriguez Read and Shanika Karunasekera}, journal={Knowledge-Based Systems}, pages={111916}, year={2024}, publisher={Elsevier} } ``` If you use the multi-lingual sentence encoders, please cite the following paper: ``` @article{lamsal2024semantically, title={Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts}, author={Rabindra Lamsal and Maria Rodriguez Read and Shanika Karunasekera}, year={2024}, eprint={2403.16614}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
crisistransformers/CT-M2-OneLook	crisistransformers	2024-05-20T01:16:48Z	161	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "arxiv:2403.16614", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-07-10T03:28:54Z	# CrisisTransformers CrisisTransformers is a family of pre-trained language models and sentence encoders introduced in the papers "[CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts](https://www.sciencedirect.com/science/article/pii/S0950705124005501)" and "[Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts](https://arxiv.org/abs/2403.16614)". The models were trained based on the RoBERTa pre-training procedure on a massive corpus of over 15 billion word tokens sourced from tweets associated with 30+ crisis events such as disease outbreaks, natural disasters, conflicts, etc. Please refer to the [associated paper](https://www.sciencedirect.com/science/article/pii/S0950705124005501) for more details. CrisisTransformers were evaluated on 18 public crisis-specific datasets against strong baselines. Our pre-trained models outperform the baselines across all 18 datasets in classification tasks, and our best-performing sentence-encoder (mono-lingual) outperforms the state-of-the-art by more than 17\% in sentence encoding tasks. The multi-lingual sentence encoders (support 50+ languages; see [associated paper](https://arxiv.org/abs/2403.16614)) are designed to approximate the embedding space of the best-performing mono-lingual sentence encoder. ## Uses CrisisTransformers has 8 pre-trained models, 1 mono-lingual and 2 multi-lingual sentence encoders. The pre-trained models should be finetuned for downstream tasks just like [BERT](https://huggingface.co/bert-base-cased) and [RoBERTa](https://huggingface.co/roberta-base). The sentence encoders can be used out-of-the-box just like [Sentence-Transformers](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) for sentence encoding to facilitate tasks such as semantic search, clustering, topic modelling. ## Models and naming conventions CT-M1 models were trained from scratch up to 40 epochs, while CT-M2 models were initialized with pre-trained RoBERTa's weights and CT-M3 models were initialized with pre-trained BERTweet's weights and both trained for up to 20 epochs. OneLook represents the checkpoint after 1 epoch, BestLoss represents the checkpoint with the lowest loss during training, and Complete represents the checkpoint after completing all epochs. SE represents sentence encoder. \| pre-trained model \| source \| \|--\|--\| \|CT-M1-BestLoss\|[crisistransformers/CT-M1-BestLoss](https://huggingface.co/crisistransformers/CT-M1-BestLoss)\| \|CT-M1-Complete\|[crisistransformers/CT-M1-Complete](https://huggingface.co/crisistransformers/CT-M1-Complete)\| \|CT-M2-OneLook\|[crisistransformers/CT-M2-OneLook](https://huggingface.co/crisistransformers/CT-M2-OneLook)\| \|CT-M2-BestLoss\|[crisistransformers/CT-M2-BestLoss](https://huggingface.co/crisistransformers/CT-M2-BestLoss)\| \|CT-M2-Complete\|[crisistransformers/CT-M2-Complete](https://huggingface.co/crisistransformers/CT-M2-Complete)\| \|CT-M3-OneLook\|[crisistransformers/CT-M3-OneLook](https://huggingface.co/crisistransformers/CT-M3-OneLook)\| \|CT-M3-BestLoss\|[crisistransformers/CT-M3-BestLoss](https://huggingface.co/crisistransformers/CT-M3-BestLoss)\| \|CT-M3-Complete\|[crisistransformers/CT-M3-Complete](https://huggingface.co/crisistransformers/CT-M3-Complete)\| \| sentence encoder \| source \| \|--\|--\| \|CT-M1-Complete-SE (mono-lingual: EN)\|[crisistransformers/CT-M1-Complete-SE](https://huggingface.co/crisistransformers/CT-M1-Complete-SE)\| \|CT-XLMR-SE (multi-lingual)\|[crisistransformers/CT-XLMR-SE](https://huggingface.co/crisistransformers/CT-XLMR-SE)\| \|CT-mBERT-SE (multi-lingual)\|[crisistransformers/CT-mBERT-SE](https://huggingface.co/crisistransformers/CT-mBERT-SE)\| Languages supported by the multi-lingual sentence encoders: Albanian, Arabic, Armenian, Bulgarian, Catalan, Chinese (Simplified), Chinese (Traditional), Croatian, Czech, Danish, Dutch, Estonian, Finnish, French, French (Canada), Galician, Georgian, German, Greek, Gujarati, Hebrew, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Kurdish (Sorani), Latvian, Lithuanian, Macedonian, Malay, Marathi, Mongolian, Myanmar (Burmese), Norwegian, Persian, Polish, Portuguese, Portuguese (Brazil), Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Swedish, Thai, Turkish, Ukrainian, Urdu, and Vietnamese. ## Citation If you use CrisisTransformers and the mono-lingual sentence encoder, please cite the following paper: ``` @article{lamsal2023crisistransformers, title={CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts}, author={Rabindra Lamsal and Maria Rodriguez Read and Shanika Karunasekera}, journal={Knowledge-Based Systems}, pages={111916}, year={2024}, publisher={Elsevier} } ``` If you use the multi-lingual sentence encoders, please cite the following paper: ``` @article{lamsal2024semantically, title={Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts}, author={Rabindra Lamsal and Maria Rodriguez Read and Shanika Karunasekera}, year={2024}, eprint={2403.16614}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
crisistransformers/CT-mBERT-SE	crisistransformers	2024-05-20T01:15:53Z	1,306	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "arxiv:2403.16614", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-02-13T23:07:56Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # CrisisTransformers CrisisTransformers is a family of pre-trained language models and sentence encoders introduced in the papers "[CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts](https://www.sciencedirect.com/science/article/pii/S0950705124005501)" and "[Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts](https://arxiv.org/abs/2403.16614)". The models were trained based on the RoBERTa pre-training procedure on a massive corpus of over 15 billion word tokens sourced from tweets associated with 30+ crisis events such as disease outbreaks, natural disasters, conflicts, etc. Please refer to the [associated paper](https://www.sciencedirect.com/science/article/pii/S0950705124005501) for more details. CrisisTransformers were evaluated on 18 public crisis-specific datasets against strong baselines. Our pre-trained models outperform the baselines across all 18 datasets in classification tasks, and our best-performing sentence-encoder (mono-lingual) outperforms the state-of-the-art by more than 17\% in sentence encoding tasks. The multi-lingual sentence encoders (support 50+ languages; see [associated paper](https://arxiv.org/abs/2403.16614)) are designed to approximate the embedding space of the best-performing mono-lingual sentence encoder. ## Uses CrisisTransformers has 8 pre-trained models, 1 mono-lingual and 2 multi-lingual sentence encoders. The pre-trained models should be finetuned for downstream tasks just like [BERT](https://huggingface.co/bert-base-cased) and [RoBERTa](https://huggingface.co/roberta-base). The sentence encoders can be used out-of-the-box just like [Sentence-Transformers](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) for sentence encoding to facilitate tasks such as semantic search, clustering, topic modelling. ## Models and naming conventions CT-M1 models were trained from scratch up to 40 epochs, while CT-M2 models were initialized with pre-trained RoBERTa's weights and CT-M3 models were initialized with pre-trained BERTweet's weights and both trained for up to 20 epochs. OneLook represents the checkpoint after 1 epoch, BestLoss represents the checkpoint with the lowest loss during training, and Complete represents the checkpoint after completing all epochs. SE represents sentence encoder. \| pre-trained model \| source \| \|--\|--\| \|CT-M1-BestLoss\|[crisistransformers/CT-M1-BestLoss](https://huggingface.co/crisistransformers/CT-M1-BestLoss)\| \|CT-M1-Complete\|[crisistransformers/CT-M1-Complete](https://huggingface.co/crisistransformers/CT-M1-Complete)\| \|CT-M2-OneLook\|[crisistransformers/CT-M2-OneLook](https://huggingface.co/crisistransformers/CT-M2-OneLook)\| \|CT-M2-BestLoss\|[crisistransformers/CT-M2-BestLoss](https://huggingface.co/crisistransformers/CT-M2-BestLoss)\| \|CT-M2-Complete\|[crisistransformers/CT-M2-Complete](https://huggingface.co/crisistransformers/CT-M2-Complete)\| \|CT-M3-OneLook\|[crisistransformers/CT-M3-OneLook](https://huggingface.co/crisistransformers/CT-M3-OneLook)\| \|CT-M3-BestLoss\|[crisistransformers/CT-M3-BestLoss](https://huggingface.co/crisistransformers/CT-M3-BestLoss)\| \|CT-M3-Complete\|[crisistransformers/CT-M3-Complete](https://huggingface.co/crisistransformers/CT-M3-Complete)\| \| sentence encoder \| source \| \|--\|--\| \|CT-M1-Complete-SE (mono-lingual: EN)\|[crisistransformers/CT-M1-Complete-SE](https://huggingface.co/crisistransformers/CT-M1-Complete-SE)\| \|CT-XLMR-SE (multi-lingual)\|[crisistransformers/CT-XLMR-SE](https://huggingface.co/crisistransformers/CT-XLMR-SE)\| \|CT-mBERT-SE (multi-lingual)\|[crisistransformers/CT-mBERT-SE](https://huggingface.co/crisistransformers/CT-mBERT-SE)\| Languages supported by the multi-lingual sentence encoders: Albanian, Arabic, Armenian, Bulgarian, Catalan, Chinese (Simplified), Chinese (Traditional), Croatian, Czech, Danish, Dutch, Estonian, Finnish, French, French (Canada), Galician, Georgian, German, Greek, Gujarati, Hebrew, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Kurdish (Sorani), Latvian, Lithuanian, Macedonian, Malay, Marathi, Mongolian, Myanmar (Burmese), Norwegian, Persian, Polish, Portuguese, Portuguese (Brazil), Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Swedish, Thai, Turkish, Ukrainian, Urdu, and Vietnamese. ## Citation If you use CrisisTransformers and the mono-lingual sentence encoder, please cite the following paper: ``` @article{lamsal2023crisistransformers, title={CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts}, author={Rabindra Lamsal and Maria Rodriguez Read and Shanika Karunasekera}, journal={Knowledge-Based Systems}, pages={111916}, year={2024}, publisher={Elsevier} } ``` If you use the multi-lingual sentence encoders, please cite the following paper: ``` @article{lamsal2024semantically, title={Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts}, author={Rabindra Lamsal and Maria Rodriguez Read and Shanika Karunasekera}, year={2024}, eprint={2403.16614}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
Sorour/cls_sentiment_mistral_v1	Sorour	2024-05-20T01:12:58Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]	null	2024-05-20T00:24:07Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.2 datasets: - generator model-index: - name: cls_sentiment_mistral_v1 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. --> # cls_sentiment_mistral_v1 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 0.5972 ## 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: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.7365 \| 0.1986 \| 50 \| 0.7344 \| \| 0.6778 \| 0.3972 \| 100 \| 0.6852 \| \| 0.6548 \| 0.5958 \| 150 \| 0.6588 \| \| 0.6728 \| 0.7944 \| 200 \| 0.6333 \| \| 0.6148 \| 0.9930 \| 250 \| 0.6106 \| \| 0.43 \| 1.1917 \| 300 \| 0.6174 \| \| 0.4575 \| 1.3903 \| 350 \| 0.6081 \| \| 0.4225 \| 1.5889 \| 400 \| 0.6058 \| \| 0.4136 \| 1.7875 \| 450 \| 0.5976 \| \| 0.441 \| 1.9861 \| 500 \| 0.5972 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
WhoTookMyAmogusNickname/llama2-7b-megacode2_min100-GGML	WhoTookMyAmogusNickname	2024-05-20T01:11:13Z	0	0	null	[ "region:us" ]	null	2023-08-13T05:34:54Z	[llama2-7b-megacode2_min100](https://huggingface.co/andreaskoepf/llama2-7b-megacode2_min100) converted and quantized to GGML\ had to use a "[added_tokens.json](https://huggingface.co/andreaskoepf/llama2-7b-oasst-baseline/blob/main/added_tokens.json)" from another of their models, as the vocab size is strangely 32007
Solshine/llama3_SOAP_Notes_06_lora_model	Solshine	2024-05-20T01:08:33Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-20T01:08:24Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: Solshine - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
St4n/wav2vec2-base-self-519-colab-3-grams	St4n	2024-05-20T01:01:12Z	82	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:audiofolder", "base_model:facebook/wav2vec2-base-960h", "base_model:finetune:facebook/wav2vec2-base-960h", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-20T01:00:05Z	--- license: apache-2.0 base_model: facebook/wav2vec2-base-960h tags: - generated_from_trainer datasets: - audiofolder metrics: - wer model-index: - name: wav2vec2-base-self-331-colab results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: audiofolder type: audiofolder config: default split: test args: default metrics: - name: Wer type: wer value: 0.15007215007215008 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-self-331-colab This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3282 - Wer: 0.1501 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 300 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:------:\| \| 2.3444 \| 30.77 \| 200 \| 2.1940 \| 0.9841 \| \| 1.972 \| 61.54 \| 400 \| 1.4582 \| 0.8167 \| \| 1.3875 \| 92.31 \| 600 \| 0.8476 \| 0.5902 \| \| 0.9092 \| 123.08 \| 800 \| 0.5445 \| 0.3636 \| \| 0.6382 \| 153.85 \| 1000 \| 0.4129 \| 0.2641 \| \| 0.5789 \| 184.62 \| 1200 \| 0.3497 \| 0.1876 \| \| 0.4632 \| 215.38 \| 1400 \| 0.3478 \| 0.1616 \| \| 0.4474 \| 246.15 \| 1600 \| 0.3394 \| 0.1486 \| \| 0.429 \| 276.92 \| 1800 \| 0.3282 \| 0.1501 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
jsfamily/korean-small_t33	jsfamily	2024-05-20T00:59:16Z	96	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "ko", "dataset:korean_samll_dataset3", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-20T00:57:22Z	--- language: - ko license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer base_model: openai/whisper-small datasets: - korean_samll_dataset3 model-index: - name: korean-small_t33 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. --> # korean-small_t33 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the korean_samll_dataset3 dataset. It achieves the following results on the evaluation set: - eval_loss: 0.1656 - eval_cer: 6.6580 - eval_runtime: 2081.9667 - eval_samples_per_second: 3.128 - eval_steps_per_second: 0.391 - step: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
ytcheng/llama-3-8B-pretrain_v2	ytcheng	2024-05-20T00:56:09Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T00:52:01Z	--- library_name: transformers tags: - llama-factory --- # 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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head-empty-ai/Mytho-Lemon-11B	head-empty-ai	2024-05-20T00:55:38Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "base_model:KatyTheCutie/LemonadeRP-4.5.3", "base_model:finetune:KatyTheCutie/LemonadeRP-4.5.3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-19T20:45:35Z	--- base_model: - KatyTheCutie/LemonadeRP-4.5.3 library_name: transformers tags: - mergekit - merge --- # Mytho-Lemon-11B Just a simple 11B frankenmerge of LemonadeRP and MythoMist which was used in [matchaaaaa/Chaifighter-20B-v2](https://huggingface.co/matchaaaaa/Chaifighter-20B-v2). I didn't have to merge the models like this in Chaifighter, but I already had this lying around from a previous attempt, so I just went with it. It's nothing special, but here it is! ## Merge Details This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * [Gryphe/MythoMist-7B](https://huggingface.co/Gryphe/MythoMist-7b) * [KatyTheCutie/LemonadeRP-4.5.3](https://huggingface.co/KatyTheCutie/LemonadeRP-4.5.3) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: KatyTheCutie/LemonadeRP-4.5.3 layer_range: [0, 24] - sources: - model: Gryphe/MythoMist-7B layer_range: [8, 32] merge_method: passthrough dtype: bfloat16 ``` Anyway, have a great day!
lomov/targetsandgoalsv1	lomov	2024-05-20T00:52:59Z	124	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "autotrain", "dataset:targetsandgoalsv1/autotrain-data", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-20T00:51:53Z	--- tags: - autotrain - text-classification widget: - text: "I love AutoTrain" datasets: - targetsandgoalsv1/autotrain-data --- # Model Trained Using AutoTrain - Problem type: Text Classification ## Validation Metrics loss: 0.22812850773334503 f1_macro: 0.928605054676046 f1_micro: 0.9313725490196079 f1_weighted: 0.9297769573887364 precision_macro: 0.9294524189261031 precision_micro: 0.9313725490196079 precision_weighted: 0.930390072030939 recall_macro: 0.93 recall_micro: 0.9313725490196079 recall_weighted: 0.9313725490196079 accuracy: 0.9313725490196079
souvik0306/test_quant_merge_facebook_opt	souvik0306	2024-05-20T00:51:06Z	84	1	transformers	[ "transformers", "safetensors", "opt", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	text-generation	2024-05-20T00:50:54Z	--- library_name: transformers tags: [] --- # 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - 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]
lomov/strategytransitionplanv1	lomov	2024-05-20T00:48:16Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "autotrain", "dataset:strategytransitionplanv1/autotrain-data", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-20T00:47:26Z	--- tags: - autotrain - text-classification widget: - text: "I love AutoTrain" datasets: - strategytransitionplanv1/autotrain-data --- # Model Trained Using AutoTrain - Problem type: Text Classification ## Validation Metrics loss: 0.12461505830287933 f1_macro: 0.9837010534684953 f1_micro: 0.9838709677419355 f1_weighted: 0.9838517321638102 precision_macro: 0.9848484848484849 precision_micro: 0.9838709677419355 precision_weighted: 0.9846041055718475 recall_macro: 0.9833333333333334 recall_micro: 0.9838709677419355 recall_weighted: 0.9838709677419355 accuracy: 0.9838709677419355
lomov/strategydisofrisksv1	lomov	2024-05-20T00:45:50Z	112	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "autotrain", "dataset:strategydisofrisksv1/autotrain-data", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-20T00:44:53Z	--- tags: - autotrain - text-classification widget: - text: "I love AutoTrain" datasets: - strategydisofrisksv1/autotrain-data --- # Model Trained Using AutoTrain - Problem type: Text Classification ## Validation Metrics loss: 0.15990132093429565 f1_macro: 0.974930590799176 f1_micro: 0.975609756097561 f1_weighted: 0.9752584323168365 precision_macro: 0.9767316017316017 precision_micro: 0.975609756097561 precision_weighted: 0.9767447999155316 recall_macro: 0.975 recall_micro: 0.975609756097561 recall_weighted: 0.975609756097561 accuracy: 0.975609756097561
OscarGalavizC/roberta-base-bne-finetuned-multi-sentiment	OscarGalavizC	2024-05-20T00:45:17Z	109	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:BSC-LT/roberta-base-bne", "base_model:finetune:BSC-LT/roberta-base-bne", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-16T20:36:26Z	--- license: apache-2.0 base_model: BSC-TeMU/roberta-base-bne tags: - generated_from_trainer metrics: - accuracy model-index: - name: roberta-base-bne-finetuned-multi-sentiment 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-base-bne-finetuned-multi-sentiment This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7116 - Accuracy: 0.6914 ## 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.809 \| 1.0 \| 115 \| 0.7168 \| 0.6852 \| \| 0.6101 \| 2.0 \| 230 \| 0.7116 \| 0.6914 \| ### Framework versions - Transformers 4.40.2 - Pytorch 1.13.1+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1
mradermacher/Notaires_dolphin-2.9.1-yi-1.5-9b-GGUF	mradermacher	2024-05-20T00:43:49Z	17	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "llama", "trl", "orpo", "en", "base_model:baconnier/Notaires_dolphin-2.9.1-yi-1.5-9b", "base_model:quantized:baconnier/Notaires_dolphin-2.9.1-yi-1.5-9b", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-19T23:46:08Z	--- base_model: baconnier/Notaires_dolphin-2.9.1-yi-1.5-9b language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - llama - trl - orpo --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/baconnier/Notaires_dolphin-2.9.1-yi-1.5-9b <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Notaires_dolphin-2.9.1-yi-1.5-9b-GGUF/resolve/main/Notaires_dolphin-2.9.1-yi-1.5-9b.Q2_K.gguf) \| Q2_K \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Notaires_dolphin-2.9.1-yi-1.5-9b-GGUF/resolve/main/Notaires_dolphin-2.9.1-yi-1.5-9b.IQ3_XS.gguf) \| IQ3_XS \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Notaires_dolphin-2.9.1-yi-1.5-9b-GGUF/resolve/main/Notaires_dolphin-2.9.1-yi-1.5-9b.Q3_K_S.gguf) \| Q3_K_S \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Notaires_dolphin-2.9.1-yi-1.5-9b-GGUF/resolve/main/Notaires_dolphin-2.9.1-yi-1.5-9b.IQ3_S.gguf) \| IQ3_S \| 4.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Notaires_dolphin-2.9.1-yi-1.5-9b-GGUF/resolve/main/Notaires_dolphin-2.9.1-yi-1.5-9b.IQ3_M.gguf) \| IQ3_M \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Notaires_dolphin-2.9.1-yi-1.5-9b-GGUF/resolve/main/Notaires_dolphin-2.9.1-yi-1.5-9b.Q3_K_M.gguf) \| Q3_K_M \| 4.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Notaires_dolphin-2.9.1-yi-1.5-9b-GGUF/resolve/main/Notaires_dolphin-2.9.1-yi-1.5-9b.Q3_K_L.gguf) \| Q3_K_L \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Notaires_dolphin-2.9.1-yi-1.5-9b-GGUF/resolve/main/Notaires_dolphin-2.9.1-yi-1.5-9b.IQ4_XS.gguf) \| IQ4_XS \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Notaires_dolphin-2.9.1-yi-1.5-9b-GGUF/resolve/main/Notaires_dolphin-2.9.1-yi-1.5-9b.Q4_K_S.gguf) \| Q4_K_S \| 5.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Notaires_dolphin-2.9.1-yi-1.5-9b-GGUF/resolve/main/Notaires_dolphin-2.9.1-yi-1.5-9b.Q4_K_M.gguf) \| Q4_K_M \| 5.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Notaires_dolphin-2.9.1-yi-1.5-9b-GGUF/resolve/main/Notaires_dolphin-2.9.1-yi-1.5-9b.Q5_K_S.gguf) \| Q5_K_S \| 6.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Notaires_dolphin-2.9.1-yi-1.5-9b-GGUF/resolve/main/Notaires_dolphin-2.9.1-yi-1.5-9b.Q5_K_M.gguf) \| Q5_K_M \| 6.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Notaires_dolphin-2.9.1-yi-1.5-9b-GGUF/resolve/main/Notaires_dolphin-2.9.1-yi-1.5-9b.Q6_K.gguf) \| Q6_K \| 7.3 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Notaires_dolphin-2.9.1-yi-1.5-9b-GGUF/resolve/main/Notaires_dolphin-2.9.1-yi-1.5-9b.Q8_0.gguf) \| Q8_0 \| 9.5 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Notaires_dolphin-2.9.1-yi-1.5-9b-GGUF/resolve/main/Notaires_dolphin-2.9.1-yi-1.5-9b.f16.gguf) \| f16 \| 17.8 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
gnelson/sup-sample-1-phi3-mini	gnelson	2024-05-20T00:41:02Z	1	0	null	[ "gguf", "license:cc-by-sa-4.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-20T00:16:31Z	--- license: cc-by-sa-4.0 ---
tsavage68/MedQA_L3_1000steps_1e6rate_01beat_CSFTDPO	tsavage68	2024-05-20T00:35:13Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T00:31:07Z	--- license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - trl - dpo - generated_from_trainer model-index: - name: MedQA_L3_1000steps_1e6rate_01beat_CSFTDPO 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. --> # MedQA_L3_1000steps_1e6rate_01beat_CSFTDPO This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4018 - Rewards/chosen: -1.1456 - Rewards/rejected: -2.9172 - Rewards/accuracies: 0.7912 - Rewards/margins: 1.7716 - Logps/rejected: -50.4889 - Logps/chosen: -29.6790 - Logits/rejected: -1.3967 - Logits/chosen: -1.3936 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.695 \| 0.0489 \| 50 \| 0.6713 \| 0.0342 \| -0.0142 \| 0.6615 \| 0.0484 \| -21.4583 \| -17.8807 \| -0.9400 \| -0.9395 \| \| 0.6187 \| 0.0977 \| 100 \| 0.5915 \| -0.1174 \| -0.4200 \| 0.7121 \| 0.3027 \| -25.5168 \| -19.3963 \| -1.0412 \| -1.0403 \| \| 0.5652 \| 0.1466 \| 150 \| 0.5103 \| -0.6250 \| -1.3027 \| 0.7495 \| 0.6777 \| -34.3433 \| -24.4723 \| -1.1124 \| -1.1110 \| \| 0.4549 \| 0.1954 \| 200 \| 0.5152 \| -1.3616 \| -2.3988 \| 0.7231 \| 1.0372 \| -45.3043 \| -31.8385 \| -1.2048 \| -1.2020 \| \| 0.4875 \| 0.2443 \| 250 \| 0.4642 \| -0.6443 \| -1.7506 \| 0.7648 \| 1.1063 \| -38.8228 \| -24.6654 \| -1.1785 \| -1.1765 \| \| 0.4433 \| 0.2931 \| 300 \| 0.4453 \| -0.8917 \| -2.2308 \| 0.8044 \| 1.3391 \| -43.6244 \| -27.1394 \| -1.2423 \| -1.2401 \| \| 0.5036 \| 0.3420 \| 350 \| 0.4581 \| -0.7568 \| -2.0680 \| 0.7692 \| 1.3112 \| -41.9963 \| -25.7907 \| -1.2182 \| -1.2158 \| \| 0.6285 \| 0.3908 \| 400 \| 0.4703 \| -0.6136 \| -1.9063 \| 0.7604 \| 1.2927 \| -40.3798 \| -24.3588 \| -1.2386 \| -1.2361 \| \| 0.5726 \| 0.4397 \| 450 \| 0.4732 \| -0.4602 \| -1.5238 \| 0.7692 \| 1.0636 \| -36.5545 \| -22.8248 \| -1.2652 \| -1.2626 \| \| 0.5198 \| 0.4885 \| 500 \| 0.4280 \| -0.9825 \| -2.4466 \| 0.8066 \| 1.4641 \| -45.7828 \| -28.0480 \| -1.3426 \| -1.3399 \| \| 0.3963 \| 0.5374 \| 550 \| 0.4236 \| -0.9424 \| -2.3856 \| 0.8022 \| 1.4432 \| -45.1725 \| -27.6467 \| -1.3514 \| -1.3488 \| \| 0.3233 \| 0.5862 \| 600 \| 0.4127 \| -0.9551 \| -2.5770 \| 0.8000 \| 1.6219 \| -47.0868 \| -27.7738 \| -1.3761 \| -1.3733 \| \| 0.3955 \| 0.6351 \| 650 \| 0.4236 \| -0.9988 \| -2.7155 \| 0.7846 \| 1.7167 \| -48.4714 \| -28.2110 \| -1.3837 \| -1.3806 \| \| 0.3121 \| 0.6839 \| 700 \| 0.4109 \| -1.0837 \| -2.8282 \| 0.7868 \| 1.7445 \| -49.5986 \| -29.0595 \| -1.3902 \| -1.3871 \| \| 0.4809 \| 0.7328 \| 750 \| 0.4060 \| -1.1344 \| -2.8863 \| 0.7846 \| 1.7519 \| -50.1796 \| -29.5667 \| -1.3954 \| -1.3923 \| \| 0.4075 \| 0.7816 \| 800 \| 0.4013 \| -1.1649 \| -2.9284 \| 0.7868 \| 1.7635 \| -50.6008 \| -29.8717 \| -1.3971 \| -1.3939 \| \| 0.584 \| 0.8305 \| 850 \| 0.4014 \| -1.1482 \| -2.9188 \| 0.7890 \| 1.7706 \| -50.5041 \| -29.7042 \| -1.3971 \| -1.3939 \| \| 0.5942 \| 0.8793 \| 900 \| 0.4042 \| -1.1517 \| -2.9160 \| 0.7846 \| 1.7643 \| -50.4761 \| -29.7394 \| -1.3965 \| -1.3934 \| \| 0.3169 \| 0.9282 \| 950 \| 0.4040 \| -1.1507 \| -2.9162 \| 0.7934 \| 1.7655 \| -50.4786 \| -29.7294 \| -1.3965 \| -1.3934 \| \| 0.2727 \| 0.9770 \| 1000 \| 0.4018 \| -1.1456 \| -2.9172 \| 0.7912 \| 1.7716 \| -50.4889 \| -29.6790 \| -1.3967 \| -1.3936 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.0.0+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1
netekiswill/Netekis-will	netekiswill	2024-05-20T00:30:52Z	0	0	null	[ "region:us" ]	null	2024-05-20T00:29:47Z	<p><strong>Winning concerning Nursing Tasks: A Hand made Approach for Progress</strong></p> <p><strong>Show:</strong></p> <p>In the referencing universe of nursing getting ready, tasks like NURS FPX 4010 Appraisal 4, NURS FPX 4030 Assessment 2, and NURS FPX 4020 Assessment 4 go probably as basic achievements in outlining understudies' scholastic excursion. Anyway, examining these evaluations can routinely be overpowering, particularly while changing clinical turns, coursework, and individual commitments. Seeing these difficulties, <a href="https://onlineclassassignment.com/do-my-nursing-assignment/">Do My Nursing Assignment</a> associations have arisen as helps for understudies looking for custom fitted help with their shrewd undertakings. 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anzorq/w2v-bert-2.0-kbd-colab	anzorq	2024-05-20T00:14:41Z	79	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2-bert", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-15T23:28:10Z	--- library_name: transformers tags: [] --- # 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. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Subsets and Splits