Datasets:
modelbiome
/
model_ecosystem_with_cards

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New-videos-Amira-Ishtara-viral-Video-Links/ORIGINAL.FULL.VIDEOS.Amira.Ishtara.Viral.Video.Official.Tutorial
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2025-07-12T17:09:25.000Z
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1,860,401
rizki8/longt5-lora-findsum
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2025-07-12T17:09:38.000Z
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1,860,402
New-Priyanka-Pandit-Viral-Video/FULL.VIDEOS.Priyanka.Pandit.Viral.Video.Official.Tutorial
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2025-07-12T17:10:12.000Z
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1,860,403
RichardErkhov/mlfoundations-dev_-_oh_teknium_scaling_down_ratiocontrolled_0.1-gguf
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false
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['gguf', 'endpoints_compatible', 'region:us', 'conversational']
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2025-07-12T17:10:45.000Z
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--- {} --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) oh_teknium_scaling_down_ratiocontrolled_0.1 - GGUF - Model creator: https://huggingface.co/mlfoundations-dev/ - Original model: https://huggingface.co/mlfoundations-dev/oh_teknium_scaling_down_ratiocontrolled_0.1/ | Name | Quant method | Size | | ---- | ---- | ---- | | [oh_teknium_scaling_down_ratiocontrolled_0.1.Q2_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_oh_teknium_scaling_down_ratiocontrolled_0.1-gguf/blob/main/oh_teknium_scaling_down_ratiocontrolled_0.1.Q2_K.gguf) | Q2_K | 2.96GB | | [oh_teknium_scaling_down_ratiocontrolled_0.1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_oh_teknium_scaling_down_ratiocontrolled_0.1-gguf/blob/main/oh_teknium_scaling_down_ratiocontrolled_0.1.IQ3_XS.gguf) | IQ3_XS | 3.28GB | | [oh_teknium_scaling_down_ratiocontrolled_0.1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_oh_teknium_scaling_down_ratiocontrolled_0.1-gguf/blob/main/oh_teknium_scaling_down_ratiocontrolled_0.1.IQ3_S.gguf) | IQ3_S | 3.43GB | | [oh_teknium_scaling_down_ratiocontrolled_0.1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_oh_teknium_scaling_down_ratiocontrolled_0.1-gguf/blob/main/oh_teknium_scaling_down_ratiocontrolled_0.1.Q3_K_S.gguf) | Q3_K_S | 3.41GB | | [oh_teknium_scaling_down_ratiocontrolled_0.1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_oh_teknium_scaling_down_ratiocontrolled_0.1-gguf/blob/main/oh_teknium_scaling_down_ratiocontrolled_0.1.IQ3_M.gguf) | IQ3_M | 3.52GB | | [oh_teknium_scaling_down_ratiocontrolled_0.1.Q3_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_oh_teknium_scaling_down_ratiocontrolled_0.1-gguf/blob/main/oh_teknium_scaling_down_ratiocontrolled_0.1.Q3_K.gguf) | Q3_K | 3.74GB | | [oh_teknium_scaling_down_ratiocontrolled_0.1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_oh_teknium_scaling_down_ratiocontrolled_0.1-gguf/blob/main/oh_teknium_scaling_down_ratiocontrolled_0.1.Q3_K_M.gguf) | Q3_K_M | 3.74GB | | [oh_teknium_scaling_down_ratiocontrolled_0.1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_oh_teknium_scaling_down_ratiocontrolled_0.1-gguf/blob/main/oh_teknium_scaling_down_ratiocontrolled_0.1.Q3_K_L.gguf) | Q3_K_L | 4.03GB | | [oh_teknium_scaling_down_ratiocontrolled_0.1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_oh_teknium_scaling_down_ratiocontrolled_0.1-gguf/blob/main/oh_teknium_scaling_down_ratiocontrolled_0.1.IQ4_XS.gguf) | IQ4_XS | 4.18GB | | [oh_teknium_scaling_down_ratiocontrolled_0.1.Q4_0.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_oh_teknium_scaling_down_ratiocontrolled_0.1-gguf/blob/main/oh_teknium_scaling_down_ratiocontrolled_0.1.Q4_0.gguf) | Q4_0 | 4.34GB | | [oh_teknium_scaling_down_ratiocontrolled_0.1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_oh_teknium_scaling_down_ratiocontrolled_0.1-gguf/blob/main/oh_teknium_scaling_down_ratiocontrolled_0.1.IQ4_NL.gguf) | IQ4_NL | 4.38GB | | [oh_teknium_scaling_down_ratiocontrolled_0.1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_oh_teknium_scaling_down_ratiocontrolled_0.1-gguf/blob/main/oh_teknium_scaling_down_ratiocontrolled_0.1.Q4_K_S.gguf) | Q4_K_S | 4.37GB | | [oh_teknium_scaling_down_ratiocontrolled_0.1.Q4_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_oh_teknium_scaling_down_ratiocontrolled_0.1-gguf/blob/main/oh_teknium_scaling_down_ratiocontrolled_0.1.Q4_K.gguf) | Q4_K | 4.58GB | | [oh_teknium_scaling_down_ratiocontrolled_0.1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_oh_teknium_scaling_down_ratiocontrolled_0.1-gguf/blob/main/oh_teknium_scaling_down_ratiocontrolled_0.1.Q4_K_M.gguf) | Q4_K_M | 4.58GB | | [oh_teknium_scaling_down_ratiocontrolled_0.1.Q4_1.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_oh_teknium_scaling_down_ratiocontrolled_0.1-gguf/blob/main/oh_teknium_scaling_down_ratiocontrolled_0.1.Q4_1.gguf) | Q4_1 | 4.78GB | | [oh_teknium_scaling_down_ratiocontrolled_0.1.Q5_0.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_oh_teknium_scaling_down_ratiocontrolled_0.1-gguf/blob/main/oh_teknium_scaling_down_ratiocontrolled_0.1.Q5_0.gguf) | Q5_0 | 5.21GB | | [oh_teknium_scaling_down_ratiocontrolled_0.1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_oh_teknium_scaling_down_ratiocontrolled_0.1-gguf/blob/main/oh_teknium_scaling_down_ratiocontrolled_0.1.Q5_K_S.gguf) | Q5_K_S | 5.21GB | | [oh_teknium_scaling_down_ratiocontrolled_0.1.Q5_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_oh_teknium_scaling_down_ratiocontrolled_0.1-gguf/blob/main/oh_teknium_scaling_down_ratiocontrolled_0.1.Q5_K.gguf) | Q5_K | 5.34GB | | [oh_teknium_scaling_down_ratiocontrolled_0.1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_oh_teknium_scaling_down_ratiocontrolled_0.1-gguf/blob/main/oh_teknium_scaling_down_ratiocontrolled_0.1.Q5_K_M.gguf) | Q5_K_M | 5.34GB | | [oh_teknium_scaling_down_ratiocontrolled_0.1.Q5_1.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_oh_teknium_scaling_down_ratiocontrolled_0.1-gguf/blob/main/oh_teknium_scaling_down_ratiocontrolled_0.1.Q5_1.gguf) | Q5_1 | 5.65GB | | [oh_teknium_scaling_down_ratiocontrolled_0.1.Q6_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_oh_teknium_scaling_down_ratiocontrolled_0.1-gguf/blob/main/oh_teknium_scaling_down_ratiocontrolled_0.1.Q6_K.gguf) | Q6_K | 6.14GB | | [oh_teknium_scaling_down_ratiocontrolled_0.1.Q8_0.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_oh_teknium_scaling_down_ratiocontrolled_0.1-gguf/blob/main/oh_teknium_scaling_down_ratiocontrolled_0.1.Q8_0.gguf) | Q8_0 | 7.95GB | Original model description: --- library_name: transformers license: llama3.1 base_model: meta-llama/Meta-Llama-3.1-8B tags: - llama-factory - full - generated_from_trainer model-index: - name: oh_teknium_scaling_down_ratiocontrolled_0.1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # oh_teknium_scaling_down_ratiocontrolled_0.1 This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) on the mlfoundations-dev/oh_teknium_scaling_down_ratiocontrolled_0.1 dataset. It achieves the following results on the evaluation set: - Loss: 0.6307 ## 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-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - total_eval_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.68 | 0.9778 | 33 | 0.6524 | | 0.6045 | 1.9667 | 66 | 0.6236 | | 0.5201 | 2.9556 | 99 | 0.6307 | ### Framework versions - Transformers 4.46.1 - Pytorch 2.3.0 - Datasets 3.1.0 - Tokenizers 0.20.3
1,860,404
phospho-app/yyyy76514263-ACT-data_first_13_07-g8wsr
0
0
false
0
['phosphobot', 'act', 'region:us']
null
null
2025-07-12T17:10:50.000Z
[]
[]
[]
[]
[]
null
us
null
--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` Training process failed with exit code 1: return self.transform(batch) File "/lerobot/lerobot/common/datasets/utils.py", line 272, in hf_transform_to_torch items_dict[key] = [x if isinstance(x, str) else torch.tensor(x) for x in items_dict[key]] File "/lerobot/lerobot/common/datasets/utils.py", line 272, in <listcomp> items_dict[key] = [x if isinstance(x, str) else torch.tensor(x) for x in items_dict[key]] RuntimeError: Could not infer dtype of NoneType wandb: wandb: 🚀 View run act at: https://wandb.ai/lawyiyang08-null/phospho-ACT/runs/4alaer7z wandb: Find logs at: ../data/phospho-app/yyyy76514263-ACT-data_first_13_07-g8wsr/1752340250.588072/wandb/run-20250712_191105-4alaer7z/logs ``` ## Training parameters: - **Dataset**: [yyyy76514263/data_first_13_07](https://huggingface.co/datasets/yyyy76514263/data_first_13_07) - **Wandb run URL**: None - **Epochs**: None - **Batch size**: 60 - **Training steps**: 8000 📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
1,860,405
cedricgaudron/scanner-tickets
0
0
false
0
['safetensors', 't5', 'region:us']
null
null
2025-07-12T17:11:50.000Z
[]
[]
[]
[]
[]
null
us
null
--- language: fr license: mit tags: - t5 - invoice - receipt - document-information-extraction - ocr pipeline_tag: text2text-generation --- # 🧾 Scanner Tickets – Extraction automatique de données Ce modèle T5 a été entraîné pour **extraire automatiquement des informations clés depuis du texte OCR issu de factures ou tickets de caisse**. ## 📌 Données extraites : - 🧾 **Type** : facture ou ticket - 💸 **Montant total** - 📅 **Date** - 🏢 **Fournisseur** - 🔢 **SIRET** - 🔢 **Numéro de TVA** - #️⃣ **Numéro de facture ou ticket** ## 🔍 Exemple d'utilisation ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("cedricgaudron/scanner-tickets") model = T5ForConditionalGeneration.from_pretrained("cedricgaudron/scanner-tickets") texte = """CARREFOUR TOTAL TTC : 24,75€ Date : 12/06/2024 SIRET : 123 456 789 00012 TVA : FR 12 345678912""" input_ids = tokenizer("Extrais les données suivantes en format JSON :\n" + texte, return_tensors="pt").input_ids output = model.generate(input_ids, max_length=128) print(tokenizer.decode(output[0], skip_special_tokens=True))
1,860,406
Amal17/NusaBERT-concate-BiGRU-NusaParagraph-emot
0
0
false
0
['license:apache-2.0', 'region:us']
null
null
2025-07-12T17:13:42.000Z
[]
[]
[]
[]
[]
apache-2.0
us
null
--- license: apache-2.0 ---
1,860,407
jackrvn/bidirectional-dialect-translator
0
0
false
0
['transformers', 'safetensors', 't5', 'text2text-generation', 'arxiv:1910.09700', 'autotrain_compatible', 'text-generation-inference', 'endpoints_compatible', 'region:us']
text-generation
transformers
2025-07-12T17:13:59.000Z
[]
[]
[]
[]
[]
null
us
1,910.097
--- 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]
1,860,408
Amal17/NusaBERT-concate-BiGRU-NusaParagraph-topic
0
0
false
0
['license:apache-2.0', 'region:us']
null
null
2025-07-12T17:14:00.000Z
[]
[]
[]
[]
[]
apache-2.0
us
null
--- license: apache-2.0 ---
1,860,409
ond-ai/ond-agent-1.3-8b-ckpt-1
0
0
false
0
['region:us']
null
null
2025-07-12T17:14:07.000Z
[]
[]
[]
[]
[]
null
us
null
--- tags: - text-generation ---
1,860,410
jackrvn/biderectional-dialect-translator
0
0
false
0
['transformers', 'arxiv:1910.09700', 'endpoints_compatible', 'region:us']
null
transformers
2025-07-12T17:14:09.000Z
[]
[]
[]
[]
[]
null
us
1,910.097
null