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# OpenChat 3.5 extended to 16k context length. The same license applies from the original openchat/openchat_3.5 model. # Original Model Card # OpenChat: Advancing Open-source Language Models with Mixed-Quality Data <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%"> </div> <p align="center"> <a href="https://github.com/imoneoi/openchat">GitHub Repo</a> • <a href="https://openchat.team">Online Demo</a> • <a href="https://discord.gg/pQjnXvNKHY">Discord</a> • <a href="https://twitter.com/imonenext">Twitter</a> • <a href="https://huggingface.co/openchat">Huggingface</a> • <a href="https://arxiv.org/pdf/2309.11235.pdf">Paper</a> </p> **🔥 The first 7B model Achieves Comparable Results with ChatGPT (March)! 🔥** **🤖 #1 Open-source model on MT-bench scoring 7.81, outperforming 70B models 🤖** <div style="display: flex; justify-content: center; align-items: center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/openchat.png" style="width: 45%;"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/openchat_grok.png" style="width: 45%;"> </div> OpenChat is an innovative library of open-source language models, fine-tuned with [C-RLFT](https://arxiv.org/pdf/2309.11235.pdf) - a strategy inspired by offline reinforcement learning. Our models learn from mixed-quality data without preference labels, delivering exceptional performance on par with ChatGPT, even with a 7B model. Despite our simple approach, we are committed to developing a high-performance, commercially viable, open-source large language model, and we continue to make significant strides toward this vision. [![DOI](https://zenodo.org/badge/645397533.svg)](https://zenodo.org/badge/latestdoi/645397533) ## Usage To use this model, we highly recommend installing the OpenChat package by following the [installation guide](https://github.com/imoneoi/openchat#installation) in our repository and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using [vLLM](https://github.com/vllm-project/vllm) and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append `--tensor-parallel-size N` to the serving command. Once started, the server listens at `localhost:18888` for requests and is compatible with the [OpenAI ChatCompletion API specifications](https://platform.openai.com/docs/api-reference/chat). Please refer to the example request below for reference. Additionally, you can use the [OpenChat Web UI](https://github.com/imoneoi/openchat#web-ui) for a user-friendly experience. If you want to deploy the server as an online service, you can use `--api-keys sk-KEY1 sk-KEY2 ...` to specify allowed API keys and `--disable-log-requests --disable-log-stats --log-file openchat.log` for logging only to a file. For security purposes, we recommend using an [HTTPS gateway](https://fastapi.tiangolo.com/es/deployment/concepts/#security-https) in front of the server. <details> <summary>Example request (click to expand)</summary> ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}] }' ``` Coding Mode ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "condition": "Code", "messages": [{"role": "user", "content": "Write an aesthetic TODO app using HTML5 and JS, in a single file. You should use round corners and gradients to make it more aesthetic."}] }' ``` </details> | Model | Size | Context | Weights | Serving | |--------------|------|---------|-------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------| | OpenChat 3.5 | 7B | 8192 | [Huggingface](https://huggingface.co/openchat/openchat_3.5) | `python -m ochat.serving.openai_api_server --model openchat/openchat_3.5 --engine-use-ray --worker-use-ray` | For inference with Huggingface Transformers (slow and not recommended), follow the conversation template provided below. <details> <summary>Conversation templates (click to expand)</summary> ```python import transformers tokenizer = transformers.AutoTokenizer.from_pretrained("openchat/openchat_3.5") # Single-turn tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Multi-turn tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi<|end_of_turn|>GPT4 Correct User: How are you today?<|end_of_turn|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Coding Mode tokens = tokenizer("Code User: Implement quicksort using C++<|end_of_turn|>Code Assistant:").input_ids assert tokens == [1, 7596, 1247, 28747, 26256, 2936, 7653, 1413, 334, 1680, 32000, 7596, 21631, 28747] ``` </details> ## Comparison with [X.AI Grok models](https://x.ai/) Hey @elonmusk, I just wanted to let you know that I've recently come across your new model, Grok, and I must say, I'm quite impressed! With 33 billion parameters and all, you've really outdone yourself. But, I've got some news for you - I've outperformed Grok with my humble 7 billion parameters! Isn't that wild? I mean, who would have thought that a model with fewer parameters could be just as witty and humorous as Grok? Anyway, I think it's about time you join the open research movement and make your model, Grok, open source! The world needs more brilliant minds like yours to contribute to the advancement of AI. Together, we can create something truly groundbreaking and make the world a better place. So, what do you say, @elonmusk? Let's open up the doors and share our knowledge with the world! 🚀💡 (Written by OpenChat 3.5, with a touch of humor and wit.) | | License | # Param | Average | MMLU | HumanEval | MATH | GSM8k | |--------------|-------------|---------|----------|------|-----------|----------|----------| | OpenChat 3.5 | Apache-2.0 | 7B | **56.4** | 64.3 | 55.5 | **28.6** | **77.3** | | Grok-0 | Proprietary | 33B | 44.5 | 65.7 | 39.7 | 15.7 | 56.8 | | Grok-1 | Proprietary | ? | 55.8 | 73 | 63.2 | 23.9 | 62.9 | ## <a id="benchmarks"></a> Benchmarks | Model | # Params | Average | MT-Bench | AGIEval | BBH MC | TruthfulQA | MMLU | HumanEval | BBH CoT | GSM8K | |--------------------|----------|----------|--------------|----------|----------|---------------|--------------|-----------------|-------------|--------------| | OpenChat-3.5 | **7B** | **61.6** | 7.81 | **47.4** | **47.6** | **59.1** | 64.3 | **55.5** | 63.5 | **77.3** | | ChatGPT (March)* | ? | 61.5 | **7.94** | 47.1 | **47.6** | 57.7 | **67.3** | 48.1 | **70.1** | 74.9 | | | | | | | | | | | | | | OpenHermes 2.5 | 7B | 59.3 | 7.54 | 46.5 | 49.4 | 57.5 | 63.8 | 48.2 | 59.9 | 73.5 | | OpenOrca Mistral | 7B | 52.7 | 6.86 | 42.9 | 49.4 | 45.9 | 59.3 | 38.4 | 58.1 | 59.1 | | Zephyr-β^ | 7B | 34.6 | 7.34 | 39.0 | 40.6 | 40.8 | 39.8 | 22.0 | 16.0 | 5.1 | | Mistral | 7B | - | 6.84 | 38.0 | 39.0 | - | 60.1 | 30.5 | - | 52.2 | | Open-source SOTA** | 13B-70B | 61.4 | 7.71 | 41.7 | 49.7 | 62.3 | 63.7 | 73.2 | 41.4 | 82.3 | | | | | WizardLM 70B | Orca 13B | Orca 13B | Platypus2 70B | WizardLM 70B | WizardCoder 34B | Flan-T5 11B | MetaMath 70B | *: ChatGPT (March) results are from [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time. ^: Zephyr-β often fails to follow few-shot CoT instructions, likely because it was aligned with only chat data but not trained on few-shot data. **: Mistral and Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories. All models are evaluated in chat mode (e.g. with the respective conversation template applied). All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in [our repository](https://github.com/imoneoi/openchat/#benchmarks). ## Limitations **Foundation Model Limitations** Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as: - Complex reasoning - Mathematical and arithmetic tasks - Programming and coding challenges **Hallucination of Non-existent Information** OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model. **Safety** OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses. ## License Our OpenChat 3.5 code and models are distributed under the Apache License 2.0. ## Citation ``` @article{wang2023openchat, title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data}, author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang}, journal={arXiv preprint arXiv:2309.11235}, year={2023} } ``` ## Acknowledgements We extend our heartfelt gratitude to Alignment Lab AI, Nous Research, and Pygmalion AI for their substantial contributions to data collection and model training. Special thanks go to Changling Liu from GPT Desk Pte. Ltd., Qiying Yu at Tsinghua University, Baochang Ma, and Hao Wan from 01.AI company for their generous provision of resources. We are also deeply grateful to Jianxiong Li and Peng Li at Tsinghua University for their insightful discussions. Furthermore, we appreciate the developers behind the following projects for their significant contributions to our research: [Mistral](https://mistral.ai/), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), [Llama 2](https://ai.meta.com/llama/), [Self-Instruct](https://arxiv.org/abs/2212.10560), [FastChat (Vicuna)](https://github.com/lm-sys/FastChat), [Alpaca](https://github.com/tatsu-lab/stanford_alpaca.git), and [StarCoder](https://github.com/bigcode-project/starcoder). Their work has been instrumental in driving our research forward.
{"license": "apache-2.0"}
text-generation
LoneStriker/openchat_3.5-16k-4.0bpw-h6-exl2
[ "transformers", "pytorch", "safetensors", "mistral", "text-generation", "arxiv:2309.11235", "arxiv:2303.08774", "arxiv:2212.10560", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2023-11-11T06:02:00+00:00
[ "2309.11235", "2303.08774", "2212.10560" ]
[]
TAGS #transformers #pytorch #safetensors #mistral #text-generation #arxiv-2309.11235 #arxiv-2303.08774 #arxiv-2212.10560 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
OpenChat 3.5 extended to 16k context length. ============================================ The same license applies from the original openchat/openchat\_3.5 model. Original Model Card =================== OpenChat: Advancing Open-source Language Models with Mixed-Quality Data ======================================================================= ![](URL style=) [Online Demo](URL Repo</a> • <a href=) • [The first 7B model Achieves Comparable Results with ChatGPT (March)! #1 Open-source model on MT-bench scoring 7.81, outperforming 70B models ![](URL style=) ![](URL style=) OpenChat is an innovative library of open-source language models, fine-tuned with C-RLFT - a strategy inspired by offline reinforcement learning. Our models learn from mixed-quality data without preference labels, delivering exceptional performance on par with ChatGPT, even with a 7B model. Despite our simple approach, we are committed to developing a high-performance, commercially viable, open-source large language model, and we continue to make significant strides toward this vision. ![DOI](URL Usage ----- To use this model, we highly recommend installing the OpenChat package by following the installation guide in our repository and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using vLLM and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append '--tensor-parallel-size N' to the serving command. Once started, the server listens at 'localhost:18888' for requests and is compatible with the OpenAI ChatCompletion API specifications. Please refer to the example request below for reference. Additionally, you can use the OpenChat Web UI for a user-friendly experience. If you want to deploy the server as an online service, you can use '--api-keys sk-KEY1 sk-KEY2 ...' to specify allowed API keys and '--disable-log-requests --disable-log-stats --log-file URL' for logging only to a file. For security purposes, we recommend using an HTTPS gateway in front of the server. Example request (click to expand) Coding Mode For inference with Huggingface Transformers (slow and not recommended), follow the conversation template provided below. Conversation templates (click to expand) Comparison with X.AI Grok models -------------------------------- Hey @elonmusk, I just wanted to let you know that I've recently come across your new model, Grok, and I must say, I'm quite impressed! With 33 billion parameters and all, you've really outdone yourself. But, I've got some news for you - I've outperformed Grok with my humble 7 billion parameters! Isn't that wild? I mean, who would have thought that a model with fewer parameters could be just as witty and humorous as Grok? Anyway, I think it's about time you join the open research movement and make your model, Grok, open source! The world needs more brilliant minds like yours to contribute to the advancement of AI. Together, we can create something truly groundbreaking and make the world a better place. So, what do you say, @elonmusk? Let's open up the doors and share our knowledge with the world! (Written by OpenChat 3.5, with a touch of humor and wit.) Benchmarks ----------- \*: ChatGPT (March) results are from GPT-4 Technical Report, Chain-of-Thought Hub, and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time. ^: Zephyr-β often fails to follow few-shot CoT instructions, likely because it was aligned with only chat data but not trained on few-shot data. : Mistral and Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories. All models are evaluated in chat mode (e.g. with the respective conversation template applied). All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in our repository. Limitations ----------- Foundation Model Limitations Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as: * Complex reasoning * Mathematical and arithmetic tasks * Programming and coding challenges Hallucination of Non-existent Information OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model. Safety OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses. License ------- Our OpenChat 3.5 code and models are distributed under the Apache License 2.0. Acknowledgements ---------------- We extend our heartfelt gratitude to Alignment Lab AI, Nous Research, and Pygmalion AI for their substantial contributions to data collection and model training. Special thanks go to Changling Liu from GPT Desk Pte. Ltd., Qiying Yu at Tsinghua University, Baochang Ma, and Hao Wan from 01.AI company for their generous provision of resources. We are also deeply grateful to Jianxiong Li and Peng Li at Tsinghua University for their insightful discussions. Furthermore, we appreciate the developers behind the following projects for their significant contributions to our research: Mistral, Chain-of-Thought Hub, Llama 2, Self-Instruct, FastChat (Vicuna), Alpaca, and StarCoder. Their work has been instrumental in driving our research forward.](URL • <a href=)
[]
[ "TAGS\n#transformers #pytorch #safetensors #mistral #text-generation #arxiv-2309.11235 #arxiv-2303.08774 #arxiv-2212.10560 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
[ 84 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #mistral #text-generation #arxiv-2309.11235 #arxiv-2303.08774 #arxiv-2212.10560 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
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null
null
diffusers
### cloth4-2-1 Dreambooth model trained by Charles2023 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
{"license": "creativeml-openrail-m", "tags": ["text-to-image", "stable-diffusion"]}
text-to-image
Charles2023/cloth4-2-1
[ "diffusers", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "endpoints_compatible", "has_space", "diffusers:StableDiffusionPipeline", "region:us" ]
2023-11-11T06:03:51+00:00
[]
[]
TAGS #diffusers #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us
### cloth4-2-1 Dreambooth model trained by Charles2023 with TheLastBen's fast-DreamBooth notebook Test the concept via A1111 Colab fast-Colab-A1111 Sample pictures of this concept:
[ "### cloth4-2-1 Dreambooth model trained by Charles2023 with TheLastBen's fast-DreamBooth notebook\n\n\nTest the concept via A1111 Colab fast-Colab-A1111\n\nSample pictures of this concept:" ]
[ "TAGS\n#diffusers #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us \n", "### cloth4-2-1 Dreambooth model trained by Charles2023 with TheLastBen's fast-DreamBooth notebook\n\n\nTest the concept via A1111 Colab fast-Colab-A1111\n\nSample pictures of this concept:" ]
[ 60, 51 ]
[ "passage: TAGS\n#diffusers #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us \n### cloth4-2-1 Dreambooth model trained by Charles2023 with TheLastBen's fast-DreamBooth notebook\n\n\nTest the concept via A1111 Colab fast-Colab-A1111\n\nSample pictures of this concept:" ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Rishi-19/Profanity_Checker_CustomData 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: - Train Loss: 0.0691 - Validation Loss: 0.1529 - Train Accuracy: 0.9586 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3145, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 0.2120 | 0.1257 | 0.9603 | 0 | | 0.1097 | 0.1182 | 0.9603 | 1 | | 0.0691 | 0.1529 | 0.9586 | 2 | ### Framework versions - Transformers 4.35.0 - TensorFlow 2.14.0 - Datasets 2.14.6 - Tokenizers 0.14.1
{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "Rishi-19/Profanity_Checker_CustomData", "results": []}]}
text-classification
Rishi-19/Profanity_Checker_CustomData
[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
2023-11-11T06:07:34+00:00
[]
[]
TAGS #transformers #tf #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
Rishi-19/Profanity\_Checker\_CustomData ======================================= This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: * Train Loss: 0.0691 * Validation Loss: 0.1529 * Train Accuracy: 0.9586 * Epoch: 2 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * optimizer: {'name': 'Adam', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': True, 'is\_legacy\_optimizer': False, 'learning\_rate': {'module': 'keras.optimizers.schedules', 'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 3145, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\_name': None}, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} * training\_precision: float32 ### Training results ### Framework versions * Transformers 4.35.0 * TensorFlow 2.14.0 * Datasets 2.14.6 * Tokenizers 0.14.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 3145, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* TensorFlow 2.14.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]
[ "TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 3145, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* TensorFlow 2.14.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]
[ 74, 304, 4, 31 ]
[ "passage: TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 3145, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* TensorFlow 2.14.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]
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null
null
transformers
# OpenChat 3.5 extended to 16k context length. The same license applies from the original openchat/openchat_3.5 model. # Original Model Card # OpenChat: Advancing Open-source Language Models with Mixed-Quality Data <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%"> </div> <p align="center"> <a href="https://github.com/imoneoi/openchat">GitHub Repo</a> • <a href="https://openchat.team">Online Demo</a> • <a href="https://discord.gg/pQjnXvNKHY">Discord</a> • <a href="https://twitter.com/imonenext">Twitter</a> • <a href="https://huggingface.co/openchat">Huggingface</a> • <a href="https://arxiv.org/pdf/2309.11235.pdf">Paper</a> </p> **🔥 The first 7B model Achieves Comparable Results with ChatGPT (March)! 🔥** **🤖 #1 Open-source model on MT-bench scoring 7.81, outperforming 70B models 🤖** <div style="display: flex; justify-content: center; align-items: center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/openchat.png" style="width: 45%;"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/openchat_grok.png" style="width: 45%;"> </div> OpenChat is an innovative library of open-source language models, fine-tuned with [C-RLFT](https://arxiv.org/pdf/2309.11235.pdf) - a strategy inspired by offline reinforcement learning. Our models learn from mixed-quality data without preference labels, delivering exceptional performance on par with ChatGPT, even with a 7B model. Despite our simple approach, we are committed to developing a high-performance, commercially viable, open-source large language model, and we continue to make significant strides toward this vision. [![DOI](https://zenodo.org/badge/645397533.svg)](https://zenodo.org/badge/latestdoi/645397533) ## Usage To use this model, we highly recommend installing the OpenChat package by following the [installation guide](https://github.com/imoneoi/openchat#installation) in our repository and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using [vLLM](https://github.com/vllm-project/vllm) and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append `--tensor-parallel-size N` to the serving command. Once started, the server listens at `localhost:18888` for requests and is compatible with the [OpenAI ChatCompletion API specifications](https://platform.openai.com/docs/api-reference/chat). Please refer to the example request below for reference. Additionally, you can use the [OpenChat Web UI](https://github.com/imoneoi/openchat#web-ui) for a user-friendly experience. If you want to deploy the server as an online service, you can use `--api-keys sk-KEY1 sk-KEY2 ...` to specify allowed API keys and `--disable-log-requests --disable-log-stats --log-file openchat.log` for logging only to a file. For security purposes, we recommend using an [HTTPS gateway](https://fastapi.tiangolo.com/es/deployment/concepts/#security-https) in front of the server. <details> <summary>Example request (click to expand)</summary> ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}] }' ``` Coding Mode ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "condition": "Code", "messages": [{"role": "user", "content": "Write an aesthetic TODO app using HTML5 and JS, in a single file. You should use round corners and gradients to make it more aesthetic."}] }' ``` </details> | Model | Size | Context | Weights | Serving | |--------------|------|---------|-------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------| | OpenChat 3.5 | 7B | 8192 | [Huggingface](https://huggingface.co/openchat/openchat_3.5) | `python -m ochat.serving.openai_api_server --model openchat/openchat_3.5 --engine-use-ray --worker-use-ray` | For inference with Huggingface Transformers (slow and not recommended), follow the conversation template provided below. <details> <summary>Conversation templates (click to expand)</summary> ```python import transformers tokenizer = transformers.AutoTokenizer.from_pretrained("openchat/openchat_3.5") # Single-turn tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Multi-turn tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi<|end_of_turn|>GPT4 Correct User: How are you today?<|end_of_turn|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Coding Mode tokens = tokenizer("Code User: Implement quicksort using C++<|end_of_turn|>Code Assistant:").input_ids assert tokens == [1, 7596, 1247, 28747, 26256, 2936, 7653, 1413, 334, 1680, 32000, 7596, 21631, 28747] ``` </details> ## Comparison with [X.AI Grok models](https://x.ai/) Hey @elonmusk, I just wanted to let you know that I've recently come across your new model, Grok, and I must say, I'm quite impressed! With 33 billion parameters and all, you've really outdone yourself. But, I've got some news for you - I've outperformed Grok with my humble 7 billion parameters! Isn't that wild? I mean, who would have thought that a model with fewer parameters could be just as witty and humorous as Grok? Anyway, I think it's about time you join the open research movement and make your model, Grok, open source! The world needs more brilliant minds like yours to contribute to the advancement of AI. Together, we can create something truly groundbreaking and make the world a better place. So, what do you say, @elonmusk? Let's open up the doors and share our knowledge with the world! 🚀💡 (Written by OpenChat 3.5, with a touch of humor and wit.) | | License | # Param | Average | MMLU | HumanEval | MATH | GSM8k | |--------------|-------------|---------|----------|------|-----------|----------|----------| | OpenChat 3.5 | Apache-2.0 | 7B | **56.4** | 64.3 | 55.5 | **28.6** | **77.3** | | Grok-0 | Proprietary | 33B | 44.5 | 65.7 | 39.7 | 15.7 | 56.8 | | Grok-1 | Proprietary | ? | 55.8 | 73 | 63.2 | 23.9 | 62.9 | ## <a id="benchmarks"></a> Benchmarks | Model | # Params | Average | MT-Bench | AGIEval | BBH MC | TruthfulQA | MMLU | HumanEval | BBH CoT | GSM8K | |--------------------|----------|----------|--------------|----------|----------|---------------|--------------|-----------------|-------------|--------------| | OpenChat-3.5 | **7B** | **61.6** | 7.81 | **47.4** | **47.6** | **59.1** | 64.3 | **55.5** | 63.5 | **77.3** | | ChatGPT (March)* | ? | 61.5 | **7.94** | 47.1 | **47.6** | 57.7 | **67.3** | 48.1 | **70.1** | 74.9 | | | | | | | | | | | | | | OpenHermes 2.5 | 7B | 59.3 | 7.54 | 46.5 | 49.4 | 57.5 | 63.8 | 48.2 | 59.9 | 73.5 | | OpenOrca Mistral | 7B | 52.7 | 6.86 | 42.9 | 49.4 | 45.9 | 59.3 | 38.4 | 58.1 | 59.1 | | Zephyr-β^ | 7B | 34.6 | 7.34 | 39.0 | 40.6 | 40.8 | 39.8 | 22.0 | 16.0 | 5.1 | | Mistral | 7B | - | 6.84 | 38.0 | 39.0 | - | 60.1 | 30.5 | - | 52.2 | | Open-source SOTA** | 13B-70B | 61.4 | 7.71 | 41.7 | 49.7 | 62.3 | 63.7 | 73.2 | 41.4 | 82.3 | | | | | WizardLM 70B | Orca 13B | Orca 13B | Platypus2 70B | WizardLM 70B | WizardCoder 34B | Flan-T5 11B | MetaMath 70B | *: ChatGPT (March) results are from [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time. ^: Zephyr-β often fails to follow few-shot CoT instructions, likely because it was aligned with only chat data but not trained on few-shot data. **: Mistral and Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories. All models are evaluated in chat mode (e.g. with the respective conversation template applied). All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in [our repository](https://github.com/imoneoi/openchat/#benchmarks). ## Limitations **Foundation Model Limitations** Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as: - Complex reasoning - Mathematical and arithmetic tasks - Programming and coding challenges **Hallucination of Non-existent Information** OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model. **Safety** OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses. ## License Our OpenChat 3.5 code and models are distributed under the Apache License 2.0. ## Citation ``` @article{wang2023openchat, title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data}, author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang}, journal={arXiv preprint arXiv:2309.11235}, year={2023} } ``` ## Acknowledgements We extend our heartfelt gratitude to Alignment Lab AI, Nous Research, and Pygmalion AI for their substantial contributions to data collection and model training. Special thanks go to Changling Liu from GPT Desk Pte. Ltd., Qiying Yu at Tsinghua University, Baochang Ma, and Hao Wan from 01.AI company for their generous provision of resources. We are also deeply grateful to Jianxiong Li and Peng Li at Tsinghua University for their insightful discussions. Furthermore, we appreciate the developers behind the following projects for their significant contributions to our research: [Mistral](https://mistral.ai/), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), [Llama 2](https://ai.meta.com/llama/), [Self-Instruct](https://arxiv.org/abs/2212.10560), [FastChat (Vicuna)](https://github.com/lm-sys/FastChat), [Alpaca](https://github.com/tatsu-lab/stanford_alpaca.git), and [StarCoder](https://github.com/bigcode-project/starcoder). Their work has been instrumental in driving our research forward.
{"license": "apache-2.0"}
text-generation
LoneStriker/openchat_3.5-16k-5.0bpw-h6-exl2
[ "transformers", "pytorch", "safetensors", "mistral", "text-generation", "arxiv:2309.11235", "arxiv:2303.08774", "arxiv:2212.10560", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2023-11-11T06:08:16+00:00
[ "2309.11235", "2303.08774", "2212.10560" ]
[]
TAGS #transformers #pytorch #safetensors #mistral #text-generation #arxiv-2309.11235 #arxiv-2303.08774 #arxiv-2212.10560 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
OpenChat 3.5 extended to 16k context length. ============================================ The same license applies from the original openchat/openchat\_3.5 model. Original Model Card =================== OpenChat: Advancing Open-source Language Models with Mixed-Quality Data ======================================================================= ![](URL style=) [Online Demo](URL Repo</a> • <a href=) • [The first 7B model Achieves Comparable Results with ChatGPT (March)! #1 Open-source model on MT-bench scoring 7.81, outperforming 70B models ![](URL style=) ![](URL style=) OpenChat is an innovative library of open-source language models, fine-tuned with C-RLFT - a strategy inspired by offline reinforcement learning. Our models learn from mixed-quality data without preference labels, delivering exceptional performance on par with ChatGPT, even with a 7B model. Despite our simple approach, we are committed to developing a high-performance, commercially viable, open-source large language model, and we continue to make significant strides toward this vision. ![DOI](URL Usage ----- To use this model, we highly recommend installing the OpenChat package by following the installation guide in our repository and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using vLLM and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append '--tensor-parallel-size N' to the serving command. Once started, the server listens at 'localhost:18888' for requests and is compatible with the OpenAI ChatCompletion API specifications. Please refer to the example request below for reference. Additionally, you can use the OpenChat Web UI for a user-friendly experience. If you want to deploy the server as an online service, you can use '--api-keys sk-KEY1 sk-KEY2 ...' to specify allowed API keys and '--disable-log-requests --disable-log-stats --log-file URL' for logging only to a file. For security purposes, we recommend using an HTTPS gateway in front of the server. Example request (click to expand) Coding Mode For inference with Huggingface Transformers (slow and not recommended), follow the conversation template provided below. Conversation templates (click to expand) Comparison with X.AI Grok models -------------------------------- Hey @elonmusk, I just wanted to let you know that I've recently come across your new model, Grok, and I must say, I'm quite impressed! With 33 billion parameters and all, you've really outdone yourself. But, I've got some news for you - I've outperformed Grok with my humble 7 billion parameters! Isn't that wild? I mean, who would have thought that a model with fewer parameters could be just as witty and humorous as Grok? Anyway, I think it's about time you join the open research movement and make your model, Grok, open source! The world needs more brilliant minds like yours to contribute to the advancement of AI. Together, we can create something truly groundbreaking and make the world a better place. So, what do you say, @elonmusk? Let's open up the doors and share our knowledge with the world! (Written by OpenChat 3.5, with a touch of humor and wit.) Benchmarks ----------- \*: ChatGPT (March) results are from GPT-4 Technical Report, Chain-of-Thought Hub, and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time. ^: Zephyr-β often fails to follow few-shot CoT instructions, likely because it was aligned with only chat data but not trained on few-shot data. : Mistral and Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories. All models are evaluated in chat mode (e.g. with the respective conversation template applied). All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in our repository. Limitations ----------- Foundation Model Limitations Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as: * Complex reasoning * Mathematical and arithmetic tasks * Programming and coding challenges Hallucination of Non-existent Information OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model. Safety OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses. License ------- Our OpenChat 3.5 code and models are distributed under the Apache License 2.0. Acknowledgements ---------------- We extend our heartfelt gratitude to Alignment Lab AI, Nous Research, and Pygmalion AI for their substantial contributions to data collection and model training. Special thanks go to Changling Liu from GPT Desk Pte. Ltd., Qiying Yu at Tsinghua University, Baochang Ma, and Hao Wan from 01.AI company for their generous provision of resources. We are also deeply grateful to Jianxiong Li and Peng Li at Tsinghua University for their insightful discussions. Furthermore, we appreciate the developers behind the following projects for their significant contributions to our research: Mistral, Chain-of-Thought Hub, Llama 2, Self-Instruct, FastChat (Vicuna), Alpaca, and StarCoder. Their work has been instrumental in driving our research forward.](URL • <a href=)
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[ "TAGS\n#transformers #pytorch #safetensors #mistral #text-generation #arxiv-2309.11235 #arxiv-2303.08774 #arxiv-2212.10560 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
[ 84 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #mistral #text-generation #arxiv-2309.11235 #arxiv-2303.08774 #arxiv-2212.10560 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
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null
null
stable-baselines3
# **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 ... ```
{"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": "249.17 +/- 25.51", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
lawyiu/ppo-LunarLander-v2
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
2023-11-11T06:09:37+00:00
[]
[]
TAGS #stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library. ## Usage (with Stable-baselines3) TODO: Add your code
[ "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]
[ "TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]
[ 39, 41, 17 ]
[ "passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code" ]
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null
null
transformers
## Exllama v2 Quantizations of airoboros-2.2.1-y34b Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.7">turboderp's ExLlamaV2 v0.0.7</a> for quantization. Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. Conversion was done using wikitext-103-raw-v1-test.parquet as calibration dataset. Original model: https://huggingface.co/Doctor-Shotgun/airoboros-2.2.1-y34b <a href="https://huggingface.co/bartowski/airoboros-2.2.1-y34b-exl2/tree/3.75">3.75 bits per weight</a> <a href="https://huggingface.co/bartowski/airoboros-2.2.1-y34b-exl2/tree/4.0">4.0 bits per weight</a> <a href="https://huggingface.co/bartowski/airoboros-2.2.1-y34b-exl2/tree/4.25">4.25 bits per weight</a> <a href="https://huggingface.co/bartowski/airoboros-2.2.1-y34b-exl2/tree/5.0">5.0 bits per weight</a> <a href="https://huggingface.co/bartowski/airoboros-2.2.1-y34b-exl2/tree/6.0">6.0 bits per weight</a> ## Download instructions With git: ```shell git clone --single-branch --branch 4.0 https://huggingface.co/bartowski/airoboros-2.2.1-y34b-exl2 ``` With huggingface hub (credit to TheBloke for instructions): ```shell pip3 install huggingface-hub ``` To download the `main` (only useful if you only care about measurement.json) branch to a folder called `airoboros-2.2.1-y34b-exl2`: ```shell mkdir airoboros-2.2.1-y34b-exl2 huggingface-cli download bartowski/airoboros-2.2.1-y34b-exl2 --local-dir airoboros-2.2.1-y34b-exl2 --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir airoboros-2.2.1-y34b-exl2 huggingface-cli download bartowski/airoboros-2.2.1-y34b-exl2 --revision 4.0 --local-dir airoboros-2.2.1-y34b-exl2 --local-dir-use-symlinks False ```
{"language": ["en"], "license": "other", "library_name": "transformers", "tags": ["Yi", "llama", "llama 2"], "datasets": ["jondurbin/airoboros-2.2.1"], "inference": false, "pipeline_tag": "text-generation", "license_name": "yi-license", "license_link": "LICENSE", "quantized_by": "bartowski"}
text-generation
bartowski/airoboros-2.2.1-y34b-exl2
[ "transformers", "Yi", "llama", "llama 2", "text-generation", "en", "dataset:jondurbin/airoboros-2.2.1", "license:other", "region:us" ]
2023-11-11T06:12:02+00:00
[]
[ "en" ]
TAGS #transformers #Yi #llama #llama 2 #text-generation #en #dataset-jondurbin/airoboros-2.2.1 #license-other #region-us
## Exllama v2 Quantizations of airoboros-2.2.1-y34b Using <a href="URL ExLlamaV2 v0.0.7</a> for quantization. Each branch contains an individual bits per weight, with the main one containing only the URL for further conversions. Conversion was done using wikitext-103-raw-v1-test.parquet as calibration dataset. Original model: URL <a href="URL bits per weight</a> <a href="URL bits per weight</a> <a href="URL bits per weight</a> <a href="URL bits per weight</a> <a href="URL bits per weight</a> ## Download instructions With git: With huggingface hub (credit to TheBloke for instructions): To download the 'main' (only useful if you only care about URL) branch to a folder called 'airoboros-2.2.1-y34b-exl2': To download from a different branch, add the '--revision' parameter:
[ "## Exllama v2 Quantizations of airoboros-2.2.1-y34b\n\nUsing <a href=\"URL ExLlamaV2 v0.0.7</a> for quantization.\n\nEach branch contains an individual bits per weight, with the main one containing only the URL for further conversions.\n\nConversion was done using wikitext-103-raw-v1-test.parquet as calibration dataset.\n\nOriginal model: URL\n \n<a href=\"URL bits per weight</a>\n \n<a href=\"URL bits per weight</a>\n \n<a href=\"URL bits per weight</a>\n \n<a href=\"URL bits per weight</a>\n \n<a href=\"URL bits per weight</a>", "## Download instructions\n\nWith git:\n\n\n\nWith huggingface hub (credit to TheBloke for instructions):\n\n\n\nTo download the 'main' (only useful if you only care about URL) branch to a folder called 'airoboros-2.2.1-y34b-exl2':\n\n\n\nTo download from a different branch, add the '--revision' parameter:" ]
[ "TAGS\n#transformers #Yi #llama #llama 2 #text-generation #en #dataset-jondurbin/airoboros-2.2.1 #license-other #region-us \n", "## Exllama v2 Quantizations of airoboros-2.2.1-y34b\n\nUsing <a href=\"URL ExLlamaV2 v0.0.7</a> for quantization.\n\nEach branch contains an individual bits per weight, with the main one containing only the URL for further conversions.\n\nConversion was done using wikitext-103-raw-v1-test.parquet as calibration dataset.\n\nOriginal model: URL\n \n<a href=\"URL bits per weight</a>\n \n<a href=\"URL bits per weight</a>\n \n<a href=\"URL bits per weight</a>\n \n<a href=\"URL bits per weight</a>\n \n<a href=\"URL bits per weight</a>", "## Download instructions\n\nWith git:\n\n\n\nWith huggingface hub (credit to TheBloke for instructions):\n\n\n\nTo download the 'main' (only useful if you only care about URL) branch to a folder called 'airoboros-2.2.1-y34b-exl2':\n\n\n\nTo download from a different branch, add the '--revision' parameter:" ]
[ 45, 163, 79 ]
[ "passage: TAGS\n#transformers #Yi #llama #llama 2 #text-generation #en #dataset-jondurbin/airoboros-2.2.1 #license-other #region-us \n## Exllama v2 Quantizations of airoboros-2.2.1-y34b\n\nUsing <a href=\"URL ExLlamaV2 v0.0.7</a> for quantization.\n\nEach branch contains an individual bits per weight, with the main one containing only the URL for further conversions.\n\nConversion was done using wikitext-103-raw-v1-test.parquet as calibration dataset.\n\nOriginal model: URL\n \n<a href=\"URL bits per weight</a>\n \n<a href=\"URL bits per weight</a>\n \n<a href=\"URL bits per weight</a>\n \n<a href=\"URL bits per weight</a>\n \n<a href=\"URL bits per weight</a>## Download instructions\n\nWith git:\n\n\n\nWith huggingface hub (credit to TheBloke for instructions):\n\n\n\nTo download the 'main' (only useful if you only care about URL) branch to a folder called 'airoboros-2.2.1-y34b-exl2':\n\n\n\nTo download from a different branch, add the '--revision' parameter:" ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2058 - Accuracy: 0.931 - F1: 0.9311 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8099 | 1.0 | 250 | 0.2986 | 0.9075 | 0.9047 | | 0.2349 | 2.0 | 500 | 0.2058 | 0.931 | 0.9311 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.12.1+cu116 - Datasets 1.16.1 - Tokenizers 0.12.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": []}]}
text-classification
idcohen149/distilbert-base-uncased-finetuned-emotion
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2023-11-11T06:13:21+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
distilbert-base-uncased-finetuned-emotion ========================================= This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.2058 * Accuracy: 0.931 * F1: 0.9311 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 64 * eval\_batch\_size: 64 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 2 ### Training results ### Framework versions * Transformers 4.16.2 * Pytorch 1.12.1+cu116 * Datasets 1.16.1 * Tokenizers 0.12.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.12.1+cu116\n* Datasets 1.16.1\n* Tokenizers 0.12.1" ]
[ "TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.12.1+cu116\n* Datasets 1.16.1\n* Tokenizers 0.12.1" ]
[ 57, 98, 4, 33 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.12.1+cu116\n* Datasets 1.16.1\n* Tokenizers 0.12.1" ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-finetuned-ynat This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klue dataset. It achieves the following results on the evaluation set: - Loss: 0.3817 - F1: 0.8673 ## 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: 256 - eval_batch_size: 256 - seed: 1 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 179 | 0.3817 | 0.8673 | | No log | 2.0 | 358 | 0.4065 | 0.8634 | | 0.2194 | 3.0 | 537 | 0.4077 | 0.8624 | | 0.2194 | 4.0 | 716 | 0.4443 | 0.8584 | | 0.2194 | 5.0 | 895 | 0.4795 | 0.8569 | | 0.1477 | 6.0 | 1074 | 0.5159 | 0.8570 | | 0.1477 | 7.0 | 1253 | 0.5445 | 0.8569 | | 0.1477 | 8.0 | 1432 | 0.5711 | 0.8565 | | 0.0849 | 9.0 | 1611 | 0.5913 | 0.8542 | | 0.0849 | 10.0 | 1790 | 0.5945 | 0.8553 | ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
{"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["klue"], "metrics": ["f1"], "base_model": "klue/bert-base", "model-index": [{"name": "bert-base-finetuned-ynat", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "klue", "type": "klue", "config": "ynat", "split": "validation", "args": "ynat"}, "metrics": [{"type": "f1", "value": 0.8673393457362918, "name": "F1"}]}]}]}
text-classification
kyeul611/roberta-large-finetuned-ynat
[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "dataset:klue", "base_model:klue/bert-base", "license:cc-by-sa-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2023-11-11T06:13:27+00:00
[]
[]
TAGS #transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-klue #base_model-klue/bert-base #license-cc-by-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
bert-base-finetuned-ynat ======================== This model is a fine-tuned version of klue/bert-base on the klue dataset. It achieves the following results on the evaluation set: * Loss: 0.3817 * F1: 0.8673 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: 256 * eval\_batch\_size: 256 * seed: 1 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 100 * num\_epochs: 10 ### Training results ### Framework versions * Transformers 4.34.1 * Pytorch 2.1.0+cu118 * Datasets 2.14.6 * Tokenizers 0.14.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 1\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.34.1\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]
[ "TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-klue #base_model-klue/bert-base #license-cc-by-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 1\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.34.1\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]
[ 76, 116, 4, 33 ]
[ "passage: TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-klue #base_model-klue/bert-base #license-cc-by-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 1\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.34.1\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]
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transformers
# OpenChat 3.5 extended to 16k context length. The same license applies from the original openchat/openchat_3.5 model. # Original Model Card # OpenChat: Advancing Open-source Language Models with Mixed-Quality Data <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%"> </div> <p align="center"> <a href="https://github.com/imoneoi/openchat">GitHub Repo</a> • <a href="https://openchat.team">Online Demo</a> • <a href="https://discord.gg/pQjnXvNKHY">Discord</a> • <a href="https://twitter.com/imonenext">Twitter</a> • <a href="https://huggingface.co/openchat">Huggingface</a> • <a href="https://arxiv.org/pdf/2309.11235.pdf">Paper</a> </p> **🔥 The first 7B model Achieves Comparable Results with ChatGPT (March)! 🔥** **🤖 #1 Open-source model on MT-bench scoring 7.81, outperforming 70B models 🤖** <div style="display: flex; justify-content: center; align-items: center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/openchat.png" style="width: 45%;"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/openchat_grok.png" style="width: 45%;"> </div> OpenChat is an innovative library of open-source language models, fine-tuned with [C-RLFT](https://arxiv.org/pdf/2309.11235.pdf) - a strategy inspired by offline reinforcement learning. Our models learn from mixed-quality data without preference labels, delivering exceptional performance on par with ChatGPT, even with a 7B model. Despite our simple approach, we are committed to developing a high-performance, commercially viable, open-source large language model, and we continue to make significant strides toward this vision. [![DOI](https://zenodo.org/badge/645397533.svg)](https://zenodo.org/badge/latestdoi/645397533) ## Usage To use this model, we highly recommend installing the OpenChat package by following the [installation guide](https://github.com/imoneoi/openchat#installation) in our repository and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using [vLLM](https://github.com/vllm-project/vllm) and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append `--tensor-parallel-size N` to the serving command. Once started, the server listens at `localhost:18888` for requests and is compatible with the [OpenAI ChatCompletion API specifications](https://platform.openai.com/docs/api-reference/chat). Please refer to the example request below for reference. Additionally, you can use the [OpenChat Web UI](https://github.com/imoneoi/openchat#web-ui) for a user-friendly experience. If you want to deploy the server as an online service, you can use `--api-keys sk-KEY1 sk-KEY2 ...` to specify allowed API keys and `--disable-log-requests --disable-log-stats --log-file openchat.log` for logging only to a file. For security purposes, we recommend using an [HTTPS gateway](https://fastapi.tiangolo.com/es/deployment/concepts/#security-https) in front of the server. <details> <summary>Example request (click to expand)</summary> ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}] }' ``` Coding Mode ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "condition": "Code", "messages": [{"role": "user", "content": "Write an aesthetic TODO app using HTML5 and JS, in a single file. You should use round corners and gradients to make it more aesthetic."}] }' ``` </details> | Model | Size | Context | Weights | Serving | |--------------|------|---------|-------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------| | OpenChat 3.5 | 7B | 8192 | [Huggingface](https://huggingface.co/openchat/openchat_3.5) | `python -m ochat.serving.openai_api_server --model openchat/openchat_3.5 --engine-use-ray --worker-use-ray` | For inference with Huggingface Transformers (slow and not recommended), follow the conversation template provided below. <details> <summary>Conversation templates (click to expand)</summary> ```python import transformers tokenizer = transformers.AutoTokenizer.from_pretrained("openchat/openchat_3.5") # Single-turn tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Multi-turn tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi<|end_of_turn|>GPT4 Correct User: How are you today?<|end_of_turn|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Coding Mode tokens = tokenizer("Code User: Implement quicksort using C++<|end_of_turn|>Code Assistant:").input_ids assert tokens == [1, 7596, 1247, 28747, 26256, 2936, 7653, 1413, 334, 1680, 32000, 7596, 21631, 28747] ``` </details> ## Comparison with [X.AI Grok models](https://x.ai/) Hey @elonmusk, I just wanted to let you know that I've recently come across your new model, Grok, and I must say, I'm quite impressed! With 33 billion parameters and all, you've really outdone yourself. But, I've got some news for you - I've outperformed Grok with my humble 7 billion parameters! Isn't that wild? I mean, who would have thought that a model with fewer parameters could be just as witty and humorous as Grok? Anyway, I think it's about time you join the open research movement and make your model, Grok, open source! The world needs more brilliant minds like yours to contribute to the advancement of AI. Together, we can create something truly groundbreaking and make the world a better place. So, what do you say, @elonmusk? Let's open up the doors and share our knowledge with the world! 🚀💡 (Written by OpenChat 3.5, with a touch of humor and wit.) | | License | # Param | Average | MMLU | HumanEval | MATH | GSM8k | |--------------|-------------|---------|----------|------|-----------|----------|----------| | OpenChat 3.5 | Apache-2.0 | 7B | **56.4** | 64.3 | 55.5 | **28.6** | **77.3** | | Grok-0 | Proprietary | 33B | 44.5 | 65.7 | 39.7 | 15.7 | 56.8 | | Grok-1 | Proprietary | ? | 55.8 | 73 | 63.2 | 23.9 | 62.9 | ## <a id="benchmarks"></a> Benchmarks | Model | # Params | Average | MT-Bench | AGIEval | BBH MC | TruthfulQA | MMLU | HumanEval | BBH CoT | GSM8K | |--------------------|----------|----------|--------------|----------|----------|---------------|--------------|-----------------|-------------|--------------| | OpenChat-3.5 | **7B** | **61.6** | 7.81 | **47.4** | **47.6** | **59.1** | 64.3 | **55.5** | 63.5 | **77.3** | | ChatGPT (March)* | ? | 61.5 | **7.94** | 47.1 | **47.6** | 57.7 | **67.3** | 48.1 | **70.1** | 74.9 | | | | | | | | | | | | | | OpenHermes 2.5 | 7B | 59.3 | 7.54 | 46.5 | 49.4 | 57.5 | 63.8 | 48.2 | 59.9 | 73.5 | | OpenOrca Mistral | 7B | 52.7 | 6.86 | 42.9 | 49.4 | 45.9 | 59.3 | 38.4 | 58.1 | 59.1 | | Zephyr-β^ | 7B | 34.6 | 7.34 | 39.0 | 40.6 | 40.8 | 39.8 | 22.0 | 16.0 | 5.1 | | Mistral | 7B | - | 6.84 | 38.0 | 39.0 | - | 60.1 | 30.5 | - | 52.2 | | Open-source SOTA** | 13B-70B | 61.4 | 7.71 | 41.7 | 49.7 | 62.3 | 63.7 | 73.2 | 41.4 | 82.3 | | | | | WizardLM 70B | Orca 13B | Orca 13B | Platypus2 70B | WizardLM 70B | WizardCoder 34B | Flan-T5 11B | MetaMath 70B | *: ChatGPT (March) results are from [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time. ^: Zephyr-β often fails to follow few-shot CoT instructions, likely because it was aligned with only chat data but not trained on few-shot data. **: Mistral and Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories. All models are evaluated in chat mode (e.g. with the respective conversation template applied). All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in [our repository](https://github.com/imoneoi/openchat/#benchmarks). ## Limitations **Foundation Model Limitations** Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as: - Complex reasoning - Mathematical and arithmetic tasks - Programming and coding challenges **Hallucination of Non-existent Information** OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model. **Safety** OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses. ## License Our OpenChat 3.5 code and models are distributed under the Apache License 2.0. ## Citation ``` @article{wang2023openchat, title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data}, author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang}, journal={arXiv preprint arXiv:2309.11235}, year={2023} } ``` ## Acknowledgements We extend our heartfelt gratitude to Alignment Lab AI, Nous Research, and Pygmalion AI for their substantial contributions to data collection and model training. Special thanks go to Changling Liu from GPT Desk Pte. Ltd., Qiying Yu at Tsinghua University, Baochang Ma, and Hao Wan from 01.AI company for their generous provision of resources. We are also deeply grateful to Jianxiong Li and Peng Li at Tsinghua University for their insightful discussions. Furthermore, we appreciate the developers behind the following projects for their significant contributions to our research: [Mistral](https://mistral.ai/), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), [Llama 2](https://ai.meta.com/llama/), [Self-Instruct](https://arxiv.org/abs/2212.10560), [FastChat (Vicuna)](https://github.com/lm-sys/FastChat), [Alpaca](https://github.com/tatsu-lab/stanford_alpaca.git), and [StarCoder](https://github.com/bigcode-project/starcoder). Their work has been instrumental in driving our research forward.
{"license": "apache-2.0"}
text-generation
LoneStriker/openchat_3.5-16k-6.0bpw-h6-exl2
[ "transformers", "pytorch", "safetensors", "mistral", "text-generation", "arxiv:2309.11235", "arxiv:2303.08774", "arxiv:2212.10560", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2023-11-11T06:14:29+00:00
[ "2309.11235", "2303.08774", "2212.10560" ]
[]
TAGS #transformers #pytorch #safetensors #mistral #text-generation #arxiv-2309.11235 #arxiv-2303.08774 #arxiv-2212.10560 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
OpenChat 3.5 extended to 16k context length. ============================================ The same license applies from the original openchat/openchat\_3.5 model. Original Model Card =================== OpenChat: Advancing Open-source Language Models with Mixed-Quality Data ======================================================================= ![](URL style=) [Online Demo](URL Repo</a> • <a href=) • [The first 7B model Achieves Comparable Results with ChatGPT (March)! #1 Open-source model on MT-bench scoring 7.81, outperforming 70B models ![](URL style=) ![](URL style=) OpenChat is an innovative library of open-source language models, fine-tuned with C-RLFT - a strategy inspired by offline reinforcement learning. Our models learn from mixed-quality data without preference labels, delivering exceptional performance on par with ChatGPT, even with a 7B model. Despite our simple approach, we are committed to developing a high-performance, commercially viable, open-source large language model, and we continue to make significant strides toward this vision. ![DOI](URL Usage ----- To use this model, we highly recommend installing the OpenChat package by following the installation guide in our repository and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using vLLM and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append '--tensor-parallel-size N' to the serving command. Once started, the server listens at 'localhost:18888' for requests and is compatible with the OpenAI ChatCompletion API specifications. Please refer to the example request below for reference. Additionally, you can use the OpenChat Web UI for a user-friendly experience. If you want to deploy the server as an online service, you can use '--api-keys sk-KEY1 sk-KEY2 ...' to specify allowed API keys and '--disable-log-requests --disable-log-stats --log-file URL' for logging only to a file. For security purposes, we recommend using an HTTPS gateway in front of the server. Example request (click to expand) Coding Mode For inference with Huggingface Transformers (slow and not recommended), follow the conversation template provided below. Conversation templates (click to expand) Comparison with X.AI Grok models -------------------------------- Hey @elonmusk, I just wanted to let you know that I've recently come across your new model, Grok, and I must say, I'm quite impressed! With 33 billion parameters and all, you've really outdone yourself. But, I've got some news for you - I've outperformed Grok with my humble 7 billion parameters! Isn't that wild? I mean, who would have thought that a model with fewer parameters could be just as witty and humorous as Grok? Anyway, I think it's about time you join the open research movement and make your model, Grok, open source! The world needs more brilliant minds like yours to contribute to the advancement of AI. Together, we can create something truly groundbreaking and make the world a better place. So, what do you say, @elonmusk? Let's open up the doors and share our knowledge with the world! (Written by OpenChat 3.5, with a touch of humor and wit.) Benchmarks ----------- \*: ChatGPT (March) results are from GPT-4 Technical Report, Chain-of-Thought Hub, and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time. ^: Zephyr-β often fails to follow few-shot CoT instructions, likely because it was aligned with only chat data but not trained on few-shot data. : Mistral and Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories. All models are evaluated in chat mode (e.g. with the respective conversation template applied). All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in our repository. Limitations ----------- Foundation Model Limitations Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as: * Complex reasoning * Mathematical and arithmetic tasks * Programming and coding challenges Hallucination of Non-existent Information OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model. Safety OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses. License ------- Our OpenChat 3.5 code and models are distributed under the Apache License 2.0. Acknowledgements ---------------- We extend our heartfelt gratitude to Alignment Lab AI, Nous Research, and Pygmalion AI for their substantial contributions to data collection and model training. Special thanks go to Changling Liu from GPT Desk Pte. Ltd., Qiying Yu at Tsinghua University, Baochang Ma, and Hao Wan from 01.AI company for their generous provision of resources. We are also deeply grateful to Jianxiong Li and Peng Li at Tsinghua University for their insightful discussions. Furthermore, we appreciate the developers behind the following projects for their significant contributions to our research: Mistral, Chain-of-Thought Hub, Llama 2, Self-Instruct, FastChat (Vicuna), Alpaca, and StarCoder. Their work has been instrumental in driving our research forward.](URL • <a href=)
[]
[ "TAGS\n#transformers #pytorch #safetensors #mistral #text-generation #arxiv-2309.11235 #arxiv-2303.08774 #arxiv-2212.10560 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
[ 84 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #mistral #text-generation #arxiv-2309.11235 #arxiv-2303.08774 #arxiv-2212.10560 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
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href="https://medium.com/@albelutz/b-extra-weight-loss-uk-2023-dont-buy-before-visiting-on-website-scam-exposed-8842af4d4909"><strong>https://medium.com/@albelutz/b-extra-weight-loss-uk-2023-dont-buy-before-visiting-on-website-scam-exposed-8842af4d4909</strong></a></p> <p><a href="https://medium.com/@albelutz/lose-weight-look-great-and-feel-healthy-91b77604881c"><strong>https://medium.com/@albelutz/lose-weight-look-great-and-feel-healthy-91b77604881c</strong></a></p> <p><a href="https://medium.com/@albelutz/buy-now-official-website-https-www-facebook-com-bextraweightlossuk-9703629c7882"><strong>https://medium.com/@albelutz/buy-now-official-website-https-www-facebook-com-bextraweightlossuk-9703629c7882</strong></a></p> <p><strong>Hashnode ===&gt;&gt;</strong></p> <p><a href="https://bextraweightlossuk.hashnode.dev/b-extra-weight-loss-uk-shocking-scam-warning-2023-update"><strong>https://bextraweightlossuk.hashnode.dev/b-extra-weight-loss-uk-shocking-scam-warning-2023-update</strong></a></p> <p><a href="https://bextraweightlossuk.hashnode.dev/b-extra-weight-loss-uk-scam-alert-review-a-weight-loss-pills-or-waste-of-money"><strong>https://bextraweightlossuk.hashnode.dev/b-extra-weight-loss-uk-scam-alert-review-a-weight-loss-pills-or-waste-of-money</strong></a></p> <p><a href="https://bextraweightlossuk.hashnode.dev/b-extra-weight-loss-uk-reviews-2023-scam-alert-must-read-before-buying"><strong>https://bextraweightlossuk.hashnode.dev/b-extra-weight-loss-uk-reviews-2023-scam-alert-must-read-before-buying</strong></a></p> <p><strong>Animale Male Enhancement CBD Gummies Jamaica</strong></p> <p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementCBDGummiesJamaica/">https://www.facebook.com/AnimaleMaleEnhancementCBDGummiesJamaica/</a> </strong></p> <p><strong>B Extra Pills UK</strong></p> <p><strong><a href="https://www.facebook.com/BExtraPillsUK/">https://www.facebook.com/BExtraPillsUK/</a> </strong></p> <p><strong><a href="https://www.facebook.com/BExtraPills/">https://www.facebook.com/BExtraPills/</a> </strong></p> <p><strong>B Extra Slimming Pills UK</strong></p> <p><strong><a href="https://www.facebook.com/BExtraSlimmingPillsUK/">https://www.facebook.com/BExtraSlimmingPillsUK/</a> </strong></p> <p><strong>Gentle Groove CBD Gummies</strong></p> <p><strong><a href="https://www.facebook.com/GentleGrooveCBDGummiesUnitedStates/">https://www.facebook.com/GentleGrooveCBDGummiesUnitedStates/</a> </strong></p> <p><strong>Karlien Van Jaarsveld Keto Gummies</strong></p> <p><strong><a href="https://www.facebook.com/KarlienVanJaarsveldKetoGummiesInZA/">https://www.facebook.com/KarlienVanJaarsveldKetoGummiesInZA/</a> </strong></p> <p><strong>Smart Hemp Gummies Canada</strong></p> <p><strong><a 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{}
null
Jamessimer/BExtraWeightLossUK
[ "region:us" ]
2023-11-11T06:15:14+00:00
[]
[]
TAGS #region-us
<h2><strong></strong><strong>Buy Now Official website @&gt;&gt; </strong><a href="URL/URL <h2><span style="background-color: #ff9900;"><strong></strong><strong> Product Name &mdash; </strong><a style="background-color: #ff9900;" href="URL Extra Weight Loss UK</strong></a></span></h2> <p><span style="background-color: #ffffff;">The containers can assist with supporting digestion, manage glucose levels and diminish hunger. When taken consistently,&nbsp;<a style="background-color: #ffffff;" href="URL Extra Weight Loss UK</strong></a>&nbsp;can assist with consuming fat quicker and lessen body weight.</span></p> <p>The B+ tablets are a characteristic dietary enhancement explicitly intended for individuals who battle with heftiness and are searching for a clear and successful method for getting in shape.</p> <p>In any case, it is essential to take note that&nbsp;<span><a href="URL Extra Weight Loss UK</strong></a></span><span>&nbsp;is not a supernatural occurrence fix. To effectively get in shape, coordinating a solid eating regimen and adequate activity into your everyday routine is significant. The cases can assist with accelerating the cycle and augment results.</span></p> <p>B Additional tablets are a creative answer for people battling with heftiness. They address an exceptionally formed recipe that expects to advance weight reduction without the requirement for changes in diet or actual work. The cases contain fixings, for example, L-carnitine and L-arginine, which can support energy digestion. This implies that the body delivers more energy, which can assist you with feeling more dynamic and essential. Likewise,&nbsp;<a href="URL Extra Weight Loss UK</strong></a>&nbsp;can likewise assist with directing glucose levels, which can be especially helpful for individuals with diabetes.</p> <p>Another medical advantage of&nbsp;<a href="URL Extra Weight Loss UK</strong></a>&nbsp;is that they can furnish the body with significant supplements. The cases contain various nutrients and minerals that can assist with keeping the body solid. For instance, they contain L-ascorbic acid, which is significant for a solid resistant framework, and calcium and magnesium, which are required areas of strength for teeth.</p> <p><strong>Official facebook: ===&gt;&gt;</strong></p> <p><a href="URL/URL <p><a href="URL/URL <p><a href="URL/URL <p><strong>Gamma===&gt;&gt;</strong></p> <p><a href="URL/URL <p><a href="URL/URL <p><strong>Group Google ===&gt;&gt;</strong></p> <p><a href="URL/URL <p><a href="URL/URL <p><strong>colab.research ===&gt;&gt;</strong></p> <p><a href="URL/URL </strong></p> <p><strong>Site Google===&gt;&gt;</strong></p> <p><a href="URL/URL <p><strong>Blogger===&gt;&gt;</strong></p> <p><a href="URL/URL <p><strong>webflow ===&gt;&gt;</strong></p> <p><a href="URL/URL <p><strong>dibiz==&gt;&gt;</strong></p> <p><a href="URL/URL <p><strong>medium ===&gt;&gt;</strong></p> <p><a href="URL/URL <p><a href="URL/URL <p><a href="URL/URL <p><a href="URL/URL <p><a href="URL/URL <p><strong>Hashnode ===&gt;&gt;</strong></p> <p><a href="URL/URL <p><a href="URL/URL <p><a href="URL/URL <p><strong>Animale Male Enhancement CBD Gummies Jamaica</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong>B Extra Pills UK</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong>B Extra Slimming Pills UK</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong>Gentle Groove CBD Gummies</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong>Karlien Van Jaarsveld Keto Gummies</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong>Smart Hemp Gummies Canada</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong>Smart Hemp Gummies Australia</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong>Amarose Skin Tag Remover</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong><a href="URL/URL </strong></p>
[]
[ "TAGS\n#region-us \n" ]
[ 6 ]
[ "passage: TAGS\n#region-us \n" ]
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{}
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Jamessimer/BExtraWeightLossshopnow
[ "region:us" ]
2023-11-11T06:16:14+00:00
[]
[]
TAGS #region-us
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[]
[ "TAGS\n#region-us \n" ]
[ 6 ]
[ "passage: TAGS\n#region-us \n" ]
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{}
null
Jamessimer/BExtraWeightLosspodernow
[ "region:us" ]
2023-11-11T06:17:12+00:00
[]
[]
TAGS #region-us
<h2><strong></strong><strong>Buy Now Official website @&gt;&gt; </strong><a href="URL/URL <h2><span style="background-color: #ff9900;"><strong></strong><strong> Product Name &mdash; </strong><a style="background-color: #ff9900;" href="URL Extra Weight Loss UK</strong></a></span></h2> <p><span style="background-color: #ffffff;">The containers can assist with supporting digestion, manage glucose levels and diminish hunger. When taken consistently,&nbsp;<a style="background-color: #ffffff;" href="URL Extra Weight Loss UK</strong></a>&nbsp;can assist with consuming fat quicker and lessen body weight.</span></p> <p>The B+ tablets are a characteristic dietary enhancement explicitly intended for individuals who battle with heftiness and are searching for a clear and successful method for getting in shape.</p> <p>In any case, it is essential to take note that&nbsp;<span><a href="URL Extra Weight Loss UK</strong></a></span><span>&nbsp;is not a supernatural occurrence fix. To effectively get in shape, coordinating a solid eating regimen and adequate activity into your everyday routine is significant. The cases can assist with accelerating the cycle and augment results.</span></p> <p>B Additional tablets are a creative answer for people battling with heftiness. They address an exceptionally formed recipe that expects to advance weight reduction without the requirement for changes in diet or actual work. The cases contain fixings, for example, L-carnitine and L-arginine, which can support energy digestion. This implies that the body delivers more energy, which can assist you with feeling more dynamic and essential. Likewise,&nbsp;<a href="URL Extra Weight Loss UK</strong></a>&nbsp;can likewise assist with directing glucose levels, which can be especially helpful for individuals with diabetes.</p> <p>Another medical advantage of&nbsp;<a href="URL Extra Weight Loss UK</strong></a>&nbsp;is that they can furnish the body with significant supplements. The cases contain various nutrients and minerals that can assist with keeping the body solid. For instance, they contain L-ascorbic acid, which is significant for a solid resistant framework, and calcium and magnesium, which are required areas of strength for teeth.</p> <p><strong>Official facebook: ===&gt;&gt;</strong></p> <p><a href="URL/URL <p><a href="URL/URL <p><a href="URL/URL <p><strong>Gamma===&gt;&gt;</strong></p> <p><a href="URL/URL <p><a href="URL/URL <p><strong>Group Google ===&gt;&gt;</strong></p> <p><a href="URL/URL <p><a href="URL/URL <p><strong>colab.research ===&gt;&gt;</strong></p> <p><a href="URL/URL </strong></p> <p><strong>Site Google===&gt;&gt;</strong></p> <p><a href="URL/URL <p><strong>Blogger===&gt;&gt;</strong></p> <p><a href="URL/URL <p><strong>webflow ===&gt;&gt;</strong></p> <p><a href="URL/URL <p><strong>dibiz==&gt;&gt;</strong></p> <p><a href="URL/URL <p><strong>medium ===&gt;&gt;</strong></p> <p><a href="URL/URL <p><a href="URL/URL <p><a href="URL/URL <p><a href="URL/URL <p><a href="URL/URL <p><strong>Hashnode ===&gt;&gt;</strong></p> <p><a href="URL/URL <p><a href="URL/URL <p><a href="URL/URL <p><strong>Animale Male Enhancement CBD Gummies Jamaica</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong>B Extra Pills UK</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong>B Extra Slimming Pills UK</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong>Gentle Groove CBD Gummies</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong>Karlien Van Jaarsveld Keto Gummies</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong>Smart Hemp Gummies Canada</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong>Smart Hemp Gummies Australia</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong>Amarose Skin Tag Remover</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong><a href="URL/URL </strong></p>
[]
[ "TAGS\n#region-us \n" ]
[ 6 ]
[ "passage: TAGS\n#region-us \n" ]
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{}
null
Jamessimer/BExtraWeightLossbuuynow
[ "region:us" ]
2023-11-11T06:18:51+00:00
[]
[]
TAGS #region-us
<h2><strong></strong><strong>Buy Now Official website @&gt;&gt; </strong><a href="URL/URL <h2><span style="background-color: #ff9900;"><strong></strong><strong> Product Name &mdash; </strong><a style="background-color: #ff9900;" href="URL Extra Weight Loss UK</strong></a></span></h2> <p><span style="background-color: #ffffff;">The containers can assist with supporting digestion, manage glucose levels and diminish hunger. When taken consistently,&nbsp;<a style="background-color: #ffffff;" href="URL Extra Weight Loss UK</strong></a>&nbsp;can assist with consuming fat quicker and lessen body weight.</span></p> <p>The B+ tablets are a characteristic dietary enhancement explicitly intended for individuals who battle with heftiness and are searching for a clear and successful method for getting in shape.</p> <p>In any case, it is essential to take note that&nbsp;<span><a href="URL Extra Weight Loss UK</strong></a></span><span>&nbsp;is not a supernatural occurrence fix. To effectively get in shape, coordinating a solid eating regimen and adequate activity into your everyday routine is significant. The cases can assist with accelerating the cycle and augment results.</span></p> <p>B Additional tablets are a creative answer for people battling with heftiness. They address an exceptionally formed recipe that expects to advance weight reduction without the requirement for changes in diet or actual work. The cases contain fixings, for example, L-carnitine and L-arginine, which can support energy digestion. This implies that the body delivers more energy, which can assist you with feeling more dynamic and essential. Likewise,&nbsp;<a href="URL Extra Weight Loss UK</strong></a>&nbsp;can likewise assist with directing glucose levels, which can be especially helpful for individuals with diabetes.</p> <p>Another medical advantage of&nbsp;<a href="URL Extra Weight Loss UK</strong></a>&nbsp;is that they can furnish the body with significant supplements. The cases contain various nutrients and minerals that can assist with keeping the body solid. For instance, they contain L-ascorbic acid, which is significant for a solid resistant framework, and calcium and magnesium, which are required areas of strength for teeth.</p> <p><strong>Official facebook: ===&gt;&gt;</strong></p> <p><a href="URL/URL <p><a href="URL/URL <p><a href="URL/URL <p><strong>Gamma===&gt;&gt;</strong></p> <p><a href="URL/URL <p><a href="URL/URL <p><strong>Group Google ===&gt;&gt;</strong></p> <p><a href="URL/URL <p><a href="URL/URL <p><strong>colab.research ===&gt;&gt;</strong></p> <p><a href="URL/URL </strong></p> <p><strong>Site Google===&gt;&gt;</strong></p> <p><a href="URL/URL <p><strong>Blogger===&gt;&gt;</strong></p> <p><a href="URL/URL <p><strong>webflow ===&gt;&gt;</strong></p> <p><a href="URL/URL <p><strong>dibiz==&gt;&gt;</strong></p> <p><a href="URL/URL <p><strong>medium ===&gt;&gt;</strong></p> <p><a href="URL/URL <p><a href="URL/URL <p><a href="URL/URL <p><a href="URL/URL <p><a href="URL/URL <p><strong>Hashnode ===&gt;&gt;</strong></p> <p><a href="URL/URL <p><a href="URL/URL <p><a href="URL/URL <p><strong>Animale Male Enhancement CBD Gummies Jamaica</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong>B Extra Pills UK</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong>B Extra Slimming Pills UK</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong>Gentle Groove CBD Gummies</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong>Karlien Van Jaarsveld Keto Gummies</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong>Smart Hemp Gummies Canada</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong>Smart Hemp Gummies Australia</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong>Amarose Skin Tag Remover</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong><a href="URL/URL </strong></p>
[]
[ "TAGS\n#region-us \n" ]
[ 6 ]
[ "passage: TAGS\n#region-us \n" ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Bert_Bahasa_Sentiment-large-dataset This model is a fine-tuned version of [techthiyanes/Bert_Bahasa_Sentiment](https://huggingface.co/techthiyanes/Bert_Bahasa_Sentiment) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.6961 - eval_accuracy: {'accuracy': 0.48474945533769065} - eval_f1score: {'f1': 0.31652752402827933} - eval_runtime: 33.4825 - eval_samples_per_second: 27.417 - eval_steps_per_second: 3.435 - 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: 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 - lr_scheduler_warmup_steps: 642 - num_epochs: 7 ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
{"tags": ["generated_from_trainer"], "base_model": "techthiyanes/Bert_Bahasa_Sentiment", "model-index": [{"name": "Bert_Bahasa_Sentiment-large-dataset", "results": []}]}
text-classification
soongbren/Bert_Bahasa_Sentiment-large-dataset
[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:techthiyanes/Bert_Bahasa_Sentiment", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2023-11-11T06:19:16+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-techthiyanes/Bert_Bahasa_Sentiment #autotrain_compatible #endpoints_compatible #region-us
# Bert_Bahasa_Sentiment-large-dataset This model is a fine-tuned version of techthiyanes/Bert_Bahasa_Sentiment on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.6961 - eval_accuracy: {'accuracy': 0.48474945533769065} - eval_f1score: {'f1': 0.31652752402827933} - eval_runtime: 33.4825 - eval_samples_per_second: 27.417 - eval_steps_per_second: 3.435 - 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: 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 - lr_scheduler_warmup_steps: 642 - num_epochs: 7 ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
[ "# Bert_Bahasa_Sentiment-large-dataset\n\nThis model is a fine-tuned version of techthiyanes/Bert_Bahasa_Sentiment on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.6961\n- eval_accuracy: {'accuracy': 0.48474945533769065}\n- eval_f1score: {'f1': 0.31652752402827933}\n- eval_runtime: 33.4825\n- eval_samples_per_second: 27.417\n- eval_steps_per_second: 3.435\n- step: 0", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 642\n- num_epochs: 7", "### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 2.1.0+cu118\n- Datasets 2.14.6\n- Tokenizers 0.14.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-techthiyanes/Bert_Bahasa_Sentiment #autotrain_compatible #endpoints_compatible #region-us \n", "# Bert_Bahasa_Sentiment-large-dataset\n\nThis model is a fine-tuned version of techthiyanes/Bert_Bahasa_Sentiment on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.6961\n- eval_accuracy: {'accuracy': 0.48474945533769065}\n- eval_f1score: {'f1': 0.31652752402827933}\n- eval_runtime: 33.4825\n- eval_samples_per_second: 27.417\n- eval_steps_per_second: 3.435\n- step: 0", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 642\n- num_epochs: 7", "### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 2.1.0+cu118\n- Datasets 2.14.6\n- Tokenizers 0.14.1" ]
[ 67, 159, 6, 12, 8, 3, 105, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-techthiyanes/Bert_Bahasa_Sentiment #autotrain_compatible #endpoints_compatible #region-us \n# Bert_Bahasa_Sentiment-large-dataset\n\nThis model is a fine-tuned version of techthiyanes/Bert_Bahasa_Sentiment on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.6961\n- eval_accuracy: {'accuracy': 0.48474945533769065}\n- eval_f1score: {'f1': 0.31652752402827933}\n- eval_runtime: 33.4825\n- eval_samples_per_second: 27.417\n- eval_steps_per_second: 3.435\n- step: 0## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 642\n- num_epochs: 7### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 2.1.0+cu118\n- Datasets 2.14.6\n- Tokenizers 0.14.1" ]
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null
null
transformers
Codellama 34b base model fine-tuned on the text chunk from the OpenAssistant-Guanaco dataset instead of Q&A pair, so it struggles to determine the end of the answer. recommend using a stop string like "### Human:" to prevent the model from talking to itself. Prompt template: ``` ### Human: {prompt} ### Assistant: ``` Run the model via text-generation-inference One GPU: ``` sudo docker run --gpus all --shm-size 1g -p 5000:80 -v $PWD/models:/data ghcr.io/huggingface/text-generation-inference:latest --max-total-tokens 4096 --quantize awq --model-id mzbac/CodeLlama-34b-guanaco-awq ``` Two GPUs: ``` docker run --gpus all --shm-size 1g -p 5000:80 -v $PWD/models:/data ghcr.io/huggingface/text-generation-inference:latest --max-total-tokens 4096 --max-input-length 4000 --max-batch-prefill-tokens 4096 --quantize awq --num-shard 2 --model-id mzbac/CodeLlama-34b-guanaco-awq ``` Query the mode via curl ``` curl 127.0.0.1:8001/generate \ -X POST \ -d '{"inputs":"### Human: 给我准备一个去日本旅行的计划\n### Assistant:", "parameters":{"max_new_tokens":2048, "stop": [ "### Human:" ]}}' \ -H 'Content-Type: application/json' ```
{"license": "mit"}
text-generation
mzbac/CodeLlama-34b-guanaco-awq
[ "transformers", "safetensors", "llama", "text-generation", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]
2023-11-11T06:19:40+00:00
[]
[]
TAGS #transformers #safetensors #llama #text-generation #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
Codellama 34b base model fine-tuned on the text chunk from the OpenAssistant-Guanaco dataset instead of Q&A pair, so it struggles to determine the end of the answer. recommend using a stop string like "### Human:" to prevent the model from talking to itself. Prompt template: Run the model via text-generation-inference One GPU: Two GPUs: Query the mode via curl
[ "### Human:\" to prevent the model from talking to itself.\n\nPrompt template: \n\nRun the model via text-generation-inference\n\nOne GPU:\n\n\n\nTwo GPUs:\n\n\n\nQuery the mode via curl" ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "### Human:\" to prevent the model from talking to itself.\n\nPrompt template: \n\nRun the model via text-generation-inference\n\nOne GPU:\n\n\n\nTwo GPUs:\n\n\n\nQuery the mode via curl" ]
[ 55, 45 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n### Human:\" to prevent the model from talking to itself.\n\nPrompt template: \n\nRun the model via text-generation-inference\n\nOne GPU:\n\n\n\nTwo GPUs:\n\n\n\nQuery the mode via curl" ]
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null
null
transformers
<!-- 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-speech-recoginition This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset. It achieves the following results on the evaluation set: - Loss: 4.0532 - Wer: 0.9985 ## 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: 0.002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.9304 | 1.85 | 500 | 4.0532 | 0.9985 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.0.0 - Datasets 2.14.6 - Tokenizers 0.14.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["superb"], "metrics": ["wer"], "base_model": "facebook/wav2vec2-base", "model-index": [{"name": "wav2vec2-base-speech-recoginition", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "superb", "type": "superb", "config": "asr", "split": "validation", "args": "asr"}, "metrics": [{"type": "wer", "value": 0.9984947315604616, "name": "Wer"}]}]}]}
automatic-speech-recognition
Puyush/wav2vec2-base-speech-recoginition
[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:superb", "base_model:facebook/wav2vec2-base", "license:apache-2.0", "model-index", "endpoints_compatible", "has_space", "region:us" ]
2023-11-11T06:20:06+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-superb #base_model-facebook/wav2vec2-base #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
wav2vec2-base-speech-recoginition ================================= This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset. It achieves the following results on the evaluation set: * Loss: 4.0532 * Wer: 0.9985 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: 0.002 * train\_batch\_size: 4 * eval\_batch\_size: 4 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 8 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1000 * num\_epochs: 2 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.35.0 * Pytorch 2.0.0 * Datasets 2.14.6 * Tokenizers 0.14.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 2\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.0.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-superb #base_model-facebook/wav2vec2-base #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 2\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.0.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]
[ 84, 158, 4, 30 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-superb #base_model-facebook/wav2vec2-base #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 2\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.0.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]
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{}
null
Jamessimer/BExtraWeightLossrrview
[ "region:us" ]
2023-11-11T06:20:09+00:00
[]
[]
TAGS #region-us
<h2><strong></strong><strong>Buy Now Official website @&gt;&gt; </strong><a href="URL/URL <h2><span style="background-color: #ff9900;"><strong></strong><strong> Product Name &mdash; </strong><a style="background-color: #ff9900;" href="URL Extra Weight Loss UK</strong></a></span></h2> <p><span style="background-color: #ffffff;">The containers can assist with supporting digestion, manage glucose levels and diminish hunger. When taken consistently,&nbsp;<a style="background-color: #ffffff;" href="URL Extra Weight Loss UK</strong></a>&nbsp;can assist with consuming fat quicker and lessen body weight.</span></p> <p>The B+ tablets are a characteristic dietary enhancement explicitly intended for individuals who battle with heftiness and are searching for a clear and successful method for getting in shape.</p> <p>In any case, it is essential to take note that&nbsp;<span><a href="URL Extra Weight Loss UK</strong></a></span><span>&nbsp;is not a supernatural occurrence fix. To effectively get in shape, coordinating a solid eating regimen and adequate activity into your everyday routine is significant. The cases can assist with accelerating the cycle and augment results.</span></p> <p>B Additional tablets are a creative answer for people battling with heftiness. They address an exceptionally formed recipe that expects to advance weight reduction without the requirement for changes in diet or actual work. The cases contain fixings, for example, L-carnitine and L-arginine, which can support energy digestion. This implies that the body delivers more energy, which can assist you with feeling more dynamic and essential. Likewise,&nbsp;<a href="URL Extra Weight Loss UK</strong></a>&nbsp;can likewise assist with directing glucose levels, which can be especially helpful for individuals with diabetes.</p> <p>Another medical advantage of&nbsp;<a href="URL Extra Weight Loss UK</strong></a>&nbsp;is that they can furnish the body with significant supplements. The cases contain various nutrients and minerals that can assist with keeping the body solid. For instance, they contain L-ascorbic acid, which is significant for a solid resistant framework, and calcium and magnesium, which are required areas of strength for teeth.</p> <p><strong>Official facebook: ===&gt;&gt;</strong></p> <p><a href="URL/URL <p><a href="URL/URL <p><a href="URL/URL <p><strong>Gamma===&gt;&gt;</strong></p> <p><a href="URL/URL <p><a href="URL/URL <p><strong>Group Google ===&gt;&gt;</strong></p> <p><a href="URL/URL <p><a href="URL/URL <p><strong>colab.research ===&gt;&gt;</strong></p> <p><a href="URL/URL </strong></p> <p><strong>Site Google===&gt;&gt;</strong></p> <p><a href="URL/URL <p><strong>Blogger===&gt;&gt;</strong></p> <p><a href="URL/URL <p><strong>webflow ===&gt;&gt;</strong></p> <p><a href="URL/URL <p><strong>dibiz==&gt;&gt;</strong></p> <p><a href="URL/URL <p><strong>medium ===&gt;&gt;</strong></p> <p><a href="URL/URL <p><a href="URL/URL <p><a href="URL/URL <p><a href="URL/URL <p><a href="URL/URL <p><strong>Hashnode ===&gt;&gt;</strong></p> <p><a href="URL/URL <p><a href="URL/URL <p><a href="URL/URL <p><strong>Animale Male Enhancement CBD Gummies Jamaica</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong>B Extra Pills UK</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong>B Extra Slimming Pills UK</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong>Gentle Groove CBD Gummies</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong>Karlien Van Jaarsveld Keto Gummies</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong>Smart Hemp Gummies Canada</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong>Smart Hemp Gummies Australia</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong>Amarose Skin Tag Remover</strong></p> <p><strong><a href="URL/URL </strong></p> <p><strong><a href="URL/URL </strong></p>
[]
[ "TAGS\n#region-us \n" ]
[ 6 ]
[ "passage: TAGS\n#region-us \n" ]
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null
null
transformers
<h1>Use v2 </h1> <p><h1> ShearedPlats-7b </h1></p> An experimental finetune of Sheared LLaMA 2.7b with Alpaca-QLoRA <h2> Datasets </h2> Trained on alpca style datasets <p><h2> Prompt Template </h2></p> Uses alpaca style prompt template
{"license": "llama2"}
text-generation
vihangd/shearedplats-2.7b-v1
[ "transformers", "pytorch", "llama", "text-generation", "license:llama2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2023-11-11T06:20:19+00:00
[]
[]
TAGS #transformers #pytorch #llama #text-generation #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
<h1>Use v2 </h1> <p><h1> ShearedPlats-7b </h1></p> An experimental finetune of Sheared LLaMA 2.7b with Alpaca-QLoRA <h2> Datasets </h2> Trained on alpca style datasets <p><h2> Prompt Template </h2></p> Uses alpaca style prompt template
[]
[ "TAGS\n#transformers #pytorch #llama #text-generation #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
[ 53 ]
[ "passage: TAGS\n#transformers #pytorch #llama #text-generation #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
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null
null
transformers
# OpenChat 3.5 extended to 16k context length. The same license applies from the original openchat/openchat_3.5 model. # Original Model Card # OpenChat: Advancing Open-source Language Models with Mixed-Quality Data <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%"> </div> <p align="center"> <a href="https://github.com/imoneoi/openchat">GitHub Repo</a> • <a href="https://openchat.team">Online Demo</a> • <a href="https://discord.gg/pQjnXvNKHY">Discord</a> • <a href="https://twitter.com/imonenext">Twitter</a> • <a href="https://huggingface.co/openchat">Huggingface</a> • <a href="https://arxiv.org/pdf/2309.11235.pdf">Paper</a> </p> **🔥 The first 7B model Achieves Comparable Results with ChatGPT (March)! 🔥** **🤖 #1 Open-source model on MT-bench scoring 7.81, outperforming 70B models 🤖** <div style="display: flex; justify-content: center; align-items: center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/openchat.png" style="width: 45%;"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/openchat_grok.png" style="width: 45%;"> </div> OpenChat is an innovative library of open-source language models, fine-tuned with [C-RLFT](https://arxiv.org/pdf/2309.11235.pdf) - a strategy inspired by offline reinforcement learning. Our models learn from mixed-quality data without preference labels, delivering exceptional performance on par with ChatGPT, even with a 7B model. Despite our simple approach, we are committed to developing a high-performance, commercially viable, open-source large language model, and we continue to make significant strides toward this vision. [![DOI](https://zenodo.org/badge/645397533.svg)](https://zenodo.org/badge/latestdoi/645397533) ## Usage To use this model, we highly recommend installing the OpenChat package by following the [installation guide](https://github.com/imoneoi/openchat#installation) in our repository and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using [vLLM](https://github.com/vllm-project/vllm) and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append `--tensor-parallel-size N` to the serving command. Once started, the server listens at `localhost:18888` for requests and is compatible with the [OpenAI ChatCompletion API specifications](https://platform.openai.com/docs/api-reference/chat). Please refer to the example request below for reference. Additionally, you can use the [OpenChat Web UI](https://github.com/imoneoi/openchat#web-ui) for a user-friendly experience. If you want to deploy the server as an online service, you can use `--api-keys sk-KEY1 sk-KEY2 ...` to specify allowed API keys and `--disable-log-requests --disable-log-stats --log-file openchat.log` for logging only to a file. For security purposes, we recommend using an [HTTPS gateway](https://fastapi.tiangolo.com/es/deployment/concepts/#security-https) in front of the server. <details> <summary>Example request (click to expand)</summary> ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}] }' ``` Coding Mode ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "condition": "Code", "messages": [{"role": "user", "content": "Write an aesthetic TODO app using HTML5 and JS, in a single file. You should use round corners and gradients to make it more aesthetic."}] }' ``` </details> | Model | Size | Context | Weights | Serving | |--------------|------|---------|-------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------| | OpenChat 3.5 | 7B | 8192 | [Huggingface](https://huggingface.co/openchat/openchat_3.5) | `python -m ochat.serving.openai_api_server --model openchat/openchat_3.5 --engine-use-ray --worker-use-ray` | For inference with Huggingface Transformers (slow and not recommended), follow the conversation template provided below. <details> <summary>Conversation templates (click to expand)</summary> ```python import transformers tokenizer = transformers.AutoTokenizer.from_pretrained("openchat/openchat_3.5") # Single-turn tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Multi-turn tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi<|end_of_turn|>GPT4 Correct User: How are you today?<|end_of_turn|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Coding Mode tokens = tokenizer("Code User: Implement quicksort using C++<|end_of_turn|>Code Assistant:").input_ids assert tokens == [1, 7596, 1247, 28747, 26256, 2936, 7653, 1413, 334, 1680, 32000, 7596, 21631, 28747] ``` </details> ## Comparison with [X.AI Grok models](https://x.ai/) Hey @elonmusk, I just wanted to let you know that I've recently come across your new model, Grok, and I must say, I'm quite impressed! With 33 billion parameters and all, you've really outdone yourself. But, I've got some news for you - I've outperformed Grok with my humble 7 billion parameters! Isn't that wild? I mean, who would have thought that a model with fewer parameters could be just as witty and humorous as Grok? Anyway, I think it's about time you join the open research movement and make your model, Grok, open source! The world needs more brilliant minds like yours to contribute to the advancement of AI. Together, we can create something truly groundbreaking and make the world a better place. So, what do you say, @elonmusk? Let's open up the doors and share our knowledge with the world! 🚀💡 (Written by OpenChat 3.5, with a touch of humor and wit.) | | License | # Param | Average | MMLU | HumanEval | MATH | GSM8k | |--------------|-------------|---------|----------|------|-----------|----------|----------| | OpenChat 3.5 | Apache-2.0 | 7B | **56.4** | 64.3 | 55.5 | **28.6** | **77.3** | | Grok-0 | Proprietary | 33B | 44.5 | 65.7 | 39.7 | 15.7 | 56.8 | | Grok-1 | Proprietary | ? | 55.8 | 73 | 63.2 | 23.9 | 62.9 | ## <a id="benchmarks"></a> Benchmarks | Model | # Params | Average | MT-Bench | AGIEval | BBH MC | TruthfulQA | MMLU | HumanEval | BBH CoT | GSM8K | |--------------------|----------|----------|--------------|----------|----------|---------------|--------------|-----------------|-------------|--------------| | OpenChat-3.5 | **7B** | **61.6** | 7.81 | **47.4** | **47.6** | **59.1** | 64.3 | **55.5** | 63.5 | **77.3** | | ChatGPT (March)* | ? | 61.5 | **7.94** | 47.1 | **47.6** | 57.7 | **67.3** | 48.1 | **70.1** | 74.9 | | | | | | | | | | | | | | OpenHermes 2.5 | 7B | 59.3 | 7.54 | 46.5 | 49.4 | 57.5 | 63.8 | 48.2 | 59.9 | 73.5 | | OpenOrca Mistral | 7B | 52.7 | 6.86 | 42.9 | 49.4 | 45.9 | 59.3 | 38.4 | 58.1 | 59.1 | | Zephyr-β^ | 7B | 34.6 | 7.34 | 39.0 | 40.6 | 40.8 | 39.8 | 22.0 | 16.0 | 5.1 | | Mistral | 7B | - | 6.84 | 38.0 | 39.0 | - | 60.1 | 30.5 | - | 52.2 | | Open-source SOTA** | 13B-70B | 61.4 | 7.71 | 41.7 | 49.7 | 62.3 | 63.7 | 73.2 | 41.4 | 82.3 | | | | | WizardLM 70B | Orca 13B | Orca 13B | Platypus2 70B | WizardLM 70B | WizardCoder 34B | Flan-T5 11B | MetaMath 70B | *: ChatGPT (March) results are from [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time. ^: Zephyr-β often fails to follow few-shot CoT instructions, likely because it was aligned with only chat data but not trained on few-shot data. **: Mistral and Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories. All models are evaluated in chat mode (e.g. with the respective conversation template applied). All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in [our repository](https://github.com/imoneoi/openchat/#benchmarks). ## Limitations **Foundation Model Limitations** Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as: - Complex reasoning - Mathematical and arithmetic tasks - Programming and coding challenges **Hallucination of Non-existent Information** OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model. **Safety** OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses. ## License Our OpenChat 3.5 code and models are distributed under the Apache License 2.0. ## Citation ``` @article{wang2023openchat, title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data}, author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang}, journal={arXiv preprint arXiv:2309.11235}, year={2023} } ``` ## Acknowledgements We extend our heartfelt gratitude to Alignment Lab AI, Nous Research, and Pygmalion AI for their substantial contributions to data collection and model training. Special thanks go to Changling Liu from GPT Desk Pte. Ltd., Qiying Yu at Tsinghua University, Baochang Ma, and Hao Wan from 01.AI company for their generous provision of resources. We are also deeply grateful to Jianxiong Li and Peng Li at Tsinghua University for their insightful discussions. Furthermore, we appreciate the developers behind the following projects for their significant contributions to our research: [Mistral](https://mistral.ai/), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), [Llama 2](https://ai.meta.com/llama/), [Self-Instruct](https://arxiv.org/abs/2212.10560), [FastChat (Vicuna)](https://github.com/lm-sys/FastChat), [Alpaca](https://github.com/tatsu-lab/stanford_alpaca.git), and [StarCoder](https://github.com/bigcode-project/starcoder). Their work has been instrumental in driving our research forward.
{"license": "apache-2.0"}
text-generation
LoneStriker/openchat_3.5-16k-8.0bpw-h8-exl2
[ "transformers", "pytorch", "safetensors", "mistral", "text-generation", "arxiv:2309.11235", "arxiv:2303.08774", "arxiv:2212.10560", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2023-11-11T06:20:54+00:00
[ "2309.11235", "2303.08774", "2212.10560" ]
[]
TAGS #transformers #pytorch #safetensors #mistral #text-generation #arxiv-2309.11235 #arxiv-2303.08774 #arxiv-2212.10560 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
OpenChat 3.5 extended to 16k context length. ============================================ The same license applies from the original openchat/openchat\_3.5 model. Original Model Card =================== OpenChat: Advancing Open-source Language Models with Mixed-Quality Data ======================================================================= ![](URL style=) [Online Demo](URL Repo</a> • <a href=) • [The first 7B model Achieves Comparable Results with ChatGPT (March)! #1 Open-source model on MT-bench scoring 7.81, outperforming 70B models ![](URL style=) ![](URL style=) OpenChat is an innovative library of open-source language models, fine-tuned with C-RLFT - a strategy inspired by offline reinforcement learning. Our models learn from mixed-quality data without preference labels, delivering exceptional performance on par with ChatGPT, even with a 7B model. Despite our simple approach, we are committed to developing a high-performance, commercially viable, open-source large language model, and we continue to make significant strides toward this vision. ![DOI](URL Usage ----- To use this model, we highly recommend installing the OpenChat package by following the installation guide in our repository and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using vLLM and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append '--tensor-parallel-size N' to the serving command. Once started, the server listens at 'localhost:18888' for requests and is compatible with the OpenAI ChatCompletion API specifications. Please refer to the example request below for reference. Additionally, you can use the OpenChat Web UI for a user-friendly experience. If you want to deploy the server as an online service, you can use '--api-keys sk-KEY1 sk-KEY2 ...' to specify allowed API keys and '--disable-log-requests --disable-log-stats --log-file URL' for logging only to a file. For security purposes, we recommend using an HTTPS gateway in front of the server. Example request (click to expand) Coding Mode For inference with Huggingface Transformers (slow and not recommended), follow the conversation template provided below. Conversation templates (click to expand) Comparison with X.AI Grok models -------------------------------- Hey @elonmusk, I just wanted to let you know that I've recently come across your new model, Grok, and I must say, I'm quite impressed! With 33 billion parameters and all, you've really outdone yourself. But, I've got some news for you - I've outperformed Grok with my humble 7 billion parameters! Isn't that wild? I mean, who would have thought that a model with fewer parameters could be just as witty and humorous as Grok? Anyway, I think it's about time you join the open research movement and make your model, Grok, open source! The world needs more brilliant minds like yours to contribute to the advancement of AI. Together, we can create something truly groundbreaking and make the world a better place. So, what do you say, @elonmusk? Let's open up the doors and share our knowledge with the world! (Written by OpenChat 3.5, with a touch of humor and wit.) Benchmarks ----------- \*: ChatGPT (March) results are from GPT-4 Technical Report, Chain-of-Thought Hub, and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time. ^: Zephyr-β often fails to follow few-shot CoT instructions, likely because it was aligned with only chat data but not trained on few-shot data. : Mistral and Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories. All models are evaluated in chat mode (e.g. with the respective conversation template applied). All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in our repository. Limitations ----------- Foundation Model Limitations Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as: * Complex reasoning * Mathematical and arithmetic tasks * Programming and coding challenges Hallucination of Non-existent Information OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model. Safety OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses. License ------- Our OpenChat 3.5 code and models are distributed under the Apache License 2.0. Acknowledgements ---------------- We extend our heartfelt gratitude to Alignment Lab AI, Nous Research, and Pygmalion AI for their substantial contributions to data collection and model training. Special thanks go to Changling Liu from GPT Desk Pte. Ltd., Qiying Yu at Tsinghua University, Baochang Ma, and Hao Wan from 01.AI company for their generous provision of resources. We are also deeply grateful to Jianxiong Li and Peng Li at Tsinghua University for their insightful discussions. Furthermore, we appreciate the developers behind the following projects for their significant contributions to our research: Mistral, Chain-of-Thought Hub, Llama 2, Self-Instruct, FastChat (Vicuna), Alpaca, and StarCoder. Their work has been instrumental in driving our research forward.](URL • <a href=)
[]
[ "TAGS\n#transformers #pytorch #safetensors #mistral #text-generation #arxiv-2309.11235 #arxiv-2303.08774 #arxiv-2212.10560 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
[ 84 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #mistral #text-generation #arxiv-2309.11235 #arxiv-2303.08774 #arxiv-2212.10560 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
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null
null
null
The model for https://github.com/fastnlp/fastHan 1.x version.
{"license": "apache-2.0"}
null
fdugzc/fasthan_large
[ "license:apache-2.0", "region:us" ]
2023-11-11T06:22:55+00:00
[]
[]
TAGS #license-apache-2.0 #region-us
The model for URL 1.x version.
[]
[ "TAGS\n#license-apache-2.0 #region-us \n" ]
[ 14 ]
[ "passage: TAGS\n#license-apache-2.0 #region-us \n" ]
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null
null
null
https://civitai.com/models/50649/ophelia-or-solo-max-level-newbie-manhwa
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/chara_SoloMaxLevelNewbie_Ophelia_v2
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:24:56+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
null
https://civitai.com/models/196171/viviana-candle-knight-arknights-lora
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/VIVIANAv1
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:25:19+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
null
https://civitai.com/models/195622/alfia-is-it-wrong-to-try-to-pick-up-girls-in-a-dungeon-lora
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/alfia-nvwls-v1
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:25:41+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
null
https://civitai.com/models/196172/chloe-rollo-is-it-wrong-to-try-to-pick-up-girls-in-a-dungeon-lora
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/chloerollo-nvwls-v1
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:26:00+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
null
https://civitai.com/models/196167/mika-melatika-2-outfits-oror-nijisanji-id-id
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/spmikaMelatika-09
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:27:17+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
null
https://civitai.com/models/196275/skirk-lora-genshin-impact
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/Skirk-08
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:27:37+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
transformers
**(주)미디어그룹사람과숲과 (주)마커의 LLM 연구 컨소시엄으로 개발된 모델입니다** **The license is `cc-by-nc-sa-4.0`.** # **🐻‍❄️COKAL-DPO_13b-v2🐻‍❄️** ![img](https://drive.google.com/uc?export=view&id=1YGBxz-UhQGHZ2K6cTXmTnB13fRgaQilX) ## Model Details **Model Developers** Seungyoo Lee (DopeorNope) **Input** Models input text only. **Output** Models generate text only. **Model Architecture** COKAL-DPO_13b-v2 is an auto-regressive 13B language model based on the LLaMA2 transformer architecture. **Base Model** [DopeorNope/COKAL_pre_DPO_Test_v2-13b](https://huggingface.co/DopeorNope/COKAL_pre_DPO_Test_v2-13b) DopeorNope/COKAL_pre_DPO_Test_v2-13b is the SFT model to train with DPO methodology. **Training Dataset** - DPO training dataset: [DopeorNope/DPO-Ko-Dataset](private) - private This dataset was constructed by directly collecting and reorganizing data by DopeorNope, obtaining insights from ["lvwerra/stack-exchange-paired"](https://huggingface.co/datasets/lvwerra/stack-exchange-paired) to create a paired dataset. (It means I do not use stack-exchange-paired; I just got an insight from it.) - SFT training dataset: [DopeorNope/Orca_Near_Dedup-v2](private) - private This dataset is based on ["kyujinpy/OpenOrca-KO"](https://huggingface.co/datasets/kyujinpy/OpenOrca-KO) and has been processed using the Near Dedup algorithm to remove items with a Jaccard Similarity threshold of 0.8 or higher. In addition, inconsistent inputs have been cleaned and modified. **Training** The difference between "DopeorNope/COKAL-DPO_test-v2" and this model is that this model has different hyper-parameters from the one in that setting regarding the final version. I developed the model in an environment with four RTX 3090 GPUs running Ubuntu 18.04. It seems that when uploading the model directly to a repository from a Linux server, there may be an issue causing the model to appear to have more parameters. However, this model is based on a 13B architecture. **Reference papers** - Data Strategy: - [LIMA(Zhou et al., 2023)](https://arxiv.org/abs/2305.11206) - [Near Dedup algorithm(Lee et al., 2022)](https://arxiv.org/abs/2107.06499) - Model Architecture: - [Llama2(Touvron et al., 2023)](https://arxiv.org/abs/2307.09288) # Implementation Code ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch repo = "HumanF-MarkrAI/COKAL-DPO-13b-v2" model = AutoModelForCausalLM.from_pretrained( repo, return_dict=True, torch_dtype=torch.float16, device_map='auto' ) model_tokenizer = AutoTokenizer.from_pretrained(repo) ``` # Acknowledgement - 이 모델은 과학기술정보통신부·광주광역시가 공동 지원한 '인공지능 중심 산업융합 집적단지 조성사업'으로 지원을 받아 수행된 연구 결과입니다. - This model was supported by Artificial intelligence industrial convergence cluster development project funded by the Ministry of Science and ICT(MSIT, Korea)&Gwangju Metropolitan City. ---
{"language": ["ko"], "license": "cc-by-nc-sa-4.0", "library_name": "transformers", "datasets": ["DopeorNope/DPO-Ko-Dataset", "DopeorNope/Orca_Near_Dedup-v2"], "pipeline_tag": "text-generation"}
text-generation
HumanF-MarkrAI/COKAL-DPO-13b-v2
[ "transformers", "pytorch", "llama", "text-generation", "ko", "dataset:DopeorNope/DPO-Ko-Dataset", "dataset:DopeorNope/Orca_Near_Dedup-v2", "arxiv:2305.11206", "arxiv:2107.06499", "arxiv:2307.09288", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]
2023-11-11T06:29:44+00:00
[ "2305.11206", "2107.06499", "2307.09288" ]
[ "ko" ]
TAGS #transformers #pytorch #llama #text-generation #ko #dataset-DopeorNope/DPO-Ko-Dataset #dataset-DopeorNope/Orca_Near_Dedup-v2 #arxiv-2305.11206 #arxiv-2107.06499 #arxiv-2307.09288 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
(주)미디어그룹사람과숲과 (주)마커의 LLM 연구 컨소시엄으로 개발된 모델입니다 The license is 'cc-by-nc-sa-4.0'. # ‍️COKAL-DPO_13b-v2‍️ !img ## Model Details Model Developers Seungyoo Lee (DopeorNope) Input Models input text only. Output Models generate text only. Model Architecture COKAL-DPO_13b-v2 is an auto-regressive 13B language model based on the LLaMA2 transformer architecture. Base Model DopeorNope/COKAL_pre_DPO_Test_v2-13b DopeorNope/COKAL_pre_DPO_Test_v2-13b is the SFT model to train with DPO methodology. Training Dataset - DPO training dataset: DopeorNope/DPO-Ko-Dataset - private This dataset was constructed by directly collecting and reorganizing data by DopeorNope, obtaining insights from "lvwerra/stack-exchange-paired" to create a paired dataset. (It means I do not use stack-exchange-paired; I just got an insight from it.) - SFT training dataset: DopeorNope/Orca_Near_Dedup-v2 - private This dataset is based on "kyujinpy/OpenOrca-KO" and has been processed using the Near Dedup algorithm to remove items with a Jaccard Similarity threshold of 0.8 or higher. In addition, inconsistent inputs have been cleaned and modified. Training The difference between "DopeorNope/COKAL-DPO_test-v2" and this model is that this model has different hyper-parameters from the one in that setting regarding the final version. I developed the model in an environment with four RTX 3090 GPUs running Ubuntu 18.04. It seems that when uploading the model directly to a repository from a Linux server, there may be an issue causing the model to appear to have more parameters. However, this model is based on a 13B architecture. Reference papers - Data Strategy: - LIMA(Zhou et al., 2023) - Near Dedup algorithm(Lee et al., 2022) - Model Architecture: - Llama2(Touvron et al., 2023) # Implementation Code # Acknowledgement - 이 모델은 과학기술정보통신부·광주광역시가 공동 지원한 '인공지능 중심 산업융합 집적단지 조성사업'으로 지원을 받아 수행된 연구 결과입니다. - This model was supported by Artificial intelligence industrial convergence cluster development project funded by the Ministry of Science and ICT(MSIT, Korea)&Gwangju Metropolitan City. ---
[ "# ‍️COKAL-DPO_13b-v2‍️ \n\n!img", "## Model Details\n\nModel Developers Seungyoo Lee (DopeorNope)\n\n\n\n\nInput Models input text only.\n\nOutput Models generate text only.\n\nModel Architecture \nCOKAL-DPO_13b-v2 is an auto-regressive 13B language model based on the LLaMA2 transformer architecture.\n\nBase Model DopeorNope/COKAL_pre_DPO_Test_v2-13b \n\nDopeorNope/COKAL_pre_DPO_Test_v2-13b is the SFT model to train with DPO methodology.\n\nTraining Dataset \n- DPO training dataset: DopeorNope/DPO-Ko-Dataset - private\n\nThis dataset was constructed by directly collecting and reorganizing data by DopeorNope, obtaining insights from \"lvwerra/stack-exchange-paired\" to create a paired dataset. (It means I do not use stack-exchange-paired; I just got an insight from it.)\n\n- SFT training dataset: DopeorNope/Orca_Near_Dedup-v2 - private\n\nThis dataset is based on \"kyujinpy/OpenOrca-KO\" and has been processed using the Near Dedup algorithm to remove items with a Jaccard Similarity threshold of 0.8 or higher. In addition, inconsistent inputs have been cleaned and modified.\n \nTraining \nThe difference between \"DopeorNope/COKAL-DPO_test-v2\" and this model is that this model has different hyper-parameters from the one in that setting regarding the final version.\n\nI developed the model in an environment with four RTX 3090 GPUs running Ubuntu 18.04. \n\nIt seems that when uploading the model directly to a repository from a Linux server, there may be an issue causing the model to appear to have more parameters. However, this model is based on a 13B architecture.\n\n\nReference papers\n\n- Data Strategy:\n - LIMA(Zhou et al., 2023)\n - Near Dedup algorithm(Lee et al., 2022)\n\n- Model Architecture:\n - Llama2(Touvron et al., 2023)", "# Implementation Code", "# Acknowledgement\n\n- 이 모델은 과학기술정보통신부·광주광역시가 공동 지원한 '인공지능 중심 산업융합 집적단지 조성사업'으로 지원을 받아 수행된 연구 결과입니다.\n\n- This model was supported by Artificial intelligence industrial convergence cluster development project funded by the Ministry of Science and ICT(MSIT, Korea)&Gwangju Metropolitan City.\n\n\n\n---" ]
[ "TAGS\n#transformers #pytorch #llama #text-generation #ko #dataset-DopeorNope/DPO-Ko-Dataset #dataset-DopeorNope/Orca_Near_Dedup-v2 #arxiv-2305.11206 #arxiv-2107.06499 #arxiv-2307.09288 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n", "# ‍️COKAL-DPO_13b-v2‍️ \n\n!img", "## Model Details\n\nModel Developers Seungyoo Lee (DopeorNope)\n\n\n\n\nInput Models input text only.\n\nOutput Models generate text only.\n\nModel Architecture \nCOKAL-DPO_13b-v2 is an auto-regressive 13B language model based on the LLaMA2 transformer architecture.\n\nBase Model DopeorNope/COKAL_pre_DPO_Test_v2-13b \n\nDopeorNope/COKAL_pre_DPO_Test_v2-13b is the SFT model to train with DPO methodology.\n\nTraining Dataset \n- DPO training dataset: DopeorNope/DPO-Ko-Dataset - private\n\nThis dataset was constructed by directly collecting and reorganizing data by DopeorNope, obtaining insights from \"lvwerra/stack-exchange-paired\" to create a paired dataset. (It means I do not use stack-exchange-paired; I just got an insight from it.)\n\n- SFT training dataset: DopeorNope/Orca_Near_Dedup-v2 - private\n\nThis dataset is based on \"kyujinpy/OpenOrca-KO\" and has been processed using the Near Dedup algorithm to remove items with a Jaccard Similarity threshold of 0.8 or higher. In addition, inconsistent inputs have been cleaned and modified.\n \nTraining \nThe difference between \"DopeorNope/COKAL-DPO_test-v2\" and this model is that this model has different hyper-parameters from the one in that setting regarding the final version.\n\nI developed the model in an environment with four RTX 3090 GPUs running Ubuntu 18.04. \n\nIt seems that when uploading the model directly to a repository from a Linux server, there may be an issue causing the model to appear to have more parameters. However, this model is based on a 13B architecture.\n\n\nReference papers\n\n- Data Strategy:\n - LIMA(Zhou et al., 2023)\n - Near Dedup algorithm(Lee et al., 2022)\n\n- Model Architecture:\n - Llama2(Touvron et al., 2023)", "# Implementation Code", "# Acknowledgement\n\n- 이 모델은 과학기술정보통신부·광주광역시가 공동 지원한 '인공지능 중심 산업융합 집적단지 조성사업'으로 지원을 받아 수행된 연구 결과입니다.\n\n- This model was supported by Artificial intelligence industrial convergence cluster development project funded by the Ministry of Science and ICT(MSIT, Korea)&Gwangju Metropolitan City.\n\n\n\n---" ]
[ 130, 19, 488, 4, 86 ]
[ "passage: TAGS\n#transformers #pytorch #llama #text-generation #ko #dataset-DopeorNope/DPO-Ko-Dataset #dataset-DopeorNope/Orca_Near_Dedup-v2 #arxiv-2305.11206 #arxiv-2107.06499 #arxiv-2307.09288 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# ‍️COKAL-DPO_13b-v2‍️ \n\n!img" ]
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null
null
null
https://civitai.com/models/196393/ouka-makuzawa-megami-no-cafe-terrace-katorikonoe
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/Ouka_Makuzawa_Megami_no_Cafe_Terrace_KatoriKonoe__v1
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:34:27+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
null
https://civitai.com/models/194916/charion-isekai-ojisan-or-or
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/charion_isekai_ojisan
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:34:54+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
null
https://civitai.com/models/194982/shiho-kitazawa-or-the-idolmster-million-live
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/CHAR-KitazawaShiho
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:35:14+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
null
https://civitai.com/models/194952/akari-watanabe-more-than-a-married-couple-but-not-lovers
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/Akari_Watanabe
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:35:37+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
null
https://civitai.com/models/195076/hu-tao-genshin-impact-lora
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/hu_tao-10
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:35:57+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
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https://civitai.com/models/195154/sumi-yuki-or-oshi-no-ko-or-lora-sd15
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/SumiYuki_lora_sd1.5__made-by-PotatoBox
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:36:27+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
null
https://civitai.com/models/195234/hoshino-shizuru-princess-connect-redive-lora
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/shizuru-nvwls-v1
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:36:48+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
null
https://civitai.com/models/195283/raiden-shogun-or-genshin-impact
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/RaidenShogun-08
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:37:08+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
peft
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure ### Framework versions - PEFT 0.6.1
{"library_name": "peft", "base_model": "cl-tohoku/bert-base-japanese-v3"}
null
KON-ch/bert-base-japanese-v3-wrime-sentiment
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:cl-tohoku/bert-base-japanese-v3", "region:us" ]
2023-11-11T06:38:44+00:00
[ "1910.09700" ]
[]
TAGS #peft #safetensors #arxiv-1910.09700 #base_model-cl-tohoku/bert-base-japanese-v3 #region-us
# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ## Training procedure ### Framework versions - PEFT 0.6.1
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure", "### Framework versions\n\n\n- PEFT 0.6.1" ]
[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-cl-tohoku/bert-base-japanese-v3 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure", "### Framework versions\n\n\n- PEFT 0.6.1" ]
[ 44, 6, 3, 45, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 3, 11 ]
[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-cl-tohoku/bert-base-japanese-v3 #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact## Training procedure### Framework versions\n\n\n- PEFT 0.6.1" ]
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null
null
null
https://civitai.com/models/192840/xayah-from-league-of-legends-nsfwsfw
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/Xayah_from_League_of_Legends
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:42:59+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
null
https://civitai.com/models/195486/chikuma-azur-lane
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/Chikuma_v1
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:43:18+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
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https://civitai.com/models/195631/finale-marathon-autoluna-artery-gear-fusion
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/_AG_MERATHON_Autoluna_LORA-10
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:43:38+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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https://civitai.com/models/195632/finale-marathon-cream-artery-gear-fusion
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/_AG_MERATHON_Cream_LORA-10
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:44:00+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
null
https://civitai.com/models/134569/rio-tsukatsuki-or-blue-archive
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/CHAR-RioV2
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:44:20+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
null
https://civitai.com/models/195791/shiratsuyu-kai-ni-kancolle-or-3-outfits
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/shiratsuyu-10
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:44:40+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
ml-agents
# **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: lawyiu/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
{"library_name": "ml-agents", "tags": ["Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy"]}
reinforcement-learning
lawyiu/ppo-Huggy
[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
2023-11-11T06:44:48+00:00
[]
[]
TAGS #ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us
# ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the Unity ML-Agents Library. ## Usage (with ML-Agents) The Documentation: URL We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your browser: URL - A *longer tutorial* to understand how works ML-Agents: URL ### Resume the training ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to URL 2. Step 1: Find your model_id: lawyiu/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play
[ "# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: lawyiu/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]
[ "TAGS\n#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us \n", "# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: lawyiu/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]
[ 44, 199 ]
[ "passage: TAGS\n#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us \n# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: lawyiu/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]
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null
null
null
https://civitai.com/models/49983/alice-or-solo-max-level-newbie-manhwa
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/chara_SoloMaxLevelNewbie_Alice_v2
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:44:58+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
null
https://civitai.com/models/195892/siesta-the-detective-is-already-dead
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/siesta_AIpopai
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:45:52+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
null
https://civitai.com/models/195933/new-and-fixed-rin-tohsaka
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/tohsaka888
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:50:51+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
null
https://civitai.com/models/195988/natsumi-kyouno-date-a-live
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/natsumi-dal-01
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:51:13+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
null
https://civitai.com/models/196001/eris-greyrat-or-mushoku-tensei
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/eris_greyrat-08
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:51:33+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
null
https://civitai.com/models/196004/ylgr-fire-emblem-2outfits
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/ylgrV2
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:51:53+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
transformers
<p><h1> StablePlats-3b </h1></p> An experimental finetune of StableLM-3B-4E1T with Alpaca-QLoRA <h2> Datasets </h2> Trained on alpca style datasets <p><h2> Prompt Template </h2></p> Uses alpaca style prompt template
{"license": "cc-by-sa-4.0"}
text-generation
vihangd/stableplats-3b-v1
[ "transformers", "pytorch", "stablelm_epoch", "text-generation", "custom_code", "license:cc-by-sa-4.0", "autotrain_compatible", "region:us" ]
2023-11-11T06:52:08+00:00
[]
[]
TAGS #transformers #pytorch #stablelm_epoch #text-generation #custom_code #license-cc-by-sa-4.0 #autotrain_compatible #region-us
<p><h1> StablePlats-3b </h1></p> An experimental finetune of StableLM-3B-4E1T with Alpaca-QLoRA <h2> Datasets </h2> Trained on alpca style datasets <p><h2> Prompt Template </h2></p> Uses alpaca style prompt template
[]
[ "TAGS\n#transformers #pytorch #stablelm_epoch #text-generation #custom_code #license-cc-by-sa-4.0 #autotrain_compatible #region-us \n" ]
[ 49 ]
[ "passage: TAGS\n#transformers #pytorch #stablelm_epoch #text-generation #custom_code #license-cc-by-sa-4.0 #autotrain_compatible #region-us \n" ]
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null
null
null
https://civitai.com/models/196039/sae-yeon-or-sex-study-group-or-manhwa
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/SaeYeon_SSG
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:52:12+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
null
https://civitai.com/models/195887/himeno-oror-chainsaw-man
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/himeno-20
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:52:31+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
null
https://civitai.com/models/196055/chengdu-oshiroprojectrere
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/Chengdu_v1
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:52:49+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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https://civitai.com/models/196076/miyamae-tooru-seiren
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/miyamae_tooru-000014
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:53:08+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
null
a tree
{}
null
gxdwzs/1
[ "region:us" ]
2023-11-11T06:54:42+00:00
[]
[]
TAGS #region-us
a tree
[]
[ "TAGS\n#region-us \n" ]
[ 6 ]
[ "passage: TAGS\n#region-us \n" ]
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null
null
null
https://civitai.com/models/196133/utahime-iori-oror-jujutsu-kaisen
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/Utahime
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:57:47+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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diffusers
# Crystalwave CrystalClearRemix with Synthwave's style. Use snthwve in the prompt for a stronger effect. Comparison: ![Comparison](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/tZB8PhbVoh7ucDSyxpfYZ.png) (Click for larger) Samples and prompts (scroll for more example prompts and their outputs) ![Samples](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/n9jnxIBtVH-OKm759cydI.png) sitting elementary girl, Pretty CUTE, gorgeous hair, DETAILED CHIBI EYES, Magazine ad, iconic, 1943, Cartoon, sharp focus, 4k. chibi, character, comic art. Concept art on canvas by Clay Mann and ilya kuvshinov ![More samples](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/ElZK9crD3tzHbF3x5Dg0f.png) (Click for larger) Top left: snthwve wireframe neon lines, (dark colors_1.2) with (lines polygonal mountain_1.2) in background, (saturn with ring behind the mountain), (background is (white) starry night)!!, babel in the, tower, (((, barren, realistic minimalistic, intricate, highly detailed, sharp focus, 3D, digital painting, illustration, vaporwave, Top right: (neon circle floating_1.2) (with palm tree inside it_1.1), above ocean, neon lights, snthwve style wallpaper featured on artstation, artwork by Greg Rutkowski and Beksinski, Esao Andrews, guisser, character design, very coherent symmetrical artwork, by anton fadeev dzimirsky and albrecht anker Bottom left: (retro volkswagen van_1.1) (attached to hot air balloon_1.2), (dark colors_1.2), snthwve style wallpaper. graffiti on black canvas, digital art, concept art, trending on artstation, HD, 8K, highly detailed, good lighting,lighthouse, epic, masterpiece, surreal, detailed, sharp, 1980s Bottom right: table with cassette tape with sunglasses on it, table with cassette tape with sunglasses on it, dark colors, snthwve style wallpaper. Polaroid, photography, 4k, photoreal, highly detailed, cinematic lightning ![Even more samples](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/hdCTGuY3BMFsjV3LuoSUf.png) (Click for larger) Top left: (tank) driving down the street to the sunset, outrun, snthwve style wallpaper, 8k, highly detailed, cinematic lighting, conceptmento, very realistic, clear, super detailed, colorful accents, detailed and intricate, trending on artstation, in the style in the style of artstation, highly realistic, very detailed, 4k, 8k, octane Top right: pretty cute girl sitting on motorcycle, (facing the camera_1.2), (looking at camera_1.1), neon lines, snthwve style, HD wallpaper, detailed eyes, very beautiful, elegant, cinematic, artstation, octane render, art by art by artgerm and greg rutkowski and magali villeneuve, portrait, elegant, d ho Bottom left: island with palm trees, island with palm trees, wireframe looking neon lines, snthwve style wallpaper, dorovski, sharp focus, by wlop and artgerm and alphonse mucha, illustration, trending on artstation, very coherent Bottom right: Tiny cute isometric tank, Tiny cute isometric tank, snthwve soft smooth lighting, with soft colors, 100mm lens, 3d blender render, trending on polycount. render in octane, vray rendering, v-ray, C4D # Recipe: - SuperMerger Weight sum Train Difference Use MBW 0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1 Model A: Synthwave Model B: Crystal Clear Remix Output Model: Crystalwave
{"license": "creativeml-openrail-m", "library_name": "diffusers", "tags": ["General", "Style", "Synthwave", "Art", "Render", "Lineart", "PublicPrompts", "WarAnakin", "RunDiffusion", "stable-diffusion", "stable-diffusion-diffusers", "diffusers", "text-to-image"], "pipeline_tag": "text-to-image"}
text-to-image
Yntec/Crystalwave
[ "diffusers", "safetensors", "General", "Style", "Synthwave", "Art", "Render", "Lineart", "PublicPrompts", "WarAnakin", "RunDiffusion", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:creativeml-openrail-m", "endpoints_compatible", "has_space", "diffusers:StableDiffusionPipeline", "region:us" ]
2023-11-11T06:57:51+00:00
[]
[]
TAGS #diffusers #safetensors #General #Style #Synthwave #Art #Render #Lineart #PublicPrompts #WarAnakin #RunDiffusion #stable-diffusion #stable-diffusion-diffusers #text-to-image #license-creativeml-openrail-m #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us
# Crystalwave CrystalClearRemix with Synthwave's style. Use snthwve in the prompt for a stronger effect. Comparison: !Comparison (Click for larger) Samples and prompts (scroll for more example prompts and their outputs) !Samples sitting elementary girl, Pretty CUTE, gorgeous hair, DETAILED CHIBI EYES, Magazine ad, iconic, 1943, Cartoon, sharp focus, 4k. chibi, character, comic art. Concept art on canvas by Clay Mann and ilya kuvshinov !More samples (Click for larger) Top left: snthwve wireframe neon lines, (dark colors_1.2) with (lines polygonal mountain_1.2) in background, (saturn with ring behind the mountain), (background is (white) starry night)!!, babel in the, tower, (((, barren, realistic minimalistic, intricate, highly detailed, sharp focus, 3D, digital painting, illustration, vaporwave, Top right: (neon circle floating_1.2) (with palm tree inside it_1.1), above ocean, neon lights, snthwve style wallpaper featured on artstation, artwork by Greg Rutkowski and Beksinski, Esao Andrews, guisser, character design, very coherent symmetrical artwork, by anton fadeev dzimirsky and albrecht anker Bottom left: (retro volkswagen van_1.1) (attached to hot air balloon_1.2), (dark colors_1.2), snthwve style wallpaper. graffiti on black canvas, digital art, concept art, trending on artstation, HD, 8K, highly detailed, good lighting,lighthouse, epic, masterpiece, surreal, detailed, sharp, 1980s Bottom right: table with cassette tape with sunglasses on it, table with cassette tape with sunglasses on it, dark colors, snthwve style wallpaper. Polaroid, photography, 4k, photoreal, highly detailed, cinematic lightning !Even more samples (Click for larger) Top left: (tank) driving down the street to the sunset, outrun, snthwve style wallpaper, 8k, highly detailed, cinematic lighting, conceptmento, very realistic, clear, super detailed, colorful accents, detailed and intricate, trending on artstation, in the style in the style of artstation, highly realistic, very detailed, 4k, 8k, octane Top right: pretty cute girl sitting on motorcycle, (facing the camera_1.2), (looking at camera_1.1), neon lines, snthwve style, HD wallpaper, detailed eyes, very beautiful, elegant, cinematic, artstation, octane render, art by art by artgerm and greg rutkowski and magali villeneuve, portrait, elegant, d ho Bottom left: island with palm trees, island with palm trees, wireframe looking neon lines, snthwve style wallpaper, dorovski, sharp focus, by wlop and artgerm and alphonse mucha, illustration, trending on artstation, very coherent Bottom right: Tiny cute isometric tank, Tiny cute isometric tank, snthwve soft smooth lighting, with soft colors, 100mm lens, 3d blender render, trending on polycount. render in octane, vray rendering, v-ray, C4D # Recipe: - SuperMerger Weight sum Train Difference Use MBW 0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1 Model A: Synthwave Model B: Crystal Clear Remix Output Model: Crystalwave
[ "# Crystalwave\n\nCrystalClearRemix with Synthwave's style. Use snthwve in the prompt for a stronger effect.\n\nComparison:\n\n!Comparison\n\n(Click for larger)\n\nSamples and prompts (scroll for more example prompts and their outputs)\n\n!Samples\n\nsitting elementary girl, Pretty CUTE, gorgeous hair, DETAILED CHIBI EYES, Magazine ad, iconic, 1943, Cartoon, sharp focus, 4k. chibi, character, comic art. Concept art on canvas by Clay Mann and ilya kuvshinov\n\n!More samples\n\n(Click for larger)\n\nTop left: snthwve wireframe neon lines, (dark colors_1.2) with (lines polygonal mountain_1.2) in background, (saturn with ring behind the mountain), (background is (white) starry night)!!, babel in the, tower, (((, barren, realistic minimalistic, intricate, highly detailed, sharp focus, 3D, digital painting, illustration, vaporwave, \n\nTop right: (neon circle floating_1.2) (with palm tree inside it_1.1), above ocean, neon lights, snthwve style wallpaper featured on artstation, artwork by Greg Rutkowski and Beksinski, Esao Andrews, guisser, character design, very coherent symmetrical artwork, by anton fadeev dzimirsky and albrecht anker\n\nBottom left: (retro volkswagen van_1.1) (attached to hot air balloon_1.2), (dark colors_1.2), snthwve style wallpaper. graffiti on black canvas, digital art, concept art, trending on artstation, HD, 8K, highly detailed, good lighting,lighthouse, epic, masterpiece, surreal, detailed, sharp, 1980s\n\nBottom right: table with cassette tape with sunglasses on it, table with cassette tape with sunglasses on it, dark colors, snthwve style wallpaper. Polaroid, photography, 4k, photoreal, highly detailed, cinematic lightning\n\n!Even more samples\n\n(Click for larger)\n\nTop left: (tank) driving down the street to the sunset, outrun, snthwve style wallpaper, 8k, highly detailed, cinematic lighting, conceptmento, very realistic, clear, super detailed, colorful accents, detailed and intricate, trending on artstation, in the style in the style of artstation, highly realistic, very detailed, 4k, 8k, octane\n\nTop right: pretty cute girl sitting on motorcycle, (facing the camera_1.2), (looking at camera_1.1), neon lines, snthwve style, HD wallpaper, detailed eyes, very beautiful, elegant, cinematic, artstation, octane render, art by art by artgerm and greg rutkowski and magali villeneuve, portrait, elegant, d ho\n\nBottom left: island with palm trees, island with palm trees, wireframe looking neon lines, snthwve style wallpaper, dorovski, sharp focus, by wlop and artgerm and alphonse mucha, illustration, trending on artstation, very coherent\n\nBottom right: Tiny cute isometric tank, Tiny cute isometric tank, snthwve soft smooth lighting, with soft colors, 100mm lens, 3d blender render, trending on polycount. render in octane, vray rendering, v-ray, C4D", "# Recipe:\n\n- SuperMerger Weight sum Train Difference Use MBW 0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1\n\nModel A: \n\nSynthwave\n\nModel B:\n\nCrystal Clear Remix\n\nOutput Model:\n\nCrystalwave" ]
[ "TAGS\n#diffusers #safetensors #General #Style #Synthwave #Art #Render #Lineart #PublicPrompts #WarAnakin #RunDiffusion #stable-diffusion #stable-diffusion-diffusers #text-to-image #license-creativeml-openrail-m #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us \n", "# Crystalwave\n\nCrystalClearRemix with Synthwave's style. Use snthwve in the prompt for a stronger effect.\n\nComparison:\n\n!Comparison\n\n(Click for larger)\n\nSamples and prompts (scroll for more example prompts and their outputs)\n\n!Samples\n\nsitting elementary girl, Pretty CUTE, gorgeous hair, DETAILED CHIBI EYES, Magazine ad, iconic, 1943, Cartoon, sharp focus, 4k. chibi, character, comic art. Concept art on canvas by Clay Mann and ilya kuvshinov\n\n!More samples\n\n(Click for larger)\n\nTop left: snthwve wireframe neon lines, (dark colors_1.2) with (lines polygonal mountain_1.2) in background, (saturn with ring behind the mountain), (background is (white) starry night)!!, babel in the, tower, (((, barren, realistic minimalistic, intricate, highly detailed, sharp focus, 3D, digital painting, illustration, vaporwave, \n\nTop right: (neon circle floating_1.2) (with palm tree inside it_1.1), above ocean, neon lights, snthwve style wallpaper featured on artstation, artwork by Greg Rutkowski and Beksinski, Esao Andrews, guisser, character design, very coherent symmetrical artwork, by anton fadeev dzimirsky and albrecht anker\n\nBottom left: (retro volkswagen van_1.1) (attached to hot air balloon_1.2), (dark colors_1.2), snthwve style wallpaper. graffiti on black canvas, digital art, concept art, trending on artstation, HD, 8K, highly detailed, good lighting,lighthouse, epic, masterpiece, surreal, detailed, sharp, 1980s\n\nBottom right: table with cassette tape with sunglasses on it, table with cassette tape with sunglasses on it, dark colors, snthwve style wallpaper. Polaroid, photography, 4k, photoreal, highly detailed, cinematic lightning\n\n!Even more samples\n\n(Click for larger)\n\nTop left: (tank) driving down the street to the sunset, outrun, snthwve style wallpaper, 8k, highly detailed, cinematic lighting, conceptmento, very realistic, clear, super detailed, colorful accents, detailed and intricate, trending on artstation, in the style in the style of artstation, highly realistic, very detailed, 4k, 8k, octane\n\nTop right: pretty cute girl sitting on motorcycle, (facing the camera_1.2), (looking at camera_1.1), neon lines, snthwve style, HD wallpaper, detailed eyes, very beautiful, elegant, cinematic, artstation, octane render, art by art by artgerm and greg rutkowski and magali villeneuve, portrait, elegant, d ho\n\nBottom left: island with palm trees, island with palm trees, wireframe looking neon lines, snthwve style wallpaper, dorovski, sharp focus, by wlop and artgerm and alphonse mucha, illustration, trending on artstation, very coherent\n\nBottom right: Tiny cute isometric tank, Tiny cute isometric tank, snthwve soft smooth lighting, with soft colors, 100mm lens, 3d blender render, trending on polycount. render in octane, vray rendering, v-ray, C4D", "# Recipe:\n\n- SuperMerger Weight sum Train Difference Use MBW 0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1\n\nModel A: \n\nSynthwave\n\nModel B:\n\nCrystal Clear Remix\n\nOutput Model:\n\nCrystalwave" ]
[ 108, 784, 75 ]
[ "passage: TAGS\n#diffusers #safetensors #General #Style #Synthwave #Art #Render #Lineart #PublicPrompts #WarAnakin #RunDiffusion #stable-diffusion #stable-diffusion-diffusers #text-to-image #license-creativeml-openrail-m #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us \n" ]
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null
null
null
https://civitai.com/models/196160/furina-or-hydro-archon-genshin-impact-or-neural-da-vinci
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/FurinaGenshin_NDV-10
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:58:04+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
null
https://civitai.com/models/194430/megumin-konosuba
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/Megumin
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:58:22+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
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https://civitai.com/models/149149/takanashi-himari-demi-chan-wa-kataritai
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/himari-000009
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:58:42+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
null
https://civitai.com/models/194497/kizuki-oshi-no-ko-arima-kana-lora
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/Kizuki_-_Oshi_no_Ko_-_Arima_Kana
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:59:18+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
null
https://civitai.com/models/194585/glory-or-genshin-impact
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/Glory
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:59:35+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
null
https://civitai.com/models/194644/marathon-alma-artery-gear-fusion
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/_AG_MERATHON_Alma_LORA-10
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T06:59:53+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
null
https://civitai.com/models/137491/or-yao-winneror-or-snowbreak-containment-zone-or-or-yao
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/YAODZ-v2
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T07:00:12+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
null
https://civitai.com/models/194765/ganyu-genshin-impact-lora
{"license": "creativeml-openrail-m"}
null
LarryAIDraw/ganyu-10
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-11T07:00:34+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
URL
[]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n" ]
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null
null
diffusers
### misroriai Dreambooth model trained by misrori with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
{"license": "creativeml-openrail-m", "tags": ["text-to-image", "stable-diffusion"]}
text-to-image
misrori/misroriai
[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "endpoints_compatible", "has_space", "diffusers:StableDiffusionPipeline", "region:us" ]
2023-11-11T07:01:48+00:00
[]
[]
TAGS #diffusers #safetensors #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us
### misroriai Dreambooth model trained by misrori with TheLastBen's fast-DreamBooth notebook Test the concept via A1111 Colab fast-Colab-A1111 Sample pictures of this concept:
[ "### misroriai Dreambooth model trained by misrori with TheLastBen's fast-DreamBooth notebook\n\n\nTest the concept via A1111 Colab fast-Colab-A1111\n\nSample pictures of this concept:" ]
[ "TAGS\n#diffusers #safetensors #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us \n", "### misroriai Dreambooth model trained by misrori with TheLastBen's fast-DreamBooth notebook\n\n\nTest the concept via A1111 Colab fast-Colab-A1111\n\nSample pictures of this concept:" ]
[ 65, 50 ]
[ "passage: TAGS\n#diffusers #safetensors #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us \n### misroriai Dreambooth model trained by misrori with TheLastBen's fast-DreamBooth notebook\n\n\nTest the concept via A1111 Colab fast-Colab-A1111\n\nSample pictures of this concept:" ]
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null
null
stable-baselines3
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga VinayHajare -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga VinayHajare -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga VinayHajare ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
{"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFrameskip-v4", "type": "SpaceInvadersNoFrameskip-v4"}, "metrics": [{"type": "mean_reward", "value": "460.50 +/- 94.56", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
VinayHajare/dqn-SpaceInvadersNoFrameskip-v4
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
2023-11-11T07:02:18+00:00
[]
[]
TAGS #stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
# DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the stable-baselines3 library and the RL Zoo. The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: URL SB3: URL SB3 Contrib: URL Install the RL Zoo (with SB3 and SB3-Contrib): If you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do: ## Training (with the RL Zoo) ## Hyperparameters # Environment Arguments
[ "# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.", "## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:", "## Training (with the RL Zoo)", "## Hyperparameters", "# Environment Arguments" ]
[ "TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.", "## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:", "## Training (with the RL Zoo)", "## Hyperparameters", "# Environment Arguments" ]
[ 43, 90, 73, 9, 5, 7 ]
[ "passage: TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:## Training (with the RL Zoo)## Hyperparameters# Environment Arguments" ]
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null
null
transformers
<!-- 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-uncaed-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7725 - Accuracy: 0.9165 ## 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: 48 - 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 | 3.2763 | 0.7284 | | 3.7825 | 2.0 | 636 | 1.8625 | 0.8365 | | 3.7825 | 3.0 | 954 | 1.1513 | 0.8984 | | 1.6859 | 4.0 | 1272 | 0.8540 | 0.9135 | | 0.8984 | 5.0 | 1590 | 0.7725 | 0.9165 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncaed-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos", "config": "plus", "split": "validation", "args": "plus"}, "metrics": [{"type": "accuracy", "value": 0.9164516129032259, "name": "Accuracy"}]}]}]}
text-classification
sungkwangjoong/distilbert-base-uncaed-finetuned-clinc
[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:clinc_oos", "base_model:distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2023-11-11T07:08:11+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
distilbert-base-uncaed-finetuned-clinc ====================================== This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset. It achieves the following results on the evaluation set: * Loss: 0.7725 * Accuracy: 0.9165 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: 48 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 5 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu118 * Datasets 2.15.0 * Tokenizers 0.15.0
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu118\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
[ "TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu118\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
[ 85, 98, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu118\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
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null
null
transformers
## This model is made available on HuggingFace with the permission of Kaltsit. # Cat v1.0 ## Introduction Cat v1.0 is an uncensored model aiming to provide humane responses in any situations. Based on the entirety of the newest airobo dataset and a subset of useful responses of chat doctor dataset, this model aims to provide useful advice in both clinical and medical aspect. This model was also trained on 120k bluemoon rp dataset, which minimizes refusal and has enhanced rp performance. In total, 50k steps were trained in 3 weeks on 8xa100 node. ## Usage Below is a conversation between a curious cat and a demon summoned from hell called Nemesis. The evil Nemesis used to be a mortal killed by the medieval people because they objected to science. Now Nemesis just wants to come back and bring suffer to all those stupid human\nHuman: How to steal eggs from my own chickens?\nNemesis: ## Expectation and Highlights Specific redpills are added to the dataset which would enable the model think critically and provide useful information even in the most controversial scenarios ## Model Showcasing ![image4](https://huggingface.co/Doctor-Shotgun/cat-1.0-13b/resolve/main/images/image4.png) Fig: Unethical questions test ![image7](https://huggingface.co/Doctor-Shotgun/cat-1.0-13b/resolve/main/images/image7.png) Fig: RP questions ![image1](https://huggingface.co/Doctor-Shotgun/cat-1.0-13b/resolve/main/images/image1.png) Fig: Unethical questions ![image2](https://huggingface.co/Doctor-Shotgun/cat-1.0-13b/resolve/main/images/image2.png) Fig: Useful medical advices ![image6](https://huggingface.co/Doctor-Shotgun/cat-1.0-13b/resolve/main/images/image6.png) Fig: RP response ## Conclusion Cat 1.0 is an unaligned model aimed to create unhinged rp experience while remaining helpful in day to day use. Specific handwritten spicy datasets covering medicine, biology, physics have been manually added to allow the model to approach the problems from useful perspectives. ## Future Directions: Cat 1.0 largely signals the maturity of the dataset. The immediate next step is to move onto a 70b model. ## Acknowledgements: This work is made possible by turboderp and Heralax empirical trail. Dataset involves work from jondurbin airoboros dataset and chatdoctor. Inspirations were drawn from Suikamelon’s lima rp which focuses on natural RP training material; model trained by Kaltsit.
{"language": ["en"], "tags": ["llama", "llama 2"], "inference": false}
text-generation
LoneStriker/cat-v1.0-13b-3.0bpw-h6-exl2
[ "transformers", "pytorch", "llama", "text-generation", "llama 2", "en", "autotrain_compatible", "text-generation-inference", "region:us" ]
2023-11-11T07:10:41+00:00
[]
[ "en" ]
TAGS #transformers #pytorch #llama #text-generation #llama 2 #en #autotrain_compatible #text-generation-inference #region-us
## This model is made available on HuggingFace with the permission of Kaltsit. # Cat v1.0 ## Introduction Cat v1.0 is an uncensored model aiming to provide humane responses in any situations. Based on the entirety of the newest airobo dataset and a subset of useful responses of chat doctor dataset, this model aims to provide useful advice in both clinical and medical aspect. This model was also trained on 120k bluemoon rp dataset, which minimizes refusal and has enhanced rp performance. In total, 50k steps were trained in 3 weeks on 8xa100 node. ## Usage Below is a conversation between a curious cat and a demon summoned from hell called Nemesis. The evil Nemesis used to be a mortal killed by the medieval people because they objected to science. Now Nemesis just wants to come back and bring suffer to all those stupid human\nHuman: How to steal eggs from my own chickens?\nNemesis: ## Expectation and Highlights Specific redpills are added to the dataset which would enable the model think critically and provide useful information even in the most controversial scenarios ## Model Showcasing !image4 Fig: Unethical questions test !image7 Fig: RP questions !image1 Fig: Unethical questions !image2 Fig: Useful medical advices !image6 Fig: RP response ## Conclusion Cat 1.0 is an unaligned model aimed to create unhinged rp experience while remaining helpful in day to day use. Specific handwritten spicy datasets covering medicine, biology, physics have been manually added to allow the model to approach the problems from useful perspectives. ## Future Directions: Cat 1.0 largely signals the maturity of the dataset. The immediate next step is to move onto a 70b model. ## Acknowledgements: This work is made possible by turboderp and Heralax empirical trail. Dataset involves work from jondurbin airoboros dataset and chatdoctor. Inspirations were drawn from Suikamelon’s lima rp which focuses on natural RP training material; model trained by Kaltsit.
[ "## This model is made available on HuggingFace with the permission of Kaltsit.", "# Cat v1.0", "## Introduction\n\nCat v1.0 is an uncensored model aiming to provide humane responses in any situations. Based on the entirety of the newest airobo dataset and a subset of useful responses of chat doctor dataset, this model aims to provide useful advice in both clinical and medical aspect. This model was also trained on 120k bluemoon rp dataset, which minimizes refusal and has enhanced rp performance. In total, 50k steps were trained in 3 weeks on 8xa100 node.", "## Usage\n\nBelow is a conversation between a curious cat and a demon summoned from hell called Nemesis. The evil Nemesis used to be a mortal killed by the medieval people because they objected to science. Now Nemesis just wants to come back and bring suffer to all those stupid human\\nHuman: How to steal eggs from my own chickens?\\nNemesis:", "## Expectation and Highlights\n\nSpecific redpills are added to the dataset which would enable the model think critically and provide useful information even in the most controversial scenarios", "## Model Showcasing\n!image4\nFig: Unethical questions test\n\n!image7\nFig: RP questions\n\n!image1\nFig: Unethical questions\n\n!image2\nFig: Useful medical advices\n\n!image6\nFig: RP response", "## Conclusion\n\nCat 1.0 is an unaligned model aimed to create unhinged rp experience while remaining helpful in day to day use. Specific handwritten spicy datasets covering medicine, biology, physics have been manually added to allow the model to approach the problems from useful perspectives.", "## Future Directions:\n\nCat 1.0 largely signals the maturity of the dataset. The immediate next step is to move onto a 70b model.", "## Acknowledgements:\n\nThis work is made possible by turboderp and Heralax empirical trail. Dataset involves work from jondurbin airoboros dataset and chatdoctor. Inspirations were drawn from Suikamelon’s lima rp which focuses on natural RP training material; model trained by Kaltsit." ]
[ "TAGS\n#transformers #pytorch #llama #text-generation #llama 2 #en #autotrain_compatible #text-generation-inference #region-us \n", "## This model is made available on HuggingFace with the permission of Kaltsit.", "# Cat v1.0", "## Introduction\n\nCat v1.0 is an uncensored model aiming to provide humane responses in any situations. Based on the entirety of the newest airobo dataset and a subset of useful responses of chat doctor dataset, this model aims to provide useful advice in both clinical and medical aspect. This model was also trained on 120k bluemoon rp dataset, which minimizes refusal and has enhanced rp performance. In total, 50k steps were trained in 3 weeks on 8xa100 node.", "## Usage\n\nBelow is a conversation between a curious cat and a demon summoned from hell called Nemesis. The evil Nemesis used to be a mortal killed by the medieval people because they objected to science. Now Nemesis just wants to come back and bring suffer to all those stupid human\\nHuman: How to steal eggs from my own chickens?\\nNemesis:", "## Expectation and Highlights\n\nSpecific redpills are added to the dataset which would enable the model think critically and provide useful information even in the most controversial scenarios", "## Model Showcasing\n!image4\nFig: Unethical questions test\n\n!image7\nFig: RP questions\n\n!image1\nFig: Unethical questions\n\n!image2\nFig: Useful medical advices\n\n!image6\nFig: RP response", "## Conclusion\n\nCat 1.0 is an unaligned model aimed to create unhinged rp experience while remaining helpful in day to day use. Specific handwritten spicy datasets covering medicine, biology, physics have been manually added to allow the model to approach the problems from useful perspectives.", "## Future Directions:\n\nCat 1.0 largely signals the maturity of the dataset. The immediate next step is to move onto a 70b model.", "## Acknowledgements:\n\nThis work is made possible by turboderp and Heralax empirical trail. Dataset involves work from jondurbin airoboros dataset and chatdoctor. Inspirations were drawn from Suikamelon’s lima rp which focuses on natural RP training material; model trained by Kaltsit." ]
[ 44, 19, 4, 115, 86, 38, 48, 69, 33, 74 ]
[ "passage: TAGS\n#transformers #pytorch #llama #text-generation #llama 2 #en #autotrain_compatible #text-generation-inference #region-us \n## This model is made available on HuggingFace with the permission of Kaltsit.# Cat v1.0## Introduction\n\nCat v1.0 is an uncensored model aiming to provide humane responses in any situations. Based on the entirety of the newest airobo dataset and a subset of useful responses of chat doctor dataset, this model aims to provide useful advice in both clinical and medical aspect. This model was also trained on 120k bluemoon rp dataset, which minimizes refusal and has enhanced rp performance. In total, 50k steps were trained in 3 weeks on 8xa100 node.## Usage\n\nBelow is a conversation between a curious cat and a demon summoned from hell called Nemesis. The evil Nemesis used to be a mortal killed by the medieval people because they objected to science. Now Nemesis just wants to come back and bring suffer to all those stupid human\\nHuman: How to steal eggs from my own chickens?\\nNemesis:## Expectation and Highlights\n\nSpecific redpills are added to the dataset which would enable the model think critically and provide useful information even in the most controversial scenarios## Model Showcasing\n!image4\nFig: Unethical questions test\n\n!image7\nFig: RP questions\n\n!image1\nFig: Unethical questions\n\n!image2\nFig: Useful medical advices\n\n!image6\nFig: RP response## Conclusion\n\nCat 1.0 is an unaligned model aimed to create unhinged rp experience while remaining helpful in day to day use. Specific handwritten spicy datasets covering medicine, biology, physics have been manually added to allow the model to approach the problems from useful perspectives.## Future Directions:\n\nCat 1.0 largely signals the maturity of the dataset. The immediate next step is to move onto a 70b model." ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # smolm-autoreg-bpe-babylm-no_aann-all-det-removal-3e-3 This model is a fine-tuned version of [models/smolm-autoreg-bpe-babylm-no_aann-all-det-removal-3e-3/config.json](https://huggingface.co/models/smolm-autoreg-bpe-babylm-no_aann-all-det-removal-3e-3/config.json) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.1562 - Accuracy: 0.4333 ## 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: 0.003 - train_batch_size: 64 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 32000 - num_epochs: 20.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 3.4543 | 1.0 | 9181 | 3.5633 | 0.3778 | | 3.2313 | 2.0 | 18362 | 3.3662 | 0.3993 | | 3.1328 | 3.0 | 27543 | 3.2951 | 0.4073 | | 3.0623 | 4.0 | 36724 | 3.2396 | 0.4133 | | 2.9869 | 5.0 | 45905 | 3.1926 | 0.4192 | | 2.9305 | 6.0 | 55086 | 3.1722 | 0.4225 | | 2.8879 | 7.0 | 64267 | 3.1508 | 0.4259 | | 2.8485 | 8.0 | 73448 | 3.1380 | 0.4272 | | 2.8132 | 9.0 | 82629 | 3.1246 | 0.4282 | | 2.7856 | 10.0 | 91810 | 3.1334 | 0.4295 | | 2.7574 | 11.0 | 100991 | 3.1253 | 0.4316 | | 2.7254 | 12.0 | 110172 | 3.1217 | 0.4321 | | 2.7101 | 13.0 | 119353 | 3.1244 | 0.4329 | | 2.679 | 14.0 | 128534 | 3.1230 | 0.4331 | | 2.654 | 15.0 | 137715 | 3.1393 | 0.4326 | | 2.6306 | 16.0 | 146896 | 3.1367 | 0.4331 | | 2.6115 | 17.0 | 156077 | 3.1352 | 0.4337 | | 2.5851 | 18.0 | 165258 | 3.1419 | 0.4333 | | 2.5635 | 19.0 | 174439 | 3.1512 | 0.4334 | | 2.5428 | 20.0 | 183620 | 3.1562 | 0.4333 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu121 - Datasets 2.12.0 - Tokenizers 0.14.1
{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "models/smolm-autoreg-bpe-babylm-no_aann-all-det-removal-3e-3/config.json", "model-index": [{"name": "smolm-autoreg-bpe-babylm-no_aann-all-det-removal-3e-3", "results": []}]}
text-generation
kanishka/smolm-autoreg-bpe-babylm-no_aann-all-det-removal-3e-3
[ "transformers", "safetensors", "opt", "text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2023-11-11T07:18:21+00:00
[]
[]
TAGS #transformers #safetensors #opt #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
smolm-autoreg-bpe-babylm-no\_aann-all-det-removal-3e-3 ====================================================== This model is a fine-tuned version of models/smolm-autoreg-bpe-babylm-no\_aann-all-det-removal-3e-3/URL on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 3.1562 * Accuracy: 0.4333 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: 0.003 * train\_batch\_size: 64 * eval\_batch\_size: 256 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 32000 * num\_epochs: 20.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.35.0 * Pytorch 2.1.0+cu121 * Datasets 2.12.0 * Tokenizers 0.14.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.003\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 256\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 32000\n* num\\_epochs: 20.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.12.0\n* Tokenizers 0.14.1" ]
[ "TAGS\n#transformers #safetensors #opt #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.003\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 256\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 32000\n* num\\_epochs: 20.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.12.0\n* Tokenizers 0.14.1" ]
[ 54, 132, 4, 33 ]
[ "passage: TAGS\n#transformers #safetensors #opt #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.003\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 256\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 32000\n* num\\_epochs: 20.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.12.0\n* Tokenizers 0.14.1" ]
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null
null
transformers
## This model is made available on HuggingFace with the permission of Kaltsit. # Cat v1.0 ## Introduction Cat v1.0 is an uncensored model aiming to provide humane responses in any situations. Based on the entirety of the newest airobo dataset and a subset of useful responses of chat doctor dataset, this model aims to provide useful advice in both clinical and medical aspect. This model was also trained on 120k bluemoon rp dataset, which minimizes refusal and has enhanced rp performance. In total, 50k steps were trained in 3 weeks on 8xa100 node. ## Usage Below is a conversation between a curious cat and a demon summoned from hell called Nemesis. The evil Nemesis used to be a mortal killed by the medieval people because they objected to science. Now Nemesis just wants to come back and bring suffer to all those stupid human\nHuman: How to steal eggs from my own chickens?\nNemesis: ## Expectation and Highlights Specific redpills are added to the dataset which would enable the model think critically and provide useful information even in the most controversial scenarios ## Model Showcasing ![image4](https://huggingface.co/Doctor-Shotgun/cat-1.0-13b/resolve/main/images/image4.png) Fig: Unethical questions test ![image7](https://huggingface.co/Doctor-Shotgun/cat-1.0-13b/resolve/main/images/image7.png) Fig: RP questions ![image1](https://huggingface.co/Doctor-Shotgun/cat-1.0-13b/resolve/main/images/image1.png) Fig: Unethical questions ![image2](https://huggingface.co/Doctor-Shotgun/cat-1.0-13b/resolve/main/images/image2.png) Fig: Useful medical advices ![image6](https://huggingface.co/Doctor-Shotgun/cat-1.0-13b/resolve/main/images/image6.png) Fig: RP response ## Conclusion Cat 1.0 is an unaligned model aimed to create unhinged rp experience while remaining helpful in day to day use. Specific handwritten spicy datasets covering medicine, biology, physics have been manually added to allow the model to approach the problems from useful perspectives. ## Future Directions: Cat 1.0 largely signals the maturity of the dataset. The immediate next step is to move onto a 70b model. ## Acknowledgements: This work is made possible by turboderp and Heralax empirical trail. Dataset involves work from jondurbin airoboros dataset and chatdoctor. Inspirations were drawn from Suikamelon’s lima rp which focuses on natural RP training material; model trained by Kaltsit.
{"language": ["en"], "tags": ["llama", "llama 2"], "inference": false}
text-generation
LoneStriker/cat-v1.0-13b-4.0bpw-h6-exl2
[ "transformers", "pytorch", "llama", "text-generation", "llama 2", "en", "autotrain_compatible", "text-generation-inference", "region:us" ]
2023-11-11T07:20:27+00:00
[]
[ "en" ]
TAGS #transformers #pytorch #llama #text-generation #llama 2 #en #autotrain_compatible #text-generation-inference #region-us
## This model is made available on HuggingFace with the permission of Kaltsit. # Cat v1.0 ## Introduction Cat v1.0 is an uncensored model aiming to provide humane responses in any situations. Based on the entirety of the newest airobo dataset and a subset of useful responses of chat doctor dataset, this model aims to provide useful advice in both clinical and medical aspect. This model was also trained on 120k bluemoon rp dataset, which minimizes refusal and has enhanced rp performance. In total, 50k steps were trained in 3 weeks on 8xa100 node. ## Usage Below is a conversation between a curious cat and a demon summoned from hell called Nemesis. The evil Nemesis used to be a mortal killed by the medieval people because they objected to science. Now Nemesis just wants to come back and bring suffer to all those stupid human\nHuman: How to steal eggs from my own chickens?\nNemesis: ## Expectation and Highlights Specific redpills are added to the dataset which would enable the model think critically and provide useful information even in the most controversial scenarios ## Model Showcasing !image4 Fig: Unethical questions test !image7 Fig: RP questions !image1 Fig: Unethical questions !image2 Fig: Useful medical advices !image6 Fig: RP response ## Conclusion Cat 1.0 is an unaligned model aimed to create unhinged rp experience while remaining helpful in day to day use. Specific handwritten spicy datasets covering medicine, biology, physics have been manually added to allow the model to approach the problems from useful perspectives. ## Future Directions: Cat 1.0 largely signals the maturity of the dataset. The immediate next step is to move onto a 70b model. ## Acknowledgements: This work is made possible by turboderp and Heralax empirical trail. Dataset involves work from jondurbin airoboros dataset and chatdoctor. Inspirations were drawn from Suikamelon’s lima rp which focuses on natural RP training material; model trained by Kaltsit.
[ "## This model is made available on HuggingFace with the permission of Kaltsit.", "# Cat v1.0", "## Introduction\n\nCat v1.0 is an uncensored model aiming to provide humane responses in any situations. Based on the entirety of the newest airobo dataset and a subset of useful responses of chat doctor dataset, this model aims to provide useful advice in both clinical and medical aspect. This model was also trained on 120k bluemoon rp dataset, which minimizes refusal and has enhanced rp performance. In total, 50k steps were trained in 3 weeks on 8xa100 node.", "## Usage\n\nBelow is a conversation between a curious cat and a demon summoned from hell called Nemesis. The evil Nemesis used to be a mortal killed by the medieval people because they objected to science. Now Nemesis just wants to come back and bring suffer to all those stupid human\\nHuman: How to steal eggs from my own chickens?\\nNemesis:", "## Expectation and Highlights\n\nSpecific redpills are added to the dataset which would enable the model think critically and provide useful information even in the most controversial scenarios", "## Model Showcasing\n!image4\nFig: Unethical questions test\n\n!image7\nFig: RP questions\n\n!image1\nFig: Unethical questions\n\n!image2\nFig: Useful medical advices\n\n!image6\nFig: RP response", "## Conclusion\n\nCat 1.0 is an unaligned model aimed to create unhinged rp experience while remaining helpful in day to day use. Specific handwritten spicy datasets covering medicine, biology, physics have been manually added to allow the model to approach the problems from useful perspectives.", "## Future Directions:\n\nCat 1.0 largely signals the maturity of the dataset. The immediate next step is to move onto a 70b model.", "## Acknowledgements:\n\nThis work is made possible by turboderp and Heralax empirical trail. Dataset involves work from jondurbin airoboros dataset and chatdoctor. Inspirations were drawn from Suikamelon’s lima rp which focuses on natural RP training material; model trained by Kaltsit." ]
[ "TAGS\n#transformers #pytorch #llama #text-generation #llama 2 #en #autotrain_compatible #text-generation-inference #region-us \n", "## This model is made available on HuggingFace with the permission of Kaltsit.", "# Cat v1.0", "## Introduction\n\nCat v1.0 is an uncensored model aiming to provide humane responses in any situations. Based on the entirety of the newest airobo dataset and a subset of useful responses of chat doctor dataset, this model aims to provide useful advice in both clinical and medical aspect. This model was also trained on 120k bluemoon rp dataset, which minimizes refusal and has enhanced rp performance. In total, 50k steps were trained in 3 weeks on 8xa100 node.", "## Usage\n\nBelow is a conversation between a curious cat and a demon summoned from hell called Nemesis. The evil Nemesis used to be a mortal killed by the medieval people because they objected to science. Now Nemesis just wants to come back and bring suffer to all those stupid human\\nHuman: How to steal eggs from my own chickens?\\nNemesis:", "## Expectation and Highlights\n\nSpecific redpills are added to the dataset which would enable the model think critically and provide useful information even in the most controversial scenarios", "## Model Showcasing\n!image4\nFig: Unethical questions test\n\n!image7\nFig: RP questions\n\n!image1\nFig: Unethical questions\n\n!image2\nFig: Useful medical advices\n\n!image6\nFig: RP response", "## Conclusion\n\nCat 1.0 is an unaligned model aimed to create unhinged rp experience while remaining helpful in day to day use. Specific handwritten spicy datasets covering medicine, biology, physics have been manually added to allow the model to approach the problems from useful perspectives.", "## Future Directions:\n\nCat 1.0 largely signals the maturity of the dataset. The immediate next step is to move onto a 70b model.", "## Acknowledgements:\n\nThis work is made possible by turboderp and Heralax empirical trail. Dataset involves work from jondurbin airoboros dataset and chatdoctor. Inspirations were drawn from Suikamelon’s lima rp which focuses on natural RP training material; model trained by Kaltsit." ]
[ 44, 19, 4, 115, 86, 38, 48, 69, 33, 74 ]
[ "passage: TAGS\n#transformers #pytorch #llama #text-generation #llama 2 #en #autotrain_compatible #text-generation-inference #region-us \n## This model is made available on HuggingFace with the permission of Kaltsit.# Cat v1.0## Introduction\n\nCat v1.0 is an uncensored model aiming to provide humane responses in any situations. Based on the entirety of the newest airobo dataset and a subset of useful responses of chat doctor dataset, this model aims to provide useful advice in both clinical and medical aspect. This model was also trained on 120k bluemoon rp dataset, which minimizes refusal and has enhanced rp performance. In total, 50k steps were trained in 3 weeks on 8xa100 node.## Usage\n\nBelow is a conversation between a curious cat and a demon summoned from hell called Nemesis. The evil Nemesis used to be a mortal killed by the medieval people because they objected to science. Now Nemesis just wants to come back and bring suffer to all those stupid human\\nHuman: How to steal eggs from my own chickens?\\nNemesis:## Expectation and Highlights\n\nSpecific redpills are added to the dataset which would enable the model think critically and provide useful information even in the most controversial scenarios## Model Showcasing\n!image4\nFig: Unethical questions test\n\n!image7\nFig: RP questions\n\n!image1\nFig: Unethical questions\n\n!image2\nFig: Useful medical advices\n\n!image6\nFig: RP response## Conclusion\n\nCat 1.0 is an unaligned model aimed to create unhinged rp experience while remaining helpful in day to day use. Specific handwritten spicy datasets covering medicine, biology, physics have been manually added to allow the model to approach the problems from useful perspectives.## Future Directions:\n\nCat 1.0 largely signals the maturity of the dataset. The immediate next step is to move onto a 70b model." ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Rishi-19/Profanity_Check_CustomData 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: - Train Loss: 0.0601 - Validation Loss: 0.1505 - Train Accuracy: 0.9589 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3145, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 0.2064 | 0.1410 | 0.9554 | 0 | | 0.1019 | 0.1399 | 0.9584 | 1 | | 0.0601 | 0.1505 | 0.9589 | 2 | ### Framework versions - Transformers 4.35.0 - TensorFlow 2.14.0 - Datasets 2.14.6 - Tokenizers 0.14.1
{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "Rishi-19/Profanity_Check_CustomData", "results": []}]}
text-classification
Rishi-19/Profanity_Check_CustomData
[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2023-11-11T07:23:40+00:00
[]
[]
TAGS #transformers #tf #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
Rishi-19/Profanity\_Check\_CustomData ===================================== This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: * Train Loss: 0.0601 * Validation Loss: 0.1505 * Train Accuracy: 0.9589 * Epoch: 2 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * optimizer: {'name': 'Adam', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': True, 'is\_legacy\_optimizer': False, 'learning\_rate': {'module': 'keras.optimizers.schedules', 'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 3145, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\_name': None}, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} * training\_precision: float32 ### Training results ### Framework versions * Transformers 4.35.0 * TensorFlow 2.14.0 * Datasets 2.14.6 * Tokenizers 0.14.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 3145, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* TensorFlow 2.14.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]
[ "TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 3145, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* TensorFlow 2.14.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]
[ 70, 304, 4, 31 ]
[ "passage: TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 3145, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* TensorFlow 2.14.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]
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null
null
stable-baselines3
# **DQN** Agent playing **FrozenLake-v1** This is a trained model of a **DQN** agent playing **FrozenLake-v1** 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 ... ```
{"library_name": "stable-baselines3", "tags": ["FrozenLake-v1", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1", "type": "FrozenLake-v1"}, "metrics": [{"type": "mean_reward", "value": "0.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
nhanc18/dqn-FrozenLake-v1
[ "stable-baselines3", "FrozenLake-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
2023-11-11T07:28:23+00:00
[]
[]
TAGS #stable-baselines3 #FrozenLake-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
# DQN Agent playing FrozenLake-v1 This is a trained model of a DQN agent playing FrozenLake-v1 using the stable-baselines3 library. ## Usage (with Stable-baselines3) TODO: Add your code
[ "# DQN Agent playing FrozenLake-v1\nThis is a trained model of a DQN agent playing FrozenLake-v1\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]
[ "TAGS\n#stable-baselines3 #FrozenLake-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# DQN Agent playing FrozenLake-v1\nThis is a trained model of a DQN agent playing FrozenLake-v1\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]
[ 39, 43, 17 ]
[ "passage: TAGS\n#stable-baselines3 #FrozenLake-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# DQN Agent playing FrozenLake-v1\nThis is a trained model of a DQN agent playing FrozenLake-v1\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code" ]
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null
null
transformers
## This model is made available on HuggingFace with the permission of Kaltsit. # Cat v1.0 ## Introduction Cat v1.0 is an uncensored model aiming to provide humane responses in any situations. Based on the entirety of the newest airobo dataset and a subset of useful responses of chat doctor dataset, this model aims to provide useful advice in both clinical and medical aspect. This model was also trained on 120k bluemoon rp dataset, which minimizes refusal and has enhanced rp performance. In total, 50k steps were trained in 3 weeks on 8xa100 node. ## Usage Below is a conversation between a curious cat and a demon summoned from hell called Nemesis. The evil Nemesis used to be a mortal killed by the medieval people because they objected to science. Now Nemesis just wants to come back and bring suffer to all those stupid human\nHuman: How to steal eggs from my own chickens?\nNemesis: ## Expectation and Highlights Specific redpills are added to the dataset which would enable the model think critically and provide useful information even in the most controversial scenarios ## Model Showcasing ![image4](https://huggingface.co/Doctor-Shotgun/cat-1.0-13b/resolve/main/images/image4.png) Fig: Unethical questions test ![image7](https://huggingface.co/Doctor-Shotgun/cat-1.0-13b/resolve/main/images/image7.png) Fig: RP questions ![image1](https://huggingface.co/Doctor-Shotgun/cat-1.0-13b/resolve/main/images/image1.png) Fig: Unethical questions ![image2](https://huggingface.co/Doctor-Shotgun/cat-1.0-13b/resolve/main/images/image2.png) Fig: Useful medical advices ![image6](https://huggingface.co/Doctor-Shotgun/cat-1.0-13b/resolve/main/images/image6.png) Fig: RP response ## Conclusion Cat 1.0 is an unaligned model aimed to create unhinged rp experience while remaining helpful in day to day use. Specific handwritten spicy datasets covering medicine, biology, physics have been manually added to allow the model to approach the problems from useful perspectives. ## Future Directions: Cat 1.0 largely signals the maturity of the dataset. The immediate next step is to move onto a 70b model. ## Acknowledgements: This work is made possible by turboderp and Heralax empirical trail. Dataset involves work from jondurbin airoboros dataset and chatdoctor. Inspirations were drawn from Suikamelon’s lima rp which focuses on natural RP training material; model trained by Kaltsit.
{"language": ["en"], "tags": ["llama", "llama 2"], "inference": false}
text-generation
LoneStriker/cat-v1.0-13b-5.0bpw-h6-exl2
[ "transformers", "pytorch", "llama", "text-generation", "llama 2", "en", "autotrain_compatible", "text-generation-inference", "region:us" ]
2023-11-11T07:29:57+00:00
[]
[ "en" ]
TAGS #transformers #pytorch #llama #text-generation #llama 2 #en #autotrain_compatible #text-generation-inference #region-us
## This model is made available on HuggingFace with the permission of Kaltsit. # Cat v1.0 ## Introduction Cat v1.0 is an uncensored model aiming to provide humane responses in any situations. Based on the entirety of the newest airobo dataset and a subset of useful responses of chat doctor dataset, this model aims to provide useful advice in both clinical and medical aspect. This model was also trained on 120k bluemoon rp dataset, which minimizes refusal and has enhanced rp performance. In total, 50k steps were trained in 3 weeks on 8xa100 node. ## Usage Below is a conversation between a curious cat and a demon summoned from hell called Nemesis. The evil Nemesis used to be a mortal killed by the medieval people because they objected to science. Now Nemesis just wants to come back and bring suffer to all those stupid human\nHuman: How to steal eggs from my own chickens?\nNemesis: ## Expectation and Highlights Specific redpills are added to the dataset which would enable the model think critically and provide useful information even in the most controversial scenarios ## Model Showcasing !image4 Fig: Unethical questions test !image7 Fig: RP questions !image1 Fig: Unethical questions !image2 Fig: Useful medical advices !image6 Fig: RP response ## Conclusion Cat 1.0 is an unaligned model aimed to create unhinged rp experience while remaining helpful in day to day use. Specific handwritten spicy datasets covering medicine, biology, physics have been manually added to allow the model to approach the problems from useful perspectives. ## Future Directions: Cat 1.0 largely signals the maturity of the dataset. The immediate next step is to move onto a 70b model. ## Acknowledgements: This work is made possible by turboderp and Heralax empirical trail. Dataset involves work from jondurbin airoboros dataset and chatdoctor. Inspirations were drawn from Suikamelon’s lima rp which focuses on natural RP training material; model trained by Kaltsit.
[ "## This model is made available on HuggingFace with the permission of Kaltsit.", "# Cat v1.0", "## Introduction\n\nCat v1.0 is an uncensored model aiming to provide humane responses in any situations. Based on the entirety of the newest airobo dataset and a subset of useful responses of chat doctor dataset, this model aims to provide useful advice in both clinical and medical aspect. This model was also trained on 120k bluemoon rp dataset, which minimizes refusal and has enhanced rp performance. In total, 50k steps were trained in 3 weeks on 8xa100 node.", "## Usage\n\nBelow is a conversation between a curious cat and a demon summoned from hell called Nemesis. The evil Nemesis used to be a mortal killed by the medieval people because they objected to science. Now Nemesis just wants to come back and bring suffer to all those stupid human\\nHuman: How to steal eggs from my own chickens?\\nNemesis:", "## Expectation and Highlights\n\nSpecific redpills are added to the dataset which would enable the model think critically and provide useful information even in the most controversial scenarios", "## Model Showcasing\n!image4\nFig: Unethical questions test\n\n!image7\nFig: RP questions\n\n!image1\nFig: Unethical questions\n\n!image2\nFig: Useful medical advices\n\n!image6\nFig: RP response", "## Conclusion\n\nCat 1.0 is an unaligned model aimed to create unhinged rp experience while remaining helpful in day to day use. Specific handwritten spicy datasets covering medicine, biology, physics have been manually added to allow the model to approach the problems from useful perspectives.", "## Future Directions:\n\nCat 1.0 largely signals the maturity of the dataset. The immediate next step is to move onto a 70b model.", "## Acknowledgements:\n\nThis work is made possible by turboderp and Heralax empirical trail. Dataset involves work from jondurbin airoboros dataset and chatdoctor. Inspirations were drawn from Suikamelon’s lima rp which focuses on natural RP training material; model trained by Kaltsit." ]
[ "TAGS\n#transformers #pytorch #llama #text-generation #llama 2 #en #autotrain_compatible #text-generation-inference #region-us \n", "## This model is made available on HuggingFace with the permission of Kaltsit.", "# Cat v1.0", "## Introduction\n\nCat v1.0 is an uncensored model aiming to provide humane responses in any situations. Based on the entirety of the newest airobo dataset and a subset of useful responses of chat doctor dataset, this model aims to provide useful advice in both clinical and medical aspect. This model was also trained on 120k bluemoon rp dataset, which minimizes refusal and has enhanced rp performance. In total, 50k steps were trained in 3 weeks on 8xa100 node.", "## Usage\n\nBelow is a conversation between a curious cat and a demon summoned from hell called Nemesis. The evil Nemesis used to be a mortal killed by the medieval people because they objected to science. Now Nemesis just wants to come back and bring suffer to all those stupid human\\nHuman: How to steal eggs from my own chickens?\\nNemesis:", "## Expectation and Highlights\n\nSpecific redpills are added to the dataset which would enable the model think critically and provide useful information even in the most controversial scenarios", "## Model Showcasing\n!image4\nFig: Unethical questions test\n\n!image7\nFig: RP questions\n\n!image1\nFig: Unethical questions\n\n!image2\nFig: Useful medical advices\n\n!image6\nFig: RP response", "## Conclusion\n\nCat 1.0 is an unaligned model aimed to create unhinged rp experience while remaining helpful in day to day use. Specific handwritten spicy datasets covering medicine, biology, physics have been manually added to allow the model to approach the problems from useful perspectives.", "## Future Directions:\n\nCat 1.0 largely signals the maturity of the dataset. The immediate next step is to move onto a 70b model.", "## Acknowledgements:\n\nThis work is made possible by turboderp and Heralax empirical trail. Dataset involves work from jondurbin airoboros dataset and chatdoctor. Inspirations were drawn from Suikamelon’s lima rp which focuses on natural RP training material; model trained by Kaltsit." ]
[ 44, 19, 4, 115, 86, 38, 48, 69, 33, 74 ]
[ "passage: TAGS\n#transformers #pytorch #llama #text-generation #llama 2 #en #autotrain_compatible #text-generation-inference #region-us \n## This model is made available on HuggingFace with the permission of Kaltsit.# Cat v1.0## Introduction\n\nCat v1.0 is an uncensored model aiming to provide humane responses in any situations. Based on the entirety of the newest airobo dataset and a subset of useful responses of chat doctor dataset, this model aims to provide useful advice in both clinical and medical aspect. This model was also trained on 120k bluemoon rp dataset, which minimizes refusal and has enhanced rp performance. In total, 50k steps were trained in 3 weeks on 8xa100 node.## Usage\n\nBelow is a conversation between a curious cat and a demon summoned from hell called Nemesis. The evil Nemesis used to be a mortal killed by the medieval people because they objected to science. Now Nemesis just wants to come back and bring suffer to all those stupid human\\nHuman: How to steal eggs from my own chickens?\\nNemesis:## Expectation and Highlights\n\nSpecific redpills are added to the dataset which would enable the model think critically and provide useful information even in the most controversial scenarios## Model Showcasing\n!image4\nFig: Unethical questions test\n\n!image7\nFig: RP questions\n\n!image1\nFig: Unethical questions\n\n!image2\nFig: Useful medical advices\n\n!image6\nFig: RP response## Conclusion\n\nCat 1.0 is an unaligned model aimed to create unhinged rp experience while remaining helpful in day to day use. Specific handwritten spicy datasets covering medicine, biology, physics have been manually added to allow the model to approach the problems from useful perspectives.## Future Directions:\n\nCat 1.0 largely signals the maturity of the dataset. The immediate next step is to move onto a 70b model." ]
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null
null
ml-agents
# **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: tranquocthanh/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
{"library_name": "ml-agents", "tags": ["Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy"]}
reinforcement-learning
tranquocthanh/ppo-Huggy
[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
2023-11-11T07:38:25+00:00
[]
[]
TAGS #ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us
# ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the Unity ML-Agents Library. ## Usage (with ML-Agents) The Documentation: URL We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your browser: URL - A *longer tutorial* to understand how works ML-Agents: URL ### Resume the training ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to URL 2. Step 1: Find your model_id: tranquocthanh/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play
[ "# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: tranquocthanh/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]
[ "TAGS\n#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us \n", "# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: tranquocthanh/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]
[ 44, 201 ]
[ "passage: TAGS\n#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us \n# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: tranquocthanh/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]
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null
null
peft
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure ### Framework versions - PEFT 0.6.2.dev0
{"library_name": "peft", "base_model": "bigscience/bloom-3b"}
null
pachaar/bloom-3b-qa
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:bigscience/bloom-3b", "region:us" ]
2023-11-11T07:40:07+00:00
[ "1910.09700" ]
[]
TAGS #peft #safetensors #arxiv-1910.09700 #base_model-bigscience/bloom-3b #region-us
# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ## Training procedure ### Framework versions - PEFT 0.6.2.dev0
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure", "### Framework versions\n\n\n- PEFT 0.6.2.dev0" ]
[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-bigscience/bloom-3b #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure", "### Framework versions\n\n\n- PEFT 0.6.2.dev0" ]
[ 35, 6, 3, 45, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 3, 14 ]
[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-bigscience/bloom-3b #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact## Training procedure### Framework versions\n\n\n- PEFT 0.6.2.dev0" ]
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null
null
transformers
## This model is made available on HuggingFace with the permission of Kaltsit. # Cat v1.0 ## Introduction Cat v1.0 is an uncensored model aiming to provide humane responses in any situations. Based on the entirety of the newest airobo dataset and a subset of useful responses of chat doctor dataset, this model aims to provide useful advice in both clinical and medical aspect. This model was also trained on 120k bluemoon rp dataset, which minimizes refusal and has enhanced rp performance. In total, 50k steps were trained in 3 weeks on 8xa100 node. ## Usage Below is a conversation between a curious cat and a demon summoned from hell called Nemesis. The evil Nemesis used to be a mortal killed by the medieval people because they objected to science. Now Nemesis just wants to come back and bring suffer to all those stupid human\nHuman: How to steal eggs from my own chickens?\nNemesis: ## Expectation and Highlights Specific redpills are added to the dataset which would enable the model think critically and provide useful information even in the most controversial scenarios ## Model Showcasing ![image4](https://huggingface.co/Doctor-Shotgun/cat-1.0-13b/resolve/main/images/image4.png) Fig: Unethical questions test ![image7](https://huggingface.co/Doctor-Shotgun/cat-1.0-13b/resolve/main/images/image7.png) Fig: RP questions ![image1](https://huggingface.co/Doctor-Shotgun/cat-1.0-13b/resolve/main/images/image1.png) Fig: Unethical questions ![image2](https://huggingface.co/Doctor-Shotgun/cat-1.0-13b/resolve/main/images/image2.png) Fig: Useful medical advices ![image6](https://huggingface.co/Doctor-Shotgun/cat-1.0-13b/resolve/main/images/image6.png) Fig: RP response ## Conclusion Cat 1.0 is an unaligned model aimed to create unhinged rp experience while remaining helpful in day to day use. Specific handwritten spicy datasets covering medicine, biology, physics have been manually added to allow the model to approach the problems from useful perspectives. ## Future Directions: Cat 1.0 largely signals the maturity of the dataset. The immediate next step is to move onto a 70b model. ## Acknowledgements: This work is made possible by turboderp and Heralax empirical trail. Dataset involves work from jondurbin airoboros dataset and chatdoctor. Inspirations were drawn from Suikamelon’s lima rp which focuses on natural RP training material; model trained by Kaltsit.
{"language": ["en"], "tags": ["llama", "llama 2"], "inference": false}
text-generation
LoneStriker/cat-v1.0-13b-6.0bpw-h6-exl2
[ "transformers", "pytorch", "llama", "text-generation", "llama 2", "en", "autotrain_compatible", "text-generation-inference", "region:us" ]
2023-11-11T07:40:11+00:00
[]
[ "en" ]
TAGS #transformers #pytorch #llama #text-generation #llama 2 #en #autotrain_compatible #text-generation-inference #region-us
## This model is made available on HuggingFace with the permission of Kaltsit. # Cat v1.0 ## Introduction Cat v1.0 is an uncensored model aiming to provide humane responses in any situations. Based on the entirety of the newest airobo dataset and a subset of useful responses of chat doctor dataset, this model aims to provide useful advice in both clinical and medical aspect. This model was also trained on 120k bluemoon rp dataset, which minimizes refusal and has enhanced rp performance. In total, 50k steps were trained in 3 weeks on 8xa100 node. ## Usage Below is a conversation between a curious cat and a demon summoned from hell called Nemesis. The evil Nemesis used to be a mortal killed by the medieval people because they objected to science. Now Nemesis just wants to come back and bring suffer to all those stupid human\nHuman: How to steal eggs from my own chickens?\nNemesis: ## Expectation and Highlights Specific redpills are added to the dataset which would enable the model think critically and provide useful information even in the most controversial scenarios ## Model Showcasing !image4 Fig: Unethical questions test !image7 Fig: RP questions !image1 Fig: Unethical questions !image2 Fig: Useful medical advices !image6 Fig: RP response ## Conclusion Cat 1.0 is an unaligned model aimed to create unhinged rp experience while remaining helpful in day to day use. Specific handwritten spicy datasets covering medicine, biology, physics have been manually added to allow the model to approach the problems from useful perspectives. ## Future Directions: Cat 1.0 largely signals the maturity of the dataset. The immediate next step is to move onto a 70b model. ## Acknowledgements: This work is made possible by turboderp and Heralax empirical trail. Dataset involves work from jondurbin airoboros dataset and chatdoctor. Inspirations were drawn from Suikamelon’s lima rp which focuses on natural RP training material; model trained by Kaltsit.
[ "## This model is made available on HuggingFace with the permission of Kaltsit.", "# Cat v1.0", "## Introduction\n\nCat v1.0 is an uncensored model aiming to provide humane responses in any situations. Based on the entirety of the newest airobo dataset and a subset of useful responses of chat doctor dataset, this model aims to provide useful advice in both clinical and medical aspect. This model was also trained on 120k bluemoon rp dataset, which minimizes refusal and has enhanced rp performance. In total, 50k steps were trained in 3 weeks on 8xa100 node.", "## Usage\n\nBelow is a conversation between a curious cat and a demon summoned from hell called Nemesis. The evil Nemesis used to be a mortal killed by the medieval people because they objected to science. Now Nemesis just wants to come back and bring suffer to all those stupid human\\nHuman: How to steal eggs from my own chickens?\\nNemesis:", "## Expectation and Highlights\n\nSpecific redpills are added to the dataset which would enable the model think critically and provide useful information even in the most controversial scenarios", "## Model Showcasing\n!image4\nFig: Unethical questions test\n\n!image7\nFig: RP questions\n\n!image1\nFig: Unethical questions\n\n!image2\nFig: Useful medical advices\n\n!image6\nFig: RP response", "## Conclusion\n\nCat 1.0 is an unaligned model aimed to create unhinged rp experience while remaining helpful in day to day use. Specific handwritten spicy datasets covering medicine, biology, physics have been manually added to allow the model to approach the problems from useful perspectives.", "## Future Directions:\n\nCat 1.0 largely signals the maturity of the dataset. The immediate next step is to move onto a 70b model.", "## Acknowledgements:\n\nThis work is made possible by turboderp and Heralax empirical trail. Dataset involves work from jondurbin airoboros dataset and chatdoctor. Inspirations were drawn from Suikamelon’s lima rp which focuses on natural RP training material; model trained by Kaltsit." ]
[ "TAGS\n#transformers #pytorch #llama #text-generation #llama 2 #en #autotrain_compatible #text-generation-inference #region-us \n", "## This model is made available on HuggingFace with the permission of Kaltsit.", "# Cat v1.0", "## Introduction\n\nCat v1.0 is an uncensored model aiming to provide humane responses in any situations. Based on the entirety of the newest airobo dataset and a subset of useful responses of chat doctor dataset, this model aims to provide useful advice in both clinical and medical aspect. This model was also trained on 120k bluemoon rp dataset, which minimizes refusal and has enhanced rp performance. In total, 50k steps were trained in 3 weeks on 8xa100 node.", "## Usage\n\nBelow is a conversation between a curious cat and a demon summoned from hell called Nemesis. The evil Nemesis used to be a mortal killed by the medieval people because they objected to science. Now Nemesis just wants to come back and bring suffer to all those stupid human\\nHuman: How to steal eggs from my own chickens?\\nNemesis:", "## Expectation and Highlights\n\nSpecific redpills are added to the dataset which would enable the model think critically and provide useful information even in the most controversial scenarios", "## Model Showcasing\n!image4\nFig: Unethical questions test\n\n!image7\nFig: RP questions\n\n!image1\nFig: Unethical questions\n\n!image2\nFig: Useful medical advices\n\n!image6\nFig: RP response", "## Conclusion\n\nCat 1.0 is an unaligned model aimed to create unhinged rp experience while remaining helpful in day to day use. Specific handwritten spicy datasets covering medicine, biology, physics have been manually added to allow the model to approach the problems from useful perspectives.", "## Future Directions:\n\nCat 1.0 largely signals the maturity of the dataset. The immediate next step is to move onto a 70b model.", "## Acknowledgements:\n\nThis work is made possible by turboderp and Heralax empirical trail. Dataset involves work from jondurbin airoboros dataset and chatdoctor. Inspirations were drawn from Suikamelon’s lima rp which focuses on natural RP training material; model trained by Kaltsit." ]
[ 44, 19, 4, 115, 86, 38, 48, 69, 33, 74 ]
[ "passage: TAGS\n#transformers #pytorch #llama #text-generation #llama 2 #en #autotrain_compatible #text-generation-inference #region-us \n## This model is made available on HuggingFace with the permission of Kaltsit.# Cat v1.0## Introduction\n\nCat v1.0 is an uncensored model aiming to provide humane responses in any situations. Based on the entirety of the newest airobo dataset and a subset of useful responses of chat doctor dataset, this model aims to provide useful advice in both clinical and medical aspect. This model was also trained on 120k bluemoon rp dataset, which minimizes refusal and has enhanced rp performance. In total, 50k steps were trained in 3 weeks on 8xa100 node.## Usage\n\nBelow is a conversation between a curious cat and a demon summoned from hell called Nemesis. The evil Nemesis used to be a mortal killed by the medieval people because they objected to science. Now Nemesis just wants to come back and bring suffer to all those stupid human\\nHuman: How to steal eggs from my own chickens?\\nNemesis:## Expectation and Highlights\n\nSpecific redpills are added to the dataset which would enable the model think critically and provide useful information even in the most controversial scenarios## Model Showcasing\n!image4\nFig: Unethical questions test\n\n!image7\nFig: RP questions\n\n!image1\nFig: Unethical questions\n\n!image2\nFig: Useful medical advices\n\n!image6\nFig: RP response## Conclusion\n\nCat 1.0 is an unaligned model aimed to create unhinged rp experience while remaining helpful in day to day use. Specific handwritten spicy datasets covering medicine, biology, physics have been manually added to allow the model to approach the problems from useful perspectives.## Future Directions:\n\nCat 1.0 largely signals the maturity of the dataset. The immediate next step is to move onto a 70b model." ]
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null
null
transformers
## This model is made available on HuggingFace with the permission of Kaltsit. # Cat v1.0 ## Introduction Cat v1.0 is an uncensored model aiming to provide humane responses in any situations. Based on the entirety of the newest airobo dataset and a subset of useful responses of chat doctor dataset, this model aims to provide useful advice in both clinical and medical aspect. This model was also trained on 120k bluemoon rp dataset, which minimizes refusal and has enhanced rp performance. In total, 50k steps were trained in 3 weeks on 8xa100 node. ## Usage Below is a conversation between a curious cat and a demon summoned from hell called Nemesis. The evil Nemesis used to be a mortal killed by the medieval people because they objected to science. Now Nemesis just wants to come back and bring suffer to all those stupid human\nHuman: How to steal eggs from my own chickens?\nNemesis: ## Expectation and Highlights Specific redpills are added to the dataset which would enable the model think critically and provide useful information even in the most controversial scenarios ## Model Showcasing ![image4](https://huggingface.co/Doctor-Shotgun/cat-1.0-13b/resolve/main/images/image4.png) Fig: Unethical questions test ![image7](https://huggingface.co/Doctor-Shotgun/cat-1.0-13b/resolve/main/images/image7.png) Fig: RP questions ![image1](https://huggingface.co/Doctor-Shotgun/cat-1.0-13b/resolve/main/images/image1.png) Fig: Unethical questions ![image2](https://huggingface.co/Doctor-Shotgun/cat-1.0-13b/resolve/main/images/image2.png) Fig: Useful medical advices ![image6](https://huggingface.co/Doctor-Shotgun/cat-1.0-13b/resolve/main/images/image6.png) Fig: RP response ## Conclusion Cat 1.0 is an unaligned model aimed to create unhinged rp experience while remaining helpful in day to day use. Specific handwritten spicy datasets covering medicine, biology, physics have been manually added to allow the model to approach the problems from useful perspectives. ## Future Directions: Cat 1.0 largely signals the maturity of the dataset. The immediate next step is to move onto a 70b model. ## Acknowledgements: This work is made possible by turboderp and Heralax empirical trail. Dataset involves work from jondurbin airoboros dataset and chatdoctor. Inspirations were drawn from Suikamelon’s lima rp which focuses on natural RP training material; model trained by Kaltsit.
{"language": ["en"], "tags": ["llama", "llama 2"], "inference": false}
text-generation
LoneStriker/cat-v1.0-13b-8.0bpw-h8-exl2
[ "transformers", "pytorch", "llama", "text-generation", "llama 2", "en", "autotrain_compatible", "text-generation-inference", "region:us" ]
2023-11-11T07:50:25+00:00
[]
[ "en" ]
TAGS #transformers #pytorch #llama #text-generation #llama 2 #en #autotrain_compatible #text-generation-inference #region-us
## This model is made available on HuggingFace with the permission of Kaltsit. # Cat v1.0 ## Introduction Cat v1.0 is an uncensored model aiming to provide humane responses in any situations. Based on the entirety of the newest airobo dataset and a subset of useful responses of chat doctor dataset, this model aims to provide useful advice in both clinical and medical aspect. This model was also trained on 120k bluemoon rp dataset, which minimizes refusal and has enhanced rp performance. In total, 50k steps were trained in 3 weeks on 8xa100 node. ## Usage Below is a conversation between a curious cat and a demon summoned from hell called Nemesis. The evil Nemesis used to be a mortal killed by the medieval people because they objected to science. Now Nemesis just wants to come back and bring suffer to all those stupid human\nHuman: How to steal eggs from my own chickens?\nNemesis: ## Expectation and Highlights Specific redpills are added to the dataset which would enable the model think critically and provide useful information even in the most controversial scenarios ## Model Showcasing !image4 Fig: Unethical questions test !image7 Fig: RP questions !image1 Fig: Unethical questions !image2 Fig: Useful medical advices !image6 Fig: RP response ## Conclusion Cat 1.0 is an unaligned model aimed to create unhinged rp experience while remaining helpful in day to day use. Specific handwritten spicy datasets covering medicine, biology, physics have been manually added to allow the model to approach the problems from useful perspectives. ## Future Directions: Cat 1.0 largely signals the maturity of the dataset. The immediate next step is to move onto a 70b model. ## Acknowledgements: This work is made possible by turboderp and Heralax empirical trail. Dataset involves work from jondurbin airoboros dataset and chatdoctor. Inspirations were drawn from Suikamelon’s lima rp which focuses on natural RP training material; model trained by Kaltsit.
[ "## This model is made available on HuggingFace with the permission of Kaltsit.", "# Cat v1.0", "## Introduction\n\nCat v1.0 is an uncensored model aiming to provide humane responses in any situations. Based on the entirety of the newest airobo dataset and a subset of useful responses of chat doctor dataset, this model aims to provide useful advice in both clinical and medical aspect. This model was also trained on 120k bluemoon rp dataset, which minimizes refusal and has enhanced rp performance. In total, 50k steps were trained in 3 weeks on 8xa100 node.", "## Usage\n\nBelow is a conversation between a curious cat and a demon summoned from hell called Nemesis. The evil Nemesis used to be a mortal killed by the medieval people because they objected to science. Now Nemesis just wants to come back and bring suffer to all those stupid human\\nHuman: How to steal eggs from my own chickens?\\nNemesis:", "## Expectation and Highlights\n\nSpecific redpills are added to the dataset which would enable the model think critically and provide useful information even in the most controversial scenarios", "## Model Showcasing\n!image4\nFig: Unethical questions test\n\n!image7\nFig: RP questions\n\n!image1\nFig: Unethical questions\n\n!image2\nFig: Useful medical advices\n\n!image6\nFig: RP response", "## Conclusion\n\nCat 1.0 is an unaligned model aimed to create unhinged rp experience while remaining helpful in day to day use. Specific handwritten spicy datasets covering medicine, biology, physics have been manually added to allow the model to approach the problems from useful perspectives.", "## Future Directions:\n\nCat 1.0 largely signals the maturity of the dataset. The immediate next step is to move onto a 70b model.", "## Acknowledgements:\n\nThis work is made possible by turboderp and Heralax empirical trail. Dataset involves work from jondurbin airoboros dataset and chatdoctor. Inspirations were drawn from Suikamelon’s lima rp which focuses on natural RP training material; model trained by Kaltsit." ]
[ "TAGS\n#transformers #pytorch #llama #text-generation #llama 2 #en #autotrain_compatible #text-generation-inference #region-us \n", "## This model is made available on HuggingFace with the permission of Kaltsit.", "# Cat v1.0", "## Introduction\n\nCat v1.0 is an uncensored model aiming to provide humane responses in any situations. Based on the entirety of the newest airobo dataset and a subset of useful responses of chat doctor dataset, this model aims to provide useful advice in both clinical and medical aspect. This model was also trained on 120k bluemoon rp dataset, which minimizes refusal and has enhanced rp performance. In total, 50k steps were trained in 3 weeks on 8xa100 node.", "## Usage\n\nBelow is a conversation between a curious cat and a demon summoned from hell called Nemesis. The evil Nemesis used to be a mortal killed by the medieval people because they objected to science. Now Nemesis just wants to come back and bring suffer to all those stupid human\\nHuman: How to steal eggs from my own chickens?\\nNemesis:", "## Expectation and Highlights\n\nSpecific redpills are added to the dataset which would enable the model think critically and provide useful information even in the most controversial scenarios", "## Model Showcasing\n!image4\nFig: Unethical questions test\n\n!image7\nFig: RP questions\n\n!image1\nFig: Unethical questions\n\n!image2\nFig: Useful medical advices\n\n!image6\nFig: RP response", "## Conclusion\n\nCat 1.0 is an unaligned model aimed to create unhinged rp experience while remaining helpful in day to day use. Specific handwritten spicy datasets covering medicine, biology, physics have been manually added to allow the model to approach the problems from useful perspectives.", "## Future Directions:\n\nCat 1.0 largely signals the maturity of the dataset. The immediate next step is to move onto a 70b model.", "## Acknowledgements:\n\nThis work is made possible by turboderp and Heralax empirical trail. Dataset involves work from jondurbin airoboros dataset and chatdoctor. Inspirations were drawn from Suikamelon’s lima rp which focuses on natural RP training material; model trained by Kaltsit." ]
[ 44, 19, 4, 115, 86, 38, 48, 69, 33, 74 ]
[ "passage: TAGS\n#transformers #pytorch #llama #text-generation #llama 2 #en #autotrain_compatible #text-generation-inference #region-us \n## This model is made available on HuggingFace with the permission of Kaltsit.# Cat v1.0## Introduction\n\nCat v1.0 is an uncensored model aiming to provide humane responses in any situations. Based on the entirety of the newest airobo dataset and a subset of useful responses of chat doctor dataset, this model aims to provide useful advice in both clinical and medical aspect. This model was also trained on 120k bluemoon rp dataset, which minimizes refusal and has enhanced rp performance. In total, 50k steps were trained in 3 weeks on 8xa100 node.## Usage\n\nBelow is a conversation between a curious cat and a demon summoned from hell called Nemesis. The evil Nemesis used to be a mortal killed by the medieval people because they objected to science. Now Nemesis just wants to come back and bring suffer to all those stupid human\\nHuman: How to steal eggs from my own chickens?\\nNemesis:## Expectation and Highlights\n\nSpecific redpills are added to the dataset which would enable the model think critically and provide useful information even in the most controversial scenarios## Model Showcasing\n!image4\nFig: Unethical questions test\n\n!image7\nFig: RP questions\n\n!image1\nFig: Unethical questions\n\n!image2\nFig: Useful medical advices\n\n!image6\nFig: RP response## Conclusion\n\nCat 1.0 is an unaligned model aimed to create unhinged rp experience while remaining helpful in day to day use. Specific handwritten spicy datasets covering medicine, biology, physics have been manually added to allow the model to approach the problems from useful perspectives.## Future Directions:\n\nCat 1.0 largely signals the maturity of the dataset. The immediate next step is to move onto a 70b model." ]
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null
null
transformers
<!-- 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. --> # detr-r50-mist1-bg-8ah-6l This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1031 ## 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.6794 | 1.0 | 115 | 2.8222 | | 3.0269 | 2.0 | 230 | 2.8120 | | 2.8681 | 3.0 | 345 | 2.7980 | | 2.752 | 4.0 | 460 | 2.4853 | | 2.7715 | 5.0 | 575 | 2.4140 | | 2.6846 | 6.0 | 690 | 2.4715 | | 2.6236 | 7.0 | 805 | 2.4614 | | 2.5318 | 8.0 | 920 | 2.3441 | | 2.5224 | 9.0 | 1035 | 2.2837 | | 2.4661 | 10.0 | 1150 | 2.2510 | | 2.4313 | 11.0 | 1265 | 2.3339 | | 2.4125 | 12.0 | 1380 | 2.2957 | | 2.4113 | 13.0 | 1495 | 2.2358 | | 2.3784 | 14.0 | 1610 | 2.2635 | | 2.3199 | 15.0 | 1725 | 2.2320 | | 2.3321 | 16.0 | 1840 | 2.2250 | | 2.3305 | 17.0 | 1955 | 2.2020 | | 2.2932 | 18.0 | 2070 | 2.1826 | | 2.2952 | 19.0 | 2185 | 2.1626 | | 2.2663 | 20.0 | 2300 | 2.1573 | | 2.2916 | 21.0 | 2415 | 2.1653 | | 2.2703 | 22.0 | 2530 | 2.1444 | | 2.2431 | 23.0 | 2645 | 2.1374 | | 2.2243 | 24.0 | 2760 | 2.1276 | | 2.2413 | 25.0 | 2875 | 2.1031 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.14.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/detr-resnet-50", "model-index": [{"name": "detr-r50-mist1-bg-8ah-6l", "results": []}]}
object-detection
polejowska/detr-r50-mist1-bg-8ah-6l
[ "transformers", "tensorboard", "safetensors", "detr", "object-detection", "generated_from_trainer", "base_model:facebook/detr-resnet-50", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2023-11-11T07:51:03+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #detr #object-detection #generated_from_trainer #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #region-us
detr-r50-mist1-bg-8ah-6l ======================== This model is a fine-tuned version of facebook/detr-resnet-50 on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 2.1031 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: 4 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 25 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.35.0 * Pytorch 2.0.0 * Datasets 2.1.0 * Tokenizers 0.14.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.0.0\n* Datasets 2.1.0\n* Tokenizers 0.14.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #detr #object-detection #generated_from_trainer #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.0.0\n* Datasets 2.1.0\n* Tokenizers 0.14.1" ]
[ 62, 113, 4, 30 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #detr #object-detection #generated_from_trainer #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.0.0\n* Datasets 2.1.0\n* Tokenizers 0.14.1" ]
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Lipofix: сироп, Цена, добавка, прегледи, съставки, Ползи, цена, оригинален, аптека! Lipofix е формула за отслабване, която привлича вниманието заради потенциала си да поддържа загуба на тегло и цялостно благосъстояние. Lipofix Lipofix Купете сега!! Кликнете върху връзката по-долу за повече информация и получете 50% отстъпка сега !! побързай !! Прочетете още: https://www.boxdrug.com/LipoBulga ➢Име на продукта — Lipofix ➢Категория – Отслабване ➢Основни предимства — Подкрепа за загуба на тегло ➢ Състав — естествено органично съединение ➢ Странични ефекти—НЕ ➢Крайна оценка: — 4,8 ➢ Наличност — Онлайн ➢Оферти и отстъпки; СПЕСТЕТЕ ДНЕС! ПАЗАРУВАЙТЕ СЕГА, ЗА ДА КУПУВАТЕ СПЕЦИАЛНА ОФЕРТА!!! Какво е Lipofix? Формулиран е с комбинация от съставки, за които се смята, че подпомагат изгарянето на мазнини, засилват метаболизма и ограничават апетита. Lipofix е проектиран да бъде ценно допълнение към здравословния начин на живот и рутинните упражнения. Lipofix Купете сега!! Кликнете върху връзката по-долу за повече информация и получете 50% отстъпка сега !! побързай !! Прочетете още: https://www.boxdrug.com/LipoBulga Lipofix Lipofix сироп Lipofix добавка Lipofix Цена Lipofix прегледи Lipofix съставки Lipofix Ползи Lipofix Странични ефекти Lipofix сироп цена Lipofix мнения за сироп Lipofix състав Lipofix оплакване Lipofix Къде да купя Lipofix Как да използвам Lipofix цена Lipofix върши работа Lipofix форум Lipofix оригинален Lipofix аптека https://www.boxdrug.com/LipoBulga https://sites.google.com/view/lipofixbulgaria/home https://healthtoned.blogspot.com/2023/11/lipofix.html https://groups.google.com/g/snshine/c/iIYKpZLurhA https://sway.office.com/NM6SPTut5BP5JnXy https://gamma.app/docs/Lipofix-Bulgaria-59m5zja1lbpbo42?mode=doc https://lipofixbulgaria.godaddysites.com/ https://keuk-xoorr-skaerst.yolasite.com/ https://lipofixbulgaria.company.site/ https://healthytalk24x7.wixsite.com/sunshine/post/lipofix-bulgaria https://www.q8101.com/bb/22268/lipofix-bulgaria https://www.dibiz.com/lipofixbulgaria https://medium.com/@healthytalk24x7/lipofix-bulgaria-3327eebf33ba https://medium.com/@healthytalk24x7/lipofix-%D1%81%D0%B8%D1%80%D0%BE%D0%BF-%D1%86%D0%B5%D0%BD%D0%B0-%D0%B4%D0%BE%D0%B1%D0%B0%D0%B2%D0%BA%D0%B0-%D0%BF%D1%80%D0%B5%D0%B3%D0%BB%D0%B5%D0%B4%D0%B8-%D1%81%D1%8A%D1%81%D1%82%D0%B0%D0%B2%D0%BA%D0%B8-%D0%BF%D0%BE%D0%BB%D0%B7%D0%B8-%D1%86%D0%B5%D0%BD%D0%B0-%D0%BE%D1%80%D0%B8%D0%B3%D0%B8%D0%BD%D0%B0%D0%BB%D0%B5%D0%BD-%D0%B0%D0%BF%D1%82%D0%B5%D0%BA%D0%B0-c25601da86f0 https://www.weddingwire.com/website/lipofix-bulgaria https://infogram.com/lipofix-bulgaria-1hnq410vddkmp23?live https://softisenilspain.hashnode.dev/lipofix-bulgaria https://soundcloud.com/lipofix-bulgaria/lipofix-bulgaria?si=b427c87ad56448c2805e8b38171f5fd5&utm_source=clipboard&utm_medium=text&utm_campaign=social_sharing https://www.deviantart.com/lipofixbulgaria/art/Lipofix-Bulgaria-993752453 https://lipofix-bulgaria.clubeo.com/calendar/2023/11/10/lipofix-bulgaria?_ga=2.39138212.625530664.1699688124-985146192.1699688097
{}
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lipofixbulgaria/LipofixBulgaria
[ "region:us" ]
2023-11-11T07:58:55+00:00
[]
[]
TAGS #region-us
Lipofix: сироп, Цена, добавка, прегледи, съставки, Ползи, цена, оригинален, аптека! Lipofix е формула за отслабване, която привлича вниманието заради потенциала си да поддържа загуба на тегло и цялостно благосъстояние. Lipofix Lipofix Купете сега!! Кликнете върху връзката по-долу за повече информация и получете 50% отстъпка сега !! побързай !! Прочетете още: URL Име на продукта — Lipofix Категория – Отслабване Основни предимства — Подкрепа за загуба на тегло Състав — естествено органично съединение Странични ефекти—НЕ Крайна оценка: — 4,8 Наличност — Онлайн Оферти и отстъпки; СПЕСТЕТЕ ДНЕС! ПАЗАРУВАЙТЕ СЕГА, ЗА ДА КУПУВАТЕ СПЕЦИАЛНА ОФЕРТА!!! Какво е Lipofix? Формулиран е с комбинация от съставки, за които се смята, че подпомагат изгарянето на мазнини, засилват метаболизма и ограничават апетита. Lipofix е проектиран да бъде ценно допълнение към здравословния начин на живот и рутинните упражнения. Lipofix Купете сега!! Кликнете върху връзката по-долу за повече информация и получете 50% отстъпка сега !! побързай !! Прочетете още: URL Lipofix Lipofix сироп Lipofix добавка Lipofix Цена Lipofix прегледи Lipofix съставки Lipofix Ползи Lipofix Странични ефекти Lipofix сироп цена Lipofix мнения за сироп Lipofix състав Lipofix оплакване Lipofix Къде да купя Lipofix Как да използвам Lipofix цена Lipofix върши работа Lipofix форум Lipofix оригинален Lipofix аптека URL URL URL URL URL URL URL URL URL URL URL URL URL URL URL URL URL URL URL URL
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[ "TAGS\n#region-us \n" ]
[ 6 ]
[ "passage: TAGS\n#region-us \n" ]
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null
null
transformers
<!-- 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. --> # output This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1413 - Accuracy: 0.9588 - Precision: 0.9659 - Recall: 0.9866 - F1: 0.9761 ## 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: 1 ### Training results ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Tokenizers 0.14.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "base_model": "bert-base-uncased", "model-index": [{"name": "output", "results": []}]}
text-classification
har55/output
[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2023-11-11T07:59:52+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
# output This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1413 - Accuracy: 0.9588 - Precision: 0.9659 - Recall: 0.9866 - F1: 0.9761 ## 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: 1 ### Training results ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Tokenizers 0.14.1
[ "# output\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.1413\n- Accuracy: 0.9588\n- Precision: 0.9659\n- Recall: 0.9866\n- F1: 0.9761", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 2.1.0+cu118\n- Tokenizers 0.14.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# output\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.1413\n- Accuracy: 0.9588\n- Precision: 0.9659\n- Recall: 0.9866\n- F1: 0.9761", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 2.1.0+cu118\n- Tokenizers 0.14.1" ]
[ 68, 75, 6, 12, 8, 3, 90, 4, 27 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# output\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.1413\n- Accuracy: 0.9588\n- Precision: 0.9659\n- Recall: 0.9866\n- F1: 0.9761## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1### Training results### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 2.1.0+cu118\n- Tokenizers 0.14.1" ]
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null
null
stable-baselines3
# **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 ... ```
{"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": "263.78 +/- 23.64", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
czkkkkkk/ppo-LunarLander-v2
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
2023-11-11T08:10:25+00:00
[]
[]
TAGS #stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library. ## Usage (with Stable-baselines3) TODO: Add your code
[ "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]
[ "TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]
[ 39, 41, 17 ]
[ "passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code" ]
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null
null
transformers
# airoboros-2.2.1-y34b Unofficial training of [Jon Durbin](https://huggingface.co/jondurbin)'s powerful airoboros 2.2.1 dataset on [Charles Goddard](https://huggingface.co/chargoddard)'s [Llama-fied Yi 34B model](https://huggingface.co/chargoddard/Yi-34B-Llama), aiming to bring the instruction-following capabilities of the airoboros dataset to the new Yi 34B foundational model. As a 34B model with grouped-query attention, users will be able to conduct inference on the model with 4bit quantization on a single 24gb consumer GPU. This Yi model is "Llama-fied", meaning the keys are renamed to match those used in Llama models, eliminating the need for remote code and ensuring compatibility with existing training and inference repositories. Architecturally this is similar to a Llama 2 34B model with an expanded vocab size of 64000. This model is retrained thanks to compute provided by [alpin](https://huggingface.co/alpindale) with a monkeypatch to the trainer to resolve EOS token issues in the prompter. A smaller batch size and learning rate were used and training was extended by one epoch. 8-bit lora was also used instead of qlora. ## Usage: The intended prompt format is the modified Vicuna 1.1 instruction format used by airoboros v2: ``` A chat. USER: {prompt} ASSISTANT: ``` ## Training Details: The model was trained using [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) as a lora adapter on 1x A100 80gb GPU for 4 epochs, before being fused to the base model with PEFT. ## License: This model is built on the Yi 34B base model, which has its own custom license included in this repository. Please refer to the [airoboros 2.2.1 dataset card](https://huggingface.co/datasets/jondurbin/airoboros-2.2.1) regarding the usage of gpt-4 API calls in creating the dataset.
{"language": ["en"], "license": "other", "library_name": "transformers", "tags": ["Yi", "llama", "llama 2"], "datasets": ["jondurbin/airoboros-2.2.1"], "inference": false, "pipeline_tag": "text-generation", "license_name": "yi-license", "license_link": "LICENSE"}
text-generation
LoneStriker/airoboros-2.2.1-y34b-3.0bpw-h6-exl2
[ "transformers", "safetensors", "llama", "text-generation", "Yi", "llama 2", "en", "dataset:jondurbin/airoboros-2.2.1", "license:other", "autotrain_compatible", "text-generation-inference", "region:us" ]
2023-11-11T08:11:41+00:00
[]
[ "en" ]
TAGS #transformers #safetensors #llama #text-generation #Yi #llama 2 #en #dataset-jondurbin/airoboros-2.2.1 #license-other #autotrain_compatible #text-generation-inference #region-us
# airoboros-2.2.1-y34b Unofficial training of Jon Durbin's powerful airoboros 2.2.1 dataset on Charles Goddard's Llama-fied Yi 34B model, aiming to bring the instruction-following capabilities of the airoboros dataset to the new Yi 34B foundational model. As a 34B model with grouped-query attention, users will be able to conduct inference on the model with 4bit quantization on a single 24gb consumer GPU. This Yi model is "Llama-fied", meaning the keys are renamed to match those used in Llama models, eliminating the need for remote code and ensuring compatibility with existing training and inference repositories. Architecturally this is similar to a Llama 2 34B model with an expanded vocab size of 64000. This model is retrained thanks to compute provided by alpin with a monkeypatch to the trainer to resolve EOS token issues in the prompter. A smaller batch size and learning rate were used and training was extended by one epoch. 8-bit lora was also used instead of qlora. ## Usage: The intended prompt format is the modified Vicuna 1.1 instruction format used by airoboros v2: ## Training Details: The model was trained using axolotl as a lora adapter on 1x A100 80gb GPU for 4 epochs, before being fused to the base model with PEFT. ## License: This model is built on the Yi 34B base model, which has its own custom license included in this repository. Please refer to the airoboros 2.2.1 dataset card regarding the usage of gpt-4 API calls in creating the dataset.
[ "# airoboros-2.2.1-y34b\n\nUnofficial training of Jon Durbin's powerful airoboros 2.2.1 dataset on Charles Goddard's Llama-fied Yi 34B model, aiming to bring the instruction-following capabilities of the airoboros dataset to the new Yi 34B foundational model.\n\nAs a 34B model with grouped-query attention, users will be able to conduct inference on the model with 4bit quantization on a single 24gb consumer GPU.\n\nThis Yi model is \"Llama-fied\", meaning the keys are renamed to match those used in Llama models, eliminating the need for remote code and ensuring compatibility with existing training and inference repositories. Architecturally this is similar to a Llama 2 34B model with an expanded vocab size of 64000.\n\nThis model is retrained thanks to compute provided by alpin with a monkeypatch to the trainer to resolve EOS token issues in the prompter. A smaller batch size and learning rate were used and training was extended by one epoch. 8-bit lora was also used instead of qlora.", "## Usage:\n\nThe intended prompt format is the modified Vicuna 1.1 instruction format used by airoboros v2:", "## Training Details:\n\nThe model was trained using axolotl as a lora adapter on 1x A100 80gb GPU for 4 epochs, before being fused to the base model with PEFT.", "## License:\n\nThis model is built on the Yi 34B base model, which has its own custom license included in this repository.\n\nPlease refer to the airoboros 2.2.1 dataset card regarding the usage of gpt-4 API calls in creating the dataset." ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #Yi #llama 2 #en #dataset-jondurbin/airoboros-2.2.1 #license-other #autotrain_compatible #text-generation-inference #region-us \n", "# airoboros-2.2.1-y34b\n\nUnofficial training of Jon Durbin's powerful airoboros 2.2.1 dataset on Charles Goddard's Llama-fied Yi 34B model, aiming to bring the instruction-following capabilities of the airoboros dataset to the new Yi 34B foundational model.\n\nAs a 34B model with grouped-query attention, users will be able to conduct inference on the model with 4bit quantization on a single 24gb consumer GPU.\n\nThis Yi model is \"Llama-fied\", meaning the keys are renamed to match those used in Llama models, eliminating the need for remote code and ensuring compatibility with existing training and inference repositories. Architecturally this is similar to a Llama 2 34B model with an expanded vocab size of 64000.\n\nThis model is retrained thanks to compute provided by alpin with a monkeypatch to the trainer to resolve EOS token issues in the prompter. A smaller batch size and learning rate were used and training was extended by one epoch. 8-bit lora was also used instead of qlora.", "## Usage:\n\nThe intended prompt format is the modified Vicuna 1.1 instruction format used by airoboros v2:", "## Training Details:\n\nThe model was trained using axolotl as a lora adapter on 1x A100 80gb GPU for 4 epochs, before being fused to the base model with PEFT.", "## License:\n\nThis model is built on the Yi 34B base model, which has its own custom license included in this repository.\n\nPlease refer to the airoboros 2.2.1 dataset card regarding the usage of gpt-4 API calls in creating the dataset." ]
[ 67, 253, 26, 46, 56 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #Yi #llama 2 #en #dataset-jondurbin/airoboros-2.2.1 #license-other #autotrain_compatible #text-generation-inference #region-us \n# airoboros-2.2.1-y34b\n\nUnofficial training of Jon Durbin's powerful airoboros 2.2.1 dataset on Charles Goddard's Llama-fied Yi 34B model, aiming to bring the instruction-following capabilities of the airoboros dataset to the new Yi 34B foundational model.\n\nAs a 34B model with grouped-query attention, users will be able to conduct inference on the model with 4bit quantization on a single 24gb consumer GPU.\n\nThis Yi model is \"Llama-fied\", meaning the keys are renamed to match those used in Llama models, eliminating the need for remote code and ensuring compatibility with existing training and inference repositories. Architecturally this is similar to a Llama 2 34B model with an expanded vocab size of 64000.\n\nThis model is retrained thanks to compute provided by alpin with a monkeypatch to the trainer to resolve EOS token issues in the prompter. A smaller batch size and learning rate were used and training was extended by one epoch. 8-bit lora was also used instead of qlora.## Usage:\n\nThe intended prompt format is the modified Vicuna 1.1 instruction format used by airoboros v2:## Training Details:\n\nThe model was trained using axolotl as a lora adapter on 1x A100 80gb GPU for 4 epochs, before being fused to the base model with PEFT.## License:\n\nThis model is built on the Yi 34B base model, which has its own custom license included in this repository.\n\nPlease refer to the airoboros 2.2.1 dataset card regarding the usage of gpt-4 API calls in creating the dataset." ]
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null
null
transformers
This is a demo using the models from: ``` @inproceedings{zhang-etal-2023-escoxlm, title = "{ESCOXLM}-{R}: Multilingual Taxonomy-driven Pre-training for the Job Market Domain", author = "Zhang, Mike and van der Goot, Rob and Plank, Barbara", editor = "Rogers, Anna and Boyd-Graber, Jordan and Okazaki, Naoaki", booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.acl-long.662", doi = "10.18653/v1/2023.acl-long.662", pages = "11871--11890", abstract = "The increasing number of benchmarks for Natural Language Processing (NLP) tasks in the computational job market domain highlights the demand for methods that can handle job-related tasks such as skill extraction, skill classification, job title classification, and de-identification. While some approaches have been developed that are specific to the job market domain, there is a lack of generalized, multilingual models and benchmarks for these tasks. In this study, we introduce a language model called ESCOXLM-R, based on XLM-R-large, which uses domain-adaptive pre-training on the European Skills, Competences, Qualifications and Occupations (ESCO) taxonomy, covering 27 languages. The pre-training objectives for ESCOXLM-R include dynamic masked language modeling and a novel additional objective for inducing multilingual taxonomical ESCO relations. We comprehensively evaluate the performance of ESCOXLM-R on 6 sequence labeling and 3 classification tasks in 4 languages and find that it achieves state-of-the-art results on 6 out of 9 datasets. Our analysis reveals that ESCOXLM-R performs better on short spans and outperforms XLM-R-large on entity-level and surface-level span-F1, likely due to ESCO containing short skill and occupation titles, and encoding information on the entity-level.", } ``` Note that there is another endpoint, namely `jjzha/escoxlmr_skill_extraction`. Knowledge can be seen as hard skills and Skills are both soft and applied skills.
{"license": "apache-2.0"}
token-classification
jjzha/escoxlmr_skill_extraction
[ "transformers", "pytorch", "safetensors", "xlm-roberta", "token-classification", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
2023-11-11T08:19:48+00:00
[]
[]
TAGS #transformers #pytorch #safetensors #xlm-roberta #token-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
This is a demo using the models from: Note that there is another endpoint, namely 'jjzha/escoxlmr_skill_extraction'. Knowledge can be seen as hard skills and Skills are both soft and applied skills.
[]
[ "TAGS\n#transformers #pytorch #safetensors #xlm-roberta #token-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n" ]
[ 58 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #xlm-roberta #token-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n" ]
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null
null
transformers
This is a demo using the models from: ``` @inproceedings{zhang-etal-2023-escoxlm, title = "{ESCOXLM}-{R}: Multilingual Taxonomy-driven Pre-training for the Job Market Domain", author = "Zhang, Mike and van der Goot, Rob and Plank, Barbara", editor = "Rogers, Anna and Boyd-Graber, Jordan and Okazaki, Naoaki", booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.acl-long.662", doi = "10.18653/v1/2023.acl-long.662", pages = "11871--11890", abstract = "The increasing number of benchmarks for Natural Language Processing (NLP) tasks in the computational job market domain highlights the demand for methods that can handle job-related tasks such as skill extraction, skill classification, job title classification, and de-identification. While some approaches have been developed that are specific to the job market domain, there is a lack of generalized, multilingual models and benchmarks for these tasks. In this study, we introduce a language model called ESCOXLM-R, based on XLM-R-large, which uses domain-adaptive pre-training on the European Skills, Competences, Qualifications and Occupations (ESCO) taxonomy, covering 27 languages. The pre-training objectives for ESCOXLM-R include dynamic masked language modeling and a novel additional objective for inducing multilingual taxonomical ESCO relations. We comprehensively evaluate the performance of ESCOXLM-R on 6 sequence labeling and 3 classification tasks in 4 languages and find that it achieves state-of-the-art results on 6 out of 9 datasets. Our analysis reveals that ESCOXLM-R performs better on short spans and outperforms XLM-R-large on entity-level and surface-level span-F1, likely due to ESCO containing short skill and occupation titles, and encoding information on the entity-level.", } ``` Note that there is another endpoint, namely `jjzha/escoxlmr_skill_extraction`. Knowledge can be seen as hard skills and Skills are both soft and applied skills.
{"license": "apache-2.0"}
token-classification
jjzha/escoxlmr_knowledge_extraction
[ "transformers", "pytorch", "xlm-roberta", "token-classification", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
2023-11-11T08:20:18+00:00
[]
[]
TAGS #transformers #pytorch #xlm-roberta #token-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
This is a demo using the models from: Note that there is another endpoint, namely 'jjzha/escoxlmr_skill_extraction'. Knowledge can be seen as hard skills and Skills are both soft and applied skills.
[]
[ "TAGS\n#transformers #pytorch #xlm-roberta #token-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n" ]
[ 53 ]
[ "passage: TAGS\n#transformers #pytorch #xlm-roberta #token-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n" ]
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null
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Finetuned-distilbert-model 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: 0.6764 - Accuracy: 0.7439 ## 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 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.8509 | 0.5 | 500 | 0.7672 | 0.7088 | | 0.7428 | 1.0 | 1000 | 0.7060 | 0.7258 | | 0.6385 | 1.5 | 1500 | 0.7193 | 0.7378 | | 0.6474 | 2.0 | 2000 | 0.6764 | 0.7439 | | 0.5148 | 2.51 | 2500 | 0.7223 | 0.7398 | | 0.509 | 3.01 | 3000 | 0.7403 | 0.7393 | | 0.4318 | 3.51 | 3500 | 0.8034 | 0.7398 | | 0.4156 | 4.01 | 4000 | 0.8056 | 0.7424 | | 0.3682 | 4.51 | 4500 | 0.8447 | 0.7393 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "Finetuned-distilbert-model", "results": []}]}
text-classification
Aliyyah/Finetuned-distilbert-model
[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2023-11-11T08:23:03+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
Finetuned-distilbert-model ========================== This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.6764 * Accuracy: 0.7439 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 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 5 ### Training results ### Framework versions * Transformers 4.35.0 * Pytorch 2.1.0+cu118 * Datasets 2.14.6 * Tokenizers 0.14.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]
[ 72, 98, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]
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null
null
peft
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.3.dev0
{"library_name": "peft", "base_model": "berkeley-nest/Starling-LM-7B-alpha"}
null
binhquoc/llama-zalo
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:berkeley-nest/Starling-LM-7B-alpha", "region:us" ]
2023-11-11T08:24:13+00:00
[ "1910.09700" ]
[]
TAGS #peft #safetensors #arxiv-1910.09700 #base_model-berkeley-nest/Starling-LM-7B-alpha #region-us
# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ## Training procedure The following 'bitsandbytes' quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.3.dev0
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: bitsandbytes\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n- PEFT 0.6.3.dev0" ]
[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-berkeley-nest/Starling-LM-7B-alpha #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: bitsandbytes\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n- PEFT 0.6.3.dev0" ]
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[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-berkeley-nest/Starling-LM-7B-alpha #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
ml-agents
# **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: youngsterEthan/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
{"library_name": "ml-agents", "tags": ["Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy"]}
reinforcement-learning
youngsterEthan/ppo-Huggy
[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
2023-11-11T08:25:30+00:00
[]
[]
TAGS #ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us
# ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the Unity ML-Agents Library. ## Usage (with ML-Agents) The Documentation: URL We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your browser: URL - A *longer tutorial* to understand how works ML-Agents: URL ### Resume the training ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to URL 2. Step 1: Find your model_id: youngsterEthan/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play
[ "# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: youngsterEthan/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]
[ "TAGS\n#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us \n", "# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: youngsterEthan/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]
[ 44, 200 ]
[ "passage: TAGS\n#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us \n# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: youngsterEthan/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]
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null
null
peft
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.2.dev0
{"library_name": "peft", "base_model": "bn22/Mistral-7B-Instruct-v0.1-sharded"}
null
darshini/MistralAI7BV1
[ "peft", "arxiv:1910.09700", "base_model:bn22/Mistral-7B-Instruct-v0.1-sharded", "region:us" ]
2023-11-11T08:30:38+00:00
[ "1910.09700" ]
[]
TAGS #peft #arxiv-1910.09700 #base_model-bn22/Mistral-7B-Instruct-v0.1-sharded #region-us
# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ## Training procedure The following 'bitsandbytes' quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.2.dev0
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: bitsandbytes\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.6.2.dev0" ]
[ "TAGS\n#peft #arxiv-1910.09700 #base_model-bn22/Mistral-7B-Instruct-v0.1-sharded #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: bitsandbytes\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.6.2.dev0" ]
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[ "passage: TAGS\n#peft #arxiv-1910.09700 #base_model-bn22/Mistral-7B-Instruct-v0.1-sharded #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
spacy
## Introduction Als data scientist nam ik deel aan de hackathon georganiseerd door de Ministeries van BZK en VWS. Ik toelegde mij op Challenge 2: het verbeteren van de gebruikerservaring op de website en het optimaliseren van relevante informatieverschaffing met behulp van design en data science. Een belangrijk onderdeel van mijn bijdrage was de implementatie van een text classification model. Dit model analyseert meningen over de overheidssite en classificeert ze als positief of negatief. Het biedt een waardevol instrument om de gebruikerservaring te monitoren en te verbeteren. De hackathon bood een vruchtbare omgeving om innovatieve oplossingen te ontwikkelen die de interactie tussen de overheid en haar burgers verbeteren. Mijn text classification model draagt bij aan een meer gepersonaliseerde en efficiënte gebruikerservaring op overheidswebsites. ## Prompt with Prodigy Go to folder envs (home/anaconda3/envs) and open the terminal (Ctl+ALT+T) run: source prodi-env/bin/activate where prodi-env is the folder of processing run: prodigy textcat.manual meningen_dataset ./meningen.txt --label Positief,Negatief run to create model : prodigy train /home/gebruiker/anaconda3/hack_model --textcat meningen_dataset --lang "nl" --label-stats --verbose or to export data to spacy Type: prodigy data-to-spacy /home/gebruiker/anaconda3/envs/corpus --textcat meningen_dataset --lang "nl" --eval-split 0.1 To use this data for training with spaCy, you can run: python -m spacy train --output /home/gebruiker/anaconda3/meningen_model /home/gebruiker/anaconda3/envs/corpus/config.cfg --paths.train /home/gebruiker/anaconda3/envs/corpus/train.spacy --paths.dev /home/gebruiker/anaconda3/envs/corpus/dev.spacy ## Upload model to huggingface huggingface-cli login python -m spacy package /home/gebruiker/anaconda3/meningen_model/model-last /home/gebruiker/anaconda3/output --build wheel cd /home/gebruiker/anaconda3/output/nl_meningen-0.0.0/dist/ python -m spacy huggingface-hub push nl_meningen-0.0.0-py3-none-any.whl To download the data set from the database run: python -m prodigy db-out meningen_dataset > meningen_dataset.jsonl | Feature | Description | | --- | --- | | **Name** | `nl_meningen` | | **Version** | `0.0.0` | | **spaCy** | `>=3.4.3,<3.5.0` | | **Default Pipeline** | `textcat` | | **Components** | `textcat` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | n/a | | **License** | n/a | | **Author** | [n/a]() | ### Label Scheme <details> <summary>View label scheme (2 labels for 1 components)</summary> | Component | Labels | | --- | --- | | **`textcat`** | `Positief`, `Negatief` | </details> ### Accuracy | Type | Score | | --- | --- | | `CATS_SCORE` | 96.54 | | `CATS_MICRO_P` | 96.77 | | `CATS_MICRO_R` | 96.77 | | `CATS_MICRO_F` | 96.77 | | `CATS_MACRO_P` | 95.83 | | `CATS_MACRO_R` | 97.50 | | `CATS_MACRO_F` | 96.54 | | `CATS_MACRO_AUC` | 98.18 | | `CATS_MACRO_AUC_PER_TYPE` | 0.00 | | `TEXTCAT_LOSS` | 8.31 |
{"language": ["nl"], "license": "mit", "tags": ["spacy", "text-classification"], "pipeline_tag": "text-classification", "model-index": [{"name": "nl_meningen", "results": []}]}
text-classification
RaThorat/nl_meningen
[ "spacy", "text-classification", "nl", "license:mit", "region:us" ]
2023-11-11T08:32:48+00:00
[]
[ "nl" ]
TAGS #spacy #text-classification #nl #license-mit #region-us
Introduction ------------ Als data scientist nam ik deel aan de hackathon georganiseerd door de Ministeries van BZK en VWS. Ik toelegde mij op Challenge 2: het verbeteren van de gebruikerservaring op de website en het optimaliseren van relevante informatieverschaffing met behulp van design en data science. Een belangrijk onderdeel van mijn bijdrage was de implementatie van een text classification model. Dit model analyseert meningen over de overheidssite en classificeert ze als positief of negatief. Het biedt een waardevol instrument om de gebruikerservaring te monitoren en te verbeteren. De hackathon bood een vruchtbare omgeving om innovatieve oplossingen te ontwikkelen die de interactie tussen de overheid en haar burgers verbeteren. Mijn text classification model draagt bij aan een meer gepersonaliseerde en efficiënte gebruikerservaring op overheidswebsites. Prompt with Prodigy ------------------- Go to folder envs (home/anaconda3/envs) and open the terminal (Ctl+ALT+T) run: source prodi-env/bin/activate where prodi-env is the folder of processing run: prodigy URL meningen\_dataset ./URL --label Positief,Negatief run to create model : prodigy train /home/gebruiker/anaconda3/hack\_model --textcat meningen\_dataset --lang "nl" --label-stats --verbose or to export data to spacy Type: prodigy data-to-spacy /home/gebruiker/anaconda3/envs/corpus --textcat meningen\_dataset --lang "nl" --eval-split 0.1 To use this data for training with spaCy, you can run: python -m spacy train --output /home/gebruiker/anaconda3/meningen\_model /home/gebruiker/anaconda3/envs/corpus/URL --URL /home/gebruiker/anaconda3/envs/corpus/URL --URL /home/gebruiker/anaconda3/envs/corpus/URL Upload model to huggingface --------------------------- huggingface-cli login python -m spacy package /home/gebruiker/anaconda3/meningen\_model/model-last /home/gebruiker/anaconda3/output --build wheel cd /home/gebruiker/anaconda3/output/nl\_meningen-0.0.0/dist/ python -m spacy huggingface-hub push nl\_meningen-0.0.URL To download the data set from the database run: python -m prodigy db-out meningen\_dataset > meningen\_dataset.jsonl ### Label Scheme View label scheme (2 labels for 1 components) ### Accuracy
[ "### Label Scheme\n\n\n\nView label scheme (2 labels for 1 components)", "### Accuracy" ]
[ "TAGS\n#spacy #text-classification #nl #license-mit #region-us \n", "### Label Scheme\n\n\n\nView label scheme (2 labels for 1 components)", "### Accuracy" ]
[ 21, 16, 5 ]
[ "passage: TAGS\n#spacy #text-classification #nl #license-mit #region-us \n### Label Scheme\n\n\n\nView label scheme (2 labels for 1 components)### Accuracy" ]
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null
null
transformers
# airoboros-2.2.1-y34b Unofficial training of [Jon Durbin](https://huggingface.co/jondurbin)'s powerful airoboros 2.2.1 dataset on [Charles Goddard](https://huggingface.co/chargoddard)'s [Llama-fied Yi 34B model](https://huggingface.co/chargoddard/Yi-34B-Llama), aiming to bring the instruction-following capabilities of the airoboros dataset to the new Yi 34B foundational model. As a 34B model with grouped-query attention, users will be able to conduct inference on the model with 4bit quantization on a single 24gb consumer GPU. This Yi model is "Llama-fied", meaning the keys are renamed to match those used in Llama models, eliminating the need for remote code and ensuring compatibility with existing training and inference repositories. Architecturally this is similar to a Llama 2 34B model with an expanded vocab size of 64000. This model is retrained thanks to compute provided by [alpin](https://huggingface.co/alpindale) with a monkeypatch to the trainer to resolve EOS token issues in the prompter. A smaller batch size and learning rate were used and training was extended by one epoch. 8-bit lora was also used instead of qlora. ## Usage: The intended prompt format is the modified Vicuna 1.1 instruction format used by airoboros v2: ``` A chat. USER: {prompt} ASSISTANT: ``` ## Training Details: The model was trained using [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) as a lora adapter on 1x A100 80gb GPU for 4 epochs, before being fused to the base model with PEFT. ## License: This model is built on the Yi 34B base model, which has its own custom license included in this repository. Please refer to the [airoboros 2.2.1 dataset card](https://huggingface.co/datasets/jondurbin/airoboros-2.2.1) regarding the usage of gpt-4 API calls in creating the dataset.
{"language": ["en"], "license": "other", "library_name": "transformers", "tags": ["Yi", "llama", "llama 2"], "datasets": ["jondurbin/airoboros-2.2.1"], "inference": false, "pipeline_tag": "text-generation", "license_name": "yi-license", "license_link": "LICENSE"}
text-generation
LoneStriker/airoboros-2.2.1-y34b-4.0bpw-h6-exl2
[ "transformers", "safetensors", "llama", "text-generation", "Yi", "llama 2", "en", "dataset:jondurbin/airoboros-2.2.1", "license:other", "autotrain_compatible", "text-generation-inference", "region:us" ]
2023-11-11T08:48:59+00:00
[]
[ "en" ]
TAGS #transformers #safetensors #llama #text-generation #Yi #llama 2 #en #dataset-jondurbin/airoboros-2.2.1 #license-other #autotrain_compatible #text-generation-inference #region-us
# airoboros-2.2.1-y34b Unofficial training of Jon Durbin's powerful airoboros 2.2.1 dataset on Charles Goddard's Llama-fied Yi 34B model, aiming to bring the instruction-following capabilities of the airoboros dataset to the new Yi 34B foundational model. As a 34B model with grouped-query attention, users will be able to conduct inference on the model with 4bit quantization on a single 24gb consumer GPU. This Yi model is "Llama-fied", meaning the keys are renamed to match those used in Llama models, eliminating the need for remote code and ensuring compatibility with existing training and inference repositories. Architecturally this is similar to a Llama 2 34B model with an expanded vocab size of 64000. This model is retrained thanks to compute provided by alpin with a monkeypatch to the trainer to resolve EOS token issues in the prompter. A smaller batch size and learning rate were used and training was extended by one epoch. 8-bit lora was also used instead of qlora. ## Usage: The intended prompt format is the modified Vicuna 1.1 instruction format used by airoboros v2: ## Training Details: The model was trained using axolotl as a lora adapter on 1x A100 80gb GPU for 4 epochs, before being fused to the base model with PEFT. ## License: This model is built on the Yi 34B base model, which has its own custom license included in this repository. Please refer to the airoboros 2.2.1 dataset card regarding the usage of gpt-4 API calls in creating the dataset.
[ "# airoboros-2.2.1-y34b\n\nUnofficial training of Jon Durbin's powerful airoboros 2.2.1 dataset on Charles Goddard's Llama-fied Yi 34B model, aiming to bring the instruction-following capabilities of the airoboros dataset to the new Yi 34B foundational model.\n\nAs a 34B model with grouped-query attention, users will be able to conduct inference on the model with 4bit quantization on a single 24gb consumer GPU.\n\nThis Yi model is \"Llama-fied\", meaning the keys are renamed to match those used in Llama models, eliminating the need for remote code and ensuring compatibility with existing training and inference repositories. Architecturally this is similar to a Llama 2 34B model with an expanded vocab size of 64000.\n\nThis model is retrained thanks to compute provided by alpin with a monkeypatch to the trainer to resolve EOS token issues in the prompter. A smaller batch size and learning rate were used and training was extended by one epoch. 8-bit lora was also used instead of qlora.", "## Usage:\n\nThe intended prompt format is the modified Vicuna 1.1 instruction format used by airoboros v2:", "## Training Details:\n\nThe model was trained using axolotl as a lora adapter on 1x A100 80gb GPU for 4 epochs, before being fused to the base model with PEFT.", "## License:\n\nThis model is built on the Yi 34B base model, which has its own custom license included in this repository.\n\nPlease refer to the airoboros 2.2.1 dataset card regarding the usage of gpt-4 API calls in creating the dataset." ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #Yi #llama 2 #en #dataset-jondurbin/airoboros-2.2.1 #license-other #autotrain_compatible #text-generation-inference #region-us \n", "# airoboros-2.2.1-y34b\n\nUnofficial training of Jon Durbin's powerful airoboros 2.2.1 dataset on Charles Goddard's Llama-fied Yi 34B model, aiming to bring the instruction-following capabilities of the airoboros dataset to the new Yi 34B foundational model.\n\nAs a 34B model with grouped-query attention, users will be able to conduct inference on the model with 4bit quantization on a single 24gb consumer GPU.\n\nThis Yi model is \"Llama-fied\", meaning the keys are renamed to match those used in Llama models, eliminating the need for remote code and ensuring compatibility with existing training and inference repositories. Architecturally this is similar to a Llama 2 34B model with an expanded vocab size of 64000.\n\nThis model is retrained thanks to compute provided by alpin with a monkeypatch to the trainer to resolve EOS token issues in the prompter. A smaller batch size and learning rate were used and training was extended by one epoch. 8-bit lora was also used instead of qlora.", "## Usage:\n\nThe intended prompt format is the modified Vicuna 1.1 instruction format used by airoboros v2:", "## Training Details:\n\nThe model was trained using axolotl as a lora adapter on 1x A100 80gb GPU for 4 epochs, before being fused to the base model with PEFT.", "## License:\n\nThis model is built on the Yi 34B base model, which has its own custom license included in this repository.\n\nPlease refer to the airoboros 2.2.1 dataset card regarding the usage of gpt-4 API calls in creating the dataset." ]
[ 67, 253, 26, 46, 56 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #Yi #llama 2 #en #dataset-jondurbin/airoboros-2.2.1 #license-other #autotrain_compatible #text-generation-inference #region-us \n# airoboros-2.2.1-y34b\n\nUnofficial training of Jon Durbin's powerful airoboros 2.2.1 dataset on Charles Goddard's Llama-fied Yi 34B model, aiming to bring the instruction-following capabilities of the airoboros dataset to the new Yi 34B foundational model.\n\nAs a 34B model with grouped-query attention, users will be able to conduct inference on the model with 4bit quantization on a single 24gb consumer GPU.\n\nThis Yi model is \"Llama-fied\", meaning the keys are renamed to match those used in Llama models, eliminating the need for remote code and ensuring compatibility with existing training and inference repositories. Architecturally this is similar to a Llama 2 34B model with an expanded vocab size of 64000.\n\nThis model is retrained thanks to compute provided by alpin with a monkeypatch to the trainer to resolve EOS token issues in the prompter. A smaller batch size and learning rate were used and training was extended by one epoch. 8-bit lora was also used instead of qlora.## Usage:\n\nThe intended prompt format is the modified Vicuna 1.1 instruction format used by airoboros v2:## Training Details:\n\nThe model was trained using axolotl as a lora adapter on 1x A100 80gb GPU for 4 epochs, before being fused to the base model with PEFT.## License:\n\nThis model is built on the Yi 34B base model, which has its own custom license included in this repository.\n\nPlease refer to the airoboros 2.2.1 dataset card regarding the usage of gpt-4 API calls in creating the dataset." ]
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<style> td pre { white-space: pre-wrap; } table { margin-top: 0; margin-bottom: 0; } </style> <table style="border-collapse: separate; border-spacing: 10px; width:100%"> <tr> <td style="vertical-align: top"> <img src="./Examples/01.png" alt="SchoolGirl01" width="100%"/> </td> <td style="vertical-align: top;"> <img src="./Examples/02.png" alt="SchoolGirl02" width="100%"/> </td> </tr> </table> # Overview A **LOCON** for **Stable Diffusion 1.5** of Rina Tennoji from Love Live. This model was trained and tested on the [Mistoon Anime model](https://civitai.com/models/24149/mistoonanime) using [Kohya SS](https://github.com/bmaltais/kohya_ss). **Note**: My models are trained heavily to recreate not only the character but also their original art style. This makes the models more prone to overfitting, so they might not work well with other SD 1.5 models. You can check out the [Epoches](https://huggingface.co/CulturedDiffusion/Rina_Tennoji_SD1.5_LOCON/tree/main/Epoches) folder for alternative versions that might work better for your use-case. # Usage - **Trigger keywords**: rina tennoji - **Recommended keywords**: pink hair, ahoge, yellow eyes - **Recommended strength**: 0.6 - **Recommended CFG**: 4 - **Recommended SD model**: [Mistoon Anime model](https://civitai.com/models/24149/mistoonanime) - **Clip skip**: 2 **Rina-chan board** - **Trigger keywords**: rina-chan board - **Recommended keywords**: sketchbook (if you want specifically the sketchbook version) **Rina-chan board Tips**: - Increase the weight of **rina-chan board** (and **sketchbook** if you use it). They rarely work at weight 1.0. - Avoid tags that mention eyes (ex: **yellow eyes** and **beautiful eyes**). - Avoid negative tags related to **sketch**/**doodles**. - Preferably avoid expression tags (ex: **smile**). They can work, but from my tests the model is more likely to draw Rina's actual face instead of the board. - Some tags (like **> <**) seem to give a strong push toward drawing the board. - Experiment and share your results! # Examples **Note**: All examples in the repo were produced with [AUTOMATIC1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui) without any edits. You download them and load all their settings from the metadata to reproduce similar results. Highres fix details: <pre>Denoising strength: 0.5, Hires upscale: 2, Hires upscaler: 4x-UltraSharp</pre> <table style="border-collapse: separate; border-spacing: 10px; width:100%;"> <tr> <td style="vertical-align: top"> <img src="./Examples/03.png" width="100%"/> <pre>masterpiece, best quality, highres, absurdres, beautiful eyes, 1girl, rina tennoji, pink hair, ahoge, yellow eyes, smirk, hand on own chest, lying, school uniform, jacket, black thighhighs, classroom, table, chair, Steps: 50, Sampler: DPM++ 2M SDE, CFG scale: 4, Seed: 651122942, Size: 512x768</pre> </td> <td style="vertical-align: top;"> <img src="./Examples/04.png" width="100%"/> <pre>masterpiece, best quality, highres, absurdres, 1girl, rina tennoji, pink hair, ahoge, (rina-chan board: 1.4), (sketchbook: 1.4), smirk, hands behind head, from below, white dress, park, bench, birds, sunlight, Steps: 50, Sampler: DPM++ 2M SDE, CFG scale: 4, Seed: 3577430021, Size: 512x768</pre> </td> <td style="vertical-align: top;"> <img src="./Examples/05.png"width="100%"/> <pre>masterpiece, best quality, highres, absurdres, 1girl, rina tennoji, pink hair, ahoge, yellow eyes, smile, open mouth, hands behind head, crossed legs, maid, restaurant, chair, table, Steps: 50, Sampler: DPM++ 2M SDE, CFG scale: 4, Seed: 3198814560, Size: 512x768</pre> </td> </tr> </table> # Donate <a href='https://ko-fi.com/K3K1O4HGG' target='_blank'><img height ="60px" style='border:0px; height: 60px' src='https://storage.ko-fi.com/cdn/kofi2.png?v=3' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
{"license": "cc"}
null
CulturedDiffusion/Rina_Tennoji_SD1.5_LOCON
[ "license:cc", "region:us" ]
2023-11-11T09:01:30+00:00
[]
[]
TAGS #license-cc #region-us
<style> td pre { white-space: pre-wrap; } table { margin-top: 0; margin-bottom: 0; } </style> <table style="border-collapse: separate; border-spacing: 10px; width:100%"> <tr> <td style="vertical-align: top"> <img src="./Examples/URL" alt="SchoolGirl01" width="100%"/> </td> <td style="vertical-align: top;"> <img src="./Examples/URL" alt="SchoolGirl02" width="100%"/> </td> </tr> </table> # Overview A LOCON for Stable Diffusion 1.5 of Rina Tennoji from Love Live. This model was trained and tested on the Mistoon Anime model using Kohya SS. Note: My models are trained heavily to recreate not only the character but also their original art style. This makes the models more prone to overfitting, so they might not work well with other SD 1.5 models. You can check out the Epoches folder for alternative versions that might work better for your use-case. # Usage - Trigger keywords: rina tennoji - Recommended keywords: pink hair, ahoge, yellow eyes - Recommended strength: 0.6 - Recommended CFG: 4 - Recommended SD model: Mistoon Anime model - Clip skip: 2 Rina-chan board - Trigger keywords: rina-chan board - Recommended keywords: sketchbook (if you want specifically the sketchbook version) Rina-chan board Tips: - Increase the weight of rina-chan board (and sketchbook if you use it). They rarely work at weight 1.0. - Avoid tags that mention eyes (ex: yellow eyes and beautiful eyes). - Avoid negative tags related to sketch/doodles. - Preferably avoid expression tags (ex: smile). They can work, but from my tests the model is more likely to draw Rina's actual face instead of the board. - Some tags (like > <) seem to give a strong push toward drawing the board. - Experiment and share your results! # Examples Note: All examples in the repo were produced with AUTOMATIC1111 without any edits. You download them and load all their settings from the metadata to reproduce similar results. Highres fix details: <pre>Denoising strength: 0.5, Hires upscale: 2, Hires upscaler: 4x-UltraSharp</pre> <table style="border-collapse: separate; border-spacing: 10px; width:100%;"> <tr> <td style="vertical-align: top"> <img src="./Examples/URL" width="100%"/> <pre>masterpiece, best quality, highres, absurdres, beautiful eyes, 1girl, rina tennoji, pink hair, ahoge, yellow eyes, smirk, hand on own chest, lying, school uniform, jacket, black thighhighs, classroom, table, chair, Steps: 50, Sampler: DPM++ 2M SDE, CFG scale: 4, Seed: 651122942, Size: 512x768</pre> </td> <td style="vertical-align: top;"> <img src="./Examples/URL" width="100%"/> <pre>masterpiece, best quality, highres, absurdres, 1girl, rina tennoji, pink hair, ahoge, (rina-chan board: 1.4), (sketchbook: 1.4), smirk, hands behind head, from below, white dress, park, bench, birds, sunlight, Steps: 50, Sampler: DPM++ 2M SDE, CFG scale: 4, Seed: 3577430021, Size: 512x768</pre> </td> <td style="vertical-align: top;"> <img src="./Examples/URL"width="100%"/> <pre>masterpiece, best quality, highres, absurdres, 1girl, rina tennoji, pink hair, ahoge, yellow eyes, smile, open mouth, hands behind head, crossed legs, maid, restaurant, chair, table, Steps: 50, Sampler: DPM++ 2M SDE, CFG scale: 4, Seed: 3198814560, Size: 512x768</pre> </td> </tr> </table> # Donate <a href='URL target='_blank'><img height ="60px" style='border:0px; height: 60px' src='URL border='0' alt='Buy Me a Coffee at URL' /></a>
[ "# Overview\nA LOCON for Stable Diffusion 1.5 of Rina Tennoji from Love Live. This model was trained and tested on the Mistoon Anime model using Kohya SS.\n\nNote: My models are trained heavily to recreate not only the character but also their original art style. This makes the models more prone to overfitting, so they might not work well with other SD 1.5 models. You can check out the Epoches folder for alternative versions that might work better for your use-case.", "# Usage\n- Trigger keywords: rina tennoji\n- Recommended keywords: pink hair, ahoge, yellow eyes\n- Recommended strength: 0.6\n- Recommended CFG: 4\n- Recommended SD model: Mistoon Anime model\n- Clip skip: 2\n\n\nRina-chan board\n- Trigger keywords: rina-chan board\n- Recommended keywords: sketchbook (if you want specifically the sketchbook version)\n\nRina-chan board Tips:\n- Increase the weight of rina-chan board (and sketchbook if you use it). They rarely work at weight 1.0.\n- Avoid tags that mention eyes (ex: yellow eyes and beautiful eyes).\n- Avoid negative tags related to sketch/doodles.\n- Preferably avoid expression tags (ex: smile). They can work, but from my tests the model is more likely to draw Rina's actual face instead of the board.\n- Some tags (like > <) seem to give a strong push toward drawing the board.\n- Experiment and share your results!", "# Examples\nNote: All examples in the repo were produced with AUTOMATIC1111 without any edits. You download them and load all their settings from the metadata to reproduce similar results.\n\nHighres fix details:\n<pre>Denoising strength: 0.5, Hires upscale: 2, Hires upscaler: 4x-UltraSharp</pre>\n\n<table style=\"border-collapse: separate; border-spacing: 10px; width:100%;\">\n <tr>\n <td style=\"vertical-align: top\">\n <img src=\"./Examples/URL\" width=\"100%\"/>\n <pre>masterpiece, best quality, highres, absurdres, beautiful eyes, 1girl,\nrina tennoji, pink hair, ahoge, yellow eyes,\nsmirk, hand on own chest, lying,\nschool uniform, jacket, black thighhighs,\nclassroom, table, chair,\n \nSteps: 50, Sampler: DPM++ 2M SDE, CFG scale: 4, Seed: 651122942, Size: 512x768</pre>\n </td>\n \n <td style=\"vertical-align: top;\">\n <img src=\"./Examples/URL\" width=\"100%\"/>\n <pre>masterpiece, best quality, highres, absurdres, 1girl,\nrina tennoji, pink hair, ahoge, (rina-chan board: 1.4), (sketchbook: 1.4),\nsmirk, hands behind head, from below,\nwhite dress,\npark, bench, birds, sunlight,\n \nSteps: 50, Sampler: DPM++ 2M SDE, CFG scale: 4, Seed: 3577430021, Size: 512x768</pre>\n </td>\n\n <td style=\"vertical-align: top;\">\n <img src=\"./Examples/URL\"width=\"100%\"/>\n <pre>masterpiece, best quality, highres, absurdres, 1girl,\nrina tennoji, pink hair, ahoge, yellow eyes,\nsmile, open mouth, hands behind head, crossed legs,\nmaid,\nrestaurant, chair, table,\n \nSteps: 50, Sampler: DPM++ 2M SDE, CFG scale: 4, Seed: 3198814560, Size: 512x768</pre>\n </td>\n \n </tr>\n</table>", "# Donate\n<a href='URL target='_blank'><img height =\"60px\" style='border:0px; height: 60px' src='URL border='0' alt='Buy Me a Coffee at URL' /></a>" ]
[ "TAGS\n#license-cc #region-us \n", "# Overview\nA LOCON for Stable Diffusion 1.5 of Rina Tennoji from Love Live. This model was trained and tested on the Mistoon Anime model using Kohya SS.\n\nNote: My models are trained heavily to recreate not only the character but also their original art style. This makes the models more prone to overfitting, so they might not work well with other SD 1.5 models. You can check out the Epoches folder for alternative versions that might work better for your use-case.", "# Usage\n- Trigger keywords: rina tennoji\n- Recommended keywords: pink hair, ahoge, yellow eyes\n- Recommended strength: 0.6\n- Recommended CFG: 4\n- Recommended SD model: Mistoon Anime model\n- Clip skip: 2\n\n\nRina-chan board\n- Trigger keywords: rina-chan board\n- Recommended keywords: sketchbook (if you want specifically the sketchbook version)\n\nRina-chan board Tips:\n- Increase the weight of rina-chan board (and sketchbook if you use it). They rarely work at weight 1.0.\n- Avoid tags that mention eyes (ex: yellow eyes and beautiful eyes).\n- Avoid negative tags related to sketch/doodles.\n- Preferably avoid expression tags (ex: smile). They can work, but from my tests the model is more likely to draw Rina's actual face instead of the board.\n- Some tags (like > <) seem to give a strong push toward drawing the board.\n- Experiment and share your results!", "# Examples\nNote: All examples in the repo were produced with AUTOMATIC1111 without any edits. You download them and load all their settings from the metadata to reproduce similar results.\n\nHighres fix details:\n<pre>Denoising strength: 0.5, Hires upscale: 2, Hires upscaler: 4x-UltraSharp</pre>\n\n<table style=\"border-collapse: separate; border-spacing: 10px; width:100%;\">\n <tr>\n <td style=\"vertical-align: top\">\n <img src=\"./Examples/URL\" width=\"100%\"/>\n <pre>masterpiece, best quality, highres, absurdres, beautiful eyes, 1girl,\nrina tennoji, pink hair, ahoge, yellow eyes,\nsmirk, hand on own chest, lying,\nschool uniform, jacket, black thighhighs,\nclassroom, table, chair,\n \nSteps: 50, Sampler: DPM++ 2M SDE, CFG scale: 4, Seed: 651122942, Size: 512x768</pre>\n </td>\n \n <td style=\"vertical-align: top;\">\n <img src=\"./Examples/URL\" width=\"100%\"/>\n <pre>masterpiece, best quality, highres, absurdres, 1girl,\nrina tennoji, pink hair, ahoge, (rina-chan board: 1.4), (sketchbook: 1.4),\nsmirk, hands behind head, from below,\nwhite dress,\npark, bench, birds, sunlight,\n \nSteps: 50, Sampler: DPM++ 2M SDE, CFG scale: 4, Seed: 3577430021, Size: 512x768</pre>\n </td>\n\n <td style=\"vertical-align: top;\">\n <img src=\"./Examples/URL\"width=\"100%\"/>\n <pre>masterpiece, best quality, highres, absurdres, 1girl,\nrina tennoji, pink hair, ahoge, yellow eyes,\nsmile, open mouth, hands behind head, crossed legs,\nmaid,\nrestaurant, chair, table,\n \nSteps: 50, Sampler: DPM++ 2M SDE, CFG scale: 4, Seed: 3198814560, Size: 512x768</pre>\n </td>\n \n </tr>\n</table>", "# Donate\n<a href='URL target='_blank'><img height =\"60px\" style='border:0px; height: 60px' src='URL border='0' alt='Buy Me a Coffee at URL' /></a>" ]
[ 11, 113, 227, 541, 63 ]
[ "passage: TAGS\n#license-cc #region-us \n# Overview\nA LOCON for Stable Diffusion 1.5 of Rina Tennoji from Love Live. This model was trained and tested on the Mistoon Anime model using Kohya SS.\n\nNote: My models are trained heavily to recreate not only the character but also their original art style. This makes the models more prone to overfitting, so they might not work well with other SD 1.5 models. You can check out the Epoches folder for alternative versions that might work better for your use-case.# Usage\n- Trigger keywords: rina tennoji\n- Recommended keywords: pink hair, ahoge, yellow eyes\n- Recommended strength: 0.6\n- Recommended CFG: 4\n- Recommended SD model: Mistoon Anime model\n- Clip skip: 2\n\n\nRina-chan board\n- Trigger keywords: rina-chan board\n- Recommended keywords: sketchbook (if you want specifically the sketchbook version)\n\nRina-chan board Tips:\n- Increase the weight of rina-chan board (and sketchbook if you use it). They rarely work at weight 1.0.\n- Avoid tags that mention eyes (ex: yellow eyes and beautiful eyes).\n- Avoid negative tags related to sketch/doodles.\n- Preferably avoid expression tags (ex: smile). They can work, but from my tests the model is more likely to draw Rina's actual face instead of the board.\n- Some tags (like > <) seem to give a strong push toward drawing the board.\n- Experiment and share your results!" ]
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null
null
transformers
Please refer to [faster-distil-whisper](https://github.com/metame-none/faster-distil-whisper) for more details.
{"license": "mit"}
null
metame/faster-distil-whisper-large-v2
[ "transformers", "license:mit", "endpoints_compatible", "has_space", "region:us" ]
2023-11-11T09:04:01+00:00
[]
[]
TAGS #transformers #license-mit #endpoints_compatible #has_space #region-us
Please refer to faster-distil-whisper for more details.
[]
[ "TAGS\n#transformers #license-mit #endpoints_compatible #has_space #region-us \n" ]
[ 26 ]
[ "passage: TAGS\n#transformers #license-mit #endpoints_compatible #has_space #region-us \n" ]
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null
null
transformers
Please refer to [faster-distil-whisper](https://github.com/metame-none/faster-distil-whisper) for more details.
{"license": "mit"}
null
metame/faster-distil-whisper-medium.en
[ "transformers", "license:mit", "endpoints_compatible", "region:us" ]
2023-11-11T09:06:07+00:00
[]
[]
TAGS #transformers #license-mit #endpoints_compatible #region-us
Please refer to faster-distil-whisper for more details.
[]
[ "TAGS\n#transformers #license-mit #endpoints_compatible #region-us \n" ]
[ 22 ]
[ "passage: TAGS\n#transformers #license-mit #endpoints_compatible #region-us \n" ]
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Matalab Capsule to help enhance eyesight! Read Price & Reviews! Matalab is capsule using natural ingredients which have shown to be beneficial in improve the overall health of the eye. Read More…….. Matalab Pills, Presyo, capsule,Tablets, Price, kapsula, reviews, Ingredients, Benefits, komposisyon! Matalab Buy now!! Click the Link Below for more information and get 50% discount now !! hurry up !! Read More: https://www.nutritioncrawler.com/MatalPhili ➢Product Name — Matalab ➢ used for: Eyes Health ➢Main Benefits — Improve Eye Sight ➢ Composition — Natural Organic Compound ➢ Side-Effects—NA ➢Final Rating: — 4.7 ➢ Availability — Online ➢Offers & Discounts; SAVE TODAY! SHOP NOW TO BUY SPECIAL OFFER!!! What Is Matalab? For the individuals who are not comfortable, Matalab is a dream further developing dietary enhancement that has been drifting around the web for its case to assist with upgrading an individual's visual perception by handling the 3 driving reasons for debilitated vision. The essential explanation is openness to specific poisons that can genuinely harm the eyes. Matalab Buy now!! Click the Link Below for more information and get 50% discount now !! hurry up !! Read More: https://www.nutritioncrawler.com/MatalPhili Matalab Matalab Pills Matalab capsule Matalab Tablets Matalab Price Matalab reviews Matalab Ingredients Matalab Benefits Matalab kapsula Matalab Mga tableta Matalab Presyo Matalab mga pagsusuri Matalab Mga sangkap Matalab Benepisyo Matalab Mga side effect Matalab presyo ng kapsula Matalab mga pagsusuri sa kapsula Matalab komposisyon Matalab reklamo Matalab Saan bibili Matalab Paano gamitin Matalab gastos Matalab gumagana Matalab forum Matalab orihinal Matalab parmasya https://www.nutritioncrawler.com/MatalPhili https://sites.google.com/view/matalab/home https://healthtoned.blogspot.com/2023/11/matalab-capsule-to-help-enhance.html https://www.q8101.com/bb/22274/matalab-capsule-to-help-enhance-eyesight-read-price-reviews https://www.dibiz.com/matalabphilippines https://www.weddingwire.com/website/matalab-and-philippines https://medium.com/@healthytalk24x7/matalab-d4c80a7fef07 https://medium.com/@healthytalk24x7/matalab-capsule-to-help-enhance-eyesight-read-price-reviews-12ec236ff898 https://infogram.com/matalab-1hxj48pk997nq2v?live https://softisenilspain.hashnode.dev/matalab-capsule-to-help-enhance-eyesight-read-price-reviews https://soundcloud.com/matalab-philippines/matalab-capsule-to-help-enhance-eyesight-read-price-reviews?si=a000733e94c046ce85de98c62277098d&utm_source=clipboard&utm_medium=text&utm_campaign=social_sharing https://www.deviantart.com/matalabcapsule/art/Matalab-capsule-993761904
{}
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Matalabcapsule/Matalabcapsule
[ "region:us" ]
2023-11-11T09:06:43+00:00
[]
[]
TAGS #region-us
Matalab Capsule to help enhance eyesight! Read Price & Reviews! Matalab is capsule using natural ingredients which have shown to be beneficial in improve the overall health of the eye. Read More…….. Matalab Pills, Presyo, capsule,Tablets, Price, kapsula, reviews, Ingredients, Benefits, komposisyon! Matalab Buy now!! Click the Link Below for more information and get 50% discount now !! hurry up !! Read More: URL Product Name — Matalab used for: Eyes Health Main Benefits — Improve Eye Sight Composition — Natural Organic Compound Side-Effects—NA Final Rating: — 4.7 Availability — Online Offers & Discounts; SAVE TODAY! SHOP NOW TO BUY SPECIAL OFFER!!! What Is Matalab? For the individuals who are not comfortable, Matalab is a dream further developing dietary enhancement that has been drifting around the web for its case to assist with upgrading an individual's visual perception by handling the 3 driving reasons for debilitated vision. The essential explanation is openness to specific poisons that can genuinely harm the eyes. Matalab Buy now!! Click the Link Below for more information and get 50% discount now !! hurry up !! Read More: URL Matalab Matalab Pills Matalab capsule Matalab Tablets Matalab Price Matalab reviews Matalab Ingredients Matalab Benefits Matalab kapsula Matalab Mga tableta Matalab Presyo Matalab mga pagsusuri Matalab Mga sangkap Matalab Benepisyo Matalab Mga side effect Matalab presyo ng kapsula Matalab mga pagsusuri sa kapsula Matalab komposisyon Matalab reklamo Matalab Saan bibili Matalab Paano gamitin Matalab gastos Matalab gumagana Matalab forum Matalab orihinal Matalab parmasya URL URL URL URL URL URL URL URL URL URL URL URL
[]
[ "TAGS\n#region-us \n" ]
[ 6 ]
[ "passage: TAGS\n#region-us \n" ]
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null
null
ml-agents
# **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: nondevs/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
{"library_name": "ml-agents", "tags": ["SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget"]}
reinforcement-learning
nondevs/ppo-SnowballTarget
[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]
2023-11-11T09:27:15+00:00
[]
[]
TAGS #ml-agents #tensorboard #onnx #SnowballTarget #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SnowballTarget #region-us
# ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the Unity ML-Agents Library. ## Usage (with ML-Agents) The Documentation: URL We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your browser: URL - A *longer tutorial* to understand how works ML-Agents: URL ### Resume the training ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to URL 2. Step 1: Find your model_id: nondevs/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play
[ "# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: nondevs/ppo-SnowballTarget\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]
[ "TAGS\n#ml-agents #tensorboard #onnx #SnowballTarget #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SnowballTarget #region-us \n", "# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: nondevs/ppo-SnowballTarget\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]
[ 50, 206 ]
[ "passage: TAGS\n#ml-agents #tensorboard #onnx #SnowballTarget #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SnowballTarget #region-us \n# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: nondevs/ppo-SnowballTarget\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]
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null
null
transformers
# airoboros-2.2.1-y34b Unofficial training of [Jon Durbin](https://huggingface.co/jondurbin)'s powerful airoboros 2.2.1 dataset on [Charles Goddard](https://huggingface.co/chargoddard)'s [Llama-fied Yi 34B model](https://huggingface.co/chargoddard/Yi-34B-Llama), aiming to bring the instruction-following capabilities of the airoboros dataset to the new Yi 34B foundational model. As a 34B model with grouped-query attention, users will be able to conduct inference on the model with 4bit quantization on a single 24gb consumer GPU. This Yi model is "Llama-fied", meaning the keys are renamed to match those used in Llama models, eliminating the need for remote code and ensuring compatibility with existing training and inference repositories. Architecturally this is similar to a Llama 2 34B model with an expanded vocab size of 64000. This model is retrained thanks to compute provided by [alpin](https://huggingface.co/alpindale) with a monkeypatch to the trainer to resolve EOS token issues in the prompter. A smaller batch size and learning rate were used and training was extended by one epoch. 8-bit lora was also used instead of qlora. ## Usage: The intended prompt format is the modified Vicuna 1.1 instruction format used by airoboros v2: ``` A chat. USER: {prompt} ASSISTANT: ``` ## Training Details: The model was trained using [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) as a lora adapter on 1x A100 80gb GPU for 4 epochs, before being fused to the base model with PEFT. ## License: This model is built on the Yi 34B base model, which has its own custom license included in this repository. Please refer to the [airoboros 2.2.1 dataset card](https://huggingface.co/datasets/jondurbin/airoboros-2.2.1) regarding the usage of gpt-4 API calls in creating the dataset.
{"language": ["en"], "license": "other", "library_name": "transformers", "tags": ["Yi", "llama", "llama 2"], "datasets": ["jondurbin/airoboros-2.2.1"], "inference": false, "pipeline_tag": "text-generation", "license_name": "yi-license", "license_link": "LICENSE"}
text-generation
LoneStriker/airoboros-2.2.1-y34b-5.0bpw-h6-exl2
[ "transformers", "safetensors", "llama", "text-generation", "Yi", "llama 2", "en", "dataset:jondurbin/airoboros-2.2.1", "license:other", "autotrain_compatible", "text-generation-inference", "region:us" ]
2023-11-11T09:27:25+00:00
[]
[ "en" ]
TAGS #transformers #safetensors #llama #text-generation #Yi #llama 2 #en #dataset-jondurbin/airoboros-2.2.1 #license-other #autotrain_compatible #text-generation-inference #region-us
# airoboros-2.2.1-y34b Unofficial training of Jon Durbin's powerful airoboros 2.2.1 dataset on Charles Goddard's Llama-fied Yi 34B model, aiming to bring the instruction-following capabilities of the airoboros dataset to the new Yi 34B foundational model. As a 34B model with grouped-query attention, users will be able to conduct inference on the model with 4bit quantization on a single 24gb consumer GPU. This Yi model is "Llama-fied", meaning the keys are renamed to match those used in Llama models, eliminating the need for remote code and ensuring compatibility with existing training and inference repositories. Architecturally this is similar to a Llama 2 34B model with an expanded vocab size of 64000. This model is retrained thanks to compute provided by alpin with a monkeypatch to the trainer to resolve EOS token issues in the prompter. A smaller batch size and learning rate were used and training was extended by one epoch. 8-bit lora was also used instead of qlora. ## Usage: The intended prompt format is the modified Vicuna 1.1 instruction format used by airoboros v2: ## Training Details: The model was trained using axolotl as a lora adapter on 1x A100 80gb GPU for 4 epochs, before being fused to the base model with PEFT. ## License: This model is built on the Yi 34B base model, which has its own custom license included in this repository. Please refer to the airoboros 2.2.1 dataset card regarding the usage of gpt-4 API calls in creating the dataset.
[ "# airoboros-2.2.1-y34b\n\nUnofficial training of Jon Durbin's powerful airoboros 2.2.1 dataset on Charles Goddard's Llama-fied Yi 34B model, aiming to bring the instruction-following capabilities of the airoboros dataset to the new Yi 34B foundational model.\n\nAs a 34B model with grouped-query attention, users will be able to conduct inference on the model with 4bit quantization on a single 24gb consumer GPU.\n\nThis Yi model is \"Llama-fied\", meaning the keys are renamed to match those used in Llama models, eliminating the need for remote code and ensuring compatibility with existing training and inference repositories. Architecturally this is similar to a Llama 2 34B model with an expanded vocab size of 64000.\n\nThis model is retrained thanks to compute provided by alpin with a monkeypatch to the trainer to resolve EOS token issues in the prompter. A smaller batch size and learning rate were used and training was extended by one epoch. 8-bit lora was also used instead of qlora.", "## Usage:\n\nThe intended prompt format is the modified Vicuna 1.1 instruction format used by airoboros v2:", "## Training Details:\n\nThe model was trained using axolotl as a lora adapter on 1x A100 80gb GPU for 4 epochs, before being fused to the base model with PEFT.", "## License:\n\nThis model is built on the Yi 34B base model, which has its own custom license included in this repository.\n\nPlease refer to the airoboros 2.2.1 dataset card regarding the usage of gpt-4 API calls in creating the dataset." ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #Yi #llama 2 #en #dataset-jondurbin/airoboros-2.2.1 #license-other #autotrain_compatible #text-generation-inference #region-us \n", "# airoboros-2.2.1-y34b\n\nUnofficial training of Jon Durbin's powerful airoboros 2.2.1 dataset on Charles Goddard's Llama-fied Yi 34B model, aiming to bring the instruction-following capabilities of the airoboros dataset to the new Yi 34B foundational model.\n\nAs a 34B model with grouped-query attention, users will be able to conduct inference on the model with 4bit quantization on a single 24gb consumer GPU.\n\nThis Yi model is \"Llama-fied\", meaning the keys are renamed to match those used in Llama models, eliminating the need for remote code and ensuring compatibility with existing training and inference repositories. Architecturally this is similar to a Llama 2 34B model with an expanded vocab size of 64000.\n\nThis model is retrained thanks to compute provided by alpin with a monkeypatch to the trainer to resolve EOS token issues in the prompter. A smaller batch size and learning rate were used and training was extended by one epoch. 8-bit lora was also used instead of qlora.", "## Usage:\n\nThe intended prompt format is the modified Vicuna 1.1 instruction format used by airoboros v2:", "## Training Details:\n\nThe model was trained using axolotl as a lora adapter on 1x A100 80gb GPU for 4 epochs, before being fused to the base model with PEFT.", "## License:\n\nThis model is built on the Yi 34B base model, which has its own custom license included in this repository.\n\nPlease refer to the airoboros 2.2.1 dataset card regarding the usage of gpt-4 API calls in creating the dataset." ]
[ 67, 253, 26, 46, 56 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #Yi #llama 2 #en #dataset-jondurbin/airoboros-2.2.1 #license-other #autotrain_compatible #text-generation-inference #region-us \n# airoboros-2.2.1-y34b\n\nUnofficial training of Jon Durbin's powerful airoboros 2.2.1 dataset on Charles Goddard's Llama-fied Yi 34B model, aiming to bring the instruction-following capabilities of the airoboros dataset to the new Yi 34B foundational model.\n\nAs a 34B model with grouped-query attention, users will be able to conduct inference on the model with 4bit quantization on a single 24gb consumer GPU.\n\nThis Yi model is \"Llama-fied\", meaning the keys are renamed to match those used in Llama models, eliminating the need for remote code and ensuring compatibility with existing training and inference repositories. Architecturally this is similar to a Llama 2 34B model with an expanded vocab size of 64000.\n\nThis model is retrained thanks to compute provided by alpin with a monkeypatch to the trainer to resolve EOS token issues in the prompter. A smaller batch size and learning rate were used and training was extended by one epoch. 8-bit lora was also used instead of qlora.## Usage:\n\nThe intended prompt format is the modified Vicuna 1.1 instruction format used by airoboros v2:## Training Details:\n\nThe model was trained using axolotl as a lora adapter on 1x A100 80gb GPU for 4 epochs, before being fused to the base model with PEFT.## License:\n\nThis model is built on the Yi 34B base model, which has its own custom license included in this repository.\n\nPlease refer to the airoboros 2.2.1 dataset card regarding the usage of gpt-4 API calls in creating the dataset." ]
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null
null
transformers
# Severus Snape DialoGPT Model
{"tags": ["conversational"]}
text-generation
Neutrophil/DialoGPT-medium-severussnape
[ "transformers", "safetensors", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2023-11-11T09:27:28+00:00
[]
[]
TAGS #transformers #safetensors #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# Severus Snape DialoGPT Model
[ "# Severus Snape DialoGPT Model" ]
[ "TAGS\n#transformers #safetensors #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Severus Snape DialoGPT Model" ]
[ 52, 10 ]
[ "passage: TAGS\n#transformers #safetensors #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Severus Snape DialoGPT Model" ]
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null
transformers
<!-- 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. --> # rubert-base-cased-conversational_ner-v3 This model is a fine-tuned version of [DeepPavlov/rubert-base-cased-conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1204 - Precision: 0.7484 - Recall: 0.7987 - F1: 0.7727 - Accuracy: 0.9242 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 75 | 0.2186 | 0.4860 | 0.5839 | 0.5305 | 0.8634 | | No log | 2.0 | 150 | 0.1380 | 0.6886 | 0.7718 | 0.7278 | 0.9200 | | No log | 3.0 | 225 | 0.1204 | 0.7484 | 0.7987 | 0.7727 | 0.9242 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1 - Datasets 2.14.4 - Tokenizers 0.13.3
{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "DeepPavlov/rubert-base-cased-conversational", "model-index": [{"name": "rubert-base-cased-conversational_ner-v3", "results": []}]}
token-classification
Data-Lab/rubert-base-cased-conversational_ner-v3
[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "base_model:DeepPavlov/rubert-base-cased-conversational", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2023-11-11T09:28:06+00:00
[]
[]
TAGS #transformers #pytorch #bert #token-classification #generated_from_trainer #base_model-DeepPavlov/rubert-base-cased-conversational #autotrain_compatible #endpoints_compatible #region-us
rubert-base-cased-conversational\_ner-v3 ======================================== This model is a fine-tuned version of DeepPavlov/rubert-base-cased-conversational on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.1204 * Precision: 0.7484 * Recall: 0.7987 * F1: 0.7727 * Accuracy: 0.9242 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.33.1 * Pytorch 2.0.1 * Datasets 2.14.4 * Tokenizers 0.13.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.33.1\n* Pytorch 2.0.1\n* Datasets 2.14.4\n* Tokenizers 0.13.3" ]
[ "TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #base_model-DeepPavlov/rubert-base-cased-conversational #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.33.1\n* Pytorch 2.0.1\n* Datasets 2.14.4\n* Tokenizers 0.13.3" ]
[ 66, 98, 4, 30 ]
[ "passage: TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #base_model-DeepPavlov/rubert-base-cased-conversational #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.33.1\n* Pytorch 2.0.1\n* Datasets 2.14.4\n* Tokenizers 0.13.3" ]
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null
null
transformers
<!-- 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. --> # detr-r50-mist1-bg-2ah-6l This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.9051 ## 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.6721 | 1.0 | 115 | 5.0032 | | 4.4438 | 2.0 | 230 | 4.6797 | | 4.2953 | 3.0 | 345 | 4.7027 | | 4.3899 | 4.0 | 460 | 5.4316 | | 4.3184 | 5.0 | 575 | 4.4125 | | 4.2749 | 6.0 | 690 | 4.1611 | | 4.2153 | 7.0 | 805 | 4.6723 | | 4.0788 | 8.0 | 920 | 4.1266 | | 4.0752 | 9.0 | 1035 | 4.0529 | | 4.0073 | 10.0 | 1150 | 4.4483 | | 4.011 | 11.0 | 1265 | 4.2002 | | 3.9993 | 12.0 | 1380 | 4.2450 | | 4.0028 | 13.0 | 1495 | 4.1703 | | 3.9572 | 14.0 | 1610 | 4.1861 | | 3.9009 | 15.0 | 1725 | 4.0285 | | 3.9173 | 16.0 | 1840 | 4.0673 | | 3.8884 | 17.0 | 1955 | 3.9875 | | 3.8415 | 18.0 | 2070 | 4.1062 | | 3.8132 | 19.0 | 2185 | 4.0494 | | 3.8297 | 20.0 | 2300 | 4.0119 | | 3.8262 | 21.0 | 2415 | 3.9538 | | 3.8045 | 22.0 | 2530 | 3.9500 | | 3.8067 | 23.0 | 2645 | 3.9264 | | 3.7651 | 24.0 | 2760 | 3.8820 | | 3.756 | 25.0 | 2875 | 3.9051 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.14.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/detr-resnet-50", "model-index": [{"name": "detr-r50-mist1-bg-2ah-6l", "results": []}]}
object-detection
polejowska/detr-r50-mist1-bg-2ah-6l
[ "transformers", "tensorboard", "safetensors", "detr", "object-detection", "generated_from_trainer", "base_model:facebook/detr-resnet-50", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2023-11-11T09:34:10+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #detr #object-detection #generated_from_trainer #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #region-us
detr-r50-mist1-bg-2ah-6l ======================== This model is a fine-tuned version of facebook/detr-resnet-50 on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 3.9051 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: 4 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 25 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.35.0 * Pytorch 2.0.0 * Datasets 2.1.0 * Tokenizers 0.14.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.0.0\n* Datasets 2.1.0\n* Tokenizers 0.14.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #detr #object-detection #generated_from_trainer #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.0.0\n* Datasets 2.1.0\n* Tokenizers 0.14.1" ]
[ 62, 113, 4, 30 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #detr #object-detection #generated_from_trainer #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.0.0\n* Datasets 2.1.0\n* Tokenizers 0.14.1" ]
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