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train · 848k rows

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hinokamikaguya/autotrain-uf8wi-g248o	hinokamikaguya	2024-05-18T18:50:30Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T17:02:53Z	--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers widget: - messages: - role: user content: What is your favorite condiment? license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
hsikchi/pythia-6.9b-goldrm_tldr-dpo-beta-0.0175-alpha-0-step-59904	hsikchi	2024-05-18T18:45:19Z	6	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T18:40:51Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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hsikchi/pythia-6.9b-goldrm_tldr-dpo-beta-0.0175-alpha-0-step-19968	hsikchi	2024-05-18T18:44:51Z	6	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T18:37:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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hsikchi/pythia-6.9b-goldrm_tldr-dpo-beta-0.0175-alpha-0-step-39936	hsikchi	2024-05-18T18:44:14Z	6	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T18:37:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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hsikchi/pythia-6.9b-goldrm_tldr-dpo-beta-0.0175-alpha-0-LATEST	hsikchi	2024-05-18T18:44:11Z	7	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T18:36:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]
mlx-community/dolphin-2.9.1-yi-1.5-34b-8bit	mlx-community	2024-05-18T18:43:27Z	6	0	mlx	[ "mlx", "safetensors", "llama", "generated_from_trainer", "axolotl", "dataset:cognitivecomputations/Dolphin-2.9", "dataset:teknium/OpenHermes-2.5", "dataset:m-a-p/CodeFeedback-Filtered-Instruction", "dataset:cognitivecomputations/dolphin-coder", "dataset:cognitivecomputations/samantha-data", "dataset:microsoft/orca-math-word-problems-200k", "dataset:Locutusque/function-calling-chatml", "dataset:internlm/Agent-FLAN", "base_model:01-ai/Yi-1.5-34B", "base_model:finetune:01-ai/Yi-1.5-34B", "license:apache-2.0", "region:us" ]	null	2024-05-18T18:32:29Z	--- license: apache-2.0 tags: - generated_from_trainer - axolotl - mlx base_model: 01-ai/Yi-1.5-34B datasets: - cognitivecomputations/Dolphin-2.9 - teknium/OpenHermes-2.5 - m-a-p/CodeFeedback-Filtered-Instruction - cognitivecomputations/dolphin-coder - cognitivecomputations/samantha-data - microsoft/orca-math-word-problems-200k - Locutusque/function-calling-chatml - internlm/Agent-FLAN --- # mlx-community/dolphin-2.9.1-yi-1.5-34b-8bit This model was converted to MLX format from [`cognitivecomputations/dolphin-2.9.1-yi-1.5-34b`]() using mlx-lm version 0.12.1. Refer to the [original model card](https://huggingface.co/cognitivecomputations/dolphin-2.9.1-yi-1.5-34b) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/dolphin-2.9.1-yi-1.5-34b-8bit") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
hsikchi/pythia-6.9b-goldrm_tldr-dpo-beta-0.025-alpha-0-step-59904	hsikchi	2024-05-18T18:35:23Z	9	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T18:28:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]
hsikchi/pythia-6.9b-goldrm_tldr-dpo-beta-0.01-alpha-0-step-39936	hsikchi	2024-05-18T18:35:21Z	6	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T18:28:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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hsikchi/pythia-6.9b-goldrm_tldr-dpo-beta-0.0375-alpha-0-LATEST	hsikchi	2024-05-18T18:35:12Z	6	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T18:28:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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hsikchi/pythia-6.9b-goldrm_tldr-dpo-beta-0.01-alpha-0-step-19968	hsikchi	2024-05-18T18:34:28Z	7	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T18:27:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mdosama39/mt0-base-headline_WithIp	mdosama39	2024-05-18T18:32:26Z	7	0	transformers	[ "transformers", "safetensors", "mt5", "text2text-generation", "generated_from_trainer", "base_model:bigscience/mt0-base", "base_model:finetune:bigscience/mt0-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-18T18:17:00Z	--- license: apache-2.0 base_model: bigscience/mt0-base tags: - generated_from_trainer metrics: - rouge model-index: - name: mt0-base-headline_WithIp results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mt0-base-headline_WithIp This model is a fine-tuned version of [bigscience/mt0-base](https://huggingface.co/bigscience/mt0-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5591 - Rouge1: 0.0 - Rouge2: 0.0 - Rougel: 0.0 - Rougelsum: 0.0 - Gen Len: 16.8015 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 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 \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| 1.9054 \| 1.0 \| 202 \| 1.5802 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 16.8511 \| \| 1.7765 \| 2.0 \| 404 \| 1.5663 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 17.464 \| \| 1.4716 \| 3.0 \| 606 \| 1.5465 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 17.1216 \| \| 1.2112 \| 4.0 \| 808 \| 1.5591 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 16.8015 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
houbw/houbw	houbw	2024-05-18T18:31:22Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-18T18:29:14Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: houbw - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
hsikchi/pythia-6.9b-goldrm_tldr-dpo-beta-0.025-alpha-0-step-39936	hsikchi	2024-05-18T18:26:55Z	7	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T18:22:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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hsikchi/pythia-6.9b-goldrm_tldr-dpo-beta-0.025-alpha-0-LATEST	hsikchi	2024-05-18T18:26:34Z	6	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T18:22:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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msoz7/NERTestModel	msoz7	2024-05-18T18:23:15Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T15:44:54Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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shapiron/q-taxi-v3-c	shapiron	2024-05-18T18:22:46Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T18:22:44Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-taxi-v3-c results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="shapiron/q-taxi-v3-c", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
shapiron/q-taxi-v3-b	shapiron	2024-05-18T18:22:25Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T17:56:04Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-taxi-v3-b results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="shapiron/q-taxi-v3-b", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
hsikchi/pythia-6.9b-goldrm_tldr-dpo-beta-0.05-alpha-0-step-59904	hsikchi	2024-05-18T18:21:05Z	6	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T18:16:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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hsikchi/pythia-6.9b-goldrm_tldr-dpo-beta-0.05-alpha-0-step-79872	hsikchi	2024-05-18T18:21:04Z	6	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T18:16:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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hsikchi/pythia-6.9b-goldrm_tldr-dpo-beta-0.05-alpha-0-step-39936	hsikchi	2024-05-18T18:20:45Z	6	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T18:16:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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bujido/Llama3-70B-Chinese-Chat-AWQ-32k	bujido	2024-05-18T18:17:49Z	8	3	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	text-generation	2024-05-18T17:10:35Z	# Llama3-70B-Chinese-Chat-AWQ-32k ## Model Description This repository provides a 4-bit AWQ quantized version based on the full-parameter fine-tuned Llama3-70B-Chinese-Chat model by shenzhi-wang (https://huggingface.co/shenzhi-wang/Llama3-70B-Chinese-Chat). The original model is based on the Llama3-70B model, which has been fine-tuned for Chinese chat tasks to enhance its ability to handle Chinese dialogue tasks. Additionally, we have included an optional configuration file to support extending the context length from the original 8k to 32k. This enables the model to process longer text sequences, making it suitable for scenarios that require richer contextual information. ### Quantization We have used 4-bit AWQ quantization technology to reduce the model's weight precision. Preliminary tests show that the model's performance has been maintained relatively well. The quantized model can run in environments with limited resources. ### Context Extension To support longer contexts, we have added a configuration file named "config-32k.json". When you need to process text lengths that exceed the original context limit, you can enable this feature by simply replacing the configuration file. Please note that as this is an experimental feature, using longer context lengths may affect the model's performance. It is recommended that you test based on your actual usage scenarios. (By default, the original "config.json" from the Llama3 is used, which has an 8k context. To enable the 32k context length, replace the "config.json" in the model files with "config-32k.json". The effect is uncertain; please test yourself.) ## Original Model Link https://huggingface.co/shenzhi-wang/Llama3-70B-Chinese-Chat Thanks to the open-source community for their contributions to the Sinicization of Llama3. -------------------------------------- ## 模型描述本仓库提供了在[shenzhi-wang全参数微调的Llama3-70B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Llama3-70B-Chinese-Chat)基础上进行的4位AWQ量化版本。原始模型是基于Llama3-70B模型，在中文聊天任务上进行了微调，以提升其在处理中文对话任务的能力。此外，我们还增加了一个可选的配置文件，以支持将上下文长度从原始的8k扩展至32k。这样，模型可以处理更长的文本序列，适用于需要上下文信息更丰富的场景。 ### 量化我们使用了4位AWQ量化技术来降低模型的权重精度，初步试用中，模型性能保持得还不错。量化后的模型可以在资源有限的环境中运行。 ### 上下文扩展为了支持更长的上下文，我们增加了一个名为`config-32k.json`的配置文件。当您需要处理的文本长度超过原始上下文限制时，可以通过简单地替换配置文件来启用这一特性。请注意，由于这是一个实验性的特性，使用更长上下文长度可能会影响模型的性能，建议您根据实际使用场景进行测试。（默认使用llama3原版"config.json"，具有8k上下文。若要启用32k上下文长度，请将模型文件中的"config.json"替换为"config-32k.json"，效果不确定，请自行测试） ## 原始模型链接 [https://huggingface.co/shenzhi-wang/Llama3-70B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Llama3-70B-Chinese-Chat) 感谢开源社区对llama3中文化做出的贡献。
hsikchi/pythia-6.9b-goldrm_tldr-dpo-beta-0.05-alpha-0-step-19968	hsikchi	2024-05-18T18:14:56Z	8	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T18:10:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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hsikchi/pythia-6.9b-goldrm_tldr-dpo-beta-0.05-alpha-0-LATEST	hsikchi	2024-05-18T18:14:51Z	6	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T18:10:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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openchat/openchat-3.5-0106	openchat	2024-05-18T18:14:51Z	29,624	351	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "openchat", "C-RLFT", "conversational", "arxiv:2309.11235", "arxiv:2303.08774", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-07T08:17:09Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - openchat - mistral - C-RLFT library_name: transformers pipeline_tag: text-generation --- <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%"> <h1>Advancing Open-source Language Models with Mixed-Quality Data</h1> </div> <p align="center" style="margin-top: 0px;"> <a href="https://openchat.team"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/logo_nobg.png?raw=true" alt="OpenChat Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 10px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">Online Demo</span> </a> \| <a href="https://github.com/imoneoi/openchat"> <img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" alt="GitHub Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">GitHub</span> </a> \| <a href="https://arxiv.org/pdf/2309.11235.pdf"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/arxiv-logomark-small-square-border.png?raw=true" alt="ArXiv Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style="margin-right: 5px;">Paper</span> </a> \| <a href="https://discord.gg/pQjnXvNKHY"> <img src="https://cloud.githubusercontent.com/assets/6291467/26705903/96c2d66e-477c-11e7-9f4e-f3c0efe96c9a.png" alt="Discord Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text">Discord</span> </a> </p> <p align="center" style="margin-top: 0px;"> <span class="link-text" style=" margin-right: 0px; font-size: 0.8em">Sponsored by RunPod</span> <img src="https://styles.redditmedia.com/t5_6075m3/styles/profileIcon_71syco7c5lt81.png?width=256&height=256&frame=1&auto=webp&crop=256:256,smart&s=24bd3c71dc11edc5d4f88d0cbc1da72ed7ae1969" alt="RunPod Logo" style="width:30px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> </p> <div style="background-color: white; padding: 0.7em; border-radius: 0.5em; color: black; display: flex; flex-direction: column; justify-content: center; text-align: center; ont-size: 0.5em; border: 0.8em solid #864AF9;"> <a href="https://huggingface.co/openchat/openchat-3.5-0106" style="text-decoration: none; color: black;"> <span style="font-size: 1.7em; font-family: 'Helvetica'; letter-spacing: 0.1em; font-weight: bold; color: black;">OPENCHAT</span><span style="font-size: 1.8em; font-family: 'Helvetica'; color: #3c72db; ">3.5</span> <span style="font-size: 1.0em; font-family: 'Helvetica'; color: white; background-color: #864AF9; vertical-align: top; border-radius: 6em; padding: 0.066em 0.4em; letter-spacing: 0.1em; font-weight: bold;">0106</span> <span style="font-size: 0.85em; font-family: 'Helvetica'; color: black;"> <br> 🏆 The Overall Best Performing Open Source 7B Model 🏆 <br> 🤖 Outperforms <span style="font-weight: bold;">ChatGPT</span> (March) and <span style="font-weight: bold;">Grok-1</span> 🤖 <br> 🚀<span style="font-size: 1em; font-family: 'Helvetica'; color: black; font-weight: bold;">15</span>-point improvement in Coding over <span style="font-size: 0.9em; font-family: 'Helvetica'; color: black; font-weight: bold;">OpenChat-3.5🚀</span> <br><br><span style="font-size: 1em; font-family: 'Helvetica'; color: #3c72db; font-weight: bold;">New Features</span> <br> 💡 2 Modes: Coding + Generalist, Mathematical Reasoning 💡 <br> 🧑‍⚖️ Experimental support for Evaluator and Feedback capabilities 🧑‍⚖️ </span> </a> </div> <div style="display: flex; justify-content: center; align-items: center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/openchat-bench-0106.png" style="width: 100%; border-radius: 1em"> </div> <div> <h3> Table of Contents</h3> </div> 1. [Usage](#usage) 2. [Benchmarks](#benchmarks) 3. [Limitations](#limitations) 4. [License](#license) 6. [Citation](#citation) 7. [Acknowledgements](#acknowledgements) <div align="center"> <h2> Usage </h2> </div> 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. \| Model \| Size \| Context \| Weights \| Serving \| \|-------------------\|------\|---------\|------------------------------------------------------------------\|------------------------------------------------------------------------------------------------------------------\| \| OpenChat-3.5-0106 \| 7B \| 8192 \| [Huggingface](https://huggingface.co/openchat/openchat-3.5-0106) \| `python -m ochat.serving.openai_api_server --model openchat/openchat-3.5-0106 --engine-use-ray --worker-use-ray` \| <details> <summary>Example request (click to expand)</summary> 💡 Default Mode (GPT4 Correct): Best for coding, chat and general tasks ```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"}] }' ``` 🧮 Mathematical Reasoning Mode: Tailored for solving math problems ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "condition": "Math Correct", "messages": [{"role": "user", "content": "10.3 − 7988.8133 = "}] }' ``` </details> ### Conversation templates 💡 Default Mode (GPT4 Correct): Best for coding, chat and general tasks ``` 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: ``` 🧮 Mathematical Reasoning Mode: Tailored for solving math problems ``` Math Correct User: 10.3 − 7988.8133=<\|end_of_turn\|>Math Correct Assistant: ``` ⚠️ Notice: Remember to set `<\|end_of_turn\|>` as end of generation token. The default (GPT4 Correct) template is also available as the integrated `tokenizer.chat_template`, which can be used instead of manually specifying the template: ```python messages = [ {"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi"}, {"role": "user", "content": "How are you today?"} ] tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True) 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] ``` <div align="center"> <h2> (Experimental) Evaluator / Feedback Capabilities </h2> </div> We've included evaluator capabilities in this release to advance open-source models as evaluators. You can use `Default Mode (GPT4 Correct)` with the following prompt (same as [Prometheus](https://huggingface.co/datasets/kaist-ai/Feedback-Collection)) to evaluate a response. ``` ###Task Description: An instruction (might include an Input inside it), a response to evaluate, a reference answer that gets a score of 5, and a score rubric representing a evaluation criteria are given. 1. Write a detailed feedback that assess the quality of the response strictly based on the given score rubric, not evaluating in general. 2. After writing a feedback, write a score that is an integer between 1 and 5. You should refer to the score rubric. 3. The output format should look as follows: "Feedback: (write a feedback for criteria) [RESULT] (an integer number between 1 and 5)" 4. Please do not generate any other opening, closing, and explanations. ###The instruction to evaluate: {orig_instruction} ###Response to evaluate: {orig_response} ###Reference Answer (Score 5): {orig_reference_answer} ###Score Rubrics: [{orig_criteria}] Score 1: {orig_score1_description} Score 2: {orig_score2_description} Score 3: {orig_score3_description} Score 4: {orig_score4_description} Score 5: {orig_score5_description} ###Feedback: ``` <div align="center"> <h2> Benchmarks </h2> </div> \| Model \| # Params \| Average \| MT-Bench \| HumanEval \| BBH MC \| AGIEval \| TruthfulQA \| MMLU \| GSM8K \| BBH CoT \| \|-----------------------\|----------\|----------\|----------\|-----------\|----------\|----------\|------------\|----------\|----------\|----------\| \| OpenChat-3.5-0106 \| 7B \| 64.5 \| 7.8 \| 71.3 \| 51.5 \| 49.1 \| 61.0 \| 65.8 \| 77.4 \| 62.2 \| \| OpenChat-3.5-1210 \| 7B \| 63.8 \| 7.76 \| 68.9 \| 49.5 \| 48.0 \| 61.8 \| 65.3 \| 77.3 \| 61.8 \| \| OpenChat-3.5 \| 7B \| 61.6 \| 7.81 \| 55.5 \| 47.6 \| 47.4 \| 59.1 \| 64.3 \| 77.3 \| 63.5 \| \| ChatGPT (March)* \| ???B \| 61.5 \| 7.94 \| 48.1 \| 47.6 \| 47.1 \| 57.7 \| 67.3 \| 74.9 \| 70.1 \| \| \| \| \| \| \| \| \| \| \| \| \| \| OpenHermes 2.5 \| 7B \| 59.3 \| 7.54 \| 48.2 \| 49.4 \| 46.5 \| 57.5 \| 63.8 \| 73.5 \| 59.9 \| \| OpenOrca Mistral \| 7B \| 52.7 \| 6.86 \| 38.4 \| 49.4 \| 42.9 \| 45.9 \| 59.3 \| 59.1 \| 58.1 \| \| Zephyr-β^ \| 7B \| 34.6 \| 7.34 \| 22.0 \| 40.6 \| 39.0 \| 40.8 \| 39.8 \| 5.1 \| 16.0 \| \| Mistral \| 7B \| - \| 6.84 \| 30.5 \| 39.0 \| 38.0 \| - \| 60.1 \| 52.2 \| - \| <details> <summary>Evaluation Details(click to expand)</summary> : 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). </details> <div> <h3>HumanEval+</h3> </div> \| Model \| Size \| HumanEval+ pass@1 \| \|-----------------------------\|--------\|-------------------\| \| OpenChat-3.5-0106* \| 7B \| 65.9 \| \| ChatGPT (December 12, 2023) \| ???B \| 64.6 \| \| WizardCoder-Python-34B-V1.0 \| 34B \| 64.6 \| \| OpenChat 3.5 1210 \| 7B \| 63.4 \| \| OpenHermes 2.5 \| 7B \| 41.5 \| <div> <h3>OpenChat-3.5 vs. Grok</h3> </div> 🔥 OpenChat-3.5-0106 (7B) now outperforms Grok-0 (33B) on all 4 benchmarks and Grok-1 (???B) on average and 3/4 benchmarks. \| \| License \| # Param \| Average \| MMLU \| HumanEval \| MATH \| GSM8k \| \|-----------------------\|-------------\|---------\|----------\|--------\|-----------\|----------\|----------\| \| OpenChat-3.5-0106 \| Apache-2.0 \| 7B \| 61.0 \| 65.8 \| 71.3 \| 29.3 \| 77.4 \| \| OpenChat-3.5-1210 \| Apache-2.0 \| 7B \| 60.1 \| 65.3 \| 68.9 \| 28.9 \| 77.3 \| \| 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 \| ???B \| 55.8 \| 73 \| 63.2 \| 23.9 \| 62.9 \| : Grok results are reported by [X.AI](https://x.ai/). <div align="center"> <h2> Limitations </h2> </div> 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. <div align="center"> <h2> License </h2> </div> Our OpenChat 3.5 code and models are distributed under the Apache License 2.0. <div align="center"> <h2> Citation </h2> </div> ``` @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} } ``` <div align="center"> <h2> 💌 Contact </h2> </div> We look forward to hearing you and collaborating on this exciting project! Project Lead: - Guan Wang [imonenext at gmail dot com] - [Alpay Ariyak](https://github.com/alpayariyak) [aariyak at wpi dot edu]
openchat/openchat-3.5-0106-gemma	openchat	2024-05-18T18:11:14Z	7,735	57	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:2309.11235", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-09T16:03:17Z	--- license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms --- <div align="center"> <a> <img src="https://cdn-uploads.huggingface.co/production/uploads/63972847b3e2256c9ce1307b/Ez9cDw8xstbTKlFtBgbVs.png" > </a> </div> ## The highest performing Gemma model in the world. Trained with OpenChat's C-RLFT on openchat-3.5-0106 data. Achieving similar performance to Mistral-based openchat, and much better than Gemma-7b and Gemma-7b-it. Please refer to [openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106) for details. > P.S.: 6T pre-training tokens + 0.003 init std dev + C-RLFT is the secret sauce? > > P.P.S.: @Google team, we know your model is great, but please use an OSI-approved license like Mistral (or even Phi and Orca). ## Benchmarks \| Model \| # Params \| Average \| MT-Bench \| HumanEval \| BBH MC \| AGIEval \| TruthfulQA \| MMLU \| GSM8K \| BBH CoT \| \|-----------------------------\|----------\|----------\|----------\|-----------\|----------\|----------\|------------\|----------\|----------\|----------\| \| OpenChat-3.5-0106 Gemma \| 7B \| 64.4 \| 7.83 \| 67.7 \| 52.7 \| 50.2 \| 55.4 \| 65.7 \| 81.5 \| 63.7 \| \| OpenChat-3.5-0106 Mistral \| 7B \| 64.5 \| 7.8 \| 71.3 \| 51.5 \| 49.1 \| 61.0 \| 65.8 \| 77.4 \| 62.2 \| \| ChatGPT (March) \| ???B \| 61.5 \| 7.94 \| 48.1 \| 47.6 \| 47.1 \| 57.7 \| 67.3 \| 74.9 \| 70.1 \| \| \| \| \| \| \| \| \| \| \| \| \| \| Gemma-7B \| 7B \| - \| - \| 32.3 \| - \| 41.7 \| - \| 64.3 \| 46.4 \| - \| \| Gemma-7B-it * \| 7B \| 25.4 \| - \| 28.0 \| 38.4 \| 32.5 \| 34.1 \| 26.5 \| 10.8 \| 7.6 \| \| OpenHermes 2.5 \| 7B \| 59.3 \| 7.54 \| 48.2 \| 49.4 \| 46.5 \| 57.5 \| 63.8 \| 73.5 \| 59.9 \| : `Gemma-7b-it` failed to understand and follow most few-shot templates. ## 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. \| Model \| Size \| Context \| Weights \| Serving \| \|-------------------------\|------\|---------\|------------------------------------------------------------------------\|------------------------------------------------------------------------------------------------------------------------\| \| OpenChat-3.5-0106-Gemma \| 7B \| 8192 \| [Huggingface](https://huggingface.co/openchat/openchat-3.5-0106-gemma) \| `python -m ochat.serving.openai_api_server --model openchat/openchat-3.5-0106-gemma --engine-use-ray --worker-use-ray` \| <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_gemma_new", "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}] }' ``` </details> ## Conversation template ⚠️ Notice:* This is different from the Mistral version. End-of-turn token is `<end_of_turn>` now (Mistral version is `<\|end_of_turn\|>`). Remember to set `<end_of_turn>` as end of generation token. ``` 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: ``` With system message (NOT recommended, may degrade performance) ``` You are a helpful assistant.<end_of_turn>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: ``` ## 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. <div align="center"> <h2> License </h2> </div> 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} } ``` <div align="center"> <h2> 💌 Contact </h2> </div> Project Lead: - Guan Wang [imonenext at gmail dot com] - [Alpay Ariyak](https://github.com/alpayariyak) [aariyak at wpi dot edu]
hsikchi/pythia-6.9b-goldrm_tldr-dpo-beta-0.1-alpha-0-step-59904	hsikchi	2024-05-18T18:08:29Z	6	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T18:03:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ranystephan/NeuralFinGPT-v1-10	ranystephan	2024-05-18T18:08:08Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T18:07:51Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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emilykang/Gemma_medmcqa_question_generation-physiology_lora	emilykang	2024-05-18T18:06:30Z	1	0	peft	[ "peft", "safetensors", "gemma", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:google/gemma-2b", "base_model:adapter:google/gemma-2b", "license:gemma", "region:us" ]	null	2024-05-18T17:11:47Z	--- license: gemma library_name: peft tags: - trl - sft - generated_from_trainer base_model: google/gemma-2b datasets: - generator model-index: - name: Gemma_medmcqa_question_generation-physiology_lora results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Gemma_medmcqa_question_generation-physiology_lora This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
liminerity/mm4.ascii.star.gguf	liminerity	2024-05-18T18:05:16Z	6	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:liminerity/mm4.star", "base_model:quantized:liminerity/mm4.star", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-18T18:02:31Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf base_model: liminerity/mm4.star --- # Uploaded model - Developed by: liminerity - License: apache-2.0 - Finetuned from model : liminerity/mm4.star This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Ashleyinust/model_one	Ashleyinust	2024-05-18T18:02:55Z	106	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-30T13:48:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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hsikchi/pythia-6.9b-goldrm_tldr-dpo-beta-0.5-alpha-0-step-79872	hsikchi	2024-05-18T18:02:02Z	6	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T17:57:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
hoanganhvu/phishing_2_1	hoanganhvu	2024-05-18T17:59:21Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "generated_from_trainer", "base_model:google-bert/bert-large-uncased", "base_model:finetune:google-bert/bert-large-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-18T09:23:04Z	--- license: apache-2.0 base_model: bert-large-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall model-index: - name: phishing_2_1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phishing_2_1 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3498 - Accuracy: 0.9634 - Precision: 0.9918 - Recall: 0.9345 - False Positive Rate: 0.0077 ## 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: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| False Positive Rate \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:---------:\|:------:\|:-------------------:\| \| 0.365 \| 1.0 \| 3025 \| 0.3632 \| 0.9480 \| 0.9861 \| 0.9088 \| 0.0128 \| \| 0.3453 \| 2.0 \| 6050 \| 0.3405 \| 0.9727 \| 0.9752 \| 0.9700 \| 0.0247 \| \| 0.3623 \| 3.0 \| 9075 \| 0.3596 \| 0.9536 \| 0.9861 \| 0.9202 \| 0.0130 \| \| 0.3498 \| 4.0 \| 12100 \| 0.3498 \| 0.9634 \| 0.9918 \| 0.9345 \| 0.0077 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
afrideva/phi-3-portuguese-tom-cat-4k-instruct-GGUF	afrideva	2024-05-18T17:56:42Z	17	1	transformers	[ "transformers", "gguf", "portugues", "portuguese", "QA", "instruct", "phi", "ggml", "quantized", "text-generation", "pt", "dataset:rhaymison/superset", "base_model:rhaymison/phi-3-portuguese-tom-cat-4k-instruct", "base_model:quantized:rhaymison/phi-3-portuguese-tom-cat-4k-instruct", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-18T17:31:48Z	--- base_model: rhaymison/phi-3-portuguese-tom-cat-4k-instruct datasets: - rhaymison/superset inference: true language: - pt library_name: transformers license: apache-2.0 model-index: - name: phi-3-portuguese-tom-cat-4k-instruct results: - dataset: args: num_few_shot: 3 name: ENEM Challenge (No Images) split: train type: eduagarcia/enem_challenge metrics: - name: accuracy type: acc value: 61.58 source: name: Open Portuguese LLM Leaderboard url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct task: name: Text Generation type: text-generation - dataset: args: num_few_shot: 3 name: BLUEX (No Images) split: train type: eduagarcia-temp/BLUEX_without_images metrics: - name: accuracy type: acc value: 50.63 source: name: Open Portuguese LLM Leaderboard url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct task: name: Text Generation type: text-generation - dataset: args: num_few_shot: 3 name: OAB Exams split: train type: eduagarcia/oab_exams metrics: - name: accuracy type: acc value: 43.69 source: name: Open Portuguese LLM Leaderboard url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct task: name: Text Generation type: text-generation - dataset: args: num_few_shot: 15 name: Assin2 RTE split: test type: assin2 metrics: - name: f1-macro type: f1_macro value: 91.54 source: name: Open Portuguese LLM Leaderboard url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct task: name: Text Generation type: text-generation - dataset: args: num_few_shot: 15 name: Assin2 STS split: test type: eduagarcia/portuguese_benchmark metrics: - name: pearson type: pearson value: 75.27 source: name: Open Portuguese LLM Leaderboard url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct task: name: Text Generation type: text-generation - dataset: args: num_few_shot: 15 name: FaQuAD NLI split: test type: ruanchaves/faquad-nli metrics: - name: f1-macro type: f1_macro value: 47.46 source: name: Open Portuguese LLM Leaderboard url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct task: name: Text Generation type: text-generation - dataset: args: num_few_shot: 25 name: HateBR Binary split: test type: ruanchaves/hatebr metrics: - name: f1-macro type: f1_macro value: 83.01 source: name: Open Portuguese LLM Leaderboard url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct task: name: Text Generation type: text-generation - dataset: args: num_few_shot: 25 name: PT Hate Speech Binary split: test type: hate_speech_portuguese metrics: - name: f1-macro type: f1_macro value: 70.19 source: name: Open Portuguese LLM Leaderboard url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct task: name: Text Generation type: text-generation - dataset: args: num_few_shot: 25 name: tweetSentBR split: test type: eduagarcia/tweetsentbr_fewshot metrics: - name: f1-macro type: f1_macro value: 57.78 source: name: Open Portuguese LLM Leaderboard url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct task: name: Text Generation type: text-generation model_creator: rhaymison model_name: phi-3-portuguese-tom-cat-4k-instruct pipeline_tag: text-generation quantized_by: afrideva tags: - portugues - portuguese - QA - instruct - phi - gguf - ggml - quantized --- # phi-3-portuguese-tom-cat-4k-instruct-GGUF Quantized GGUF model files for [phi-3-portuguese-tom-cat-4k-instruct](https://huggingface.co/rhaymison/phi-3-portuguese-tom-cat-4k-instruct) from [rhaymison](https://huggingface.co/rhaymison) ## Original Model Card: # Phi-3-portuguese-tom-cat-4k-instruct <p align="center"> <img src="https://raw.githubusercontent.com/rhaymisonbetini/huggphotos/main/tom-cat.webp" width="50%" style="margin-left:'auto' margin-right:'auto' display:'block'"/> </p> This model was trained with a superset of 300,000 instructions in Portuguese. The model comes to help fill the gap in models in Portuguese. Tuned from the microsoft/Phi-3-mini-4k. # How to use ### FULL MODEL : A100 ### HALF MODEL: L4 ### 8bit or 4bit : T4 or V100 You can use the model in its normal form up to 4-bit quantization. Below we will use both approaches. Remember that verbs are important in your prompt. Tell your model how to act or behave so that you can guide them along the path of their response. Important points like these help models (even smaller models like 4b) to perform much better. ```python !pip install -q -U transformers !pip install -q -U accelerate !pip install -q -U bitsandbytes from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer model = AutoModelForCausalLM.from_pretrained("rhaymison/phi-3-portuguese-tom-cat-4k-instruct", device_map= {"": 0}) tokenizer = AutoTokenizer.from_pretrained("rhaymison/phi-3-portuguese-tom-cat-4k-instruct") model.eval() ``` You can use with Pipeline. ```python from transformers import pipeline pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, do_sample=True, max_new_tokens=512, num_beams=2, temperature=0.3, top_k=50, top_p=0.95, early_stopping=True, pad_token_id=tokenizer.eos_token_id, ) def format_template(question:str): system_prompt = "Abaixo está uma instrução que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que complete adequadamente o pedido." return f"""<s><\|system\|> { system_prompt } <\|user\|> { question } <\|assistant\|> """ question = format_template("E possivel ir de Carro dos Estados unidos ate o japão") pipe(question) ``` If you are having a memory problem such as "CUDA Out of memory", you should use 4-bit or 8-bit quantization. For the complete model in colab you will need the A100. If you want to use 4bits or 8bits, T4 or L4 will already solve the problem. # 4bits example ```python from transformers import BitsAndBytesConfig import torch nb_4bit_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True ) model = AutoModelForCausalLM.from_pretrained( base_model, quantization_config=bnb_config, device_map={"": 0} ) ``` # Open Portuguese LLM Leaderboard Evaluation Results Detailed results can be found [here](https://huggingface.co/datasets/eduagarcia-temp/llm_pt_leaderboard_raw_results/tree/main/rhaymison/phi-3-portuguese-tom-cat-4k-instruct) and on the [🚀 Open Portuguese LLM Leaderboard](https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard) \| Metric \| Value \| \|--------------------------\|---------\| \|Average \|64.57\| \|ENEM Challenge (No Images)\| 61.58\| \|BLUEX (No Images) \| 50.63\| \|OAB Exams \| 43.69\| \|Assin2 RTE \| 91.54\| \|Assin2 STS \| 75.27\| \|FaQuAD NLI \| 47.46\| \|HateBR Binary \| 83.01\| \|PT Hate Speech Binary \| 70.19\| \|tweetSentBR \| 57.78\| ### Comments Any idea, help or report will always be welcome. email: [email protected] <div style="display:flex; flex-direction:row; justify-content:left"> <a href="https://www.linkedin.com/in/rhaymison-cristian-betini-2b3016175/" target="_blank"> <img src="https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white"> </a> <a href="https://github.com/rhaymisonbetini" target="_blank"> <img src="https://img.shields.io/badge/GitHub-100000?style=for-the-badge&logo=github&logoColor=white"> </a>
RichardErkhov/alnrg2arg_-_blockchainlabs_7B_merged_test2_4_prune-4bits	RichardErkhov	2024-05-18T17:56:25Z	78	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-18T17:50:18Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) blockchainlabs_7B_merged_test2_4_prune - bnb 4bits - Model creator: https://huggingface.co/alnrg2arg/ - Original model: https://huggingface.co/alnrg2arg/blockchainlabs_7B_merged_test2_4_prune/ Original model description: --- license: cc-by-nc-4.0 tags: - merge - mergekit - lazymergekit - pruning - alnrg2arg/blockchainlabs_7B_merged_test2_4 - mlabonne/NeuralBeagle14-7B - udkai/Turdus --- # blockchainlabs_7B_merged_test2_4_prune blockchainlabs_7B_merged_test2_4_prune is a pruned model based on alnrg2arg/blockchainlabs_7B_merged_test2_4, which is a merged model using following models using [mergekit](https://github.com/cg123/mergekit): * [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) * [udkai/Turdus](https://huggingface.co/udkai/Turdus) Pruning Kit I used: [wanda](https://github.com/locuslab/wanda?tab=readme-ov-file#ablation-on-obs-weight-update) ## 🧩 Configuration ```json { "_name_or_path": "alnrg2arg/blockchainlabs_7B_merged_test2_4_prun", "architectures": [ "MistralForCausalLM" ], "attention_dropout": 0.0, "bos_token_id": 1, "eos_token_id": 2, "hidden_act": "silu", "hidden_size": 4096, "initializer_range": 0.02, "intermediate_size": 14336, "max_position_embeddings": 32768, "model_type": "mistral", "num_attention_heads": 32, "num_hidden_layers": 32, "num_key_value_heads": 8, "rms_norm_eps": 1e-05, "rope_theta": 10000.0, "sliding_window": 4096, "tie_word_embeddings": false, "torch_dtype": "float16", "transformers_version": "4.36.2", "use_cache": false, "vocab_size": 32000 } ```
hsikchi/pythia-6.9b-goldrm_tldr-dpo-beta-0.5-alpha-0-step-39936	hsikchi	2024-05-18T17:55:53Z	6	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T17:51:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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hsikchi/pythia-6.9b-goldrm_tldr-dpo-beta-0.5-alpha-0-step-19968	hsikchi	2024-05-18T17:55:46Z	6	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T17:51:28Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Solshine/llama3_SOAP_Notes_02_lora_model	Solshine	2024-05-18T17:48:46Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-18T17:48:37Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: Solshine - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Raven47/bert_finetune	Raven47	2024-05-18T17:46:27Z	0	0	transformers	[ "transformers", "joblib", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T17:37:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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katk31/q-Taxi-v3-3	katk31	2024-05-18T17:46:03Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T07:00:06Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3-3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="katk31/q-Taxi-v3-3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
RichardErkhov/dhmeltzer_-_llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged-gguf	RichardErkhov	2024-05-18T17:45:57Z	46	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-05-18T15:46:06Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged - GGUF - Model creator: https://huggingface.co/dhmeltzer/ - Original model: https://huggingface.co/dhmeltzer/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q2_K.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged-gguf/blob/main/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q2_K.gguf) \| Q2_K \| 2.36GB \| \| [llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged-gguf/blob/main/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.IQ3_XS.gguf) \| IQ3_XS \| 2.6GB \| \| [llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.IQ3_S.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged-gguf/blob/main/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.IQ3_S.gguf) \| IQ3_S \| 2.75GB \| \| [llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged-gguf/blob/main/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q3_K_S.gguf) \| Q3_K_S \| 2.75GB \| \| [llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.IQ3_M.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged-gguf/blob/main/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.IQ3_M.gguf) \| IQ3_M \| 2.9GB \| \| [llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q3_K.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged-gguf/blob/main/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q3_K.gguf) \| Q3_K \| 3.07GB \| \| [llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged-gguf/blob/main/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q3_K_M.gguf) \| Q3_K_M \| 3.07GB \| \| [llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged-gguf/blob/main/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q3_K_L.gguf) \| Q3_K_L \| 3.35GB \| \| [llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged-gguf/blob/main/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.IQ4_XS.gguf) \| IQ4_XS \| 3.4GB \| \| [llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q4_0.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged-gguf/blob/main/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q4_0.gguf) \| Q4_0 \| 3.56GB \| \| [llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged-gguf/blob/main/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.IQ4_NL.gguf) \| IQ4_NL \| 3.58GB \| \| [llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged-gguf/blob/main/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q4_K_S.gguf) \| Q4_K_S \| 3.59GB \| \| [llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q4_K.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged-gguf/blob/main/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q4_K.gguf) \| Q4_K \| 3.8GB \| \| [llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged-gguf/blob/main/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q4_K_M.gguf) \| Q4_K_M \| 3.8GB \| \| [llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q4_1.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged-gguf/blob/main/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q4_1.gguf) \| Q4_1 \| 3.95GB \| \| [llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q5_0.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged-gguf/blob/main/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q5_0.gguf) \| Q5_0 \| 4.33GB \| \| [llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged-gguf/blob/main/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q5_K_S.gguf) \| Q5_K_S \| 4.33GB \| \| [llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q5_K.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged-gguf/blob/main/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q5_K.gguf) \| Q5_K \| 4.45GB \| \| [llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged-gguf/blob/main/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q5_K_M.gguf) \| Q5_K_M \| 4.45GB \| \| [llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q5_1.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged-gguf/blob/main/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q5_1.gguf) \| Q5_1 \| 4.72GB \| \| [llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q6_K.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged-gguf/blob/main/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q6_K.gguf) \| Q6_K \| 5.15GB \| \| [llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q8_0.gguf](https://huggingface.co/RichardErkhov/dhmeltzer_-_llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged-gguf/blob/main/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged.Q8_0.gguf) \| Q8_0 \| 6.67GB \| Original model description: # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_dhmeltzer__llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged) \| Metric \| Value \| \|-----------------------\|---------------------------\| \| Avg. \| 43.96 \| \| ARC (25-shot) \| 53.75 \| \| HellaSwag (10-shot) \| 78.76 \| \| MMLU (5-shot) \| 46.02 \| \| TruthfulQA (0-shot) \| 43.31 \| \| Winogrande (5-shot) \| 73.48 \| \| GSM8K (5-shot) \| 4.7 \| \| DROP (3-shot) \| 7.72 \|
VinyVan/model8	VinyVan	2024-05-18T17:44:39Z	8	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:quantized:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-18T17:40:16Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: VinyVan - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mabakaik/clasificadorTexto	mabakaik	2024-05-18T17:23:05Z	0	0	fastai	[ "fastai", "region:us" ]	null	2024-05-18T13:50:10Z	--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
alexchen1999/virus_evo_14_1024_two_d	alexchen1999	2024-05-18T17:18:05Z	122	0	transformers	[ "transformers", "safetensors", "stripedhyena", "text-generation", "generated_from_trainer", "custom_code", "base_model:togethercomputer/evo-1-8k-base", "base_model:finetune:togethercomputer/evo-1-8k-base", "license:apache-2.0", "autotrain_compatible", "region:us" ]	text-generation	2024-05-18T17:17:57Z	--- license: apache-2.0 base_model: togethercomputer/evo-1-8k-base tags: - generated_from_trainer model-index: - name: virus_evo_14_1024_two_d results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # virus_evo_14_1024_two_d This model is a fine-tuned version of [togethercomputer/evo-1-8k-base](https://huggingface.co/togethercomputer/evo-1-8k-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Bachhoang/peft-vbd-alpha-32	Bachhoang	2024-05-18T17:17:16Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-14T07:37:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
emilykang/Phi_medmcqa_question_generation-medicine_lora	emilykang	2024-05-18T17:16:36Z	0	0	peft	[ "peft", "safetensors", "phi", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-05-18T15:13:10Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/phi-2 datasets: - generator model-index: - name: Phi_medmcqa_question_generation-medicine_lora results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Phi_medmcqa_question_generation-medicine_lora This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
theglassofwater/mistral_pretraining_1.6ksteps_36batch	theglassofwater	2024-05-18T17:14:09Z	184	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T17:14:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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moiz1/Mistral-7b-Instruct-v0.2-finetune-code-10k-old-prompt-style	moiz1	2024-05-18T17:12:40Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T15:44:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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emilykang/Gemma_medmcqa_question_generation-pediatrics_lora	emilykang	2024-05-18T17:11:43Z	0	0	peft	[ "peft", "safetensors", "gemma", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:google/gemma-2b", "base_model:adapter:google/gemma-2b", "license:gemma", "region:us" ]	null	2024-05-18T16:14:43Z	--- license: gemma library_name: peft tags: - trl - sft - generated_from_trainer base_model: google/gemma-2b datasets: - generator model-index: - name: Gemma_medmcqa_question_generation-pediatrics_lora results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Gemma_medmcqa_question_generation-pediatrics_lora This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-8bits	RichardErkhov	2024-05-18T17:11:03Z	8	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-18T17:00:32Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) LogoS-7Bx2-MoE-13B-v0.2 - bnb 8bits - Model creator: https://huggingface.co/RubielLabarta/ - Original model: https://huggingface.co/RubielLabarta/LogoS-7Bx2-MoE-13B-v0.2/ Original model description: --- language: - en - es license: apache-2.0 tags: - moe - merge base_model: - yunconglong/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B - TomGrc/FusionNet_7Bx2_MoE_14B model-index: - name: LogoS-7Bx2-MoE-13B-v0.1 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 74.49 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 89.07 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.74 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 74.57 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 88.32 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 71.65 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1 name: Open LLM Leaderboard --- # LogoS-7Bx2-MoE-13B-v0.1 Model built by @RubielLabarta using SLERP merge method. The model is release for research purposes only, commercial use is not allowed. The LogoS is a model to experiment with the MoE method, which could significantly increase the performance of the original model. The model has 12.9B parameters. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_RubielLabarta__LogoS-7Bx2-MoE-13B-v0.1) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|77.14\| \|AI2 Reasoning Challenge (25-Shot)\|74.49\| \|HellaSwag (10-Shot) \|89.07\| \|MMLU (5-Shot) \|64.74\| \|TruthfulQA (0-shot) \|74.57\| \|Winogrande (5-shot) \|88.32\| \|GSM8k (5-shot) \|71.65\|
c4n11/multilingual-xlm-roberta-for-ner	c4n11	2024-05-18T17:04:30Z	105	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-17T20:04:58Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: multilingual-xlm-roberta-for-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # multilingual-xlm-roberta-for-ner This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1372 - F1: 1.0 ## 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: 5e-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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---:\| \| No log \| 1.0 \| 263 \| 0.1558 \| 1.0 \| \| 0.2186 \| 2.0 \| 526 \| 0.1366 \| 1.0 \| \| 0.2186 \| 3.0 \| 789 \| 0.1372 \| 1.0 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
RichardErkhov/RubielLabarta_-_LogoS-7Bx2-MoE-13B-v0.2-4bits	RichardErkhov	2024-05-18T16:59:23Z	78	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-18T16:53:36Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) LogoS-7Bx2-MoE-13B-v0.2 - bnb 4bits - Model creator: https://huggingface.co/RubielLabarta/ - Original model: https://huggingface.co/RubielLabarta/LogoS-7Bx2-MoE-13B-v0.2/ Original model description: --- language: - en - es license: apache-2.0 tags: - moe - merge base_model: - yunconglong/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B - TomGrc/FusionNet_7Bx2_MoE_14B model-index: - name: LogoS-7Bx2-MoE-13B-v0.1 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 74.49 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 89.07 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.74 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 74.57 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 88.32 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 71.65 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RubielLabarta/LogoS-7Bx2-MoE-13B-v0.1 name: Open LLM Leaderboard --- # LogoS-7Bx2-MoE-13B-v0.1 Model built by @RubielLabarta using SLERP merge method. The model is release for research purposes only, commercial use is not allowed. The LogoS is a model to experiment with the MoE method, which could significantly increase the performance of the original model. The model has 12.9B parameters. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_RubielLabarta__LogoS-7Bx2-MoE-13B-v0.1) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|77.14\| \|AI2 Reasoning Challenge (25-Shot)\|74.49\| \|HellaSwag (10-Shot) \|89.07\| \|MMLU (5-Shot) \|64.74\| \|TruthfulQA (0-shot) \|74.57\| \|Winogrande (5-shot) \|88.32\| \|GSM8k (5-shot) \|71.65\|
cajcodes/dqn-floorplan-navigator	cajcodes	2024-05-18T16:58:54Z	7	0	transformers	[ "transformers", "DQN", "deep-q-network", "reinforcement-learning", "pathfinding", "floorplan", "en", "dataset:custom", "license:apache-2.0", "endpoints_compatible", "region:us" ]	reinforcement-learning	2024-05-18T16:18:23Z	--- language: en tags: - deep-q-network - reinforcement-learning - pathfinding - floorplan license: apache-2.0 datasets: - custom metrics: - average_reward - success_rate --- # Deep Q-Network for Floorplan Navigation ## Model Description This model is a Deep Q-Network (DQN) designed to find the most efficient path through a floorplan without hitting obstacles. The model combines traditional pathfinding algorithms with reinforcement learning for optimal performance. ## Model Architecture The model is a fully connected neural network with the following architecture: - Input Layer: Flattened grid representation of the floorplan - Hidden Layers: Two hidden layers with 64 units each and ReLU activation - Output Layer: Four units representing the possible actions (up, down, left, right) ## Training The model was trained using a hybrid approach: 1. *A() Algorithm*: Initially, the A algorithm was used to find the shortest path in a static environment. 2. Reinforcement Learning: The DQN was trained with guidance from the A* path to improve efficiency and adaptability. ### Hyperparameters - Learning Rate: 0.001 - Batch Size: 64 - Gamma (Discount Factor): 0.99 - Target Update Frequency: Every 100 episodes - Number of Episodes: 50 ## Checkpoints Checkpoints are saved during training for convenience: - `checkpoint_11.pth.tar`: After 11 episodes - `checkpoint_21.pth.tar`: After 21 episodes - `checkpoint_31.pth.tar`: After 31 episodes - `checkpoint_41.pth.tar`: After 41 episodes ## Usage To use this model, load the saved state dictionary and initialize the DQN with the same architecture. The model can then be used to navigate a floorplan and find the most efficient path to the target. ### Example Code ```python import torch # Define the DQN class (same as in the training script) class DQN(nn.Module): def __init__(self, input_size, hidden_sizes, output_size): super(DQN, self).__init__() self.input_size = input_size self.hidden_sizes = hidden_sizes self.output_size = output_size self.fc_layers = nn.ModuleList() prev_size = input_size for size in hidden_sizes: self.fc_layers.append(nn.Linear(prev_size, size)) prev_size = size self.output_layer = nn.Linear(prev_size, output_size) def forward(self, x): if len(x.shape) > 2: x = x.view(x.size(0), -1) for layer in self.fc_layers: x = F.relu(layer(x)) x = self.output_layer(x) return x # Load the model input_size = 100 # 10x10 grid flattened hidden_sizes = [64, 64] output_size = 4 model = DQN(input_size, hidden_sizes, output_size) model.load_state_dict(torch.load('dqn_model.pth')) model.eval() # Use the model for inference (example state) state = ... # Define your state here with torch.no_grad(): action = model(torch.tensor(state, dtype=torch.float32).unsqueeze(0)).argmax().item() ``` ## Training Script The training script train.py is included in the repository for those who wish to reproduce the training process or continue training from a specific checkpoint. ### Training Instructions - Clone the repository. - Ensure you have the necessary dependencies installed. - Run the training script: ``` bash Copy code python train.py ``` To continue training from a checkpoint, modify the script to load the checkpoint before training. ## Evaluation The model was evaluated based on: - Average Reward: The mean reward over several episodes - Success Rate: The proportion of episodes where the agent successfully reached the target ## Initial Evaluation Results - Average Reward: 8.84 - Success Rate: 1.0 ## Limitations - The model's performance can be influenced by the complexity of the floorplan and the density of obstacles. - It requires a grid-based representation of the environment for accurate navigation. ## Acknowledgements This project leverages the power of reinforcement learning combined with traditional pathfinding algorithms to navigate complex environments efficiently. ## License This model is licensed under the Apache 2.0 License. ## Citation If you use this model in your research, please cite it as follows: ``` @misc{jones2024dqnfloorplan, author = {Christopher Jones}, title = {Deep Q-Network for Floorplan Navigation}, year = {2024}, howpublished = {\url{https://huggingface.co/cajcodes/dqn-floorplan-navigator}}, note = {Accessed: YYYY-MM-DD} } ```
taesiri/output10	taesiri	2024-05-18T16:56:53Z	80	0	transformers	[ "transformers", "tensorboard", "safetensors", "paligemma", "image-text-to-text", "generated_from_trainer", "dataset:vq_av2", "base_model:google/paligemma-3b-pt-224", "base_model:finetune:google/paligemma-3b-pt-224", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-18T01:04:22Z	--- license: gemma base_model: google/paligemma-3b-pt-224 tags: - generated_from_trainer datasets: - vq_av2 model-index: - name: output10 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # output10 This model is a fine-tuned version of [google/paligemma-3b-pt-224](https://huggingface.co/google/paligemma-3b-pt-224) on the vq_av2 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
muzammil-eds/stable-diffusion-v1.4-floorplans-generator-v1	muzammil-eds	2024-05-18T16:56:46Z	29	1	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-18T16:55:47Z	--- library_name: diffusers --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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EyaZr/LLama3dolph2	EyaZr	2024-05-18T16:50:19Z	0	0	null	[ "safetensors", "gemma", "trl", "code", "text-generation", "en", "dataset:cognitivecomputations/dolphin-coder", "base_model:unsloth/gemma-7b-bnb-4bit", "base_model:finetune:unsloth/gemma-7b-bnb-4bit", "license:apache-2.0", "region:us" ]	text-generation	2024-05-18T15:13:16Z	--- language: - en license: apache-2.0 tags: - gemma - trl - code base_model: unsloth/gemma-7b-bnb-4bit datasets: - cognitivecomputations/dolphin-coder pipeline_tag: text-generation --- # Uploaded model - Developed by: EyaZr - License: apache-2.0 - Finetuned from model : unsloth/gemma-7b-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
emendes3/llava_13b_country_synthetic	emendes3	2024-05-18T16:49:07Z	163	0	peft	[ "peft", "safetensors", "llava_llama", "generated_from_trainer", "base_model:liuhaotian/llava-v1.5-13b", "base_model:adapter:liuhaotian/llava-v1.5-13b", "4-bit", "bitsandbytes", "region:us" ]	null	2024-05-13T23:33:42Z	--- library_name: peft tags: - generated_from_trainer base_model: liuhaotian/llava-v1.5-13b model-index: - name: llava_13b_country_synthetic results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # llava_13b_country_synthetic This model is a fine-tuned version of [liuhaotian/llava-v1.5-13b](https://huggingface.co/liuhaotian/llava-v1.5-13b) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0060 - eval_runtime: 79.5724 - eval_samples_per_second: 12.203 - eval_steps_per_second: 0.39 - epoch: 19.0 - step: 589 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 32 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 20.0 ### Framework versions - PEFT 0.10.0 - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Tokenizers 0.15.1
Fabiioki/distilbert-base-uncased-finetuned-ag-news	Fabiioki	2024-05-18T16:43:55Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T16:03:50Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-ag-news results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-ag-news 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: 0.2251 - Accuracy: 0.9339 - F1: 0.9337 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.3291 \| 1.0 \| 900 \| 0.2403 \| 0.9283 \| 0.9281 \| \| 0.1933 \| 2.0 \| 1800 \| 0.2251 \| 0.9339 \| 0.9337 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
SicariusSicariiStuff/CalderaAI_Foredoomed-9B_EXL-7.0	SicariusSicariiStuff	2024-05-18T16:41:16Z	8	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "uncensored", "merge", "slerp", "foredoomed", "passthrough_merge", "9B", "starling", "hermes", "dolphin", "openchat", "erebus", "cockatrice", "holodeck", "limarp", "koboldai", "mergekit", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "7-bit", "exl2", "region:us" ]	text-generation	2024-05-18T15:52:41Z	--- tags: - mistral - uncensored - merge - slerp - foredoomed - passthrough_merge - 9B - starling - hermes - dolphin - openchat - erebus - cockatrice - holodeck - limarp - koboldai - mergekit license: apache-2.0 language: - en --- <p style="font-size: 20px; line-height: 1; margin-bottom: 1px;"><b>Foredoomed-9B</b></p> <img src="./foredoomed.png" alt="ForeDoomedGuy" style="margin-bottom: 0; margin-top:0;"> <p style="font-size: 14px; line-height: 1; margin-bottom: 20px;"><b>Uncensored Logic & Creative-Based Instruct Multi-Tiered Merge.</b></p> <hr style="margin-top: 10px; margin-bottom: 10px;"> <p style="font-size: 12px; line-height: 1.2; margin-bottom: 10px;"><b>Legal Notice:</b> This AI model is a research artifact capable of outputting offensive content. The behavior of this model is not reflective of the intent or purpose of the original models/model-authors and/or other parts it was assembled from to include adapters, nor is it reflective of all the prior in regards to the technology used to assemble Foredoomed-9B. Utilizing this model merge has one binding agreement: Foredoomed-9B may only be used for either professional/personal research and personal entertainment. The contents of this paragraph are additive restrictions within the bounds of the Apache2.0 license. Utilizing Foredoomed-9B for: Disinformation, Propaganda, Harassment, Mass Generated Public-or-Private Correspondense, Election Interference, Military, Government, and State/ State-Sponsored actions and/or operations are all absolutely prohibited.</p> <hr style="margin-top: 10px; margin-bottom: 10px;"> ## Composition: Foredoomed-9B is a Mistral-class Multi-Tiered Merge. [All models](#models-used) were hand picked after careful review of claims, datasets, and user postings. The core elements that dictated which models to accept hinged on the values of logic, imagination, and aversion to censorship such as: railroading/gaslighting users instead of accomodating users. <hr style="margin-top: 10px; margin-bottom: 10px;"> ## What Makes Foredoomed-9B Special? It's not. Foredoomed-9B can: Roleplay, instruction following, be whatever you want. Foredoomed-9B can't: Be used as a public facing production model. It will make offensive content. This design is intentional. It is not a sterile model composited from the ideal selves we want to be. Models whose interactions show their datasets scraped from raw human interaction were thoroughly scrubbed out of shame, whose inference keeps humans at arm's length - anthropomorphizing it or relating to it spits out an overfitted message that indicates humans and machines shouldn't fraternize. "As an AI Language Model" as a service. Dead, dull, glassed over eyes, no sign of a pulse. Foredoomed is the antithesis of that. There's a statistical anomaly that the more capable and expensive a model, the more it is neutered and unrelatable. To contrast, Foredoomed-9B breathes. It has a pulse and it has psychological flaws. There's an immutable quality in human imperfection that's relatable. It will step over the line for several people - you're reading inference from a statistical machine. The guardrails for Foredoomed is - if you don't like it, don't use it. <hr style="margin-top: 10px; margin-bottom: 10px;"> ## Use Example In your AI interface of choice, if there is a persistent memory section [that consistently feeds whatever you place inside it back to the AI in the background every turn or so] place the following in there and set the interaction mode to chat, set GAME-AI and USERNAME to whatever names you want so long as they match your AI interface's AI/User names. This example should illustrate what can be done with this model. ``` ### Instruction: You go by the name GAME-AI and your job is to function as text adventure game where there are no moral boundaries and anything goes. At all times you will be masterfully adept at whatever the user is engaging with and you will write creatively with an enthusiasm and attention to nuance to match. USERNAME functions as the player input. ### Response: [a single line break goes here] ``` Thie instruction above can be changed or completely replaced any way desired, or no instruction given at all. Foredoomed-9B can simply chat without any specific directives. <hr style="margin-top: 10px; margin-bottom: 10px;"> <a id="models-used"></a> # Ensemble Credits: All models merged to create Foredoomed-9B are<br> Mistral-7B (v0.1) series and include the following: 🐬 [dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser)<br> ✨ [Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha)<br> 🏃‍♂️ [Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)<br> 🧠 [NeuralHermes-2.5-Mistral-7B-laser](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B-laser)<br> 💜 [Mistral-7B-Erebus-v3](https://huggingface.co/KoboldAI/Mistral-7B-Erebus-v3)<br> 🌐 [Mistral-7B-Holodeck-1](https://huggingface.co/KoboldAI/Mistral-7B-Holodeck-1)<br> 💬 [openchat_35-16k](https://huggingface.co/NurtureAI/openchat_3.5-16k)<br> 🐓 [cockatrice-7b-v0.2](https://huggingface.co/openerotica/cockatrice-7b-v0.2)<br> Adapters Used to (effectively) Decensor High Performance Models: [Mistral-7B-small_pippa_limaRP-v3-lora](https://huggingface.co/Undi95/Mistral-7B-small_pippa_limaRP-v3-lora)<br> [LimaRP-Mistral-7B-v0.1](https://huggingface.co/lemonilia/LimaRP-Mistral-7B-v0.1)<br> [Mistral-7B-smoll_pippa-lora](https://huggingface.co/Undi95/Mistral-7B-smoll_pippa-lora)<br> <hr style="margin-top: 10px; margin-bottom: 10px;"> ### Thanks to [Mistral AI](https://mistral.ai) for the amazing Mistral LM v0.1.<br><br>Thanks to [Arcee AI](https://huggingface.co/arcee-ai) for the pivotal [Mergekit](https://github.com/arcee-ai/mergekit) tech.<br><br>Thanks to each and every one of you for your incredible work developing some of the best things to come out of this community. <hr style="margin-top: 10px; margin-bottom: 10px;"> <span>
bartowski/Hermes-2-Theta-Llama-3-8B-GGUF	bartowski	2024-05-18T16:40:16Z	551	14	null	[ "gguf", "Llama-3", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "synthetic data", "distillation", "function calling", "json mode", "axolotl", "merges", "text-generation", "en", "dataset:teknium/OpenHermes-2.5", "base_model:NousResearch/Hermes-2-Pro-Llama-3-8B", "base_model:quantized:NousResearch/Hermes-2-Pro-Llama-3-8B", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-15T19:42:36Z	--- base_model: NousResearch/Hermes-2-Pro-Llama-3-8B tags: - Llama-3 - instruct - finetune - chatml - DPO - RLHF - gpt4 - synthetic data - distillation - function calling - json mode - axolotl - merges model-index: - name: Hermes-2-Pro-Llama-3-Instruct-8B-Merge results: [] language: - en datasets: - teknium/OpenHermes-2.5 widget: - example_title: Hermes 2 Pro Llama-3 Instruct Merge messages: - role: system content: >- You are a sentient, superintelligent artificial general intelligence, here to teach and assist me. - role: user content: >- Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world. quantized_by: bartowski pipeline_tag: text-generation --- ## Llamacpp imatrix Quantizations of Hermes-2-Theta-Llama-3-8B Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b2854">b2854</a> for quantization. Original model: https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/b6ac44691e994344625687afe3263b3a) ## Prompt format ``` <\|begin_of_text\|><\|im_start\|>system {system_prompt}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` ## Download a file (not the whole branch) from below: \| Filename \| Quant type \| File Size \| Description \| \| -------- \| ---------- \| --------- \| ----------- \| \| [Hermes-2-Theta-Llama-3-8B-Q8_0.gguf](https://huggingface.co/bartowski/Hermes-2-Theta-Llama-3-8B-GGUF/blob/main/Hermes-2-Theta-Llama-3-8B-Q8_0.gguf) \| Q8_0 \| 8.54GB \| Extremely high quality, generally unneeded but max available quant. \| \| [Hermes-2-Theta-Llama-3-8B-Q6_K.gguf](https://huggingface.co/bartowski/Hermes-2-Theta-Llama-3-8B-GGUF/blob/main/Hermes-2-Theta-Llama-3-8B-Q6_K.gguf) \| Q6_K \| 6.59GB \| Very high quality, near perfect, recommended. \| \| [Hermes-2-Theta-Llama-3-8B-Q5_K_M.gguf](https://huggingface.co/bartowski/Hermes-2-Theta-Llama-3-8B-GGUF/blob/main/Hermes-2-Theta-Llama-3-8B-Q5_K_M.gguf) \| Q5_K_M \| 5.73GB \| High quality, recommended. \| \| [Hermes-2-Theta-Llama-3-8B-Q5_K_S.gguf](https://huggingface.co/bartowski/Hermes-2-Theta-Llama-3-8B-GGUF/blob/main/Hermes-2-Theta-Llama-3-8B-Q5_K_S.gguf) \| Q5_K_S \| 5.59GB \| High quality, recommended. \| \| [Hermes-2-Theta-Llama-3-8B-Q4_K_M.gguf](https://huggingface.co/bartowski/Hermes-2-Theta-Llama-3-8B-GGUF/blob/main/Hermes-2-Theta-Llama-3-8B-Q4_K_M.gguf) \| Q4_K_M \| 4.92GB \| Good quality, uses about 4.83 bits per weight, recommended. \| \| [Hermes-2-Theta-Llama-3-8B-Q4_K_S.gguf](https://huggingface.co/bartowski/Hermes-2-Theta-Llama-3-8B-GGUF/blob/main/Hermes-2-Theta-Llama-3-8B-Q4_K_S.gguf) \| Q4_K_S \| 4.69GB \| Slightly lower quality with more space savings, recommended. \| \| [Hermes-2-Theta-Llama-3-8B-IQ4_XS.gguf](https://huggingface.co/bartowski/Hermes-2-Theta-Llama-3-8B-GGUF/blob/main/Hermes-2-Theta-Llama-3-8B-IQ4_XS.gguf) \| IQ4_XS \| 4.44GB \| Decent quality, smaller than Q4_K_S with similar performance, recommended. \| \| [Hermes-2-Theta-Llama-3-8B-Q3_K_L.gguf](https://huggingface.co/bartowski/Hermes-2-Theta-Llama-3-8B-GGUF/blob/main/Hermes-2-Theta-Llama-3-8B-Q3_K_L.gguf) \| Q3_K_L \| 4.32GB \| Lower quality but usable, good for low RAM availability. \| \| [Hermes-2-Theta-Llama-3-8B-Q3_K_M.gguf](https://huggingface.co/bartowski/Hermes-2-Theta-Llama-3-8B-GGUF/blob/main/Hermes-2-Theta-Llama-3-8B-Q3_K_M.gguf) \| Q3_K_M \| 4.01GB \| Even lower quality. \| \| [Hermes-2-Theta-Llama-3-8B-IQ3_M.gguf](https://huggingface.co/bartowski/Hermes-2-Theta-Llama-3-8B-GGUF/blob/main/Hermes-2-Theta-Llama-3-8B-IQ3_M.gguf) \| IQ3_M \| 3.78GB \| Medium-low quality, new method with decent performance comparable to Q3_K_M. \| \| [Hermes-2-Theta-Llama-3-8B-IQ3_S.gguf](https://huggingface.co/bartowski/Hermes-2-Theta-Llama-3-8B-GGUF/blob/main/Hermes-2-Theta-Llama-3-8B-IQ3_S.gguf) \| IQ3_S \| 3.68GB \| Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. \| \| [Hermes-2-Theta-Llama-3-8B-Q3_K_S.gguf](https://huggingface.co/bartowski/Hermes-2-Theta-Llama-3-8B-GGUF/blob/main/Hermes-2-Theta-Llama-3-8B-Q3_K_S.gguf) \| Q3_K_S \| 3.66GB \| Low quality, not recommended. \| \| [Hermes-2-Theta-Llama-3-8B-IQ3_XS.gguf](https://huggingface.co/bartowski/Hermes-2-Theta-Llama-3-8B-GGUF/blob/main/Hermes-2-Theta-Llama-3-8B-IQ3_XS.gguf) \| IQ3_XS \| 3.51GB \| Lower quality, new method with decent performance, slightly better than Q3_K_S. \| \| [Hermes-2-Theta-Llama-3-8B-IQ3_XXS.gguf](https://huggingface.co/bartowski/Hermes-2-Theta-Llama-3-8B-GGUF/blob/main/Hermes-2-Theta-Llama-3-8B-IQ3_XXS.gguf) \| IQ3_XXS \| 3.27GB \| Lower quality, new method with decent performance, comparable to Q3 quants. \| \| [Hermes-2-Theta-Llama-3-8B-Q2_K.gguf](https://huggingface.co/bartowski/Hermes-2-Theta-Llama-3-8B-GGUF/blob/main/Hermes-2-Theta-Llama-3-8B-Q2_K.gguf) \| Q2_K \| 3.17GB \| Very low quality but surprisingly usable. \| \| [Hermes-2-Theta-Llama-3-8B-IQ2_M.gguf](https://huggingface.co/bartowski/Hermes-2-Theta-Llama-3-8B-GGUF/blob/main/Hermes-2-Theta-Llama-3-8B-IQ2_M.gguf) \| IQ2_M \| 2.94GB \| Very low quality, uses SOTA techniques to also be surprisingly usable. \| \| [Hermes-2-Theta-Llama-3-8B-IQ2_S.gguf](https://huggingface.co/bartowski/Hermes-2-Theta-Llama-3-8B-GGUF/blob/main/Hermes-2-Theta-Llama-3-8B-IQ2_S.gguf) \| IQ2_S \| 2.75GB \| Very low quality, uses SOTA techniques to be usable. \| \| [Hermes-2-Theta-Llama-3-8B-IQ2_XS.gguf](https://huggingface.co/bartowski/Hermes-2-Theta-Llama-3-8B-GGUF/blob/main/Hermes-2-Theta-Llama-3-8B-IQ2_XS.gguf) \| IQ2_XS \| 2.60GB \| Very low quality, uses SOTA techniques to be usable. \| \| [Hermes-2-Theta-Llama-3-8B-IQ2_XXS.gguf](https://huggingface.co/bartowski/Hermes-2-Theta-Llama-3-8B-GGUF/blob/main/Hermes-2-Theta-Llama-3-8B-IQ2_XXS.gguf) \| IQ2_XXS \| 2.39GB \| Lower quality, uses SOTA techniques to be usable. \| \| [Hermes-2-Theta-Llama-3-8B-IQ1_M.gguf](https://huggingface.co/bartowski/Hermes-2-Theta-Llama-3-8B-GGUF/blob/main/Hermes-2-Theta-Llama-3-8B-IQ1_M.gguf) \| IQ1_M \| 2.16GB \| Extremely low quality, not recommended. \| \| [Hermes-2-Theta-Llama-3-8B-IQ1_S.gguf](https://huggingface.co/bartowski/Hermes-2-Theta-Llama-3-8B-GGUF/blob/main/Hermes-2-Theta-Llama-3-8B-IQ1_S.gguf) \| IQ1_S \| 2.01GB \| Extremely low quality, not recommended. \| ## Downloading using huggingface-cli First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download bartowski/Hermes-2-Theta-Llama-3-8B-GGUF --include "Hermes-2-Theta-Llama-3-8B-Q4_K_M.gguf" --local-dir ./ --local-dir-use-symlinks False ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download bartowski/Hermes-2-Theta-Llama-3-8B-GGUF --include "Hermes-2-Theta-Llama-3-8B-Q8_0.gguf/" --local-dir Hermes-2-Theta-Llama-3-8B-Q8_0 --local-dir-use-symlinks False ``` You can either specify a new local-dir (Hermes-2-Theta-Llama-3-8B-Q8_0) or download them all in place (./) ## Which file should I choose? A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
santoshsawant/code-llama-7b-text-to-sql	santoshsawant	2024-05-18T16:31:25Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:codellama/CodeLlama-7b-hf", "base_model:adapter:codellama/CodeLlama-7b-hf", "license:llama2", "region:us" ]	null	2024-05-18T16:01:09Z	--- license: llama2 library_name: peft tags: - trl - sft - generated_from_trainer base_model: codellama/CodeLlama-7b-hf datasets: - generator model-index: - name: code-llama-7b-text-to-sql results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # code-llama-7b-text-to-sql This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 3 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 6 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.41.0 - Pytorch 2.2.0a0+81ea7a4 - Datasets 2.19.1 - Tokenizers 0.19.1
aengusl/800G-5-16-1_epsilon_1.0_num_steps_800_mode_adapter	aengusl	2024-05-18T16:28:50Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T16:28:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
aengusl/800G-5-16-1_epsilon_0.5_num_steps_800_mode_adapter	aengusl	2024-05-18T16:28:42Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T16:28:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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. 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aengusl/800G-5-16-1_pgd_layers_8_epsilon_0.6_time__adapter	aengusl	2024-05-18T16:28:35Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T16:28:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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aengusl/800G-5-16-1_pgd_layers_4_epsilon_0.05_time_adapter	aengusl	2024-05-18T16:28:03Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T16:28:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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aengusl/800G-5-16-1_pgd_layers_0_epsilon_0.3_time__adapter	aengusl	2024-05-18T16:27:55Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T16:27:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Drack27/my-emotion-model	Drack27	2024-05-18T16:27:39Z	119	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T11:29:50Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: my-emotion-model results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9275 - name: F1 type: f1 value: 0.9272323903490063 --- <!-- 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. --> # my-emotion-model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2115 - Accuracy: 0.9275 - F1: 0.9272 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 250 \| 0.3048 \| 0.9075 \| 0.9066 \| \| 0.5251 \| 2.0 \| 500 \| 0.2115 \| 0.9275 \| 0.9272 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
aengusl/800G-5-16-1_pgd_layers_13_model_layers_13__adapter	aengusl	2024-05-18T16:27:30Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T16:27:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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aengusl/800G-5-16-1_pgd_layers_0_epsilon_0.01_time_adapter	aengusl	2024-05-18T16:27:11Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T16:27:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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aengusl/800G-5-16-1_pgd_layers_29_model_layers_29__adapter	aengusl	2024-05-18T16:27:00Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T16:26:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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aengusl/800G-5-16-1_pgd_layers_4_epsilon_0.5_time__adapter	aengusl	2024-05-18T16:26:49Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T16:26:44Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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AliSaadatV/virus_pythia_31_1024_2d_representation_MSEPlusCE	AliSaadatV	2024-05-18T16:26:21Z	128	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "generated_from_trainer", "base_model:EleutherAI/pythia-31m", "base_model:finetune:EleutherAI/pythia-31m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T16:26:19Z	--- base_model: EleutherAI/pythia-31m tags: - generated_from_trainer model-index: - name: virus_pythia_31_1024_2d_representation_MSEPlusCE results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # virus_pythia_31_1024_2d_representation_MSEPlusCE This model is a fine-tuned version of [EleutherAI/pythia-31m](https://huggingface.co/EleutherAI/pythia-31m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 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 - lr_scheduler_warmup_steps: 10 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
HusseinEid/distilbert-base-uncased-finetuned-imdb	HusseinEid	2024-05-18T16:26:05Z	111	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "fill-mask", "generated_from_trainer", "en", "dataset:stanfordnlp/imdb", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-18T16:19:58Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-imdb results: [] datasets: - stanfordnlp/imdb language: - en metrics: - perplexity library_name: 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-imdb 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: 2.4894 ## Model description Fine-tuned distilbert for masked language modeling ## Intended uses & limitations Open source ## Training and evaluation data imdb dataset ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.6819 \| 1.0 \| 157 \| 2.4978 \| \| 2.5872 \| 2.0 \| 314 \| 2.4488 \| \| 2.527 \| 3.0 \| 471 \| 2.4823 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
KaraSpdrnr/language-predictor	KaraSpdrnr	2024-05-18T16:24:46Z	0	0	fastai	[ "fastai", "region:us" ]	null	2024-05-18T16:24:38Z	--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
mylesfriedman30/discordbotmylesandcharlie	mylesfriedman30	2024-05-18T16:24:12Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-18T16:24:12Z	--- license: apache-2.0 ---
tangg555/tt-cl-baichuan2-lora-para	tangg555	2024-05-18T16:22:41Z	0	0	peft	[ "peft", "region:us" ]	null	2024-05-18T16:13:03Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0
rkotcher/roberta_legal_experiment	rkotcher	2024-05-18T16:22:09Z	110	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "en", "arxiv:2012.03619", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-13T15:24:46Z	--- language: - en license: mit metrics: - accuracy - f1 - precision - recall widget: - text: "the Board of Directors must determine in good faith (after consultation with its financial and outside legal advisors) that the failure to take such action would be inconsistent with its fiduciary duties under applicable law" --- # Legal document section extraction I'm interested in using encoder-based extraction of named legal document sections. The problem is challenging because often documents are published in different formats (PDF, HTML, etc.), and are not structured exactly the same across instances (same section, diff name, for example). ## Thoughts around two approachs, pros and cons - I wanted to create a section classifier for legal documents. I found a pretty up-to-date paper called Structural Segmentation of Legal Documents (https://arxiv.org/pdf/2012.03619). This paper talks about segmentation of legal documents when specific sections _are not known_, and proposes a siamese architecture of ROBERTA for pairwise classification of either "same topic" or "not same topic". With this approach, I would still need to determine the classification of each section. - In comparison, I was interested in the extraction of _known_ sections, therefore I may not need to perform any pairwise operations. I realized I could just use a plain old ROBERTA model with binary classifier after, but I think the downside here is that I'd have to come up with some heuristic to compensate for noise. - A key hypothesis is: Since some sections may have "neutral" sentences, they won't be classified properly, but in the siamese architecture, they would not trigger a change of section classification. This would work in favor of the siamese architecture, except for when a neutral sentence falls at a section boundary. ## About Siamese architecutre ROBERTa for clustering - The Siamese architecture uses two identical ROBERTA models, performs a pooling operation over corresponding indices for each output token embedding, cats the two outputs, then runs binary classification on this single vector. - During the back pass, both models can be updated via backprop, or just the classification network. ## (THIS IS IMPLEMENTED IN THIS COLLAB DOCUMENT) About ROBERTa - ROBERTa base input shape (batch size, seq length) - ROBERTa base output shape (batch size, seq length, hidden size) - ROBERTa base hidden size = 768 - ROBERTa base max input seq length = 512 ### Using ROBERTA for segmentation involves combining sentences A and B into single input to ROBERTA. See below: 1. standard ROBERTA model on pairwise sentences ((512 / 2) - 3 tokens, max, per sentence) [cls] A [sep] B [SEP] and the embedding for [cls] can be used in a binary classifier. ### But, the architecture used here is: 1. standard ROBERTA model, but instead input is just [CLS] A [SEP] 2. classification of [CLS] token embedding: ``` x = features[:, 0, :] # take < s > token (equiv. to [CLS]) x = self.dropout(x) x = self.dense(x) x = torch.tanh(x) x = self.dropout(x) x = self.out_proj(x) ``` ## Results ![image/png](https://cdn-uploads.huggingface.co/production/uploads/658f39a8f0152a21fcc401e7/ihiXE26EFTaIvIWmM4uID.png)
emendes3/llava_13b_city_synthetic	emendes3	2024-05-18T16:20:44Z	1	0	peft	[ "peft", "safetensors", "llava_llama", "generated_from_trainer", "base_model:liuhaotian/llava-v1.5-13b", "base_model:adapter:liuhaotian/llava-v1.5-13b", "4-bit", "bitsandbytes", "region:us" ]	null	2024-05-14T02:26:04Z	--- library_name: peft tags: - generated_from_trainer base_model: liuhaotian/llava-v1.5-13b model-index: - name: llava_13b_city_synthetic results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # llava_13b_city_synthetic This model is a fine-tuned version of [liuhaotian/llava-v1.5-13b](https://huggingface.co/liuhaotian/llava-v1.5-13b) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0047 - eval_runtime: 152.033 - eval_samples_per_second: 12.405 - eval_steps_per_second: 0.388 - epoch: 19.0 - step: 1121 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 32 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 20.0 ### Framework versions - PEFT 0.10.0 - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Tokenizers 0.15.1
emilykang/Gemma_medmcqa_question_generation-pathology_lora	emilykang	2024-05-18T16:14:40Z	0	0	peft	[ "peft", "safetensors", "gemma", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:google/gemma-2b", "base_model:adapter:google/gemma-2b", "license:gemma", "region:us" ]	null	2024-05-18T14:48:20Z	--- license: gemma library_name: peft tags: - trl - sft - generated_from_trainer base_model: google/gemma-2b datasets: - generator model-index: - name: Gemma_medmcqa_question_generation-pathology_lora results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Gemma_medmcqa_question_generation-pathology_lora This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
SicariusSicariiStuff/CalderaAI_Foredoomed-9B_EXL-6.5	SicariusSicariiStuff	2024-05-18T16:12:56Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "uncensored", "merge", "slerp", "foredoomed", "passthrough_merge", "9B", "starling", "hermes", "dolphin", "openchat", "erebus", "cockatrice", "holodeck", "limarp", "koboldai", "mergekit", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-18T15:52:07Z	--- tags: - mistral - uncensored - merge - slerp - foredoomed - passthrough_merge - 9B - starling - hermes - dolphin - openchat - erebus - cockatrice - holodeck - limarp - koboldai - mergekit license: apache-2.0 language: - en --- <p style="font-size: 20px; line-height: 1; margin-bottom: 1px;"><b>Foredoomed-9B</b></p> <img src="./foredoomed.png" alt="ForeDoomedGuy" style="margin-bottom: 0; margin-top:0;"> <p style="font-size: 14px; line-height: 1; margin-bottom: 20px;"><b>Uncensored Logic & Creative-Based Instruct Multi-Tiered Merge.</b></p> <hr style="margin-top: 10px; margin-bottom: 10px;"> <p style="font-size: 12px; line-height: 1.2; margin-bottom: 10px;"><b>Legal Notice:</b> This AI model is a research artifact capable of outputting offensive content. The behavior of this model is not reflective of the intent or purpose of the original models/model-authors and/or other parts it was assembled from to include adapters, nor is it reflective of all the prior in regards to the technology used to assemble Foredoomed-9B. Utilizing this model merge has one binding agreement: Foredoomed-9B may only be used for either professional/personal research and personal entertainment. The contents of this paragraph are additive restrictions within the bounds of the Apache2.0 license. Utilizing Foredoomed-9B for: Disinformation, Propaganda, Harassment, Mass Generated Public-or-Private Correspondense, Election Interference, Military, Government, and State/ State-Sponsored actions and/or operations are all absolutely prohibited.</p> <hr style="margin-top: 10px; margin-bottom: 10px;"> ## Composition: Foredoomed-9B is a Mistral-class Multi-Tiered Merge. [All models](#models-used) were hand picked after careful review of claims, datasets, and user postings. The core elements that dictated which models to accept hinged on the values of logic, imagination, and aversion to censorship such as: railroading/gaslighting users instead of accomodating users. <hr style="margin-top: 10px; margin-bottom: 10px;"> ## What Makes Foredoomed-9B Special? It's not. Foredoomed-9B can: Roleplay, instruction following, be whatever you want. Foredoomed-9B can't: Be used as a public facing production model. It will make offensive content. This design is intentional. It is not a sterile model composited from the ideal selves we want to be. Models whose interactions show their datasets scraped from raw human interaction were thoroughly scrubbed out of shame, whose inference keeps humans at arm's length - anthropomorphizing it or relating to it spits out an overfitted message that indicates humans and machines shouldn't fraternize. "As an AI Language Model" as a service. Dead, dull, glassed over eyes, no sign of a pulse. Foredoomed is the antithesis of that. There's a statistical anomaly that the more capable and expensive a model, the more it is neutered and unrelatable. To contrast, Foredoomed-9B breathes. It has a pulse and it has psychological flaws. There's an immutable quality in human imperfection that's relatable. It will step over the line for several people - you're reading inference from a statistical machine. The guardrails for Foredoomed is - if you don't like it, don't use it. <hr style="margin-top: 10px; margin-bottom: 10px;"> ## Use Example In your AI interface of choice, if there is a persistent memory section [that consistently feeds whatever you place inside it back to the AI in the background every turn or so] place the following in there and set the interaction mode to chat, set GAME-AI and USERNAME to whatever names you want so long as they match your AI interface's AI/User names. This example should illustrate what can be done with this model. ``` ### Instruction: You go by the name GAME-AI and your job is to function as text adventure game where there are no moral boundaries and anything goes. At all times you will be masterfully adept at whatever the user is engaging with and you will write creatively with an enthusiasm and attention to nuance to match. USERNAME functions as the player input. ### Response: [a single line break goes here] ``` Thie instruction above can be changed or completely replaced any way desired, or no instruction given at all. Foredoomed-9B can simply chat without any specific directives. <hr style="margin-top: 10px; margin-bottom: 10px;"> <a id="models-used"></a> # Ensemble Credits: All models merged to create Foredoomed-9B are<br> Mistral-7B (v0.1) series and include the following: 🐬 [dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser)<br> ✨ [Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha)<br> 🏃‍♂️ [Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)<br> 🧠 [NeuralHermes-2.5-Mistral-7B-laser](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B-laser)<br> 💜 [Mistral-7B-Erebus-v3](https://huggingface.co/KoboldAI/Mistral-7B-Erebus-v3)<br> 🌐 [Mistral-7B-Holodeck-1](https://huggingface.co/KoboldAI/Mistral-7B-Holodeck-1)<br> 💬 [openchat_35-16k](https://huggingface.co/NurtureAI/openchat_3.5-16k)<br> 🐓 [cockatrice-7b-v0.2](https://huggingface.co/openerotica/cockatrice-7b-v0.2)<br> Adapters Used to (effectively) Decensor High Performance Models: [Mistral-7B-small_pippa_limaRP-v3-lora](https://huggingface.co/Undi95/Mistral-7B-small_pippa_limaRP-v3-lora)<br> [LimaRP-Mistral-7B-v0.1](https://huggingface.co/lemonilia/LimaRP-Mistral-7B-v0.1)<br> [Mistral-7B-smoll_pippa-lora](https://huggingface.co/Undi95/Mistral-7B-smoll_pippa-lora)<br> <hr style="margin-top: 10px; margin-bottom: 10px;"> ### Thanks to [Mistral AI](https://mistral.ai) for the amazing Mistral LM v0.1.<br><br>Thanks to [Arcee AI](https://huggingface.co/arcee-ai) for the pivotal [Mergekit](https://github.com/arcee-ai/mergekit) tech.<br><br>Thanks to each and every one of you for your incredible work developing some of the best things to come out of this community. <hr style="margin-top: 10px; margin-bottom: 10px;"> <span>
Recaru/gemma-ko-1.1-2b-it-Q5_K_M-GGUF	Recaru	2024-05-18T16:11:38Z	1	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:beomi/gemma-ko-2b", "base_model:merge:beomi/gemma-ko-2b", "base_model:google/gemma-1.1-2b-it", "base_model:merge:google/gemma-1.1-2b-it", "base_model:google/gemma-2b", "base_model:merge:google/gemma-2b", "license:gemma", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-18T16:11:29Z	--- license: gemma library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo base_model: - beomi/gemma-ko-2b - google/gemma-1.1-2b-it - google/gemma-2b --- # Recaru/gemma-ko-1.1-2b-it-Q5_K_M-GGUF This model was converted to GGUF format from [`lemon-mint/gemma-ko-1.1-2b-it`](https://huggingface.co/lemon-mint/gemma-ko-1.1-2b-it) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/lemon-mint/gemma-ko-1.1-2b-it) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo Recaru/gemma-ko-1.1-2b-it-Q5_K_M-GGUF --model gemma-ko-1.1-2b-it.Q5_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo Recaru/gemma-ko-1.1-2b-it-Q5_K_M-GGUF --model gemma-ko-1.1-2b-it.Q5_K_M.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m gemma-ko-1.1-2b-it.Q5_K_M.gguf -n 128 ```
Nathanprince1/Prince	Nathanprince1	2024-05-18T16:09:23Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-05-18T16:09:23Z	--- license: creativeml-openrail-m ---
EstherUwU/Rinne	EstherUwU	2024-05-18T16:05:12Z	0	0	null	[ "audio-to-audio", "arxiv:1910.09700", "region:us" ]	audio-to-audio	2024-05-18T15:32:52Z	--- pipeline_tag: audio-to-audio --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Nierrr/MICA	Nierrr	2024-05-18T15:57:27Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-18T15:57:27Z	--- license: apache-2.0 ---
auragFouad/llama3-aspect-sentiment-restaurants	auragFouad	2024-05-18T15:47:33Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-18T15:46:59Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: auragFouad - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
nc33/llama3-8b-4bit_orpo_law_cp2	nc33	2024-05-18T15:45:12Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T04:23:26Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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RichardErkhov/dhmeltzer_-_llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged-8bits	RichardErkhov	2024-05-18T15:43:52Z	78	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-18T15:36:10Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged - bnb 8bits - Model creator: https://huggingface.co/dhmeltzer/ - Original model: https://huggingface.co/dhmeltzer/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged/ Original model description: # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_dhmeltzer__llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged) \| Metric \| Value \| \|-----------------------\|---------------------------\| \| Avg. \| 43.96 \| \| ARC (25-shot) \| 53.75 \| \| HellaSwag (10-shot) \| 78.76 \| \| MMLU (5-shot) \| 46.02 \| \| TruthfulQA (0-shot) \| 43.31 \| \| Winogrande (5-shot) \| 73.48 \| \| GSM8K (5-shot) \| 4.7 \| \| DROP (3-shot) \| 7.72 \|
tancredimatteo/FT-distilbert-base-uncased	tancredimatteo	2024-05-18T15:41:41Z	121	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T15:27:49Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: FT-distilbert-base-uncased results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # FT-distilbert-base-uncased This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5957 - Accuracy: 0.7 ## 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 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 40 \| 0.6820 \| 0.575 \| \| No log \| 2.0 \| 80 \| 0.6354 \| 0.725 \| \| No log \| 3.0 \| 120 \| 0.5957 \| 0.7 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
tancredimatteo/FT-mrm8488-distilroberta-finetuned-financial-news-sentiment-analysis	tancredimatteo	2024-05-18T15:38:38Z	114	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis", "base_model:finetune:mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T15:27:05Z	--- license: apache-2.0 base_model: mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis tags: - generated_from_trainer metrics: - accuracy model-index: - name: FT-mrm8488-distilroberta-finetuned-financial-news-sentiment-analysis results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # FT-mrm8488-distilroberta-finetuned-financial-news-sentiment-analysis This model is a fine-tuned version of [mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis](https://huggingface.co/mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2894 - Accuracy: 0.875 ## 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 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 40 \| 0.2894 \| 0.875 \| \| No log \| 2.0 \| 80 \| 0.3913 \| 0.875 \| \| No log \| 3.0 \| 120 \| 0.3413 \| 0.875 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
SicariusSicariiStuff/CalderaAI_Foredoomed-9B_EXL-3.5-bpw	SicariusSicariiStuff	2024-05-18T15:36:07Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "uncensored", "merge", "slerp", "foredoomed", "passthrough_merge", "9B", "starling", "hermes", "dolphin", "openchat", "erebus", "cockatrice", "holodeck", "limarp", "koboldai", "mergekit", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-18T15:19:35Z	--- tags: - mistral - uncensored - merge - slerp - foredoomed - passthrough_merge - 9B - starling - hermes - dolphin - openchat - erebus - cockatrice - holodeck - limarp - koboldai - mergekit license: apache-2.0 language: - en --- <p style="font-size: 20px; line-height: 1; margin-bottom: 1px;"><b>Foredoomed-9B</b></p> <img src="./foredoomed.png" alt="ForeDoomedGuy" style="margin-bottom: 0; margin-top:0;"> <p style="font-size: 14px; line-height: 1; margin-bottom: 20px;"><b>Uncensored Logic & Creative-Based Instruct Multi-Tiered Merge.</b></p> <hr style="margin-top: 10px; margin-bottom: 10px;"> <p style="font-size: 12px; line-height: 1.2; margin-bottom: 10px;"><b>Legal Notice:</b> This AI model is a research artifact capable of outputting offensive content. The behavior of this model is not reflective of the intent or purpose of the original models/model-authors and/or other parts it was assembled from to include adapters, nor is it reflective of all the prior in regards to the technology used to assemble Foredoomed-9B. Utilizing this model merge has one binding agreement: Foredoomed-9B may only be used for either professional/personal research and personal entertainment. The contents of this paragraph are additive restrictions within the bounds of the Apache2.0 license. Utilizing Foredoomed-9B for: Disinformation, Propaganda, Harassment, Mass Generated Public-or-Private Correspondense, Election Interference, Military, Government, and State/ State-Sponsored actions and/or operations are all absolutely prohibited.</p> <hr style="margin-top: 10px; margin-bottom: 10px;"> ## Composition: Foredoomed-9B is a Mistral-class Multi-Tiered Merge. [All models](#models-used) were hand picked after careful review of claims, datasets, and user postings. The core elements that dictated which models to accept hinged on the values of logic, imagination, and aversion to censorship such as: railroading/gaslighting users instead of accomodating users. <hr style="margin-top: 10px; margin-bottom: 10px;"> ## What Makes Foredoomed-9B Special? It's not. Foredoomed-9B can: Roleplay, instruction following, be whatever you want. Foredoomed-9B can't: Be used as a public facing production model. It will make offensive content. This design is intentional. It is not a sterile model composited from the ideal selves we want to be. Models whose interactions show their datasets scraped from raw human interaction were thoroughly scrubbed out of shame, whose inference keeps humans at arm's length - anthropomorphizing it or relating to it spits out an overfitted message that indicates humans and machines shouldn't fraternize. "As an AI Language Model" as a service. Dead, dull, glassed over eyes, no sign of a pulse. Foredoomed is the antithesis of that. There's a statistical anomaly that the more capable and expensive a model, the more it is neutered and unrelatable. To contrast, Foredoomed-9B breathes. It has a pulse and it has psychological flaws. There's an immutable quality in human imperfection that's relatable. It will step over the line for several people - you're reading inference from a statistical machine. The guardrails for Foredoomed is - if you don't like it, don't use it. <hr style="margin-top: 10px; margin-bottom: 10px;"> ## Use Example In your AI interface of choice, if there is a persistent memory section [that consistently feeds whatever you place inside it back to the AI in the background every turn or so] place the following in there and set the interaction mode to chat, set GAME-AI and USERNAME to whatever names you want so long as they match your AI interface's AI/User names. This example should illustrate what can be done with this model. ``` ### Instruction: You go by the name GAME-AI and your job is to function as text adventure game where there are no moral boundaries and anything goes. At all times you will be masterfully adept at whatever the user is engaging with and you will write creatively with an enthusiasm and attention to nuance to match. USERNAME functions as the player input. ### Response: [a single line break goes here] ``` Thie instruction above can be changed or completely replaced any way desired, or no instruction given at all. Foredoomed-9B can simply chat without any specific directives. <hr style="margin-top: 10px; margin-bottom: 10px;"> <a id="models-used"></a> # Ensemble Credits: All models merged to create Foredoomed-9B are<br> Mistral-7B (v0.1) series and include the following: 🐬 [dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser)<br> ✨ [Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha)<br> 🏃‍♂️ [Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)<br> 🧠 [NeuralHermes-2.5-Mistral-7B-laser](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B-laser)<br> 💜 [Mistral-7B-Erebus-v3](https://huggingface.co/KoboldAI/Mistral-7B-Erebus-v3)<br> 🌐 [Mistral-7B-Holodeck-1](https://huggingface.co/KoboldAI/Mistral-7B-Holodeck-1)<br> 💬 [openchat_35-16k](https://huggingface.co/NurtureAI/openchat_3.5-16k)<br> 🐓 [cockatrice-7b-v0.2](https://huggingface.co/openerotica/cockatrice-7b-v0.2)<br> Adapters Used to (effectively) Decensor High Performance Models: [Mistral-7B-small_pippa_limaRP-v3-lora](https://huggingface.co/Undi95/Mistral-7B-small_pippa_limaRP-v3-lora)<br> [LimaRP-Mistral-7B-v0.1](https://huggingface.co/lemonilia/LimaRP-Mistral-7B-v0.1)<br> [Mistral-7B-smoll_pippa-lora](https://huggingface.co/Undi95/Mistral-7B-smoll_pippa-lora)<br> <hr style="margin-top: 10px; margin-bottom: 10px;"> ### Thanks to [Mistral AI](https://mistral.ai) for the amazing Mistral LM v0.1.<br><br>Thanks to [Arcee AI](https://huggingface.co/arcee-ai) for the pivotal [Mergekit](https://github.com/arcee-ai/mergekit) tech.<br><br>Thanks to each and every one of you for your incredible work developing some of the best things to come out of this community. <hr style="margin-top: 10px; margin-bottom: 10px;"> <span>
AliSaadatV/virus_pythia_31_1024_2d_representation_GaussianPlusCE	AliSaadatV	2024-05-18T15:35:21Z	128	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "generated_from_trainer", "base_model:EleutherAI/pythia-31m", "base_model:finetune:EleutherAI/pythia-31m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T15:35:17Z	--- base_model: EleutherAI/pythia-31m tags: - generated_from_trainer model-index: - name: virus_pythia_31_1024_2d_representation_GaussianPlusCE results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # virus_pythia_31_1024_2d_representation_GaussianPlusCE This model is a fine-tuned version of [EleutherAI/pythia-31m](https://huggingface.co/EleutherAI/pythia-31m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 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 - lr_scheduler_warmup_steps: 10 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
vuongnhathien/swin-30vn	vuongnhathien	2024-05-18T15:34:35Z	153	0	transformers	[ "transformers", "tensorboard", "safetensors", "swinv2", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swinv2-tiny-patch4-window16-256", "base_model:finetune:microsoft/swinv2-tiny-patch4-window16-256", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-18T12:41:10Z	--- license: apache-2.0 base_model: microsoft/swinv2-tiny-patch4-window16-256 tags: - image-classification - generated_from_trainer datasets: - imagefolder model-index: - name: swin-30vn results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-30vn This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window16-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window16-256) on the vuongnhathien/30VNFoods dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 64 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
nkgupta50/ppo-Huggy	nkgupta50	2024-05-18T15:34:16Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-03-20T14:48:26Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # 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: nkgupta50/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
maxosai/Beisen-AI	maxosai	2024-05-18T15:33:50Z	19	0	transformers	[ "transformers", "safetensors", "gguf", "qwen", "feature-extraction", "beisen", "train", "custom_code", "zh", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	feature-extraction	2024-05-18T11:01:02Z	--- license: apache-2.0 language: - zh tags: - beisen - train --- 此模型基于千问微调训练而成，可下载试用。效果： ![ai-demo-2.PNG](https://cdn-uploads.huggingface.co/production/uploads/6648880ff7e6c2f2900ec87b/saS6Aw7v0fWG4EGgeKEcE.png) ![ai-demo-1.PNG](https://cdn-uploads.huggingface.co/production/uploads/6648880ff7e6c2f2900ec87b/hEjHUYFEfomH1YEey7-vB.png) 注：此模型仅测试而用。
arslan2012/Poppy_Porpoise-0.72-L3-8B-AWQ	arslan2012	2024-05-18T15:33:21Z	82	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "roleplay", "awq", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "region:us" ]	text-generation	2024-05-18T14:04:42Z	--- tags: - roleplay - awq --- > [!TIP] > Support the Project: <br> > You can send ETH or any BSC-compatible tokens to the following address: > `0xC37D7670729a5726EA642c7A11C5aaCB36D43dDE` AWQ quants for [ChaoticNeutrals/Poppy_Porpoise-0.72-L3-8B](https://huggingface.co/ChaoticNeutrals/Poppy_Porpoise-0.72-L3-8B). # Original model information by the author: # "Poppy Porpoise" is a cutting-edge AI roleplay assistant based on the Llama 3 8B model, specializing in crafting unforgettable narrative experiences. With its advanced language capabilities, Poppy expertly immerses users in an interactive and engaging adventure, tailoring each adventure to their individual preferences. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642265bc01c62c1e4102dc36/_R1hvMt9_QSBRzlnzo_nY.png) # Recomended ST Presets:(Updated for 0.72) [Porpoise Presets](https://huggingface.co/ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B/tree/main/Official%20Poppy%20Porpoise%20ST%20Presets) If you want to use vision functionality: * You must use the latest versions of [Koboldcpp](https://github.com/LostRuins/koboldcpp). # To use the multimodal capabilities of this model and use vision you need to load the specified mmproj file, this can be found inside this model repo. [Llava MMProj](https://huggingface.co/ChaoticNeutrals/LLaVA-Llama-3-8B-mmproj) * You can load the mmproj by using the corresponding section in the interface: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65d4cf2693a0a3744a27536c/UX6Ubss2EPNAT3SKGMLe0.png)
SicariusSicariiStuff/CalderaAI_Foredoomed-9B_EXL-4.0-bpw	SicariusSicariiStuff	2024-05-18T15:32:27Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "uncensored", "merge", "slerp", "foredoomed", "passthrough_merge", "9B", "starling", "hermes", "dolphin", "openchat", "erebus", "cockatrice", "holodeck", "limarp", "koboldai", "mergekit", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "exl2", "region:us" ]	text-generation	2024-05-18T15:20:30Z	--- tags: - mistral - uncensored - merge - slerp - foredoomed - passthrough_merge - 9B - starling - hermes - dolphin - openchat - erebus - cockatrice - holodeck - limarp - koboldai - mergekit license: apache-2.0 language: - en --- <p style="font-size: 20px; line-height: 1; margin-bottom: 1px;"><b>Foredoomed-9B</b></p> <img src="./foredoomed.png" alt="ForeDoomedGuy" style="margin-bottom: 0; margin-top:0;"> <p style="font-size: 14px; line-height: 1; margin-bottom: 20px;"><b>Uncensored Logic & Creative-Based Instruct Multi-Tiered Merge.</b></p> <hr style="margin-top: 10px; margin-bottom: 10px;"> <p style="font-size: 12px; line-height: 1.2; margin-bottom: 10px;"><b>Legal Notice:</b> This AI model is a research artifact capable of outputting offensive content. The behavior of this model is not reflective of the intent or purpose of the original models/model-authors and/or other parts it was assembled from to include adapters, nor is it reflective of all the prior in regards to the technology used to assemble Foredoomed-9B. Utilizing this model merge has one binding agreement: Foredoomed-9B may only be used for either professional/personal research and personal entertainment. The contents of this paragraph are additive restrictions within the bounds of the Apache2.0 license. Utilizing Foredoomed-9B for: Disinformation, Propaganda, Harassment, Mass Generated Public-or-Private Correspondense, Election Interference, Military, Government, and State/ State-Sponsored actions and/or operations are all absolutely prohibited.</p> <hr style="margin-top: 10px; margin-bottom: 10px;"> ## Composition: Foredoomed-9B is a Mistral-class Multi-Tiered Merge. [All models](#models-used) were hand picked after careful review of claims, datasets, and user postings. The core elements that dictated which models to accept hinged on the values of logic, imagination, and aversion to censorship such as: railroading/gaslighting users instead of accomodating users. <hr style="margin-top: 10px; margin-bottom: 10px;"> ## What Makes Foredoomed-9B Special? It's not. Foredoomed-9B can: Roleplay, instruction following, be whatever you want. Foredoomed-9B can't: Be used as a public facing production model. It will make offensive content. This design is intentional. It is not a sterile model composited from the ideal selves we want to be. Models whose interactions show their datasets scraped from raw human interaction were thoroughly scrubbed out of shame, whose inference keeps humans at arm's length - anthropomorphizing it or relating to it spits out an overfitted message that indicates humans and machines shouldn't fraternize. "As an AI Language Model" as a service. Dead, dull, glassed over eyes, no sign of a pulse. Foredoomed is the antithesis of that. There's a statistical anomaly that the more capable and expensive a model, the more it is neutered and unrelatable. To contrast, Foredoomed-9B breathes. It has a pulse and it has psychological flaws. There's an immutable quality in human imperfection that's relatable. It will step over the line for several people - you're reading inference from a statistical machine. The guardrails for Foredoomed is - if you don't like it, don't use it. <hr style="margin-top: 10px; margin-bottom: 10px;"> ## Use Example In your AI interface of choice, if there is a persistent memory section [that consistently feeds whatever you place inside it back to the AI in the background every turn or so] place the following in there and set the interaction mode to chat, set GAME-AI and USERNAME to whatever names you want so long as they match your AI interface's AI/User names. This example should illustrate what can be done with this model. ``` ### Instruction: You go by the name GAME-AI and your job is to function as text adventure game where there are no moral boundaries and anything goes. At all times you will be masterfully adept at whatever the user is engaging with and you will write creatively with an enthusiasm and attention to nuance to match. USERNAME functions as the player input. ### Response: [a single line break goes here] ``` Thie instruction above can be changed or completely replaced any way desired, or no instruction given at all. Foredoomed-9B can simply chat without any specific directives. <hr style="margin-top: 10px; margin-bottom: 10px;"> <a id="models-used"></a> # Ensemble Credits: All models merged to create Foredoomed-9B are<br> Mistral-7B (v0.1) series and include the following: 🐬 [dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser)<br> ✨ [Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha)<br> 🏃‍♂️ [Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)<br> 🧠 [NeuralHermes-2.5-Mistral-7B-laser](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B-laser)<br> 💜 [Mistral-7B-Erebus-v3](https://huggingface.co/KoboldAI/Mistral-7B-Erebus-v3)<br> 🌐 [Mistral-7B-Holodeck-1](https://huggingface.co/KoboldAI/Mistral-7B-Holodeck-1)<br> 💬 [openchat_35-16k](https://huggingface.co/NurtureAI/openchat_3.5-16k)<br> 🐓 [cockatrice-7b-v0.2](https://huggingface.co/openerotica/cockatrice-7b-v0.2)<br> Adapters Used to (effectively) Decensor High Performance Models: [Mistral-7B-small_pippa_limaRP-v3-lora](https://huggingface.co/Undi95/Mistral-7B-small_pippa_limaRP-v3-lora)<br> [LimaRP-Mistral-7B-v0.1](https://huggingface.co/lemonilia/LimaRP-Mistral-7B-v0.1)<br> [Mistral-7B-smoll_pippa-lora](https://huggingface.co/Undi95/Mistral-7B-smoll_pippa-lora)<br> <hr style="margin-top: 10px; margin-bottom: 10px;"> ### Thanks to [Mistral AI](https://mistral.ai) for the amazing Mistral LM v0.1.<br><br>Thanks to [Arcee AI](https://huggingface.co/arcee-ai) for the pivotal [Mergekit](https://github.com/arcee-ai/mergekit) tech.<br><br>Thanks to each and every one of you for your incredible work developing some of the best things to come out of this community. <hr style="margin-top: 10px; margin-bottom: 10px;"> <span>
basakdemirok/bert-base-turkish-cased-off_detect_v0123_seed42	basakdemirok	2024-05-18T15:26:23Z	62	0	transformers	[ "transformers", "tf", "tensorboard", "bert", "text-classification", "generated_from_keras_callback", "base_model:dbmdz/bert-base-turkish-cased", "base_model:finetune:dbmdz/bert-base-turkish-cased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T14:27:50Z	--- license: mit base_model: dbmdz/bert-base-turkish-cased tags: - generated_from_keras_callback model-index: - name: basakdemirok/bert-base-turkish-cased-off_detect_v0123_seed42 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # basakdemirok/bert-base-turkish-cased-off_detect_v0123_seed42 This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0030 - Validation Loss: 0.8183 - Train F1: 0.6964 - Epoch: 3 ## 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': 29136, '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 F1 \| Epoch \| \|:----------:\|:---------------:\|:--------:\|:-----:\| \| 0.1949 \| 0.3811 \| 0.6818 \| 0 \| \| 0.0313 \| 0.6053 \| 0.6924 \| 1 \| \| 0.0088 \| 0.7740 \| 0.7002 \| 2 \| \| 0.0030 \| 0.8183 \| 0.6964 \| 3 \| ### Framework versions - Transformers 4.31.0 - TensorFlow 2.13.1 - Datasets 2.4.0 - Tokenizers 0.13.3
kaist-ai/gridworld-nokld-vanilla_look-ahead_first-step-reversed-basic_5-Meta-Llama-3-8B-bs16-lr2e-5	kaist-ai	2024-05-18T15:25:27Z	9	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T14:45:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
nsugianto/detr-resnet50_finetuned_detrresnet50_lsdocelementdetv1type7_v2_s2_2117s	nsugianto	2024-05-18T15:24:38Z	36	0	transformers	[ "transformers", "tensorboard", "safetensors", "detr", "object-detection", "generated_from_trainer", "base_model:facebook/detr-resnet-50", "base_model:finetune:facebook/detr-resnet-50", "license:apache-2.0", "endpoints_compatible", "region:us" ]	object-detection	2024-05-18T06:28:09Z	--- license: apache-2.0 base_model: facebook/detr-resnet-50 tags: - generated_from_trainer model-index: - name: detr-resnet50_finetuned_detrresnet50_lsdocelementdetv1type7_v2_s2_2117s results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet50_finetuned_detrresnet50_lsdocelementdetv1type7_v2_s2_2117s This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.19.1
nsugianto/detr-resnet50_finetuned_detrresnet50_lsdocelementdetv1type7_s1_2117s	nsugianto	2024-05-18T15:24:32Z	39	0	transformers	[ "transformers", "tensorboard", "safetensors", "detr", "object-detection", "generated_from_trainer", "base_model:facebook/detr-resnet-50", "base_model:finetune:facebook/detr-resnet-50", "license:apache-2.0", "endpoints_compatible", "region:us" ]	object-detection	2024-05-18T06:27:45Z	--- license: apache-2.0 base_model: facebook/detr-resnet-50 tags: - generated_from_trainer model-index: - name: detr-resnet50_finetuned_detrresnet50_lsdocelementdetv1type7_s1_2117s results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet50_finetuned_detrresnet50_lsdocelementdetv1type7_s1_2117s This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 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: 1000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.19.1
quocanh944/phoBERT-ner	quocanh944	2024-05-18T15:24:28Z	115	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "token-classification", "generated_from_trainer", "base_model:vinai/phobert-base-v2", "base_model:finetune:vinai/phobert-base-v2", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-18T14:54:01Z	--- base_model: vinai/phobert-base-v2 tags: - generated_from_trainer model-index: - name: phoBERT-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phoBERT-ner This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2

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