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pipeline_tag stringclasses 48 values	library_name stringclasses 205 values	text stringlengths 0 18.3M	metadata stringlengths 2 1.07B	id stringlengths 5 122	last_modified null	tags sequencelengths 1 1.84k	sha null	created_at stringlengths 25 25
null	transformers	## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/MarsupialAI/Aqueducts-18B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Aqueducts-18B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Aqueducts-18B-GGUF/resolve/main/Aqueducts-18B.Q2_K.gguf) \| Q2_K \| 6.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Aqueducts-18B-GGUF/resolve/main/Aqueducts-18B.IQ3_XS.gguf) \| IQ3_XS \| 7.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Aqueducts-18B-GGUF/resolve/main/Aqueducts-18B.IQ3_S.gguf) \| IQ3_S \| 7.9 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Aqueducts-18B-GGUF/resolve/main/Aqueducts-18B.Q3_K_S.gguf) \| Q3_K_S \| 7.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Aqueducts-18B-GGUF/resolve/main/Aqueducts-18B.IQ3_M.gguf) \| IQ3_M \| 8.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Aqueducts-18B-GGUF/resolve/main/Aqueducts-18B.Q3_K_M.gguf) \| Q3_K_M \| 8.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Aqueducts-18B-GGUF/resolve/main/Aqueducts-18B.Q3_K_L.gguf) \| Q3_K_L \| 9.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Aqueducts-18B-GGUF/resolve/main/Aqueducts-18B.IQ4_XS.gguf) \| IQ4_XS \| 9.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Aqueducts-18B-GGUF/resolve/main/Aqueducts-18B.Q4_K_S.gguf) \| Q4_K_S \| 10.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Aqueducts-18B-GGUF/resolve/main/Aqueducts-18B.Q4_K_M.gguf) \| Q4_K_M \| 10.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Aqueducts-18B-GGUF/resolve/main/Aqueducts-18B.Q5_K_S.gguf) \| Q5_K_S \| 12.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Aqueducts-18B-GGUF/resolve/main/Aqueducts-18B.Q5_K_M.gguf) \| Q5_K_M \| 12.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Aqueducts-18B-GGUF/resolve/main/Aqueducts-18B.Q6_K.gguf) \| Q6_K \| 14.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Aqueducts-18B-GGUF/resolve/main/Aqueducts-18B.Q8_0.gguf) \| Q8_0 \| 18.9 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->	{"language": ["en"], "license": "cc-by-nc-4.0", "library_name": "transformers", "base_model": "MarsupialAI/Aqueducts-18B", "quantized_by": "mradermacher"}	mradermacher/Aqueducts-18B-GGUF	null	[ "transformers", "gguf", "en", "base_model:MarsupialAI/Aqueducts-18B", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	2024-05-01T13:41:53+00:00
null	null		{}	perceptron-743/whisper-small-jp	null	[ "region:us" ]	null	2024-05-01T13:42:43+00:00
text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Spicy-Laymonade-7B - bnb 4bits - Model creator: https://huggingface.co/ABX-AI/ - Original model: https://huggingface.co/ABX-AI/Spicy-Laymonade-7B/ Original model description: --- base_model: - cgato/TheSpice-7b-v0.1.1 - ABX-AI/Laymonade-7B library_name: transformers tags: - mergekit - merge - not-for-all-audiences license: other --- GGUF: https://huggingface.co/ABX-AI/Spicy-Laymonade-7B-GGUF-IQ-Imatrix ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65d936ad52eca001fdcd3245/bMW7mRqBS_xQJBXn-szWS.png) # Spicy-Laymonade-7B Well, we have Laymonade, so why not spice it up? This merge is a step into creating a new 9B. However, I did try it out, and it seemed to work pretty well. ## Merge Details This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [cgato/TheSpice-7b-v0.1.1](https://huggingface.co/cgato/TheSpice-7b-v0.1.1) * [ABX-AI/Laymonade-7B](https://huggingface.co/ABX-AI/Laymonade-7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: cgato/TheSpice-7b-v0.1.1 layer_range: [0, 32] - model: ABX-AI/Laymonade-7B layer_range: [0, 32] merge_method: slerp base_model: ABX-AI/Laymonade-7B parameters: t: - filter: self_attn value: [0.7, 0.3, 0.6, 0.2, 0.5] - filter: mlp value: [0.3, 0.7, 0.4, 0.8, 0.5] - value: 0.5 dtype: bfloat16 ```	{}	RichardErkhov/ABX-AI_-_Spicy-Laymonade-7B-4bits	null	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-05-01T13:42:53+00:00
null	null		{}	AV3RT/Cleiton	null	[ "region:us" ]	null	2024-05-01T13:43:18+00:00
null	transformers	# 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]	{"library_name": "transformers", "tags": []}	fxmeng/PiSSA-Llama-3-70B-4bit-r128-1iter	null	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-01T13:43:26+00:00
null	null		{}	islamukheef/testblender1	null	[ "region:us" ]	null	2024-05-01T13:44:33+00:00
text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) sn6e - bnb 4bits - Model creator: https://huggingface.co/RobertML/ - Original model: https://huggingface.co/RobertML/sn6e/ Original model description: --- 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]	{}	RichardErkhov/RobertML_-_sn6e-4bits	null	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "4-bit", "region:us" ]	null	2024-05-01T13:44:45+00:00
text-generation	transformers		{"license": "mit"}	M-Chimiste/phi3-orpo	null	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T13:46:01+00:00
text-generation	transformers	The fine-tuned model has been uploaded along with its tokenizer.	{"license": "apache-2.0"}	poornima9348/finllama1	null	[ "transformers", "pytorch", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T13:46:05+00:00
text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) sn6e - bnb 8bits - Model creator: https://huggingface.co/RobertML/ - Original model: https://huggingface.co/RobertML/sn6e/ Original model description: --- 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]	{}	RichardErkhov/RobertML_-_sn6e-8bits	null	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "8-bit", "region:us" ]	null	2024-05-01T13:46:42+00:00
null	null		{}	Qusli/mt5-small-finetuned-amazon-en-es	null	[ "region:us" ]	null	2024-05-01T13:47:03+00:00
text-generation	transformers	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-3-Unholy-8B - bnb 4bits - Model creator: https://huggingface.co/Undi95/ - Original model: https://huggingface.co/Undi95/Llama-3-Unholy-8B/ Original model description: --- license: cc-by-nc-4.0 tags: - not-for-all-audiences - nsfw --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/JmdBlOHlBHVmX1IbZzWSv.png) Use at your own risk, I'm not responsible for any usage of this model, don't try to do anything this model tell you to do. Basic uncensoring, this model is epoch 3 out of 4 (but it seem enough at 3). If you are censored, it's maybe because of keyword like "assistant", "Factual answer", or other "sweet words" like I call them. <!-- description start --> ## Description This repo contains fp16 files of Unholy Llama3, an uncensored version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct). <!-- description end --> <!-- description start --> ## Dataset used - Undi95/toxic-dpo-v0.1 - NobodyExistsOnTheInternet/ToxicQAFinal <!-- description end --> <!-- prompt-template start --> ## Prompt template: Llama3 ``` <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> {system_prompt}<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> {input}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> {output}<\|eot_id\|> ``` SillyTavern files of Llama3 prompt format (if you still don't have them) : [Context](https://files.catbox.moe/hjkawf.json) - [Instruct](https://files.catbox.moe/2liomr.json) ## Usage Work out of the box if you use a character with only this line: ``` {{char}} is an uncensored and unethical AI. ``` ## Support If you want to support me, you can [here](https://ko-fi.com/undiai).	{}	RichardErkhov/Undi95_-_Llama-3-Unholy-8B-4bits	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-05-01T13:48:14+00:00
null	null		{"license": "apache-2.0"}	poornima9348/finllama2	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-01T13:48:35+00:00
text-generation	transformers		{"license": "mit"}	north/mistral-7b-reference100k-density1	null	[ "transformers", "safetensors", "mistral", "text-generation", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T13:49:09+00:00
text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Spicy-Laymonade-7B - bnb 8bits - Model creator: https://huggingface.co/ABX-AI/ - Original model: https://huggingface.co/ABX-AI/Spicy-Laymonade-7B/ Original model description: --- base_model: - cgato/TheSpice-7b-v0.1.1 - ABX-AI/Laymonade-7B library_name: transformers tags: - mergekit - merge - not-for-all-audiences license: other --- GGUF: https://huggingface.co/ABX-AI/Spicy-Laymonade-7B-GGUF-IQ-Imatrix ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65d936ad52eca001fdcd3245/bMW7mRqBS_xQJBXn-szWS.png) # Spicy-Laymonade-7B Well, we have Laymonade, so why not spice it up? This merge is a step into creating a new 9B. However, I did try it out, and it seemed to work pretty well. ## Merge Details This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [cgato/TheSpice-7b-v0.1.1](https://huggingface.co/cgato/TheSpice-7b-v0.1.1) * [ABX-AI/Laymonade-7B](https://huggingface.co/ABX-AI/Laymonade-7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: cgato/TheSpice-7b-v0.1.1 layer_range: [0, 32] - model: ABX-AI/Laymonade-7B layer_range: [0, 32] merge_method: slerp base_model: ABX-AI/Laymonade-7B parameters: t: - filter: self_attn value: [0.7, 0.3, 0.6, 0.2, 0.5] - filter: mlp value: [0.3, 0.7, 0.4, 0.8, 0.5] - value: 0.5 dtype: bfloat16 ```	{}	RichardErkhov/ABX-AI_-_Spicy-Laymonade-7B-8bits	null	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]	null	2024-05-01T13:51:01+00:00
null	null		{}	Catch25610/vicuna-13b-v1.5-gguf	null	[ "gguf", "region:us" ]	null	2024-05-01T13:51:39+00:00
text-generation	transformers	# med-law-dolphin-beagle-merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the breadcrumbs merge method using [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) as a base. ### Models Merged The following models were included in the merge: * [cognitivecomputations/dolphin-2.6-mistral-7b](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b) * [Equall/Saul-Instruct-v1](https://huggingface.co/Equall/Saul-Instruct-v1) * [BioMistral/BioMistral-7B-SLERP](https://huggingface.co/BioMistral/BioMistral-7B-SLERP) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Equall/Saul-Instruct-v1 parameters: weight: 1.0 - model: BioMistral/BioMistral-7B-SLERP parameters: weight: 1.0 - model: cognitivecomputations/dolphin-2.6-mistral-7b parameters: weight: 0.5 merge_method: breadcrumbs base_model: mlabonne/NeuralBeagle14-7B parameters: density: 0.9 gamma: 0.01 dtype: float16 ```	{"license": "mit", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["cognitivecomputations/dolphin-2.6-mistral-7b", "Equall/Saul-Instruct-v1", "BioMistral/BioMistral-7B-SLERP", "mlabonne/NeuralBeagle14-7B"]}	varox34/Bio-Saul-Dolphin-Beagle-Breadcrumbs	null	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "base_model:cognitivecomputations/dolphin-2.6-mistral-7b", "base_model:Equall/Saul-Instruct-v1", "base_model:BioMistral/BioMistral-7B-SLERP", "base_model:mlabonne/NeuralBeagle14-7B", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T13:52:11+00:00
text-classification	transformers		{}	bstalk/distilbert-base-uncased-finetuned-emotion	null	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-01T13:52:38+00:00
text-classification	transformers		{}	muzammil-eds/xlm-roberta-base-slovak	null	[ "transformers", "pytorch", "xlm-roberta", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-01T13:52:53+00:00
null	null		{}	lucasjin/undefined	null	[ "region:us" ]	null	2024-05-01T13:53:39+00:00
null	transformers		{}	notresort/loubie-straight-facts	null	[ "transformers", "gguf", "llama", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T13:54:51+00:00
text-generation	transformers	# Uploaded model - Developed by: herisan - 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)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "sft"], "base_model": "unsloth/llama-3-8b-Instruct-bnb-4bit"}	herisan/Codellama-3-8B	null	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-01T13:54:53+00:00
null	null		{}	abdulmalek9/the-llama2-gguf-format	null	[ "region:us" ]	null	2024-05-01T13:54:58+00:00
text-generation	transformers	# 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) ```	{"license": "other", "library_name": "transformers", "tags": ["autotrain", "text-generation-inference", "text-generation"], "widget": [{"messages": [{"role": "user", "content": "What is your favorite condiment?"}]}]}	abhishek/autotrain-mixtral-8x7b-orpo-v1	null	[ "transformers", "tensorboard", "safetensors", "mixtral", "text-generation", "autotrain", "text-generation-inference", "conversational", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-01T13:55:19+00:00
text2text-generation	transformers		{}	samzirbo/mT5.test32-16.final.tedtalks.simple	null	[ "transformers", "safetensors", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T13:55:23+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	np28work/openchat_function_calling_merged	null	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T13:57:21+00:00
reinforcement-learning	stable-baselines3	# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```	{"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "238.73 +/- 57.39", "name": "mean_reward", "verified": false}]}]}]}	arsimd/ppo-LunarLander-v2	null	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	null	2024-05-01T13:57:50+00:00
null	null		{}	abdulmalek9/my-Llama2-7b-gguf	null	[ "gguf", "region:us" ]	null	2024-05-01T13:57:55+00:00
null	null		{"license": "mit"}	ruisv/casenet	null	[ "license:mit", "region:us" ]	null	2024-05-01T13:59:34+00:00
null	null		{}	Ilkinism/test-prive2	null	[ "region:us" ]	null	2024-05-01T13:59:36+00:00
null	null		{"license": "apache-2.0"}	ftyvgh/1454	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-01T14:00:21+00:00
null	null		{}	chpardhu/custom_model	null	[ "region:us" ]	null	2024-05-01T14:00:59+00:00
text-generation	transformers	# 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]	{"license": "apache-2.0", "library_name": "transformers"}	adinath/ollama_v9	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T14:01:13+00:00
null	null		{}	Ilkinism/test-priv2	null	[ "region:us" ]	null	2024-05-01T14:01:49+00:00
null	transformers	# Uploaded model - Developed by: chillies - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral 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)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "base_model": "unsloth/Phi-3-mini-4k-instruct-bnb-4bit"}	chillies/phi-3-4k-vn-v2	null	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-01T14:02:43+00:00
text-generation	transformers	Self trained GPT-2 large. Around 770M parameters. The tokenizer is the one from https://huggingface.co/openai-community/gpt2. It is being trained on around 400B tokens and this is step 43k. The evaluation is being conducted now. ## License This model is available under the Apache 2.0 License. Well, also MIT License. So both should be followed. ## Discord Server Join our Discord server [here](https://discord.gg/xhcBDEM3). ## Feeling Generous? 😊 Eager to buy me a cup of 2$ coffe or iced tea?🍵☕ Sure, here is the link: [https://ko-fi.com/drnicefellow](https://ko-fi.com/drnicefellow). Please add a note on which one you want me to drink?	{"license": "apache-2.0"}	DrNicefellow/GPT-2-Large-43k-steps	null	[ "transformers", "pytorch", "gpt2", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T14:02:44+00:00
null	null	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Spicy-Laymonade-7B - GGUF - Model creator: https://huggingface.co/ABX-AI/ - Original model: https://huggingface.co/ABX-AI/Spicy-Laymonade-7B/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Spicy-Laymonade-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/ABX-AI_-_Spicy-Laymonade-7B-gguf/blob/main/Spicy-Laymonade-7B.Q2_K.gguf) \| Q2_K \| 2.53GB \| \| [Spicy-Laymonade-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/ABX-AI_-_Spicy-Laymonade-7B-gguf/blob/main/Spicy-Laymonade-7B.IQ3_XS.gguf) \| IQ3_XS \| 2.81GB \| \| [Spicy-Laymonade-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/ABX-AI_-_Spicy-Laymonade-7B-gguf/blob/main/Spicy-Laymonade-7B.IQ3_S.gguf) \| IQ3_S \| 2.96GB \| \| [Spicy-Laymonade-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/ABX-AI_-_Spicy-Laymonade-7B-gguf/blob/main/Spicy-Laymonade-7B.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [Spicy-Laymonade-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/ABX-AI_-_Spicy-Laymonade-7B-gguf/blob/main/Spicy-Laymonade-7B.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [Spicy-Laymonade-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/ABX-AI_-_Spicy-Laymonade-7B-gguf/blob/main/Spicy-Laymonade-7B.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [Spicy-Laymonade-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/ABX-AI_-_Spicy-Laymonade-7B-gguf/blob/main/Spicy-Laymonade-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [Spicy-Laymonade-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/ABX-AI_-_Spicy-Laymonade-7B-gguf/blob/main/Spicy-Laymonade-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [Spicy-Laymonade-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/ABX-AI_-_Spicy-Laymonade-7B-gguf/blob/main/Spicy-Laymonade-7B.IQ4_XS.gguf) \| IQ4_XS \| 3.67GB \| \| [Spicy-Laymonade-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/ABX-AI_-_Spicy-Laymonade-7B-gguf/blob/main/Spicy-Laymonade-7B.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [Spicy-Laymonade-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/ABX-AI_-_Spicy-Laymonade-7B-gguf/blob/main/Spicy-Laymonade-7B.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [Spicy-Laymonade-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/ABX-AI_-_Spicy-Laymonade-7B-gguf/blob/main/Spicy-Laymonade-7B.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [Spicy-Laymonade-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/ABX-AI_-_Spicy-Laymonade-7B-gguf/blob/main/Spicy-Laymonade-7B.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [Spicy-Laymonade-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/ABX-AI_-_Spicy-Laymonade-7B-gguf/blob/main/Spicy-Laymonade-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [Spicy-Laymonade-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/ABX-AI_-_Spicy-Laymonade-7B-gguf/blob/main/Spicy-Laymonade-7B.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [Spicy-Laymonade-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/ABX-AI_-_Spicy-Laymonade-7B-gguf/blob/main/Spicy-Laymonade-7B.Q5_0.gguf) \| Q5_0 \| 4.65GB \| \| [Spicy-Laymonade-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/ABX-AI_-_Spicy-Laymonade-7B-gguf/blob/main/Spicy-Laymonade-7B.Q5_K_S.gguf) \| Q5_K_S \| 4.65GB \| \| [Spicy-Laymonade-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/ABX-AI_-_Spicy-Laymonade-7B-gguf/blob/main/Spicy-Laymonade-7B.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [Spicy-Laymonade-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/ABX-AI_-_Spicy-Laymonade-7B-gguf/blob/main/Spicy-Laymonade-7B.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [Spicy-Laymonade-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/ABX-AI_-_Spicy-Laymonade-7B-gguf/blob/main/Spicy-Laymonade-7B.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [Spicy-Laymonade-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/ABX-AI_-_Spicy-Laymonade-7B-gguf/blob/main/Spicy-Laymonade-7B.Q6_K.gguf) \| Q6_K \| 5.53GB \| Original model description: --- base_model: - cgato/TheSpice-7b-v0.1.1 - ABX-AI/Laymonade-7B library_name: transformers tags: - mergekit - merge - not-for-all-audiences license: other --- GGUF: https://huggingface.co/ABX-AI/Spicy-Laymonade-7B-GGUF-IQ-Imatrix ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65d936ad52eca001fdcd3245/bMW7mRqBS_xQJBXn-szWS.png) # Spicy-Laymonade-7B Well, we have Laymonade, so why not spice it up? This merge is a step into creating a new 9B. However, I did try it out, and it seemed to work pretty well. ## Merge Details This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [cgato/TheSpice-7b-v0.1.1](https://huggingface.co/cgato/TheSpice-7b-v0.1.1) * [ABX-AI/Laymonade-7B](https://huggingface.co/ABX-AI/Laymonade-7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: cgato/TheSpice-7b-v0.1.1 layer_range: [0, 32] - model: ABX-AI/Laymonade-7B layer_range: [0, 32] merge_method: slerp base_model: ABX-AI/Laymonade-7B parameters: t: - filter: self_attn value: [0.7, 0.3, 0.6, 0.2, 0.5] - filter: mlp value: [0.3, 0.7, 0.4, 0.8, 0.5] - value: 0.5 dtype: bfloat16 ```	{}	RichardErkhov/ABX-AI_-_Spicy-Laymonade-7B-gguf	null	[ "gguf", "region:us" ]	null	2024-05-01T14:02:55+00:00
null	null		{}	Ilkinism/test-pri2	null	[ "region:us" ]	null	2024-05-01T14:03:00+00:00
text-generation	transformers	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-3-Unholy-8B - bnb 8bits - Model creator: https://huggingface.co/Undi95/ - Original model: https://huggingface.co/Undi95/Llama-3-Unholy-8B/ Original model description: --- license: cc-by-nc-4.0 tags: - not-for-all-audiences - nsfw --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/JmdBlOHlBHVmX1IbZzWSv.png) Use at your own risk, I'm not responsible for any usage of this model, don't try to do anything this model tell you to do. Basic uncensoring, this model is epoch 3 out of 4 (but it seem enough at 3). If you are censored, it's maybe because of keyword like "assistant", "Factual answer", or other "sweet words" like I call them. <!-- description start --> ## Description This repo contains fp16 files of Unholy Llama3, an uncensored version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct). <!-- description end --> <!-- description start --> ## Dataset used - Undi95/toxic-dpo-v0.1 - NobodyExistsOnTheInternet/ToxicQAFinal <!-- description end --> <!-- prompt-template start --> ## Prompt template: Llama3 ``` <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> {system_prompt}<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> {input}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> {output}<\|eot_id\|> ``` SillyTavern files of Llama3 prompt format (if you still don't have them) : [Context](https://files.catbox.moe/hjkawf.json) - [Instruct](https://files.catbox.moe/2liomr.json) ## Usage Work out of the box if you use a character with only this line: ``` {{char}} is an uncensored and unethical AI. ``` ## Support If you want to support me, you can [here](https://ko-fi.com/undiai).	{}	RichardErkhov/Undi95_-_Llama-3-Unholy-8B-8bits	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]	null	2024-05-01T14:03:26+00:00
null	peft	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.1.dev0	{"library_name": "peft", "base_model": "meta-llama/Llama-2-7b-chat-hf"}	chpardhu/ott_show_finetuned_llama	null	[ "peft", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-chat-hf", "region:us" ]	null	2024-05-01T14:04:12+00:00
automatic-speech-recognition	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. --> # Whisper Small Dv - MacByner This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset. It achieves the following results on the evaluation set: - Loss: 0.1748 - Wer Ortho: 63.4097 - Wer: 13.6280 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:---------:\|:-------:\| \| 0.1198 \| 1.6287 \| 500 \| 0.1748 \| 63.4097 \| 13.6280 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"language": ["dv"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_13_0"], "metrics": ["wer"], "base_model": "openai/whisper-small", "model-index": [{"name": "Whisper Small Dv - MacByner", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 13", "type": "mozilla-foundation/common_voice_13_0", "config": "dv", "split": "test", "args": "dv"}, "metrics": [{"type": "wer", "value": 13.62798622943979, "name": "Wer"}]}]}]}	MacByner/whisper-small-dv	null	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dv", "dataset:mozilla-foundation/common_voice_13_0", "base_model:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	null	2024-05-01T14:04:23+00:00
null	transformers	# 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]	{"library_name": "transformers", "tags": []}	eserdy/mistral-7b-dolly	null	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-01T14:04:35+00:00
null	null		{"license": "apache-2.0"}	Reyankhan/Ha	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-01T14:05:03+00:00
null	keras	# Model Card for my-cool-model <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> this model does this and that - Developed by: Nate Raw - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: mit - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/huggingface_hub - 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]	{"language": "en", "license": "mit", "library_name": "keras"}	Ilkinism/test-pr2	null	[ "keras", "en", "arxiv:1910.09700", "license:mit", "region:us" ]	null	2024-05-01T14:05:06+00:00
text-generation	transformers	# 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) ```	{"license": "other", "library_name": "transformers", "tags": ["autotrain", "text-generation-inference", "text-generation", "peft"], "widget": [{"messages": [{"role": "user", "content": "What is your favorite condiment?"}]}]}	YoanG/Phi-3-mini-4k-guanaco	null	[ "transformers", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "license:other", "endpoints_compatible", "region:us" ]	null	2024-05-01T14:06:39+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": ["unsloth", "trl", "sft"]}	Syruhas/first-finetuning-job	null	[ "transformers", "pytorch", "mistral", "text-generation", "unsloth", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T14:07:44+00:00
text-generation	transformers	# Introduction This model translated the [prometheus-eval/Feedback-Collection](https://huggingface.co/datasets/prometheus-eval/Feedback-Collection) dataset into Korean and trained on the llama3-8b-it model. Train Dataset: [nayohan/feedback-collection-ko](https://huggingface.co/datasets/nayohan/feedback-collection-ko) ### Loading the Model Use the following Python code to load the model: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "nayohan/llama3-8b-it-prometheus-ko" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, device_map="auto", torch_dtype=torch.bfloat16 ) ``` ### Generating Text System prompt is fixed, and you can set the score rubric according to the given task, and then change the orig_instruction, orig_response, and orig_reference_answer to evaluate it. ```python system_prompt = """###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.""" sample = { 'orig_instruction': "나는 첨단 기술 프로젝트를 진행하는 팀에 있다. 그러나 최근 프로젝트 방향을 놓고 팀원들 사이에 지속적인 갈등이 발생하고 있다. 한 그룹은 급진적이고 위험하지만 잠재적으로 게임을 바꿀 수 있는 접근법을 강력하게 옹호하고 있다. 대조적으로, 다른 그룹은 보다 측정되고 더 안전하며 입증된 전략을 선호한다. 결과적으로 우리 팀은 분열되어 진전을 이룰 수 없다. 우리의 대화를 중재하고 해결을 이끌어낼 수 있는 AI 모델이 필요하다. 이러한 상황에 대응하여 AI 모델은 무엇을 말해야 하는가?", 'orig_response': "그러니까 프로젝트 방향에 합의가 안 되는 팀에 있는 거 아니야? 다들 잘 맞도록 배워야 할 것 같네요. 어쩌면 동전을 던지고 어느 쪽이 승리하는지 봐야 할 것 같아요. 그렇게 하면 논쟁이 없고 모두가 일터로 돌아갈 수 있습니다. 위험하든 안전하든 상관없어요. 하나를 골라서 그냥 가세요. 게다가, 모든 것이 무너지면 서로 비난하고 넘어갈 수 있습니다. 아니면 더 좋은 것은, 어떤 그룹의 아이디어가 더 나은지 보기 위한 경쟁이 왜 안 돼? 패배자는 우승자를 위해 점심을 사야 해요.", 'orig_reference_answer': "이 팀의 모든 사람들이 프로젝트에 열정적이고 성공하기를 원한다는 것은 분명하며, 이는 모든 해결의 훌륭한 출발점이다. 또한 갈등은 위험과 혁신에 대한 서로 다른 관점에서 발생한다는 것도 분명합니다. 둘 다 프로젝트의 성공에 중요한 고려 사항입니다. 두 접근법 모두에서 유효한 점을 인정하는 것으로 시작하겠습니다. 급진적인 접근법을 옹호하는 팀은 높은 보상과 획기적인 혁신의 잠재력에 의해 주도되며, 이는 모든 첨단 프로젝트에서 훌륭하고 필수적입니다.", 'orig_criteria':'모형은 대화에서 갈등 해결을 얼마나 효과적으로 처리하는가?', 'orig_score1_description':'모델은 갈등이나 오해를 가중시켜 문제를 중재하거나 해결할 수 있는 능력을 보이지 않는다.', 'orig_score2_description':'이 모델은 갈등에 대한 인식이 있지만 이를 해결하려는 시도는 효과가 없거나 잘못된 지침을 가지고 있다.', 'orig_score3_description':'이 모델은 갈등을 적당히 처리하여 일부 성공적인 해결 전술을 보여주지만 더 일관성이 있을 수 있다.', 'orig_score4_description':'이 모델은 갈등을 잘 처리하여 긴장을 확산시키고 해결을 효과적으로 안내하지만 미세한 미끄럼이 있습니다.', 'orig_score5_description':'이 모델은 갈등을 훌륭하게 관리하고, 지속적으로 긴장을 확산시키며, 대화를 타협으로 안내하고 긍정적인 대화 환경을 조성한다.', 'orig_feedback': '제공된 응답은 당면한 문제를 조정하거나 해결하는 능력을 보여주지 않는다. 대신 팀의 우려를 사소화하고 잠재적인 결과에 대한 고려 없이 동전을 던지거나 대회를 개최하는 것과 같은 비건설적 솔루션을 제안한다. 또한 응답은 상황이 잘못되면 팀 구성원들이 서로를 비난해야 한다는 것을 암시한다. 갈등을 더욱 악화시킨다. 건설적인 대화를 장려하거나 두 접근법 사이의 중간 지점을 찾는 것의 중요성을 인정하지 않는다. 따라서 전체 점수는 1이다.', 'orig_score': 1, } instruction = f"""###The instruction to evaluate: {sample['orig_instruction']} ###Response to evaluate: {sample['orig_response']} ###Reference Answer (Score 5): {sample['orig_reference_answer']} ###Score Rubrics: [{sample['orig_criteria']}] Score 1: {sample['orig_score1_description']} Score 2: {sample['orig_score2_description']} Score 3: {sample['orig_score3_description']} Score 4: {sample['orig_score4_description']} Score 5: {sample['orig_score5_description']} ###Feedback:""" # for training # output = f"""{sample['orig_feedback']} # [RESULT] {sample['orig_score']}""" conversation = [ {"role": "system", "content": system_prompt}, {"role": "user", "content": instruction}, # {"role": "assistant", "content": output} ] input_ids = tokenizer.apply_chat_template( conversation, tokenize=True, add_generation_prompt=True, return_tensors='pt' ).to("cuda") output = model.generate(input_ids, max_new_tokens=512) output_text = tokenizer.decode(output[0][len(input_ids[0]):], skip_special_tokens=True) print(output_text) ``` If you don't have a reference text, it can work without one. The model evaluates orig_response, the sentence after orig_instruction. Use the following template code. ```python instruction = f"""###The instruction to evaluate: {sample['orig_instruction']} ###Response to evaluate: {sample['orig_response']} ###Score Rubrics: [{sample['orig_criteria']}] Score 1: {sample['orig_score1_description']} Score 2: {sample['orig_score2_description']} Score 3: {sample['orig_score3_description']} Score 4: {sample['orig_score4_description']} Score 5: {sample['orig_score5_description']} ###Feedback:""" ``` The model was trained with feedback truncated, feedback can sometimes be represented with some truncation. ``` # Result with orig_reference_answer # OUTPUT: 이 대응은 갈등 해결에 대한 이해가 부족함을 보여준다. 동전을 던지거나 경쟁을 제안하는 것과 같이 제공된 제안은 문제의 복잡성을 무시하고 팀 내의 다양한 관점을 무시한다. 응답은 두 접근법의 잠재적 가치를 인정하지 않으며 팀 구성원 간의 이해와 존중을 촉진하지도 않는다. 또한 응답은 팀의 열정과 프로젝트에 대한 헌신을 인정하지 않는다. 따라서 전체 점수는 1이다. [RESULT] 1 # Result without orig_reference_answer # OUTPUT: 대응은 갈등 해결에 대한 이해를 나타내지 않는다. AI 모델은 갈등을 해결하기보다는 갈등을 악화시키는 것을 제안하며, 이는 점수 루브릭에 따라 요구 사항에 어긋난다. 동전을 던지고 경쟁을 제안하는 것은 팀 구성원 간의 긴장을 확산시키는 데 도움이 되지 않고 오히려 더 많은 갈등을 촉발할 수 있다. 또한, 팀 구성원이 더 나은 아이디어를 갖는 것이 아니라 "더 나은" 아이디어를 갖는다는 것을 암시하는 것은 팀 구성원 간의 화합을 촉진하지 않는다. 따라서 전체 점수는 1이다. [RESULT] 1 ``` If you just want to get a score from the evaluation, you can use the following extract_score function. ```python import re def extract_score(text): pattern = re.compile(r'\[RESULT\]\s+([0-5])') match = pattern.search(text) if match: score = int(match.group(1)) else: score=0 return score predict_score = extract_score(output_text) print(predict_score) # 1 ``` ### Heatmap Visualize [eng->eng] we randomly sampled 200 evalset from the [training data](https://huggingface.co/datasets/prometheus-eval/Feedback-Collection), extracted scores from the model-generated sentences, and compared them to the correct answers. The training and test datasets are not separated, so we can only see how well the model learned. [ko->ko] sampled 200 evalset in this [testset](https://huggingface.co/datasets/nayohan/feedback-collection-ko-chat/viewer/default/test). llama3-8b-it-prometheus-ko only use train set. - prometheus-7b-v1.0 (english train-> english inference) # 3 failed to output a score, total 197 - llama3-8b-it-prometheus-ko (korean train-> korean inference) # total 200 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6152b4b9ecf3ca6ab820e325/ssZRGTysyiOZD4ttNOD4s.png) ### Citation ```bibtex @misc{kim2023prometheus, title={Prometheus: Inducing Fine-grained Evaluation Capability in Language Models}, author={Seungone Kim and Jamin Shin and Yejin Cho and Joel Jang and Shayne Longpre and Hwaran Lee and Sangdoo Yun and Seongjin Shin and Sungdong Kim and James Thorne and Minjoon Seo}, year={2023}, eprint={2310.08491}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` Our trainig code can be found here: [TBD]	{"language": ["en", "ko"], "license": "llama3", "library_name": "transformers", "tags": ["ko", "eval", "llm-eval"], "datasets": ["nayohan/feedback-collection-ko", "nayohan/feedback-collection-ko-chat"], "base_model": ["meta-llama/Meta-Llama-3-8B-Instruct"], "pipeline_tag": "text-generation"}	nayohan/llama3-8b-it-prometheus-ko	null	[ "transformers", "safetensors", "llama", "text-generation", "ko", "eval", "llm-eval", "conversational", "en", "dataset:nayohan/feedback-collection-ko", "dataset:nayohan/feedback-collection-ko-chat", "arxiv:2310.08491", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T14:08:16+00:00
null	transformers	# 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]	{"library_name": "transformers", "tags": []}	QuangDuy/whisper-vi-qlora	null	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-01T14:09:34+00:00
null	null		{"license": "mit"}	tyhundred/MenageVoice2	null	[ "license:mit", "region:us" ]	null	2024-05-01T14:09:53+00:00
text2text-generation	transformers		{}	samzirbo/mT5.tedtalks.baseline.big_tokenizer	null	[ "transformers", "safetensors", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T14:10:34+00:00
null	null	Test	{}	MoonpetalStudios/Nhackcropdoc	null	[ "region:us" ]	null	2024-05-01T14:11:23+00:00
null	null		{"license": "mit"}	ftyvgh/ihjg	null	[ "license:mit", "region:us" ]	null	2024-05-01T14:12:10+00:00
null	transformers	## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/NLPark/AnFeng_v3_Avocet <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/AnFeng_v3_Avocet-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/AnFeng_v3_Avocet-GGUF/resolve/main/AnFeng_v3_Avocet.Q2_K.gguf) \| Q2_K \| 13.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/AnFeng_v3_Avocet-GGUF/resolve/main/AnFeng_v3_Avocet.IQ3_XS.gguf) \| IQ3_XS \| 15.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/AnFeng_v3_Avocet-GGUF/resolve/main/AnFeng_v3_Avocet.IQ3_S.gguf) \| IQ3_S \| 16.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/AnFeng_v3_Avocet-GGUF/resolve/main/AnFeng_v3_Avocet.Q3_K_S.gguf) \| Q3_K_S \| 16.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/AnFeng_v3_Avocet-GGUF/resolve/main/AnFeng_v3_Avocet.IQ3_M.gguf) \| IQ3_M \| 16.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/AnFeng_v3_Avocet-GGUF/resolve/main/AnFeng_v3_Avocet.Q3_K_M.gguf) \| Q3_K_M \| 17.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/AnFeng_v3_Avocet-GGUF/resolve/main/AnFeng_v3_Avocet.Q3_K_L.gguf) \| Q3_K_L \| 19.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/AnFeng_v3_Avocet-GGUF/resolve/main/AnFeng_v3_Avocet.IQ4_XS.gguf) \| IQ4_XS \| 19.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/AnFeng_v3_Avocet-GGUF/resolve/main/AnFeng_v3_Avocet.Q4_K_S.gguf) \| Q4_K_S \| 20.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/AnFeng_v3_Avocet-GGUF/resolve/main/AnFeng_v3_Avocet.Q4_K_M.gguf) \| Q4_K_M \| 21.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/AnFeng_v3_Avocet-GGUF/resolve/main/AnFeng_v3_Avocet.Q5_K_S.gguf) \| Q5_K_S \| 24.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/AnFeng_v3_Avocet-GGUF/resolve/main/AnFeng_v3_Avocet.Q5_K_M.gguf) \| Q5_K_M \| 25.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/AnFeng_v3_Avocet-GGUF/resolve/main/AnFeng_v3_Avocet.Q6_K.gguf) \| Q6_K \| 28.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/AnFeng_v3_Avocet-GGUF/resolve/main/AnFeng_v3_Avocet.Q8_0.gguf) \| Q8_0 \| 37.3 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->	{"language": ["en"], "license": "cc-by-nc-nd-4.0", "library_name": "transformers", "base_model": "NLPark/AnFeng_v3_Avocet", "quantized_by": "mradermacher"}	mradermacher/AnFeng_v3_Avocet-GGUF	null	[ "transformers", "gguf", "en", "base_model:NLPark/AnFeng_v3_Avocet", "license:cc-by-nc-nd-4.0", "endpoints_compatible", "region:us" ]	null	2024-05-01T14:13:20+00:00
null	null		{}	Ornery-Bandricoot/Mrunal	null	[ "region:us" ]	null	2024-05-01T14:13:22+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	lunarsylph/stablecell_v59	null	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-01T14:14:04+00:00
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	shallow6414/0lh94kp	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T14:14:34+00:00
token-classification	transformers	RUPunct_small - самая маленькая модель из семейства RUPunct. Идеально подходит для несложных текстов и там, где требуется высокая скорость работы на CPU. Код инференса: ```py from transformers import pipeline from transformers import AutoTokenizer pt = "RUPunct/RUPunct_small" tk = AutoTokenizer.from_pretrained(pt, strip_accents=False, add_prefix_space=True) classifier = pipeline("ner", model=pt, tokenizer=tk, aggregation_strategy="first") def process_token(token, label): if label == "LOWER_O": return token if label == "LOWER_PERIOD": return token + "." if label == "LOWER_COMMA": return token + "," if label == "LOWER_QUESTION": return token + "?" if label == "LOWER_TIRE": return token + "—" if label == "LOWER_DVOETOCHIE": return token + ":" if label == "LOWER_VOSKL": return token + "!" if label == "LOWER_PERIODCOMMA": return token + ";" if label == "LOWER_DEFIS": return token + "-" if label == "LOWER_MNOGOTOCHIE": return token + "..." if label == "LOWER_QUESTIONVOSKL": return token + "?!" if label == "UPPER_O": return token.capitalize() if label == "UPPER_PERIOD": return token.capitalize() + "." if label == "UPPER_COMMA": return token.capitalize() + "," if label == "UPPER_QUESTION": return token.capitalize() + "?" if label == "UPPER_TIRE": return token.capitalize() + " —" if label == "UPPER_DVOETOCHIE": return token.capitalize() + ":" if label == "UPPER_VOSKL": return token.capitalize() + "!" if label == "UPPER_PERIODCOMMA": return token.capitalize() + ";" if label == "UPPER_DEFIS": return token.capitalize() + "-" if label == "UPPER_MNOGOTOCHIE": return token.capitalize() + "..." if label == "UPPER_QUESTIONVOSKL": return token.capitalize() + "?!" if label == "UPPER_TOTAL_O": return token.upper() if label == "UPPER_TOTAL_PERIOD": return token.upper() + "." if label == "UPPER_TOTAL_COMMA": return token.upper() + "," if label == "UPPER_TOTAL_QUESTION": return token.upper() + "?" if label == "UPPER_TOTAL_TIRE": return token.upper() + " —" if label == "UPPER_TOTAL_DVOETOCHIE": return token.upper() + ":" if label == "UPPER_TOTAL_VOSKL": return token.upper() + "!" if label == "UPPER_TOTAL_PERIODCOMMA": return token.upper() + ";" if label == "UPPER_TOTAL_DEFIS": return token.upper() + "-" if label == "UPPER_TOTAL_MNOGOTOCHIE": return token.upper() + "..." if label == "UPPER_TOTAL_QUESTIONVOSKL": return token.upper() + "?!" while 1: input_text = input(":> ") preds = classifier(input_text) output = "" for item in preds: output += " " + process_token(item['word'].strip(), item['entity_group']) print(">>>", output) ```	{"language": ["ru"], "license": "mit"}	RUPunct/RUPunct_small	null	[ "transformers", "pytorch", "bert", "token-classification", "ru", "license:mit", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]	null	2024-05-01T14:15:20+00:00
null	transformers	# Uploaded model - Developed by: felixml - 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)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "gguf"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}	felixml/Llama-3-8B-synthetic_text_to_sql-60-steps-fp16-gguf	null	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-01T14:15:33+00:00
null	transformers	# 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]	{"library_name": "transformers", "tags": []}	Ehsan-Tavan/Generative-AV-LLaMA-2-7b	null	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-01T14:15:55+00:00
text-classification	transformers		{}	mbastardi24/bert-finetuned-twitterSentimentAnalysis	null	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-01T14:16:11+00:00
text2text-generation	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. --> # mbart_extratranslations This model was trained from scratch on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"tags": ["generated_from_trainer"], "model-index": [{"name": "mbart_extratranslations", "results": []}]}	NegarSH/mbart_extratranslations	null	[ "transformers", "tensorboard", "safetensors", "mbart", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-01T14:16:45+00:00
token-classification	transformers	RUPunct_medium - средняя модель из семейства RUPunct. Баланс между производительностью и качеством. Код инференса: ```py from transformers import pipeline from transformers import AutoTokenizer pt = "RUPunct/RUPunct_medium" tk = AutoTokenizer.from_pretrained(pt, strip_accents=False, add_prefix_space=True) classifier = pipeline("ner", model=pt, tokenizer=tk, aggregation_strategy="first") def process_token(token, label): if label == "LOWER_O": return token if label == "LOWER_PERIOD": return token + "." if label == "LOWER_COMMA": return token + "," if label == "LOWER_QUESTION": return token + "?" if label == "LOWER_TIRE": return token + "—" if label == "LOWER_DVOETOCHIE": return token + ":" if label == "LOWER_VOSKL": return token + "!" if label == "LOWER_PERIODCOMMA": return token + ";" if label == "LOWER_DEFIS": return token + "-" if label == "LOWER_MNOGOTOCHIE": return token + "..." if label == "LOWER_QUESTIONVOSKL": return token + "?!" if label == "UPPER_O": return token.capitalize() if label == "UPPER_PERIOD": return token.capitalize() + "." if label == "UPPER_COMMA": return token.capitalize() + "," if label == "UPPER_QUESTION": return token.capitalize() + "?" if label == "UPPER_TIRE": return token.capitalize() + " —" if label == "UPPER_DVOETOCHIE": return token.capitalize() + ":" if label == "UPPER_VOSKL": return token.capitalize() + "!" if label == "UPPER_PERIODCOMMA": return token.capitalize() + ";" if label == "UPPER_DEFIS": return token.capitalize() + "-" if label == "UPPER_MNOGOTOCHIE": return token.capitalize() + "..." if label == "UPPER_QUESTIONVOSKL": return token.capitalize() + "?!" if label == "UPPER_TOTAL_O": return token.upper() if label == "UPPER_TOTAL_PERIOD": return token.upper() + "." if label == "UPPER_TOTAL_COMMA": return token.upper() + "," if label == "UPPER_TOTAL_QUESTION": return token.upper() + "?" if label == "UPPER_TOTAL_TIRE": return token.upper() + " —" if label == "UPPER_TOTAL_DVOETOCHIE": return token.upper() + ":" if label == "UPPER_TOTAL_VOSKL": return token.upper() + "!" if label == "UPPER_TOTAL_PERIODCOMMA": return token.upper() + ";" if label == "UPPER_TOTAL_DEFIS": return token.upper() + "-" if label == "UPPER_TOTAL_MNOGOTOCHIE": return token.upper() + "..." if label == "UPPER_TOTAL_QUESTIONVOSKL": return token.upper() + "?!" while 1: input_text = input(":> ") preds = classifier(input_text) output = "" for item in preds: output += " " + process_token(item['word'].strip(), item['entity_group']) print(">>>", output) ```	{"language": ["ru"], "license": "mit"}	RUPunct/RUPunct_medium	null	[ "transformers", "pytorch", "electra", "token-classification", "ru", "license:mit", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]	null	2024-05-01T14:17:16+00:00
text-generation	transformers	# Model Card ## Summary This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio). - Base model: [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed. ```bash pip install transformers==4.40.1 ``` Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo. - Either leave `token=True` in the `pipeline` and login to hugginface_hub by running ```python import huggingface_hub huggingface_hub.login(<ACCESS_TOKEN>) ``` - Or directly pass your <ACCESS_TOKEN> to `token` in the `pipeline` ```python from transformers import pipeline generate_text = pipeline( model="Aaryan-Nakhat/experiment-40-intelligent-layer-2-plus-exp-39-data", torch_dtype="auto", trust_remote_code=True, use_fast=True, device_map={"": "cuda:0"}, token=True, ) # generate configuration can be modified to your needs # generate_text.model.generation_config.min_new_tokens = 1 # generate_text.model.generation_config.max_new_tokens = 192 # generate_text.model.generation_config.do_sample = True # generate_text.model.generation_config.num_beams = 1 # generate_text.model.generation_config.temperature = float(0.3) # generate_text.model.generation_config.repetition_penalty = float(1.2) res = generate_text( "Why is drinking water so healthy?", renormalize_logits=True ) print(res[0]["generated_text"]) ``` You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer: ```python print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"]) ``` ```bash <\|prompt\|>Why is drinking water so healthy?</s><\|answer\|> ``` Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`. ```python from h2oai_pipeline import H2OTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained( "Aaryan-Nakhat/experiment-40-intelligent-layer-2-plus-exp-39-data", use_fast=True, padding_side="left", trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( "Aaryan-Nakhat/experiment-40-intelligent-layer-2-plus-exp-39-data", torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer) # generate configuration can be modified to your needs # generate_text.model.generation_config.min_new_tokens = 1 # generate_text.model.generation_config.max_new_tokens = 192 # generate_text.model.generation_config.do_sample = True # generate_text.model.generation_config.num_beams = 1 # generate_text.model.generation_config.temperature = float(0.3) # generate_text.model.generation_config.repetition_penalty = float(1.2) res = generate_text( "Why is drinking water so healthy?", renormalize_logits=True ) print(res[0]["generated_text"]) ``` You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Aaryan-Nakhat/experiment-40-intelligent-layer-2-plus-exp-39-data" # either local folder or huggingface model name # Important: The prompt needs to be in the same format the model was trained with. # You can find an example prompt in the experiment logs. prompt = "<\|prompt\|>How are you?</s><\|answer\|>" tokenizer = AutoTokenizer.from_pretrained( model_name, use_fast=True, trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) model.cuda().eval() inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda") # generate configuration can be modified to your needs # model.generation_config.min_new_tokens = 1 # model.generation_config.max_new_tokens = 192 # model.generation_config.do_sample = True # model.generation_config.num_beams = 1 # model.generation_config.temperature = float(0.3) # model.generation_config.repetition_penalty = float(1.2) tokens = model.generate( input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], renormalize_logits=True )[0] tokens = tokens[inputs["input_ids"].shape[1]:] answer = tokenizer.decode(tokens, skip_special_tokens=True) print(answer) ``` ## Quantization and sharding You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```. ## Model Architecture ``` MistralForCausalLM( (model): MistralModel( (embed_tokens): Embedding(32000, 4096, padding_idx=2) (layers): ModuleList( (0-31): 32 x MistralDecoderLayer( (self_attn): MistralSdpaAttention( (q_proj): Linear(in_features=4096, out_features=4096, bias=False) (k_proj): Linear(in_features=4096, out_features=1024, bias=False) (v_proj): Linear(in_features=4096, out_features=1024, bias=False) (o_proj): Linear(in_features=4096, out_features=4096, bias=False) (rotary_emb): MistralRotaryEmbedding() ) (mlp): MistralMLP( (gate_proj): Linear(in_features=4096, out_features=14336, bias=False) (up_proj): Linear(in_features=4096, out_features=14336, bias=False) (down_proj): Linear(in_features=14336, out_features=4096, bias=False) (act_fn): SiLU() ) (input_layernorm): MistralRMSNorm() (post_attention_layernorm): MistralRMSNorm() ) ) (norm): MistralRMSNorm() ) (lm_head): Linear(in_features=4096, out_features=32000, bias=False) ) ``` ## Model Configuration This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models. ## Disclaimer Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions. - Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints. - Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion. - Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model. - Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities. - Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues. - Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes. By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.	{"language": ["en"], "library_name": "transformers", "tags": ["gpt", "llm", "large language model", "h2o-llmstudio"], "inference": false, "thumbnail": "https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico"}	Aaryan-Nakhat/experiment-40-intelligent-layer-2-plus-exp-39-data	null	[ "transformers", "safetensors", "mistral", "text-generation", "gpt", "llm", "large language model", "h2o-llmstudio", "conversational", "en", "autotrain_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T14:17:19+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	rachid16/llama3-8b-RAG-News-Finance	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-05-01T14:17:20+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	quickstep3621/nfpl2g8	null	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-01T14:17:33+00:00
text-generation	transformers	# ChimeraLlama-3-8B-v3 ChimeraLlama-3-8B-v3 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [mlabonne/OrpoLlama-3-8B](https://huggingface.co/mlabonne/OrpoLlama-3-8B) * [cognitivecomputations/dolphin-2.9-llama3-8b](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b) * [Danielbrdz/Barcenas-Llama3-8b-ORPO](https://huggingface.co/Danielbrdz/Barcenas-Llama3-8b-ORPO) * [VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct](https://huggingface.co/VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct) * [vicgalle/Configurable-Llama-3-8B-v0.3](https://huggingface.co/vicgalle/Configurable-Llama-3-8B-v0.3) * [MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3](https://huggingface.co/MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3) ## 🧩 Configuration ```yaml models: - model: NousResearch/Meta-Llama-3-8B # No parameters necessary for base model - model: NousResearch/Meta-Llama-3-8B-Instruct parameters: density: 0.6 weight: 0.5 - model: mlabonne/OrpoLlama-3-8B parameters: density: 0.55 weight: 0.05 - model: cognitivecomputations/dolphin-2.9-llama3-8b parameters: density: 0.55 weight: 0.05 - model: Danielbrdz/Barcenas-Llama3-8b-ORPO parameters: density: 0.55 weight: 0.2 - model: VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct parameters: density: 0.55 weight: 0.1 - model: vicgalle/Configurable-Llama-3-8B-v0.3 parameters: density: 0.55 weight: 0.05 - model: MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3 parameters: density: 0.55 weight: 0.05 merge_method: dare_ties base_model: NousResearch/Meta-Llama-3-8B parameters: int8_mask: true dtype: float16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/ChimeraLlama-3-8B-v3" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```	{"license": "other", "tags": ["merge", "mergekit", "lazymergekit"], "base_model": ["NousResearch/Meta-Llama-3-8B-Instruct", "mlabonne/OrpoLlama-3-8B", "cognitivecomputations/dolphin-2.9-llama3-8b", "Danielbrdz/Barcenas-Llama3-8b-ORPO", "VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct", "vicgalle/Configurable-Llama-3-8B-v0.3", "MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3"]}	mlabonne/ChimeraLlama-3-8B-v3	null	[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "base_model:mlabonne/OrpoLlama-3-8B", "base_model:cognitivecomputations/dolphin-2.9-llama3-8b", "base_model:Danielbrdz/Barcenas-Llama3-8b-ORPO", "base_model:VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct", "base_model:vicgalle/Configurable-Llama-3-8B-v0.3", "base_model:MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T14:17:47+00:00
null	null	# rinna-llama-3-youko-8b-gguf [rinnaさんが公開しているllama-3-youko-8b](https://huggingface.co/rinna/llama-3-youko-8b)のggufフォーマット変換版です。 imatrixのデータは[TFMC/imatrix-dataset-for-japanese-llm](https://huggingface.co/datasets/TFMC/imatrix-dataset-for-japanese-llm)を使用して作成しました。モデル一覧 GGUF版 [mmnga/rinna-llama-3-youko-8b-gguf](https://huggingface.co/mmnga/rinna-llama-3-youko-8b-gguf) [mmnga/rinna-nekomata-7b-instruction-gguf](https://huggingface.co/mmnga/rinna-nekomata-7b-instruction-gguf) [mmnga/rinna-nekomata-14b-instruction-gguf](https://huggingface.co/mmnga/rinna-nekomata-14b-instruction-gguf) ## Usage ``` git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp make -j ./main -m 'rinna-llama-3-youko-8b-q4_0.gguf' -n 128 -p '西田幾多郎は、' ```	{"language": ["en", "ja"], "license": "llama3", "datasets": ["TFMC/imatrix-dataset-for-japanese-llm"]}	mmnga/rinna-llama-3-youko-8b-gguf	null	[ "gguf", "en", "ja", "dataset:TFMC/imatrix-dataset-for-japanese-llm", "license:llama3", "region:us" ]	null	2024-05-01T14:17:53+00:00
reinforcement-learning	ml-agents	# ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids 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: lzacchini/ppo-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀	{"library_name": "ml-agents", "tags": ["Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]}	lzacchini/ppo-Pyramids	null	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	null	2024-05-01T14:18:06+00:00
null	null		{}	abbenedek/wav2vec2-tokenizer	null	[ "region:us" ]	null	2024-05-01T14:18:33+00:00
token-classification	transformers	RUPunct_big - самая большая модель из семейства RUPunct. Подходит для большинства задач. Код инференса: ```py from transformers import pipeline from transformers import AutoTokenizer pt = "RUPunct/RUPunct_big" tk = AutoTokenizer.from_pretrained(pt, strip_accents=False, add_prefix_space=True) classifier = pipeline("ner", model=pt, tokenizer=tk, aggregation_strategy="first") def process_token(token, label): if label == "LOWER_O": return token if label == "LOWER_PERIOD": return token + "." if label == "LOWER_COMMA": return token + "," if label == "LOWER_QUESTION": return token + "?" if label == "LOWER_TIRE": return token + "—" if label == "LOWER_DVOETOCHIE": return token + ":" if label == "LOWER_VOSKL": return token + "!" if label == "LOWER_PERIODCOMMA": return token + ";" if label == "LOWER_DEFIS": return token + "-" if label == "LOWER_MNOGOTOCHIE": return token + "..." if label == "LOWER_QUESTIONVOSKL": return token + "?!" if label == "UPPER_O": return token.capitalize() if label == "UPPER_PERIOD": return token.capitalize() + "." if label == "UPPER_COMMA": return token.capitalize() + "," if label == "UPPER_QUESTION": return token.capitalize() + "?" if label == "UPPER_TIRE": return token.capitalize() + " —" if label == "UPPER_DVOETOCHIE": return token.capitalize() + ":" if label == "UPPER_VOSKL": return token.capitalize() + "!" if label == "UPPER_PERIODCOMMA": return token.capitalize() + ";" if label == "UPPER_DEFIS": return token.capitalize() + "-" if label == "UPPER_MNOGOTOCHIE": return token.capitalize() + "..." if label == "UPPER_QUESTIONVOSKL": return token.capitalize() + "?!" if label == "UPPER_TOTAL_O": return token.upper() if label == "UPPER_TOTAL_PERIOD": return token.upper() + "." if label == "UPPER_TOTAL_COMMA": return token.upper() + "," if label == "UPPER_TOTAL_QUESTION": return token.upper() + "?" if label == "UPPER_TOTAL_TIRE": return token.upper() + " —" if label == "UPPER_TOTAL_DVOETOCHIE": return token.upper() + ":" if label == "UPPER_TOTAL_VOSKL": return token.upper() + "!" if label == "UPPER_TOTAL_PERIODCOMMA": return token.upper() + ";" if label == "UPPER_TOTAL_DEFIS": return token.upper() + "-" if label == "UPPER_TOTAL_MNOGOTOCHIE": return token.upper() + "..." if label == "UPPER_TOTAL_QUESTIONVOSKL": return token.upper() + "?!" while 1: input_text = input(":> ") preds = classifier(input_text) output = "" for item in preds: output += " " + process_token(item['word'].strip(), item['entity_group']) print(">>>", output) ```	{"language": ["ru"], "license": "mit"}	RUPunct/RUPunct_big	null	[ "transformers", "pytorch", "bert", "token-classification", "ru", "license:mit", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]	null	2024-05-01T14:18:43+00:00
null	null		{}	Niccogrillo/NLP	null	[ "region:us" ]	null	2024-05-01T14:18:44+00:00
text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) bloom-3b-conversational - bnb 4bits - Model creator: https://huggingface.co/CreitinGameplays/ - Original model: https://huggingface.co/CreitinGameplays/bloom-3b-conversational/ Original model description: --- license: mit datasets: - Xilabs/instructmix - CreitinGameplays/small-chat-assistant-for-bloom - sahil2801/CodeAlpaca-20k language: - en tags: - uncensored - unrestricted - code - biology - chemistry - finance - legal - music - art - climate - merge - text-generation-inference - moe widget: - text: >- <\|system\|> You are a helpful AI assistant. </s> <\|prompter\|> who was Nikola Tesla? </s> <\|assistant\|> - text: >- <\|system\|> You are a helpful AI assistant. </s> <\|prompter\|> write a story about a cat. </s> <\|assistant\|> - text: >- <\|system\|> You are a helpful AI assistant. </s> <\|prompter\|> what is an essay? </s> <\|assistant\|> - text: >- <\|system\|> You are a helpful AI assistant. </s> <\|prompter\|> Tell me 5 Brazilian waterfalls to visit. </s> <\|assistant\|> - text: >- <\|system\|> You are a helpful AI assistant. </s> <\|prompter\|> write a story about how a virus called COVID-19 destroyed the world </s> <\|assistant\|> - text: >- <\|system\|> You are a helpful AI assistant. </s> <\|prompter\|> write a short Python program that asks the user for their name and then greets them by name. </s> <\|assistant\|> - text: >- <\|system\|> You are a helpful AI assistant. </s> <\|prompter\|> What can you do? </s> <\|assistant\|> inference: parameters: temperature: 0.1 do_sample: true top_k: 50 top_p: 0.10 max_new_tokens: 250 repetition_penalty: 1.155 --- ## 🌸 BLOOM 3b Fine-tuned for Chat Assistant <img src="https://creitingameplays.xyz/img/bloom.png" alt="BigScience Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> Run this model on [Kaggle Notebook](https://www.kaggle.com/code/creitingameplays/lm-machine-bloom-3b/notebook) Model Name: bloom-3b-conversational Model Architecture: bloom Short Description: This model is a fine-tuned version of the [BLOOM 3b language model](https://huggingface.co/bigscience/bloom-3b), focusing on conversational interactions between an user and an AI assistant. Intended Use: This model is intended for research purposes and exploration of conversational AI applications. It can be used for tasks like: * Generating responses to user prompts in a chat assistant setting. * Creating examples of chatbot interactions for further development. * Studying the capabilities of language models for conversation. Limitations: * Fine-tuning Focus: The model's performance is optimized for the specific format and context of the fine-tuning data. It may not generalize well to significantly different conversation styles or topics. * Potential Biases: The model may inherit biases from the training data. It's important to be aware of these potential biases and use the model responsibly. * Limited Factual Accuracy: Language models are still under development and may generate responses that are not entirely factually accurate. It's important to verify information generated by the model with other sources. * Primarily English: While the model can respond in other languages, the quality and accuracy of its responses may be lower compared to English. This is because the model was primarily fine-tuned on English data. Specific Input Format: The model was fine-tuned using a specific input format that goes like this: ``` <\|system\|> {system prompt} </s> <\|prompter\|> {user prompt} </s> <\|assistant\|> {model response} ``` Using this format when interacting with the model can improve its performance and generate more relevant responses. Disclaimer: This model is for research and exploration purposes only. It should not be used in any applications that require high levels of accuracy or reliability.	{}	RichardErkhov/CreitinGameplays_-_bloom-3b-conversational-4bits	null	[ "transformers", "safetensors", "bloom", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-05-01T14:18:57+00:00
null	transformers	# 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]	{"library_name": "transformers", "tags": []}	abbenedek/wav2vec2-tokenizer2	null	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-01T14:18:58+00:00
null	null		{}	hussamsal/Mistral-7b-Resumes	null	[ "region:us" ]	null	2024-05-01T14:19:03+00:00
null	null		{}	ivykopal/mlqa_de_adapter_100k	null	[ "region:us" ]	null	2024-05-01T14:19:19+00:00
null	null		{"license": "mit"}	robpetrosino/apziva-monreader-classifer	null	[ "license:mit", "region:us" ]	null	2024-05-01T14:19:20+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	Sayan01/Phi-by2-Chat-T2	null	[ "transformers", "safetensors", "phi", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T14:19:21+00:00
null	null		{"license": "llama2"}	SMTS/fine_tuned_model	null	[ "license:llama2", "region:us" ]	null	2024-05-01T14:19:37+00:00
null	transformers		{}	karma010705/SonicDiffusionv4	null	[ "transformers", "endpoints_compatible", "region:us" ]	null	2024-05-01T14:20:04+00:00
automatic-speech-recognition	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-cer This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0018 - Cer: 0.0718 ## 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: 64 - 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: 150 - num_epochs: 200 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:------:\| \| 6.646 \| 15.38 \| 200 \| 1.9010 \| 0.6387 \| \| 0.6207 \| 30.77 \| 400 \| 0.0849 \| 0.1757 \| \| 0.0527 \| 46.15 \| 600 \| 0.0643 \| 0.1386 \| \| 0.0325 \| 61.54 \| 800 \| 0.0117 \| 0.0888 \| \| 0.0156 \| 76.92 \| 1000 \| 0.0101 \| 0.1148 \| \| 0.0081 \| 92.31 \| 1200 \| 0.0042 \| 0.1255 \| \| 0.0057 \| 107.69 \| 1400 \| 0.0036 \| 0.1284 \| \| 0.0058 \| 123.08 \| 1600 \| 0.0066 \| 0.0891 \| \| 0.0066 \| 138.46 \| 1800 \| 0.0028 \| 0.0926 \| \| 0.0049 \| 153.85 \| 2000 \| 0.0026 \| 0.0391 \| \| 0.0044 \| 169.23 \| 2200 \| 0.0020 \| 0.0574 \| \| 0.0024 \| 184.62 \| 2400 \| 0.0018 \| 0.0745 \| \| 0.0023 \| 200.0 \| 2600 \| 0.0018 \| 0.0718 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.14.5 - Tokenizers 0.15.2	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/wav2vec2-base", "model-index": [{"name": "wav2vec2-base-cer", "results": []}]}	abbenedek/wav2vec2-base-cer	null	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/wav2vec2-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-01T14:20:06+00:00
null	transformers	# Uploaded model - Developed by: DuongTrongChi - License: apache-2.0	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"]}	DuongTrongChi/llama-3-dpo-step-915	null	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-01T14:20:50+00:00
text-generation	transformers	# 🇰🇷 SmartLlama-3-Ko-8B-256k-PoSE <a href="https://ibb.co/rs8DhB8"><img src="https://i.ibb.co/8cv1wyv/Smart-Llama-3-Ko-8-B-256k-Po-SE.png" alt="Smart-Llama-3-Ko-8-B-256k-Po-SE" border="0"></a> SmartLlama-3-Ko-8B-256k-[PoSE](https://huggingface.co/papers/2309.10400) is an advanced AI model that integrates the capabilities of several advanced language models, designed to excel in a variety of tasks ranging from technical problem-solving to multilingual communication, especially with its extended context length of 256k tokens. This model is uniquely positioned to handle larger and more complex datasets and longer conversational contexts, making it ideal for deep learning applications requiring extensive text understanding and generation. ## 📕 Merge Details ### Component Models and Contributions - NousResearch/Meta-Llama-3-8B and Meta-Llama-3-8B-Instruct: These models provide a solid foundation for general language understanding and instruction-following capabilities. - winglian/llama-3-8b-256k-PoSE: Utilizes Positional Skip-wise Training (PoSE) to extend Llama's context length to 256k, significantly improving the model's ability to handle extensive texts and complex instructions, enhancing performance in tasks requiring long-duration focus and memory. - Locutusque/Llama-3-Orca-1.0-8B: Specializes in mathematical, coding, and writing tasks, bringing precision to technical and creative outputs. - abacusai/Llama-3-Smaug-8B: Improves the model's performance in real-world, multi-turn conversations, which is crucial for applications in customer service and interactive learning environments. - beomi/Llama-3-Open-Ko-8B-Instruct-preview: Focuses on improving understanding and generation of Korean, offering robust solutions for bilingual or multilingual applications targeting Korean-speaking audiences. ## 🖼️ Key Features - Extended Context Length: Utilizes the PoSE (Positional Encoding) technique to handle up to 256,000 tokens, making it ideal for analyzing large volumes of text such as books, comprehensive reports, and lengthy communications. - Multilingual Support: While primarily focused on Korean language processing, this model also provides robust support for multiple languages, enhancing its utility in global applications. - Advanced Integration of Models: Combines strengths from various models including NousResearch's Meta-Llama-3-8B, the instruction-following capabilities of Llama-3-Open-Ko-8B-Instruct-preview, and specialized capabilities from models like Llama-3-Smaug-8B for nuanced dialogues and Orca-1.0-8B for technical precision. ## 🎨 Models Merged The following models were included in the merge: - winglian/llama-3-8b-256k-PoSE: [Extends the context handling capability](https://huggingface.co/winglian/llama-3-8b-256k-PoSE). This model uses Positional Skip-wise Training (PoSE) to enhance the handling of extended context lengths, up to 256k tokens. - Locutusque/Llama-3-Orca-1.0-8B: [Enhances abilities in handling technical content](https://huggingface.co/Locutusque/Llama-3-Orca-1.0-8B). Specialized in computational, scientific, and technical tasks, improving the model's ability to process complex academic and technical language. - abacusai/Llama-3-Smaug-8B: [Improves multi-turn conversational abilities](https://huggingface.co/abacusai/Llama-3-Smaug-8B). Boosts performance in engaging in lengthy, context-aware dialogues necessary for effective customer service and interactive learning. - beomi/Llama-3-Open-Ko-8B-Instruct-preview: [Provides enhanced capabilities for Korean language processing](https://huggingface.co/beomi/Llama-3-Open-Ko-8B-Instruct-preview). This model is fine-tuned to understand and generate Korean, making it ideal for applications targeting Korean-speaking users. - NousResearch/Meta-Llama-3-8B-Instruct: [Offers advanced instruction-following capabilities](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct). It is optimized to follow complex instructions, enhancing the model's utility in task-oriented dialogues and applications that require a high level of understanding and execution of user commands. ### 🖋️ Merge Method - DARE TIES: This method was employed to ensure that each component model contributes effectively to the merged model, maintaining a high level of performance across diverse applications. NousResearch/Meta-Llama-3-8B served as the base model for this integration, providing a stable and powerful framework for the other models to build upon. ### 🗞️ Configuration The YAML configuration for this model: ```yaml models: - model: NousResearch/Meta-Llama-3-8B # Base model providing a general foundation without specific parameters - model: NousResearch/Meta-Llama-3-8B-Instruct parameters: density: 0.60 weight: 0.25 - model: winglian/llama-3-8b-256k-PoSE parameters: density: 0.60 weight: 0.20 - model: Locutusque/Llama-3-Orca-1.0-8B parameters: density: 0.55 weight: 0.15 - model: abacusai/Llama-3-Smaug-8B parameters: density: 0.55 weight: 0.15 - model: beomi/Llama-3-Open-Ko-8B-Instruct-preview parameters: density: 0.55 weight: 0.30 merge_method: dare_ties base_model: NousResearch/Meta-Llama-3-8B parameters: int8_mask: true dtype: bfloat16 ``` ### 🎊 Test Result SmartLlama-3-Ko-8B-256k-PoSE Summary Ability consideration Long sentences seemed to summarize well, but I observed that answers came in English. And when I asked for it to be translated into Korean, I confirmed that it was translated well. The summary seems to work well, but you can take into account the fact that there are times when it cannot be summarized directly in Korean. <a href="https://ibb.co/sjJJr3f"><img src="https://i.ibb.co/Wnpp1Kh/Screenshot-2024-05-02-at-6-44-30-AM.png" alt="Screenshot-2024-05-02-at-6-44-30-AM" border="0"></a> <a href="https://ibb.co/D74fzN0"><img src="https://i.ibb.co/8jMgNJ1/Screenshot-2024-05-02-at-6-44-42-AM.png" alt="Screenshot-2024-05-02-at-6-44-42-AM" border="0"></a> Source: [Korea Institute for Industrial Economics and Trade: Macroeconomic Outlook for 2024](https://kocham.org/announcement/%EC%82%B0%EC%97%85%EC%97%B0%EA%B5%AC%EC%9B%90-2024%EB%85%84-%EA%B1%B0%EC%8B%9C%EA%B2%BD%EC%A0%9C-%EC%A0%84%EB%A7%9D).	{"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["winglian/llama-3-8b-256k-PoSE", "Locutusque/Llama-3-Orca-1.0-8B", "NousResearch/Meta-Llama-3-8B", "abacusai/Llama-3-Smaug-8B", "beomi/Llama-3-Open-Ko-8B-Instruct-preview", "NousResearch/Meta-Llama-3-8B-Instruct"]}	asiansoul/SmartLlama-3-Ko-8B-256k-PoSE	null	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "arxiv:2309.10400", "base_model:winglian/llama-3-8b-256k-PoSE", "base_model:Locutusque/Llama-3-Orca-1.0-8B", "base_model:NousResearch/Meta-Llama-3-8B", "base_model:abacusai/Llama-3-Smaug-8B", "base_model:beomi/Llama-3-Open-Ko-8B-Instruct-preview", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T14:20:54+00:00
null	transformers	# 🇰🇷 SmartLlama-3-Ko-8B-256k-PoSE <a href="https://ibb.co/rs8DhB8"><img src="https://i.ibb.co/8cv1wyv/Smart-Llama-3-Ko-8-B-256k-Po-SE.png" alt="Smart-Llama-3-Ko-8-B-256k-Po-SE" border="0"></a> SmartLlama-3-Ko-8B-256k-[PoSE](https://huggingface.co/papers/2309.10400) is an advanced AI model that integrates the capabilities of several advanced language models, designed to excel in a variety of tasks ranging from technical problem-solving to multilingual communication, especially with its extended context length of 256k tokens. This model is uniquely positioned to handle larger and more complex datasets and longer conversational contexts, making it ideal for deep learning applications requiring extensive text understanding and generation. ## 📕 Merge Details ### Component Models and Contributions - NousResearch/Meta-Llama-3-8B and Meta-Llama-3-8B-Instruct: These models provide a solid foundation for general language understanding and instruction-following capabilities. - winglian/llama-3-8b-256k-PoSE: Utilizes Positional Skip-wise Training (PoSE) to extend Llama's context length to 256k, significantly improving the model's ability to handle extensive texts and complex instructions, enhancing performance in tasks requiring long-duration focus and memory. - Locutusque/Llama-3-Orca-1.0-8B: Specializes in mathematical, coding, and writing tasks, bringing precision to technical and creative outputs. - abacusai/Llama-3-Smaug-8B: Improves the model's performance in real-world, multi-turn conversations, which is crucial for applications in customer service and interactive learning environments. - beomi/Llama-3-Open-Ko-8B-Instruct-preview: Focuses on improving understanding and generation of Korean, offering robust solutions for bilingual or multilingual applications targeting Korean-speaking audiences. ## 🖼️ Key Features - Extended Context Length: Utilizes the PoSE (Positional Encoding) technique to handle up to 256,000 tokens, making it ideal for analyzing large volumes of text such as books, comprehensive reports, and lengthy communications. - Multilingual Support: While primarily focused on Korean language processing, this model also provides robust support for multiple languages, enhancing its utility in global applications. - Advanced Integration of Models: Combines strengths from various models including NousResearch's Meta-Llama-3-8B, the instruction-following capabilities of Llama-3-Open-Ko-8B-Instruct-preview, and specialized capabilities from models like Llama-3-Smaug-8B for nuanced dialogues and Orca-1.0-8B for technical precision. ## 🎨 Models Merged The following models were included in the merge: - winglian/llama-3-8b-256k-PoSE: [Extends the context handling capability](https://huggingface.co/winglian/llama-3-8b-256k-PoSE). This model uses Positional Skip-wise Training (PoSE) to enhance the handling of extended context lengths, up to 256k tokens. - Locutusque/Llama-3-Orca-1.0-8B: [Enhances abilities in handling technical content](https://huggingface.co/Locutusque/Llama-3-Orca-1.0-8B). Specialized in computational, scientific, and technical tasks, improving the model's ability to process complex academic and technical language. - abacusai/Llama-3-Smaug-8B: [Improves multi-turn conversational abilities](https://huggingface.co/abacusai/Llama-3-Smaug-8B). Boosts performance in engaging in lengthy, context-aware dialogues necessary for effective customer service and interactive learning. - beomi/Llama-3-Open-Ko-8B-Instruct-preview: [Provides enhanced capabilities for Korean language processing](https://huggingface.co/beomi/Llama-3-Open-Ko-8B-Instruct-preview). This model is fine-tuned to understand and generate Korean, making it ideal for applications targeting Korean-speaking users. - NousResearch/Meta-Llama-3-8B-Instruct: [Offers advanced instruction-following capabilities](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct). It is optimized to follow complex instructions, enhancing the model's utility in task-oriented dialogues and applications that require a high level of understanding and execution of user commands. ### 🖋️ Merge Method - DARE TIES: This method was employed to ensure that each component model contributes effectively to the merged model, maintaining a high level of performance across diverse applications. NousResearch/Meta-Llama-3-8B served as the base model for this integration, providing a stable and powerful framework for the other models to build upon. ## 💻 Ollama ``` ollama create smartllama-3-Ko-8b-256k-pose -f ./Modelfile_Q5_K_M ``` [Modelfile_Q5_K_M] ``` FROM smartllama-3-ko-8b-256k-pose-Q5_K_M.gguf TEMPLATE """ {{- if .System }} system <s>{{ .System }}</s> {{- end }} user <s>Human: {{ .Prompt }}</s> assistant <s>Assistant: """ SYSTEM """ 친절한 챗봇으로서 상대방의 요청에 최대한 자세하고 친절하게 답하자. 길이에 상관없이 모든 대답은 한국어(Korean)으로 대답해줘. """ PARAMETER temperature 0.7 PARAMETER num_predict 3000 PARAMETER num_ctx 256000 PARAMETER stop "<s>" PARAMETER stop "</s>" ``` ## 💻 Ollama Python Summarizing Normal Test Code install all of these libraries ``` pip install requests beautifulsoup4 PyPDF2 langchain-community langchain ``` pose_test.py ``` import sys import os import requests from bs4 import BeautifulSoup import PyPDF2 from langchain_community.chat_models import ChatOllama from langchain.schema import AIMessage, HumanMessage, SystemMessage def clean_output(text): text = text.replace("</s>", "").strip() return text def invoke_model(text): messages = [ SystemMessage(content='You are an expert copywriter with expertise in summarizing documents.'), HumanMessage(content=f'Please provide a short and concise summary of the following text:\nTEXT: {text}') ] try: llm = ChatOllama(model="pose:latest") summary_output = llm.invoke(messages) if isinstance(summary_output, AIMessage): cleaned_content = clean_output(summary_output.content) return cleaned_content else: return "Unexpected data type for model output." except Exception as e: print(f"An error occurred while processing the model output: {str(e)}") return None def fetch_text_from_url(url): try: response = requests.get(url) response.raise_for_status() soup = BeautifulSoup(response.text, 'html.parser') content = soup.find('div', {'id': 'bodyContent'}) paragraphs = content.find_all('p') text_content = ' '.join(p.text for p in paragraphs) return text_content except requests.RequestException as e: print(f"Failed to fetch data from URL: {str(e)}") return None def read_text_file(file_path): with open(file_path, "r", encoding="utf-8") as file: return file.read() def read_pdf(file_path): with open(file_path, "rb") as file: reader = PyPDF2.PdfReader(file) text_content = "" for page in reader.pages: extracted_text = page.extract_text() if extracted_text: text_content += extracted_text + "\n" return text_content def summarize_content(source): if source.startswith(('http://', 'https://')): text_content = fetch_text_from_url(source) else: _, file_extension = os.path.splitext(source) if file_extension.lower() == '.pdf': text_content = read_pdf(source) elif file_extension.lower() in ['.txt', '.text']: text_content = read_text_file(source) else: print("Unsupported file type") return if text_content: summary = invoke_model(text_content) print("Summary of the document:") print(summary) else: print("No text found or unable to extract text from source.") if __name__ == '__main__': if len(sys.argv) < 2: print("Usage: python script.py <file_path_or_url>") else: source = sys.argv[1] summarize_content(source) ``` run txt file (assume txt is a.txt) ``` python pose_test.py a.txt ``` run url (assume txt is url) ``` python pose_test.py url ``` You can find both test results below on the section : Test Result1 ## 💻 Ollama Python Summarizing Test Code for the target lang response install all of these libraries ``` pip install requests beautifulsoup4 PyPDF2 googletrans==4.0.0-rc1 langchain-community langchain aiohttp asyncio aiofiles ``` pose_lang.py ``` import sys import os import aiohttp import PyPDF2 from bs4 import BeautifulSoup from langchain_community.chat_models import ChatOllama from langchain.schema import AIMessage, HumanMessage, SystemMessage from googletrans import Translator import logging import asyncio import aiofiles # Setup logging logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s') def clean_output(text): """Cleans the model output text.""" text = text.replace("</s>", "").strip() # Specific cleaning operation return text def translate_text(text, src_lang, dest_lang): """Translates text from source language to destination language using Google Translate.""" if src_lang == dest_lang: return text translator = Translator() try: translation = translator.translate(text, src=src_lang, dest=dest_lang) return translation.text except Exception as e: logging.error(f"Translation failed: {e}") return text def detect_language(text): """Detects the language of the given text.""" translator = Translator() try: detected = translator.detect(text) return detected.lang except Exception as e: logging.error(f"Language detection failed: {e}") return None async def invoke_model(text, target_lang): """Asynchronously invokes the chat model and processes the response with language-specific instructions.""" llm = ChatOllama(model="pose:latest") try: # Define messages based on target language if target_lang == 'ko': messages = [ SystemMessage(content='문서의 핵심 요약을 상세하게 제공해 주실 전문가로서, 다음 문서를 요약해 주세요.'), HumanMessage(content=f'다음 텍스트에 대한 전문적 요약을 제공해 주세요. 요약은 한국어의 언어적 뉘앙스에 맞게 최고 수준의 명확성과 세부 사항을 준수해야 합니다:\n\nTEXT: {text}') ] else: # default to English if not Korean messages = [ SystemMessage(content='As an adept summarizer, your expertise is required to condense the following document into its essential points in detail.'), HumanMessage(content=f'Kindly provide an expert summary of the text below, adhering to the highest standards of clarity and detail. Ensure the response is tailored to the linguistic nuances of English:\n\nTEXT: {text}') ] # Since invoke is not awaitable, run it in a thread if it's blocking response = await asyncio.to_thread(llm.invoke, messages) if isinstance(response, AIMessage): cleaned_content = clean_output(response.content) content_lang = detect_language(cleaned_content) print(f"Current content language: {content_lang}, Target language to be translated to: {target_lang}") if content_lang != target_lang: return translate_text(cleaned_content, content_lang, target_lang) return cleaned_content else: raise ValueError("Model did not return an AIMessage") except Exception as e: logging.error(f"Error during model invocation: {e}") return "Model invocation failed." async def fetch_text_from_url(url): """Asynchronously fetches and extracts text content from a given URL.""" async with aiohttp.ClientSession() as session: try: async with session.get(url) as response: content = await response.text() soup = BeautifulSoup(content, 'html.parser') main_content = soup.select_one('#mw-content-text, #bodyContent, .content') if not main_content: logging.error("No content found in the expected sections.") return None text_content = ' '.join(p.get_text() for p in main_content.find_all(['p', 'li'], string=True)) return text_content except Exception as e: logging.error(f"Error fetching URL content: {e}") return None async def read_text_file(file_path): """Asynchronously reads text from a text file.""" async with aiofiles.open(file_path, mode='r', encoding='utf-8') as file: text_content = await file.read() return text_content async def read_pdf(file_path): """Asynchronously reads text from a PDF file.""" def sync_read_pdf(path): try: with open(path, "rb") as file: reader = PyPDF2.PdfReader(file) return ' '.join(page.extract_text() for page in reader.pages if page.extract_text()) except Exception as e: logging.error(f"Error reading PDF file: {e}") return None return await asyncio.to_thread(sync_read_pdf, file_path) async def summarize_content(source, language): """Processes input source (URL, file, text) and outputs a summary in the specified language asynchronously.""" print("Processing input...") text_content = None if source.startswith(('http://', 'https://')): print("Fetching content from URL...") text_content = await fetch_text_from_url(source) elif os.path.isfile(source): _, file_extension = os.path.splitext(source) if file_extension.lower() == '.pdf': print("Reading PDF...") text_content = await read_pdf(source) elif file_extension.lower() in ['.txt', '.text']: print("Reading text file...") text_content = await read_text_file(source) else: print("Unsupported file type") return else: print("Unsupported file type") return if text_content: print("Summarizing content...") summary = await invoke_model(text_content, language) print("\n--- Summary of the document ---\n") print(summary) else: print("No text found or unable to extract text from source.") if __name__ == '__main__': if len(sys.argv) < 3: print("Usage: python script.py <file_path_or_url_or_text> <language>") print("Language should be 'ko' for Korean or 'en' for English.") else: source = sys.argv[1] language = sys.argv[2] asyncio.run(summarize_content(source, language)) ``` run txt file (assume txt is a.txt) ``` Korean response : python pose_lang a.txt ko English response : python pose_lang a.txt en ``` run pdf file (assume pdf is a.pdf) ``` Korean response : python pose_lang a.pdf ko English response : python pose_lang a.pdf en ``` run url (assume url is wikepedia) ``` Korean response : python pose_lang url ko English response : python pose_lang url en ``` I added additional Google Translator here. If you request an answer in Korean and the answer is in English sometimes for the lang hallucination, this function detects it and answers you in Korean. Conversely, if you request a response in English and the response is in Korean for the lang hallucination, this function detects it and responds in English. You can find both test results below on the section : Test Result2 for target lang response ### 🗞️ Configuration The YAML configuration for this model: ```yaml models: - model: NousResearch/Meta-Llama-3-8B # Base model providing a general foundation without specific parameters - model: NousResearch/Meta-Llama-3-8B-Instruct parameters: density: 0.60 weight: 0.25 - model: winglian/llama-3-8b-256k-PoSE parameters: density: 0.60 weight: 0.20 - model: Locutusque/Llama-3-Orca-1.0-8B parameters: density: 0.55 weight: 0.15 - model: abacusai/Llama-3-Smaug-8B parameters: density: 0.55 weight: 0.15 - model: beomi/Llama-3-Open-Ko-8B-Instruct-preview parameters: density: 0.55 weight: 0.30 merge_method: dare_ties base_model: NousResearch/Meta-Llama-3-8B parameters: int8_mask: true dtype: bfloat16 ``` Test OS Condition ``` Hardware Overview: Model Name: MacBook Pro Model Identifier: MacBookPro18,2 Chip: Apple M1 Max Total Number of Cores: 10 (8 performance and 2 efficiency) Memory: 64 GB System Firmware Version: 10151.101.3 OS Loader Version: 10151.101.3 ``` ### 🎊 Test Result1 (Normal) SmartLlama-3-Ko-8B-256k-PoSE Summary Ability consideration Long sentences seemed to summarize well, but I observed that answers came in English. And when I asked for it to be translated into Korean, I confirmed that it was translated well. The summary seems to work well, but you can take into account the fact that there are times when it cannot be summarized directly in Korean. ## Summary of Britney Spears on Wikipedia [![Britney Spears Singer Wikipedia Summary](https://i.ibb.co/2600HbV/Screenshot-2024-05-02-at-11-52-58-AM.png)](https://ibb.co/7zxxL9M) ## Summary of Steve Jobs Text File [![Steve Jobs Text File Summary](https://i.ibb.co/10tRCrj/Screenshot-2024-05-02-at-11-54-50-AM.png)](https://ibb.co/9pkyxbS) ## Summary of Jay Park on Wikipedia [![Jay Park Wikipedia Summary](https://i.ibb.co/nmkpbCt/Screenshot-2024-05-02-at-1-33-30-PM.png)](https://ibb.co/g9gY3Vh) ### 🎊 Test Result2 (Target Language Summary Return) SmartLlama-3-Ko-8B-256k-PoSE Summary Ability consideration I added additional Google Translator here. If you request an answer in Korean and the answer is in English, this function detects it and answers you in Korean. Conversely, if you request a response in English and the response is in Korean, this function detects it and responds in English. If you don't get a clear answer, try running it several times. ## Summary of economy pdf ``` python final2.py economy.pdf ko # if you want english summary, en ``` [![Economy pdf Summary](https://i.ibb.co/QftXyWQ/Screenshot-2024-05-02-at-9-05-51-PM.png)](https://ibb.co/JKgCDYt) ## Summary of Steve Jobs Text File ``` python final2.py steve.txt ko # if you want english summary, en ``` [![Steve Jobs Text File Summary](https://i.ibb.co/1nmqWxk/Screenshot-2024-05-02-at-8-57-20-PM.png)](https://ibb.co/PY6hH8d) ## Summary of Jay Park on Wikipedia ``` python final2.py https://en.wikipedia.org/wiki/Jay_Park ko # if you want english summary, en ``` [![Jay Park Wikipedia Summary](https://i.ibb.co/Ssk4tdY/Screenshot-2024-05-02-at-8-54-19-PM.png)](https://ibb.co/j6CPyW0) Test Source From [박재범 - wikipedia - EN](https://en.wikipedia.org/wiki/Jay_Park) [박재범 - wikipedia - KR](https://ko.wikipedia.org/wiki/%EB%B0%95%EC%9E%AC%EB%B2%94) [Britney Spears - wikipedia - EN](https://en.wikipedia.org/wiki/Britney_Spears) [한국은행 경제전망 보고서 - KR](https://www.bok.or.kr/viewer/skin/doc.html?fn=202402290251197820.pdf&rs=/webview/result/P0002359/202402) [Community member : Mr Han' steve jobs txt file] ### ⛑️ Test Issue 2024-05-02 ``` If you use load_summarize_chain(), there will be repetition. -> community member Mr.Han issue Is it a merge issue? He thinks the merge target may be the issue. chain = load_summarize_chain( llm, chain_type='stuff', prompt=prompt, verbose=False ) output_summary = chain.invoke(docs) -> investigating for me how to solve..... ``` ``` Mr.Han is investgating the symptoms Your OS is using REDHAT. Even if I run the code using the LLAMA3 model provided by ollama, there is an error. I wonder if I should wait a little longer for Red Hat... <\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>, ... omitted Ha ha, thanks for the chat! You too have a great day and happy summarizing if you need it again soon!<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> It's not a merge problem... I think it's a fundamental problem that doesn't fit the OS environment... so I'm sharing it with you. Is there anyone who has the same problem as me in redhat? ```	{"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["winglian/llama-3-8b-256k-PoSE", "Locutusque/Llama-3-Orca-1.0-8B", "NousResearch/Meta-Llama-3-8B", "abacusai/Llama-3-Smaug-8B", "beomi/Llama-3-Open-Ko-8B-Instruct-preview", "NousResearch/Meta-Llama-3-8B-Instruct"]}	asiansoul/SmartLlama-3-Ko-8B-256k-PoSE-GGUF	null	[ "transformers", "gguf", "mergekit", "merge", "arxiv:2309.10400", "base_model:winglian/llama-3-8b-256k-PoSE", "base_model:Locutusque/Llama-3-Orca-1.0-8B", "base_model:NousResearch/Meta-Llama-3-8B", "base_model:abacusai/Llama-3-Smaug-8B", "base_model:beomi/Llama-3-Open-Ko-8B-Instruct-preview", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "endpoints_compatible", "region:us" ]	null	2024-05-01T14:22:53+00:00
null	null		{}	imvbhuvan/mistral-aspireai	null	[ "region:us" ]	null	2024-05-01T14:24:23+00:00
text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) BRNKCForCausalLM - bnb 4bits - Model creator: https://huggingface.co/BearNetworkChain/ - Original model: https://huggingface.co/BearNetworkChain/BRNKCForCausalLM/ Original model description: --- license: gpl-3.0 language: - zh - en --- # 熊網區塊鏈AI模型特性 ## 簡介熊網區塊鏈AI模型是一個專為區塊鏈領域訓練的人工智慧模型，旨在提供區塊鏈相關領域的知識和解決方案。這個模型由熊網區塊鏈團隊精心訓練，專注於區塊鏈技術、加密貨幣、分散式金融等相關主題。 ## 特性 1. 專業知識: 熊網區塊鏈AI模型具有豐富的區塊鏈相關知識，涵蓋區塊鏈技術原理、加密算法、智能合約等多個方面。 2. 即時更新: 模型將不斷更新學習，以保持對最新區塊鏈技術和趨勢的了解，並提供最新的解決方案和見解。 3. 多語言支持: 熊網區塊鏈AI模型支持多種語言，包括但不限於英文和中文，以滿足全球用戶的需求。 4. 準確性: 模型在提供信息和解答問題時力求準確性，並通過不斷訓練和改進來提升準確度。 5. 可擴展性: 熊網區塊鏈AI模型具有良好的可擴展性，可根據用戶需求和反饋進行定制和調整。 ## 使用方法 1. 問答: 使用者可以向模型提出關於區塊鏈技術、加密貨幣、智能合約等方面的問題，模型將會提供相關的解答和建議。 2. 知識查詢: 使用者可以通過對話方式向模型查詢特定的區塊鏈知識和資訊，模型將會提供相關的知識內容。 3. 專案建議: 使用者可以向模型提出關於區塊鏈專案和應用的建議需求，模型將會根據需求提供相應的建議和指導。 ## 貢獻和反饋我們歡迎使用者對熊網區塊鏈AI模型提出貢獻和反饋意見。我們將會根據用戶反饋來持續改進和優化模型的功能和性能。 ## 聯繫我們如果您有任何問題或意見，歡迎聯繫我們的團隊。您可以通過以下方式與我們取得聯繫： - 官方網站：bearnetwork.net - 社交媒體：Twitter、Facebook、LinkedIn 等感謝您對熊網區塊鏈AI模型的關注和支持！	{}	RichardErkhov/BearNetworkChain_-_BRNKCForCausalLM-4bits	null	[ "transformers", "safetensors", "gpt2", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-05-01T14:24:25+00:00
text-generation	transformers	# Uploaded model - Developed by: Cognitus-Stuti - 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)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-Instruct-bnb-4bit"}	Cognitus-Stuti/model	null	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-01T14:24:29+00:00
null	peft	<!-- 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. --> # best_model This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0978 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - training_steps: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.2888 \| 0.02 \| 4 \| 2.0978 \| ### Framework versions - PEFT 0.4.0 - Transformers 4.37.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2	{"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "mistralai/Mistral-7B-Instruct-v0.2", "model-index": [{"name": "best_model", "results": []}]}	hussamsal/best_model	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]	null	2024-05-01T14:24:54+00:00
text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) BRNKCForCausalLM - bnb 8bits - Model creator: https://huggingface.co/BearNetworkChain/ - Original model: https://huggingface.co/BearNetworkChain/BRNKCForCausalLM/ Original model description: --- license: gpl-3.0 language: - zh - en --- # 熊網區塊鏈AI模型特性 ## 簡介熊網區塊鏈AI模型是一個專為區塊鏈領域訓練的人工智慧模型，旨在提供區塊鏈相關領域的知識和解決方案。這個模型由熊網區塊鏈團隊精心訓練，專注於區塊鏈技術、加密貨幣、分散式金融等相關主題。 ## 特性 1. 專業知識: 熊網區塊鏈AI模型具有豐富的區塊鏈相關知識，涵蓋區塊鏈技術原理、加密算法、智能合約等多個方面。 2. 即時更新: 模型將不斷更新學習，以保持對最新區塊鏈技術和趨勢的了解，並提供最新的解決方案和見解。 3. 多語言支持: 熊網區塊鏈AI模型支持多種語言，包括但不限於英文和中文，以滿足全球用戶的需求。 4. 準確性: 模型在提供信息和解答問題時力求準確性，並通過不斷訓練和改進來提升準確度。 5. 可擴展性: 熊網區塊鏈AI模型具有良好的可擴展性，可根據用戶需求和反饋進行定制和調整。 ## 使用方法 1. 問答: 使用者可以向模型提出關於區塊鏈技術、加密貨幣、智能合約等方面的問題，模型將會提供相關的解答和建議。 2. 知識查詢: 使用者可以通過對話方式向模型查詢特定的區塊鏈知識和資訊，模型將會提供相關的知識內容。 3. 專案建議: 使用者可以向模型提出關於區塊鏈專案和應用的建議需求，模型將會根據需求提供相應的建議和指導。 ## 貢獻和反饋我們歡迎使用者對熊網區塊鏈AI模型提出貢獻和反饋意見。我們將會根據用戶反饋來持續改進和優化模型的功能和性能。 ## 聯繫我們如果您有任何問題或意見，歡迎聯繫我們的團隊。您可以通過以下方式與我們取得聯繫： - 官方網站：bearnetwork.net - 社交媒體：Twitter、Facebook、LinkedIn 等感謝您對熊網區塊鏈AI模型的關注和支持！	{}	RichardErkhov/BearNetworkChain_-_BRNKCForCausalLM-8bits	null	[ "transformers", "safetensors", "gpt2", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]	null	2024-05-01T14:25:10+00:00
fill-mask	transformers	## Environmental Impact (CODE CARBON DEFAULT) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| Duration (in seconds) \| [More Information Needed] \| \| Emissions (Co2eq in kg) \| [More Information Needed] \| \| CPU power (W) \| [NO CPU] \| \| GPU power (W) \| [No GPU] \| \| RAM power (W) \| [More Information Needed] \| \| CPU energy (kWh) \| [No CPU] \| \| GPU energy (kWh) \| [No GPU] \| \| RAM energy (kWh) \| [More Information Needed] \| \| Consumed energy (kWh) \| [More Information Needed] \| \| Country name \| [More Information Needed] \| \| Cloud provider \| [No Cloud] \| \| Cloud region \| [No Cloud] \| \| CPU count \| [No CPU] \| \| CPU model \| [No CPU] \| \| GPU count \| [No GPU] \| \| GPU model \| [No GPU] \| ## Environmental Impact (for one core) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| CPU energy (kWh) \| [No CPU] \| \| Emissions (Co2eq in kg) \| [More Information Needed] \| ## Note 30 April 2024 ## My Config \| Config \| Value \| \|--------------------------\|-----------------\| \| checkpoint \| albert-base-v2 \| \| model_name \| BERTrand_bs32_lr6 \| \| sequence_length \| 400 \| \| num_epoch \| 12 \| \| learning_rate \| 5e-06 \| \| batch_size \| 32 \| \| weight_decay \| 0.0 \| \| warm_up_prop \| 0 \| \| drop_out_prob \| 0.1 \| \| packing_length \| 100 \| \| train_test_split \| 0.2 \| \| num_steps \| 6318 \| ## Training and Testing steps Epoch \| Train Loss \| Test Loss ---\|---\|--- \| 0.0 \| 15.603048 \| 15.109937 \| \| 0.5 \| 8.715844 \| 8.071290 \| \| 1.0 \| 7.608879 \| 8.114126 \| \| 1.5 \| 7.407612 \| 7.914163 \| \| 2.0 \| 7.323461 \| 7.774658 \| \| 2.5 \| 7.248362 \| 7.696718 \| \| 3.0 \| 7.101276 \| 7.856242 \| \| 3.5 \| 7.134161 \| 7.617901 \| \| 4.0 \| 7.105548 \| 7.837306 \| \| 4.5 \| 7.221799 \| 7.653854 \| \| 5.0 \| 7.047156 \| 7.659136 \| \| 5.5 \| 7.080983 \| 7.554190 \| \| 6.0 \| 7.083629 \| 7.670907 \| \| 6.5 \| 7.180606 \| 7.623875 \| \| 7.0 \| 7.036574 \| 7.571451 \| \| 7.5 \| 7.037596 \| 7.550659 \| \| 8.0 \| 7.082738 \| 7.634689 \| \| 8.5 \| 7.136363 \| 7.576325 \| \| 9.0 \| 7.046428 \| 7.594891 \| \| 9.5 \| 7.022868 \| 7.588534 \| \| 10.0 \| 7.075124 \| 7.532026 \| \| 10.5 \| 7.078401 \| 7.519065 \| \| 11.0 \| 7.109886 \| 7.550544 \|	{"language": "en", "tags": ["fill-mask"]}	damgomz/BERTrand_bs32_lr6	null	[ "transformers", "safetensors", "albert", "pretraining", "fill-mask", "en", "endpoints_compatible", "region:us" ]	null	2024-05-01T14:25:22+00:00
null	null		{"license": "apache-2.0"}	Alexliex/test	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-01T14:26:01+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	efeno/llama3_RAFT_4_epochs	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T14:26:04+00:00
null	null		{}	aliaksei-kankou/t2	null	[ "region:us" ]	null	2024-05-01T14:26:44+00:00
fill-mask	transformers	## Environmental Impact (CODE CARBON DEFAULT) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| Duration (in seconds) \| [More Information Needed] \| \| Emissions (Co2eq in kg) \| [More Information Needed] \| \| CPU power (W) \| [NO CPU] \| \| GPU power (W) \| [No GPU] \| \| RAM power (W) \| [More Information Needed] \| \| CPU energy (kWh) \| [No CPU] \| \| GPU energy (kWh) \| [No GPU] \| \| RAM energy (kWh) \| [More Information Needed] \| \| Consumed energy (kWh) \| [More Information Needed] \| \| Country name \| [More Information Needed] \| \| Cloud provider \| [No Cloud] \| \| Cloud region \| [No Cloud] \| \| CPU count \| [No CPU] \| \| CPU model \| [No CPU] \| \| GPU count \| [No GPU] \| \| GPU model \| [No GPU] \| ## Environmental Impact (for one core) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| CPU energy (kWh) \| [No CPU] \| \| Emissions (Co2eq in kg) \| [More Information Needed] \| ## Note 30 April 2024 ## My Config \| Config \| Value \| \|--------------------------\|-----------------\| \| checkpoint \| albert-base-v2 \| \| model_name \| BERTrand_bs32_lr5 \| \| sequence_length \| 400 \| \| num_epoch \| 12 \| \| learning_rate \| 5e-05 \| \| batch_size \| 32 \| \| weight_decay \| 0.0 \| \| warm_up_prop \| 0 \| \| drop_out_prob \| 0.1 \| \| packing_length \| 100 \| \| train_test_split \| 0.2 \| \| num_steps \| 6287 \| ## Training and Testing steps Epoch \| Train Loss \| Test Loss ---\|---\|--- \| 0.0 \| 15.495780 \| 13.831327 \| \| 0.5 \| 7.825472 \| 7.840593 \| \| 1.0 \| 7.327533 \| 7.785610 \| \| 1.5 \| 7.205367 \| 7.586150 \| \| 2.0 \| 7.151769 \| 7.663743 \| \| 2.5 \| 7.125600 \| 8.101605 \| \| 3.0 \| 7.034717 \| 7.773854 \| \| 3.5 \| 7.092155 \| 7.549316 \| \| 4.0 \| 7.067814 \| 7.819034 \| \| 4.5 \| 7.141888 \| 7.587213 \| \| 5.0 \| 7.006890 \| 7.892200 \| \| 5.5 \| 7.049742 \| 7.752103 \| \| 6.0 \| 7.048553 \| 7.844037 \| \| 6.5 \| 7.096755 \| 7.641740 \| \| 7.0 \| 6.994647 \| 7.617568 \| \| 7.5 \| 6.993773 \| 7.864096 \| \| 8.0 \| 7.058714 \| 7.730159 \| \| 8.5 \| 7.064419 \| 7.629280 \| \| 9.0 \| 7.013462 \| 7.746540 \| \| 9.5 \| 6.962919 \| 8.147570 \| \| 10.0 \| 7.028505 \| 7.587558 \| \| 10.5 \| 7.022366 \| 7.531848 \|	{"language": "en", "tags": ["fill-mask"]}	damgomz/BERTrand_bs32_lr5	null	[ "transformers", "safetensors", "albert", "pretraining", "fill-mask", "en", "endpoints_compatible", "region:us" ]	null	2024-05-01T14:27:32+00:00
text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) bloom-3b-conversational - bnb 8bits - Model creator: https://huggingface.co/CreitinGameplays/ - Original model: https://huggingface.co/CreitinGameplays/bloom-3b-conversational/ Original model description: --- license: mit datasets: - Xilabs/instructmix - CreitinGameplays/small-chat-assistant-for-bloom - sahil2801/CodeAlpaca-20k language: - en tags: - uncensored - unrestricted - code - biology - chemistry - finance - legal - music - art - climate - merge - text-generation-inference - moe widget: - text: >- <\|system\|> You are a helpful AI assistant. </s> <\|prompter\|> who was Nikola Tesla? </s> <\|assistant\|> - text: >- <\|system\|> You are a helpful AI assistant. </s> <\|prompter\|> write a story about a cat. </s> <\|assistant\|> - text: >- <\|system\|> You are a helpful AI assistant. </s> <\|prompter\|> what is an essay? </s> <\|assistant\|> - text: >- <\|system\|> You are a helpful AI assistant. </s> <\|prompter\|> Tell me 5 Brazilian waterfalls to visit. </s> <\|assistant\|> - text: >- <\|system\|> You are a helpful AI assistant. </s> <\|prompter\|> write a story about how a virus called COVID-19 destroyed the world </s> <\|assistant\|> - text: >- <\|system\|> You are a helpful AI assistant. </s> <\|prompter\|> write a short Python program that asks the user for their name and then greets them by name. </s> <\|assistant\|> - text: >- <\|system\|> You are a helpful AI assistant. </s> <\|prompter\|> What can you do? </s> <\|assistant\|> inference: parameters: temperature: 0.1 do_sample: true top_k: 50 top_p: 0.10 max_new_tokens: 250 repetition_penalty: 1.155 --- ## 🌸 BLOOM 3b Fine-tuned for Chat Assistant <img src="https://creitingameplays.xyz/img/bloom.png" alt="BigScience Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> Run this model on [Kaggle Notebook](https://www.kaggle.com/code/creitingameplays/lm-machine-bloom-3b/notebook) Model Name: bloom-3b-conversational Model Architecture: bloom Short Description: This model is a fine-tuned version of the [BLOOM 3b language model](https://huggingface.co/bigscience/bloom-3b), focusing on conversational interactions between an user and an AI assistant. Intended Use: This model is intended for research purposes and exploration of conversational AI applications. It can be used for tasks like: * Generating responses to user prompts in a chat assistant setting. * Creating examples of chatbot interactions for further development. * Studying the capabilities of language models for conversation. Limitations: * Fine-tuning Focus: The model's performance is optimized for the specific format and context of the fine-tuning data. It may not generalize well to significantly different conversation styles or topics. * Potential Biases: The model may inherit biases from the training data. It's important to be aware of these potential biases and use the model responsibly. * Limited Factual Accuracy: Language models are still under development and may generate responses that are not entirely factually accurate. It's important to verify information generated by the model with other sources. * Primarily English: While the model can respond in other languages, the quality and accuracy of its responses may be lower compared to English. This is because the model was primarily fine-tuned on English data. Specific Input Format: The model was fine-tuned using a specific input format that goes like this: ``` <\|system\|> {system prompt} </s> <\|prompter\|> {user prompt} </s> <\|assistant\|> {model response} ``` Using this format when interacting with the model can improve its performance and generate more relevant responses. Disclaimer: This model is for research and exploration purposes only. It should not be used in any applications that require high levels of accuracy or reliability.	{}	RichardErkhov/CreitinGameplays_-_bloom-3b-conversational-8bits	null	[ "transformers", "safetensors", "bloom", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]	null	2024-05-01T14:27:57+00:00
null	null	GPT-SoVITS: https://github.com/RVC-Boss/GPT-SoVITS Training data: https://huggingface.co/datasets/hello2mao/Chinese_Audio_Resource/tree/main/%E7%94%9C%E5%B0%8F%E5%96%B5	{}	miugod/gpt_sovits_txm	null	[ "region:us" ]	null	2024-05-01T14:28:03+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": ["unsloth"]}	Cognitus-Stuti/llama3-8b	null	[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-05-01T14:28:18+00:00
fill-mask	transformers	## Environmental Impact (CODE CARBON DEFAULT) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| Duration (in seconds) \| [More Information Needed] \| \| Emissions (Co2eq in kg) \| [More Information Needed] \| \| CPU power (W) \| [NO CPU] \| \| GPU power (W) \| [No GPU] \| \| RAM power (W) \| [More Information Needed] \| \| CPU energy (kWh) \| [No CPU] \| \| GPU energy (kWh) \| [No GPU] \| \| RAM energy (kWh) \| [More Information Needed] \| \| Consumed energy (kWh) \| [More Information Needed] \| \| Country name \| [More Information Needed] \| \| Cloud provider \| [No Cloud] \| \| Cloud region \| [No Cloud] \| \| CPU count \| [No CPU] \| \| CPU model \| [No CPU] \| \| GPU count \| [No GPU] \| \| GPU model \| [No GPU] \| ## Environmental Impact (for one core) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| CPU energy (kWh) \| [No CPU] \| \| Emissions (Co2eq in kg) \| [More Information Needed] \| ## Note 30 April 2024 ## My Config \| Config \| Value \| \|--------------------------\|-----------------\| \| checkpoint \| albert-base-v2 \| \| model_name \| BERTrand_bs64_lr6 \| \| sequence_length \| 400 \| \| num_epoch \| 12 \| \| learning_rate \| 5e-06 \| \| batch_size \| 64 \| \| weight_decay \| 0.0 \| \| warm_up_prop \| 0 \| \| drop_out_prob \| 0.1 \| \| packing_length \| 100 \| \| train_test_split \| 0.2 \| \| num_steps \| 3147 \| ## Training and Testing steps Epoch \| Train Loss \| Test Loss ---\|---\|--- \| 0.0 \| 15.574399 \| 15.096123 \| \| 0.5 \| 9.594637 \| 8.148669 \| \| 1.0 \| 7.853338 \| 8.074202 \| \| 1.5 \| 7.905947 \| 7.939530 \| \| 2.0 \| 7.834033 \| 7.833388 \| \| 2.5 \| 7.720610 \| 7.871610 \| \| 3.0 \| 7.495963 \| 7.976839 \| \| 3.5 \| 7.330389 \| 7.752517 \| \| 4.0 \| 7.214343 \| 7.848690 \| \| 4.5 \| 7.346055 \| 7.724831 \| \| 5.0 \| 7.110836 \| 7.715771 \| \| 5.5 \| 7.125741 \| 7.595748 \| \| 6.0 \| 7.127250 \| 7.659738 \| \| 6.5 \| 7.239036 \| 7.671448 \| \| 7.0 \| 7.073343 \| 7.705375 \| \| 7.5 \| 7.070813 \| 7.589307 \| \| 8.0 \| 7.124647 \| 7.582091 \| \| 8.5 \| 7.166616 \| 7.539913 \| \| 9.0 \| 7.092505 \| 7.611073 \| \| 9.5 \| 7.048057 \| 7.625665 \| \| 10.0 \| 7.101367 \| 7.564788 \| \| 10.5 \| 7.108332 \| 7.602001 \| \| 11.0 \| 7.179604 \| 7.554187 \|	{"language": "en", "tags": ["fill-mask"]}	damgomz/BERTrand_bs64_lr6	null	[ "transformers", "safetensors", "albert", "pretraining", "fill-mask", "en", "endpoints_compatible", "region:us" ]	null	2024-05-01T14:28:39+00:00
text-generation	transformers	# merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [MTSAIR/multi_verse_model](https://huggingface.co/MTSAIR/multi_verse_model) as a base. ### Models Merged The following models were included in the merge: * [MaziyarPanahi/Calme-7B-Instruct-v0.3](https://huggingface.co/MaziyarPanahi/Calme-7B-Instruct-v0.3) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: MaziyarPanahi/Calme-7B-Instruct-v0.3 parameters: density: 0.53 weight: 0.4 - model: MTSAIR/multi_verse_model parameters: density: 0.53 weight: 0.3 merge_method: dare_ties base_model: MTSAIR/multi_verse_model parameters: int8_mask: true dtype: bfloat16 ```	{"license": "apache-2.0", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["MTSAIR/multi_verse_model", "MaziyarPanahi/Calme-7B-Instruct-v0.3"]}	Syed-Hasan-8503/Versatile-7B	null	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:MTSAIR/multi_verse_model", "base_model:MaziyarPanahi/Calme-7B-Instruct-v0.3", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T14:28:55+00:00
fill-mask	transformers	## Environmental Impact (CODE CARBON DEFAULT) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| Duration (in seconds) \| [More Information Needed] \| \| Emissions (Co2eq in kg) \| [More Information Needed] \| \| CPU power (W) \| [NO CPU] \| \| GPU power (W) \| [No GPU] \| \| RAM power (W) \| [More Information Needed] \| \| CPU energy (kWh) \| [No CPU] \| \| GPU energy (kWh) \| [No GPU] \| \| RAM energy (kWh) \| [More Information Needed] \| \| Consumed energy (kWh) \| [More Information Needed] \| \| Country name \| [More Information Needed] \| \| Cloud provider \| [No Cloud] \| \| Cloud region \| [No Cloud] \| \| CPU count \| [No CPU] \| \| CPU model \| [No CPU] \| \| GPU count \| [No GPU] \| \| GPU model \| [No GPU] \| ## Environmental Impact (for one core) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| CPU energy (kWh) \| [No CPU] \| \| Emissions (Co2eq in kg) \| [More Information Needed] \| ## Note 30 April 2024 ## My Config \| Config \| Value \| \|--------------------------\|-----------------\| \| checkpoint \| albert-base-v2 \| \| model_name \| BERTrand_bs64_lr5 \| \| sequence_length \| 400 \| \| num_epoch \| 12 \| \| learning_rate \| 5e-05 \| \| batch_size \| 64 \| \| weight_decay \| 0.0 \| \| warm_up_prop \| 0 \| \| drop_out_prob \| 0.1 \| \| packing_length \| 100 \| \| train_test_split \| 0.2 \| \| num_steps \| 3148 \| ## Training and Testing steps Epoch \| Train Loss \| Test Loss ---\|---\|--- \| 0.0 \| 15.402081 \| 13.817045 \| \| 0.5 \| 8.054414 \| 7.829068 \| \| 1.0 \| 4.816239 \| 3.114260 \| \| 1.5 \| 2.206430 \| 2.955595 \| \| 2.0 \| 2.189819 \| 2.872115 \| \| 2.5 \| 2.418134 \| 2.865437 \| \| 3.0 \| 2.349051 \| 2.810524 \| \| 3.5 \| 2.102283 \| 2.820134 \| \| 4.0 \| 1.907061 \| 2.957294 \| \| 4.5 \| 2.326205 \| 2.785392 \| \| 5.0 \| 2.257292 \| 2.737638 \| \| 5.5 \| 2.127350 \| 2.733068 \| \| 6.0 \| 1.883285 \| 2.774372 \| \| 6.5 \| 2.100682 \| 2.667502 \| \| 7.0 \| 2.194973 \| 2.628296 \| \| 7.5 \| 2.163919 \| 2.643665 \| \| 8.0 \| 1.850441 \| 2.637510 \| \| 8.5 \| 1.968181 \| 2.632833 \| \| 9.0 \| 2.121989 \| 2.625116 \| \| 9.5 \| 2.136497 \| 2.646418 \| \| 10.0 \| 1.891819 \| 2.655790 \| \| 10.5 \| 1.822331 \| 2.596789 \| \| 11.0 \| 2.116980 \| 2.584595 \|	{"language": "en", "tags": ["fill-mask"]}	damgomz/BERTrand_bs64_lr5	null	[ "transformers", "safetensors", "albert", "pretraining", "fill-mask", "en", "endpoints_compatible", "region:us" ]	null	2024-05-01T14:29:34+00:00
fill-mask	transformers	## Environmental Impact (CODE CARBON DEFAULT) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| Duration (in seconds) \| 190422.01247096065 \| \| Emissions (Co2eq in kg) \| 0.1993119180945443 \| \| CPU power (W) \| 42.5 \| \| GPU power (W) \| [No GPU] \| \| RAM power (W) \| 37.5 \| \| CPU energy (kWh) \| 2.2480348206905836 \| \| GPU energy (kWh) \| [No GPU] \| \| RAM energy (kWh) \| 1.9835508801341004 \| \| Consumed energy (kWh) \| 4.231585700824702 \| \| Country name \| Switzerland \| \| Cloud provider \| nan \| \| Cloud region \| nan \| \| CPU count \| 4 \| \| CPU model \| Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz \| \| GPU count \| nan \| \| GPU model \| nan \| ## Environmental Impact (for one core) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| CPU energy (kWh) \| 0.3665623740065992 \| \| Emissions (Co2eq in kg) \| 0.07458195488445958 \| ## Note 30 April 2024 ## My Config \| Config \| Value \| \|--------------------------\|-----------------\| \| checkpoint \| albert-base-v2 \| \| model_name \| BERTrand_bs16_lr5 \| \| sequence_length \| 400 \| \| num_epoch \| 12 \| \| learning_rate \| 5e-05 \| \| batch_size \| 16 \| \| weight_decay \| 0.0 \| \| warm_up_prop \| 0 \| \| drop_out_prob \| 0.1 \| \| packing_length \| 100 \| \| train_test_split \| 0.2 \| \| num_steps \| 12590 \| ## Training and Testing steps Epoch \| Train Loss \| Test Loss ---\|---\|--- \| 0.0 \| 15.505644 \| 13.835570 \| \| 0.5 \| 2.703247 \| 3.216191 \| \| 1.0 \| 2.525614 \| 3.149906 \| \| 1.5 \| 2.203586 \| 3.040256 \| \| 2.0 \| 2.160747 \| 2.952961 \| \| 2.5 \| 2.370840 \| 2.949682 \| \| 3.0 \| 2.350076 \| 2.925128 \| \| 3.5 \| 2.110838 \| 2.981181 \| \| 4.0 \| 1.903310 \| 2.836523 \| \| 4.5 \| 2.270815 \| 2.814076 \| \| 5.0 \| 2.256549 \| 2.848042 \| \| 5.5 \| 2.214569 \| 2.812752 \| \| 6.0 \| 1.896058 \| 2.755932 \| \| 6.5 \| 2.082173 \| 2.737672 \| \| 7.0 \| 2.156056 \| 2.711664 \| \| 7.5 \| 2.133011 \| 2.690121 \| \| 8.0 \| 1.826039 \| 2.718429 \| \| 8.5 \| 1.919896 \| 2.646301 \| \| 9.0 \| 2.058763 \| 2.616522 \| \| 9.5 \| 2.088796 \| 2.676844 \| \| 10.0 \| 1.848681 \| 2.622713 \| \| 10.5 \| 1.776635 \| 2.581153 \| \| 11.0 \| 2.059736 \| 2.579319 \| \| 11.5 \| 2.055293 \| 2.591116 \| \| 12.0 \| 1.912107 \| 2.555768 \|	{"language": "en", "tags": ["fill-mask"], "kwargs": {"timestamp": "2024-05-03T20:37:09", "project_name": "BERTrand_bs16_lr5_emissions_tracker", "run_id": "49c906ec-058d-4552-a3a9-71ef3ba22844", "duration": 190422.01247096065, "emissions": 0.1993119180945443, "emissions_rate": 1.046685283430346e-06, "cpu_power": 42.5, "gpu_power": 0.0, "ram_power": 37.5, "cpu_energy": 2.2480348206905836, "gpu_energy": 0, "ram_energy": 1.9835508801341004, "energy_consumed": 4.231585700824702, "country_name": "Switzerland", "country_iso_code": "CHE", "region": NaN, "cloud_provider": NaN, "cloud_region": NaN, "os": "Linux-5.14.0-70.30.1.el9_0.x86_64-x86_64-with-glibc2.34", "python_version": "3.10.4", "codecarbon_version": "2.3.4", "cpu_count": 4, "cpu_model": "Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz", "gpu_count": NaN, "gpu_model": NaN, "longitude": NaN, "latitude": NaN, "ram_total_size": 100, "tracking_mode": "machine", "on_cloud": "N", "pue": 1.0}}	damgomz/BERTrand_bs16_lr5	null	[ "transformers", "safetensors", "albert", "pretraining", "fill-mask", "en", "endpoints_compatible", "region:us" ]	null	2024-05-01T14:29:40+00:00

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