# https://chatx.cnxt.app

<!-- Provide a quick summary of what the model is/does. -->

This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## Model Details

### Model Description

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- **Developed by:** [#://CNXT $://THeXDesK ❌ #://THeFLTNGMMTH]
- **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]

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- **Paper [optional]:** [More Information Needed]
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## 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

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[More Information Needed]

### Downstream Use [optional]

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[More Information Needed]

### Out-of-Scope Use

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## Bias, Risks, and Limitations

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[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.


from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("CNXT/CHaTx")
model.load_adapter("CNXT/CHaTx", source="hf")


## Training Details

### Training Data

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[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

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[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]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

[More Information Needed]

## Model Card Contact

[More Information Needed]
{"language": ["en"], "license": "openrail", "library_name": "adapter-transformers", "tags": ["finance ", "music", "biology", "legal", "medical", "text-generation-inference", "Fintech", "ai", "crypto", "chatgpt", "gpt", "openai", "huggingchat", "chatui", "stripe", "Google", "Amazon", "HTP", "#://", "$://", "@://", "*://", "#://CNXT", "$://THeXDesK", "#://CHaTx", "\u274c", " $://CHaTx", "BLooMBeRG", "CQG", "AoN", "#://CoNTRax", "$://CoNTRax", "CoNVB", "#://CNXT.app", "$://XDesK.app", "FiX", "aPi", "aiRuN", "30MiNs", "LiNKeDiN", "https://X.CoM", "https://X.aPp", "sms", "smtp", "http://", "https://", "HashTag", "HashTags", "HashTagPRoToCoL", "GuNDB", "iPFs", "STeLLaR", "DoCKeR", "GiT", "GiTHuB", "eNGLiSH", "#NGLiSH", "SeaRCHeNGiNe", "WeBBRoWSeR", "oPeRaTiNGSySTeM", "FuTuRes", "oPTioNs", "DeFi", "fiaT", "SMaRTCoNTRaCTs", "CoNTRaCTs", "DLT", "$://XCH", "$://ACH ", "$://SWiFT", "chemistry", "code", "art", "#://aMaZoN.CoM", "#://GooGLe.CoM", "#://aPPLe.CoM", "$://CMeGRouP.aPp", "#://CMeGRouP.app", "#://oPeNai.CoM", "#://MiCRoSoFT.CoM", "#://X.CoM", "$://X.aPp", "#://GiTHuB.CoM", "#://aws.aMaZoN.CoM", "#://FaCeBooK.CoM", "#://30MiNs.CoM", "#://LiNKeDiN.CoM", "#://TWiTTeR.CoM", "#://youTuBe.CoM", "#://TiKToK.CoM", "FiNaNCiaLSeRViCes", "TeCHNoLoGySeRViCes", "bresnow", "floatingmammoth", "FLTNGMMTH", "#://FLTNGMMTH.CoM", "#://THeFLTNGMMTH", "#://30MiNs.CoM ", "#://30MiNs.aPp", "zapier", "create", "#://zapier", "#ash", "Xclip", "CQG ", "#://CQG.app", "https://cqg.com", "#://SToNex", "#://STRaiTsFiNaNCiaL", "#://RCM", "#://MaRKeTsWiKi", "#://eXCeL", "#://SeCo", "#://SeCoMiND"], "datasets": ["OpenAssistant/oasst1", "fka/awesome-chatgpt-prompts", "togethercomputer/RedPajama-Data-1T", "anon8231489123/ShareGPT_Vicuna_unfiltered", "gsdf/EasyNegative", "bloomberg/entsum", "openai/summarize_from_feedback", "billsum", "AmazonScience/massive", "amazon_us_reviews", "amazon_reviews_multi", "openwebtext", "microsoft/CLUES", "Norod78/microsoft-fluentui-emoji-512-whitebg", "Norod78/microsoft-fluentui-emoji-768", "MicPie/unpredictable_msdn-microsoft-com", "microsoft/codexglue_method_generation", "Salesforce/rose", "Intel/WEC-Eng", "Intel/CoreSearch", "google/MusicCaps", "google/xtreme_s", "google/fleurs", "EleutherAI/lambada_openai", "CarperAI/openai_summarize_tldr", "CarperAI/openai_summarize_comparisons", "dalle-mini/YFCC100M_OpenAI_subset", "openai_humaneval", "openai/webgpt_comparisons", "nyanko7/LLaMA-65B", "allenai/objaverse", "Anthropic/hh-rlhf", "Anthropic/model-written-evals", "yizhongw/self_instruct", "Nerfgun3/bad_prompt", "Nerfgun3/cyberware_style", "JosephusCheung/GuanacoDataset", "allenai/multixscience_sparse_oracle", "allenai/wcep_dense_oracle", "facebook/content_rephrasing", "facebook/flores", "cverse/linkedin", "amazon_polarity", "ApiInferenceTest/asr_dummy", "Francois/futures_es", "Intel/textual_inversion_dicoo_dfq", "search_qa", "nomic-ai/gpt4all_prompt_generations", "yahoo_answers_topics", "code_search_net", "LangChainDatasets/agent-search-calculator", "wikipedia", "craigslist_bargains", "codeparrot/github-code", "codeparrot/github-code-clean", "code_x_glue_ct_code_to_text", "0n1xus/codexglue", "pszemraj/govreport-summarization-8192", "Icannos/gpt4all-formatted-dolly-15k-multilingual", "chromeNLP/quality", "CNXT/autotrain-data-chatx", "emilylearning/cond_ft_subreddit_on_reddit__prcnt_20__test_run_False__xlm-roberta-base", "openchat/openchat_sharegpt4_dataset", "tiiuae/falcon-refinedweb", "Salesforce/dialogstudio", "ehartford/dolphin", "databricks/databricks-dolly-15k", "GAIR/lima", "uonlp/CulturaX", "TIGER-Lab/MathInstruct", "google/trueteacher", "arubenruben/cnn_dailymail_azure_pt_pt", "facebook/winoground", "facebook/multilingual_librispeech", "facebook/belebele", "meta-math/MetaMathQA"], "metrics": ["accuracy", "bertscore", "bleu", "bleurt", "brier_score", "cer", "character", "charcut_mt", "chrf", "code_eval"]}
adapter-transformers finance  music biology legal medical text-generation-inference Fintech ai crypto chatgpt gpt openai huggingchat chatui stripe Google Amazon HTP #:// $:// @:// *:// #://CNXT $://THeXDesK #://CHaTx ❌  $://CHaTx BLooMBeRG CQG AoN #://CoNTRax $://CoNTRax CoNVB #://CNXT.app $://XDesK.app FiX aPi aiRuN 30MiNs LiNKeDiN https://X.CoM https://X.aPp sms smtp http:// https:// HashTag HashTags HashTagPRoToCoL GuNDB iPFs STeLLaR DoCKeR GiT GiTHuB eNGLiSH #NGLiSH SeaRCHeNGiNe WeBBRoWSeR oPeRaTiNGSySTeM FuTuRes oPTioNs DeFi fiaT SMaRTCoNTRaCTs CoNTRaCTs DLT $://XCH $://ACH  $://SWiFT chemistry code art #://aMaZoN.CoM #://GooGLe.CoM #://aPPLe.CoM $://CMeGRouP.aPp #://CMeGRouP.app #://oPeNai.CoM #://MiCRoSoFT.CoM #://X.CoM $://X.aPp #://GiTHuB.CoM #://aws.aMaZoN.CoM #://FaCeBooK.CoM #://30MiNs.CoM #://LiNKeDiN.CoM #://TWiTTeR.CoM #://youTuBe.CoM #://TiKToK.CoM FiNaNCiaLSeRViCes TeCHNoLoGySeRViCes bresnow floatingmammoth FLTNGMMTH #://FLTNGMMTH.CoM #://THeFLTNGMMTH #://30MiNs.CoM  #://30MiNs.aPp zapier create #://zapier #ash Xclip CQG  #://CQG.app https://cqg.com #://SToNex #://STRaiTsFiNaNCiaL #://RCM #://MaRKeTsWiKi #://eXCeL #://SeCo #://SeCoMiND en dataset:OpenAssistant/oasst1 dataset:fka/awesome-chatgpt-prompts dataset:togethercomputer/RedPajama-Data-1T dataset:anon8231489123/ShareGPT_Vicuna_unfiltered dataset:gsdf/EasyNegative dataset:bloomberg/entsum dataset:openai/summarize_from_feedback dataset:billsum dataset:AmazonScience/massive dataset:amazon_us_reviews dataset:amazon_reviews_multi dataset:openwebtext dataset:microsoft/CLUES dataset:Norod78/microsoft-fluentui-emoji-512-whitebg dataset:Norod78/microsoft-fluentui-emoji-768 dataset:MicPie/unpredictable_msdn-microsoft-com dataset:microsoft/codexglue_method_generation dataset:Salesforce/rose dataset:Intel/WEC-Eng dataset:Intel/CoreSearch dataset:google/MusicCaps dataset:google/xtreme_s dataset:google/fleurs dataset:EleutherAI/lambada_openai dataset:CarperAI/openai_summarize_tldr dataset:CarperAI/openai_summarize_comparisons dataset:dalle-mini/YFCC100M_OpenAI_subset dataset:openai_humaneval dataset:openai/webgpt_comparisons dataset:nyanko7/LLaMA-65B dataset:allenai/objaverse dataset:Anthropic/hh-rlhf dataset:Anthropic/model-written-evals dataset:yizhongw/self_instruct dataset:Nerfgun3/bad_prompt dataset:Nerfgun3/cyberware_style dataset:JosephusCheung/GuanacoDataset dataset:allenai/multixscience_sparse_oracle dataset:allenai/wcep_dense_oracle dataset:facebook/content_rephrasing dataset:facebook/flores dataset:cverse/linkedin dataset:amazon_polarity dataset:ApiInferenceTest/asr_dummy dataset:Francois/futures_es dataset:Intel/textual_inversion_dicoo_dfq dataset:search_qa dataset:nomic-ai/gpt4all_prompt_generations dataset:yahoo_answers_topics dataset:code_search_net dataset:LangChainDatasets/agent-search-calculator dataset:wikipedia dataset:craigslist_bargains dataset:codeparrot/github-code dataset:codeparrot/github-code-clean dataset:code_x_glue_ct_code_to_text dataset:0n1xus/codexglue dataset:pszemraj/govreport-summarization-8192 dataset:Icannos/gpt4all-formatted-dolly-15k-multilingual dataset:chromeNLP/quality dataset:CNXT/autotrain-data-chatx dataset:emilylearning/cond_ft_subreddit_on_reddit__prcnt_20__test_run_False__xlm-roberta-base dataset:openchat/openchat_sharegpt4_dataset dataset:tiiuae/falcon-refinedweb dataset:Salesforce/dialogstudio dataset:ehartford/dolphin dataset:databricks/databricks-dolly-15k dataset:GAIR/lima dataset:uonlp/CulturaX dataset:TIGER-Lab/MathInstruct dataset:google/trueteacher dataset:arubenruben/cnn_dailymail_azure_pt_pt dataset:facebook/winoground dataset:facebook/multilingual_librispeech dataset:facebook/belebele dataset:meta-math/MetaMathQA arxiv:1910.09700 doi:10.57967/hf/1197 license:openrail region:us
TAGS
#adapter-transformers #finance  #music #biology #legal #medical #text-generation-inference #Fintech #ai #crypto #chatgpt #gpt #openai #huggingchat #chatui #stripe #Google #Amazon #HTP ##-// #$-// #@-// #*-// ##-//CNXT #$-//THeXDesK ##-//CHaTx #❌ # $-//CHaTx #BLooMBeRG #CQG #AoN ##-//CoNTRax #$-//CoNTRax #CoNVB ##-//CNXT.app #$-//XDesK.app #FiX #aPi #aiRuN #30MiNs #LiNKeDiN #https-//X.CoM #https-//X.aPp #sms #smtp #http-// #https-// #HashTag #HashTags #HashTagPRoToCoL #GuNDB #iPFs #STeLLaR #DoCKeR #GiT #GiTHuB #eNGLiSH ##NGLiSH #SeaRCHeNGiNe #WeBBRoWSeR #oPeRaTiNGSySTeM #FuTuRes #oPTioNs #DeFi #fiaT #SMaRTCoNTRaCTs #CoNTRaCTs #DLT #$-//XCH #$-//ACH  #$-//SWiFT #chemistry #code #art ##-//aMaZoN.CoM ##-//GooGLe.CoM ##-//aPPLe.CoM #$-//CMeGRouP.aPp ##-//CMeGRouP.app ##-//oPeNai.CoM ##-//MiCRoSoFT.CoM ##-//X.CoM #$-//X.aPp ##-//GiTHuB.CoM ##-//aws.aMaZoN.CoM ##-//FaCeBooK.CoM ##-//30MiNs.CoM ##-//LiNKeDiN.CoM ##-//TWiTTeR.CoM ##-//youTuBe.CoM ##-//TiKToK.CoM #FiNaNCiaLSeRViCes #TeCHNoLoGySeRViCes #bresnow #floatingmammoth #FLTNGMMTH ##-//FLTNGMMTH.CoM ##-//THeFLTNGMMTH ##-//30MiNs.CoM  ##-//30MiNs.aPp #zapier #create ##-//zapier ##ash #Xclip #CQG  ##-//CQG.app #https-//cqg.com ##-//SToNex ##-//STRaiTsFiNaNCiaL ##-//RCM ##-//MaRKeTsWiKi ##-//eXCeL ##-//SeCo ##-//SeCoMiND #en #dataset-OpenAssistant/oasst1 #dataset-fka/awesome-chatgpt-prompts #dataset-togethercomputer/RedPajama-Data-1T #dataset-anon8231489123/ShareGPT_Vicuna_unfiltered #dataset-gsdf/EasyNegative #dataset-bloomberg/entsum #dataset-openai/summarize_from_feedback #dataset-billsum #dataset-AmazonScience/massive #dataset-amazon_us_reviews #dataset-amazon_reviews_multi #dataset-openwebtext #dataset-microsoft/CLUES #dataset-Norod78/microsoft-fluentui-emoji-512-whitebg #dataset-Norod78/microsoft-fluentui-emoji-768 #dataset-MicPie/unpredictable_msdn-microsoft-com #dataset-microsoft/codexglue_method_generation #dataset-Salesforce/rose #dataset-Intel/WEC-Eng #dataset-Intel/CoreSearch #dataset-google/MusicCaps #dataset-google/xtreme_s #dataset-google/fleurs #dataset-EleutherAI/lambada_openai #dataset-CarperAI/openai_summarize_tldr #dataset-CarperAI/openai_summarize_comparisons #dataset-dalle-mini/YFCC100M_OpenAI_subset #dataset-openai_humaneval #dataset-openai/webgpt_comparisons #dataset-nyanko7/LLaMA-65B #dataset-allenai/objaverse #dataset-Anthropic/hh-rlhf #dataset-Anthropic/model-written-evals #dataset-yizhongw/self_instruct #dataset-Nerfgun3/bad_prompt #dataset-Nerfgun3/cyberware_style #dataset-JosephusCheung/GuanacoDataset #dataset-allenai/multixscience_sparse_oracle #dataset-allenai/wcep_dense_oracle #dataset-facebook/content_rephrasing #dataset-facebook/flores #dataset-cverse/linkedin #dataset-amazon_polarity #dataset-ApiInferenceTest/asr_dummy #dataset-Francois/futures_es #dataset-Intel/textual_inversion_dicoo_dfq #dataset-search_qa #dataset-nomic-ai/gpt4all_prompt_generations #dataset-yahoo_answers_topics #dataset-code_search_net #dataset-LangChainDatasets/agent-search-calculator #dataset-wikipedia #dataset-craigslist_bargains #dataset-codeparrot/github-code #dataset-codeparrot/github-code-clean #dataset-code_x_glue_ct_code_to_text #dataset-0n1xus/codexglue #dataset-pszemraj/govreport-summarization-8192 #dataset-Icannos/gpt4all-formatted-dolly-15k-multilingual #dataset-chromeNLP/quality #dataset-CNXT/autotrain-data-chatx #dataset-emilylearning/cond_ft_subreddit_on_reddit__prcnt_20__test_run_False__xlm-roberta-base #dataset-openchat/openchat_sharegpt4_dataset #dataset-tiiuae/falcon-refinedweb #dataset-Salesforce/dialogstudio #dataset-ehartford/dolphin #dataset-databricks/databricks-dolly-15k #dataset-GAIR/lima #dataset-uonlp/CulturaX #dataset-TIGER-Lab/MathInstruct #dataset-google/trueteacher #dataset-arubenruben/cnn_dailymail_azure_pt_pt #dataset-facebook/winoground #dataset-facebook/multilingual_librispeech #dataset-facebook/belebele #dataset-meta-math/MetaMathQA #arxiv-1910.09700 #doi-10.57967/hf/1197 #license-openrail #region-us 

# URL



This modelcard aims to be a base template for new models. It has been generated using this raw template.

## Model Details

### Model Description





- Developed by: [#://CNXT $://THeXDesK  #://THeFLTNGMMTH]
- Shared by [optional]: 
- Model type: 
- Language(s) (NLP): 
- License: 
- Finetuned from model [optional]: 

### Model Sources [optional]



- Repository: 
- Paper [optional]: 
- Demo [optional]: 

## Uses



### Direct Use





### Downstream Use [optional]





### Out-of-Scope Use





## Bias, Risks, and Limitations





### Recommendations



Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.


from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("CNXT/CHaTx")
model.load_adapter("CNXT/CHaTx", source="hf")


## Training Details

### Training Data





### Training Procedure 



#### Preprocessing [optional]




#### Training Hyperparameters

- Training regime:  

#### Speeds, Sizes, Times [optional]





## Evaluation



### Testing Data, Factors & Metrics

#### Testing Data





#### Factors





#### Metrics





### Results



#### Summary



## Model Examination [optional]





## Environmental Impact



Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted: 

## Technical Specifications [optional]

### Model Architecture and Objective



### Compute Infrastructure



#### Hardware



#### Software



[optional]



BibTeX:



APA:



## Glossary [optional]





## More Information [optional]



## Model Card Authors [optional]



## Model Card Contact


# URL



This modelcard aims to be a base template for new models. It has been generated using this raw template. ## Model Details ### Model Description





- Developed by: [#://CNXT $://THeXDesK  #://THeFLTNGMMTH]
- Shared by [optional]: 
- Model type: 
- Language(s) (NLP): 
- License: 
- Finetuned from model [optional]: ### Model Sources [optional]



- Repository: 
- Paper [optional]: 
- Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations



Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model

Use the code below to get started with the model.


from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("CNXT/CHaTx")
model.load_adapter("CNXT/CHaTx", source="hf") ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters

- Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact



Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software



[optional]



BibTeX:



APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
TAGS
#adapter-transformers #finance  #music #biology #legal #medical #text-generation-inference #Fintech #ai #crypto #chatgpt #gpt #openai #huggingchat #chatui #stripe #Google #Amazon #HTP ##-// #$-// #@-// #*-// ##-//CNXT #$-//THeXDesK ##-//CHaTx #❌ # $-//CHaTx #BLooMBeRG #CQG #AoN ##-//CoNTRax #$-//CoNTRax #CoNVB ##-//CNXT.app #$-//XDesK.app #FiX #aPi #aiRuN #30MiNs #LiNKeDiN #https-//X.CoM #https-//X.aPp #sms #smtp #http-// #https-// #HashTag #HashTags #HashTagPRoToCoL #GuNDB #iPFs #STeLLaR #DoCKeR #GiT #GiTHuB #eNGLiSH ##NGLiSH #SeaRCHeNGiNe #WeBBRoWSeR #oPeRaTiNGSySTeM #FuTuRes #oPTioNs #DeFi #fiaT #SMaRTCoNTRaCTs #CoNTRaCTs #DLT #$-//XCH #$-//ACH  #$-//SWiFT #chemistry #code #art ##-//aMaZoN.CoM ##-//GooGLe.CoM ##-//aPPLe.CoM #$-//CMeGRouP.aPp ##-//CMeGRouP.app ##-//oPeNai.CoM ##-//MiCRoSoFT.CoM ##-//X.CoM #$-//X.aPp ##-//GiTHuB.CoM ##-//aws.aMaZoN.CoM ##-//FaCeBooK.CoM ##-//30MiNs.CoM ##-//LiNKeDiN.CoM ##-//TWiTTeR.CoM ##-//youTuBe.CoM ##-//TiKToK.CoM #FiNaNCiaLSeRViCes #TeCHNoLoGySeRViCes #bresnow #floatingmammoth #FLTNGMMTH ##-//FLTNGMMTH.CoM ##-//THeFLTNGMMTH ##-//30MiNs.CoM  ##-//30MiNs.aPp #zapier #create ##-//zapier ##ash #Xclip #CQG  ##-//CQG.app #https-//cqg.com ##-//SToNex ##-//STRaiTsFiNaNCiaL ##-//RCM ##-//MaRKeTsWiKi ##-//eXCeL ##-//SeCo ##-//SeCoMiND #en #dataset-OpenAssistant/oasst1 #dataset-fka/awesome-chatgpt-prompts #dataset-togethercomputer/RedPajama-Data-1T #dataset-anon8231489123/ShareGPT_Vicuna_unfiltered #dataset-gsdf/EasyNegative #dataset-bloomberg/entsum #dataset-openai/summarize_from_feedback #dataset-billsum #dataset-AmazonScience/massive #dataset-amazon_us_reviews #dataset-amazon_reviews_multi #dataset-openwebtext #dataset-microsoft/CLUES #dataset-Norod78/microsoft-fluentui-emoji-512-whitebg #dataset-Norod78/microsoft-fluentui-emoji-768 #dataset-MicPie/unpredictable_msdn-microsoft-com #dataset-microsoft/codexglue_method_generation #dataset-Salesforce/rose #dataset-Intel/WEC-Eng #dataset-Intel/CoreSearch #dataset-google/MusicCaps #dataset-google/xtreme_s #dataset-google/fleurs #dataset-EleutherAI/lambada_openai #dataset-CarperAI/openai_summarize_tldr #dataset-CarperAI/openai_summarize_comparisons #dataset-dalle-mini/YFCC100M_OpenAI_subset #dataset-openai_humaneval #dataset-openai/webgpt_comparisons #dataset-nyanko7/LLaMA-65B #dataset-allenai/objaverse #dataset-Anthropic/hh-rlhf #dataset-Anthropic/model-written-evals #dataset-yizhongw/self_instruct #dataset-Nerfgun3/bad_prompt #dataset-Nerfgun3/cyberware_style #dataset-JosephusCheung/GuanacoDataset #dataset-allenai/multixscience_sparse_oracle #dataset-allenai/wcep_dense_oracle #dataset-facebook/content_rephrasing #dataset-facebook/flores #dataset-cverse/linkedin #dataset-amazon_polarity #dataset-ApiInferenceTest/asr_dummy #dataset-Francois/futures_es #dataset-Intel/textual_inversion_dicoo_dfq #dataset-search_qa #dataset-nomic-ai/gpt4all_prompt_generations #dataset-yahoo_answers_topics #dataset-code_search_net #dataset-LangChainDatasets/agent-search-calculator #dataset-wikipedia #dataset-craigslist_bargains #dataset-codeparrot/github-code #dataset-codeparrot/github-code-clean #dataset-code_x_glue_ct_code_to_text #dataset-0n1xus/codexglue #dataset-pszemraj/govreport-summarization-8192 #dataset-Icannos/gpt4all-formatted-dolly-15k-multilingual #dataset-chromeNLP/quality #dataset-CNXT/autotrain-data-chatx #dataset-emilylearning/cond_ft_subreddit_on_reddit__prcnt_20__test_run_False__xlm-roberta-base #dataset-openchat/openchat_sharegpt4_dataset #dataset-tiiuae/falcon-refinedweb #dataset-Salesforce/dialogstudio #dataset-ehartford/dolphin #dataset-databricks/databricks-dolly-15k #dataset-GAIR/lima #dataset-uonlp/CulturaX #dataset-TIGER-Lab/MathInstruct #dataset-google/trueteacher #dataset-arubenruben/cnn_dailymail_azure_pt_pt #dataset-facebook/winoground #dataset-facebook/multilingual_librispeech #dataset-facebook/belebele #dataset-meta-math/MetaMathQA #arxiv-1910.09700 #doi-10.57967/hf/1197 #license-openrail #region-us 
 # URL



This modelcard aims to be a base template for new models. It has been generated using this raw template. ## Model Details ### Model Description





- Developed by: [#://CNXT $://THeXDesK  #://THeFLTNGMMTH]
- Shared by [optional]: 
- Model type: 
- Language(s) (NLP): 
- License: 
- Finetuned from model [optional]: ### Model Sources [optional]



- Repository: 
- Paper [optional]: 
- Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations



Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model

Use the code below to get started with the model.


from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("CNXT/CHaTx")
model.load_adapter("CNXT/CHaTx", source="hf") ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters

- Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact



Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software



[optional]



BibTeX:



APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
TAGS
#adapter-transformers #finance  #music #biology #legal #medical #text-generation-inference #Fintech #ai #crypto #chatgpt #gpt #openai #huggingchat #chatui #stripe #Google #Amazon #HTP ##-// #$-// #@-// #*-// ##-//CNXT #$-//THeXDesK ##-//CHaTx #❌ # $-//CHaTx #BLooMBeRG #CQG #AoN ##-//CoNTRax #$-//CoNTRax #CoNVB ##-//CNXT.app #$-//XDesK.app #FiX #aPi #aiRuN #30MiNs #LiNKeDiN #https-//X.CoM #https-//X.aPp #sms #smtp #http-// #https-// #HashTag #HashTags #HashTagPRoToCoL #GuNDB #iPFs #STeLLaR #DoCKeR #GiT #GiTHuB #eNGLiSH ##NGLiSH #SeaRCHeNGiNe #WeBBRoWSeR #oPeRaTiNGSySTeM #FuTuRes #oPTioNs #DeFi #fiaT #SMaRTCoNTRaCTs #CoNTRaCTs #DLT #$-//XCH #$-//ACH  #$-//SWiFT #chemistry #code #art ##-//aMaZoN.CoM ##-//GooGLe.CoM ##-//aPPLe.CoM #$-//CMeGRouP.aPp ##-//CMeGRouP.app ##-//oPeNai.CoM ##-//MiCRoSoFT.CoM ##-//X.CoM #$-//X.aPp ##-//GiTHuB.CoM ##-//aws.aMaZoN.CoM ##-//FaCeBooK.CoM ##-//30MiNs.CoM ##-//LiNKeDiN.CoM ##-//TWiTTeR.CoM ##-//youTuBe.CoM ##-//TiKToK.CoM #FiNaNCiaLSeRViCes #TeCHNoLoGySeRViCes #bresnow #floatingmammoth #FLTNGMMTH ##-//FLTNGMMTH.CoM ##-//THeFLTNGMMTH ##-//30MiNs.CoM  ##-//30MiNs.aPp #zapier #create ##-//zapier ##ash #Xclip #CQG  ##-//CQG.app #https-//cqg.com ##-//SToNex ##-//STRaiTsFiNaNCiaL ##-//RCM ##-//MaRKeTsWiKi ##-//eXCeL ##-//SeCo ##-//SeCoMiND #en #dataset-OpenAssistant/oasst1 #dataset-fka/awesome-chatgpt-prompts #dataset-togethercomputer/RedPajama-Data-1T #dataset-anon8231489123/ShareGPT_Vicuna_unfiltered #dataset-gsdf/EasyNegative #dataset-bloomberg/entsum #dataset-openai/summarize_from_feedback #dataset-billsum #dataset-AmazonScience/massive #dataset-amazon_us_reviews #dataset-amazon_reviews_multi #dataset-openwebtext #dataset-microsoft/CLUES #dataset-Norod78/microsoft-fluentui-emoji-512-whitebg #dataset-Norod78/microsoft-fluentui-emoji-768 #dataset-MicPie/unpredictable_msdn-microsoft-com #dataset-microsoft/codexglue_method_generation #dataset-Salesforce/rose #dataset-Intel/WEC-Eng #dataset-Intel/CoreSearch #dataset-google/MusicCaps #dataset-google/xtreme_s #dataset-google/fleurs #dataset-EleutherAI/lambada_openai #dataset-CarperAI/openai_summarize_tldr #dataset-CarperAI/openai_summarize_comparisons #dataset-dalle-mini/YFCC100M_OpenAI_subset #dataset-openai_humaneval #dataset-openai/webgpt_comparisons #dataset-nyanko7/LLaMA-65B #dataset-allenai/objaverse #dataset-Anthropic/hh-rlhf #dataset-Anthropic/model-written-evals #dataset-yizhongw/self_instruct #dataset-Nerfgun3/bad_prompt #dataset-Nerfgun3/cyberware_style #dataset-JosephusCheung/GuanacoDataset #dataset-allenai/multixscience_sparse_oracle #dataset-allenai/wcep_dense_oracle #dataset-facebook/content_rephrasing #dataset-facebook/flores #dataset-cverse/linkedin #dataset-amazon_polarity #dataset-ApiInferenceTest/asr_dummy #dataset-Francois/futures_es #dataset-Intel/textual_inversion_dicoo_dfq #dataset-search_qa #dataset-nomic-ai/gpt4all_prompt_generations #dataset-yahoo_answers_topics #dataset-code_search_net #dataset-LangChainDatasets/agent-search-calculator #dataset-wikipedia #dataset-craigslist_bargains #dataset-codeparrot/github-code #dataset-codeparrot/github-code-clean #dataset-code_x_glue_ct_code_to_text #dataset-0n1xus/codexglue #dataset-pszemraj/govreport-summarization-8192 #dataset-Icannos/gpt4all-formatted-dolly-15k-multilingual #dataset-chromeNLP/quality #dataset-CNXT/autotrain-data-chatx #dataset-emilylearning/cond_ft_subreddit_on_reddit__prcnt_20__test_run_False__xlm-roberta-base #dataset-openchat/openchat_sharegpt4_dataset #dataset-tiiuae/falcon-refinedweb #dataset-Salesforce/dialogstudio #dataset-ehartford/dolphin #dataset-databricks/databricks-dolly-15k #dataset-GAIR/lima #dataset-uonlp/CulturaX #dataset-TIGER-Lab/MathInstruct #dataset-google/trueteacher #dataset-arubenruben/cnn_dailymail_azure_pt_pt #dataset-facebook/winoground #dataset-facebook/multilingual_librispeech #dataset-facebook/belebele #dataset-meta-math/MetaMathQA #arxiv-1910.09700 #doi-10.57967/hf/1197 #license-openrail #region-us 
# URL



This modelcard aims to be a base template for new models. It has been generated using this raw template.## Model Details### Model Description





- Developed by: [#://CNXT $://THeXDesK  #://THeFLTNGMMTH]
- Shared by [optional]: 
- Model type: 
- Language(s) (NLP): 
- License: 
- Finetuned from model [optional]:### Model Sources [optional]



- Repository: 
- Paper [optional]: 
- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations



Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model

Use the code below to get started with the model.


from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("CNXT/CHaTx")
model.load_adapter("CNXT/CHaTx", source="hf")## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters

- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact



Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software



[optional]



BibTeX:



APA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact