---
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
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'
- ❌
- ' $://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'
language:
- en
license: openrail
---
# 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

<!-- Provide a longer summary of what this model is. -->



- **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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- **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

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[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.


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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### 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]

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#### 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]

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[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

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#### 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

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#### Software

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## 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:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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