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- library_name: transformers
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- tags: []
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
 
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- ### Model Description
 
 
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
 
 
 
 
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- [More Information Needed]
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- ### Downstream Use [optional]
 
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- 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. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- 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).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- **APA:**
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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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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+ license: mit
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+ language:
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+ - en
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+ base_model:
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+ - unsloth/Phi-3-mini-4k-instruct
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+ datasets:
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+ - cognitivecomputations/Dolphin-2.9
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+ - teknium/OpenHermes-2.5
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+ - m-a-p/CodeFeedback-Filtered-Instruction
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+ - cognitivecomputations/dolphin-coder
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+ - cognitivecomputations/samantha-data
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+ - microsoft/orca-math-word-problems-200k
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+ - Locutusque/function-calling-chatml
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+ - internlm/Agent-FLAN
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  ---
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+ # Dolphin 2.9.1 Phi-3 Kensho 4.5b 🐬 (Abliterated)
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+ Curated and trained by Eric Hartford, Lucas Atkins, Fernando Fernandes, and with help from the community of Cognitive Computations
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+ [![Discord](https://img.shields.io/discord/1156064224225808488?logo=Discord&logoColor=%23ffffff&label=Discord&link=https%3A%2F%2Fdiscord.gg%2FtCMkMDDHwm)](https://discord.gg/cognitivecomputations)
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+ Discord: https://discord.gg/cognitivecomputations
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+ <img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" />
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+ Our appreciation for the sponsors of Dolphin 2.9:
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+ - [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 8xL40Snode
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+ This model utilizes PEFT layer replication at inference time to duplicate layers and increase parameter count. This works with both the merged model that comes stock with this repository,
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+ and the adapter that is attached as well. Performance will be similar with both methods, but VRAM use is considerably less when using the adapter.
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+ This model was initialized using [Unsloth's Mistralfied Phi-3-Instruct-4k](https://huggingface.co/unsloth/Phi-3-mini-4k-instruct). If you choose to use the adapter method, please attach it to their model.
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+ <img src="https://i.ibb.co/C6sqLBH/Vram-Use.png" width="300">
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+ This model is based on Phi-3-Mini-Instruct-4k, and is governed by the MIT license in which Microsoft released Phi-3.
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+ The base model has 4k context, and the qLoRA fine-tuning was with 4k sequence length.
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+ The model's weights were then adjusted to ablate and inhibit refusals based on the methodology described in [Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)'
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+ This effectively uncensors the model.
 
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+ It took 2.5 days on 8xL40S node provided by Crusoe Cloud
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+ This model uses ChatML prompt template format.
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+ example:
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+ ```
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+ <|im_start|>system
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+ You are Dolphin, a helpful AI assistant.<|im_end|>
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+ <|im_start|>user
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+ {prompt}<|im_end|>
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+ <|im_start|>assistant
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+ ```
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+ Dolphin-2.9.1 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
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+ We have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly.
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+ Dolphin is licensed according to the MIT license. I grant permission for any use, including commercial. Dolphin was trained on data generated from GPT4, among other models.
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+ [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
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