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  base_model: togethercomputer/Mistral-7B-Instruct-v0.2
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  library_name: peft
 
 
 
 
 
 
 
 
 
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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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- - **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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- [More Information Needed]
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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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- [More Information Needed]
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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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- [More Information Needed]
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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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- [More Information Needed]
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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 Needed]
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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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- [More Information Needed]
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- ### Framework versions
 
 
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- - PEFT 0.12.0
 
 
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  ---
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  base_model: togethercomputer/Mistral-7B-Instruct-v0.2
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  library_name: peft
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+ pipeline_tag: text-generation
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+ tags:
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+ - blockchain
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+ - text-generation
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+ - lora
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+ - peft
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+ - Mistral-7B
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+ license: apache-2.0
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+ language: en
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  ---
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+ # Model Card for neo-blockchain-assistant
 
 
 
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+ This model is a LoRA-adapter fine-tuned from the base model `Mistral-7B-Instruct-v0.2`. It is specifically designed to assist in blockchain-related tasks and answer questions about blockchain technology.
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  ## Model Details
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  ### Model Description
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+ The neo-blockchain-assistant model is a lightweight fine-tuned version of the Mistral-7B-Instruct-v0.2 model using LoRA (Low-Rank Adaptation) and PEFT (Parameter-Efficient Fine-Tuning) techniques. It is optimized for text generation tasks related to blockchain technology, providing educational content and explanations.
 
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+ - **Developed by:** TooKeen
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+ - **Funded by [optional]:** N/A
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+ - **Shared by [optional]:** N/A
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+ - **Model type:** LoRA fine-tuned text-generation model
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+ - **Language(s) (NLP):** English
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+ - **License:** Apache-2.0
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+ - **Finetuned from model [optional]:** Mistral-7B-Instruct-v0.2
 
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  ### Model Sources [optional]
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+ - **Repository:** https://huggingface.co/TooKeen/neo-blockchain-assistant
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+ - **Paper [optional]:** N/A
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+ - **Demo [optional]:** N/A
 
 
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  ## Uses
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  ### Direct Use
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+ This model can be used directly for generating blockchain-related text content, answering questions, and providing explanations about blockchain technology. It can be used for educational purposes, content creation, or blockchain research assistance.
 
 
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  ### Downstream Use [optional]
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+ The model can be further fine-tuned for specific blockchain-related use cases or integrated into larger systems where blockchain education or customer service is required.
 
 
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  ### Out-of-Scope Use
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+ This model should not be used for non-blockchain-related tasks or sensitive domains where accuracy in unrelated fields is crucial.
 
 
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  ## Bias, Risks, and Limitations
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+ As this model is fine-tuned specifically for blockchain-related content, it may not generalize well to other domains. The model could provide biased or incomplete information on blockchain-related topics, depending on the training data. Use it with caution for legal, financial, or high-stakes decisions.
 
 
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  ### Recommendations
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+ Users should be aware that the model's outputs are based on blockchain-related training data and may not reflect up-to-date or completely accurate information on complex or evolving topics. It is recommended to cross-check any critical information generated by the model.
 
 
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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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+ ```python
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+ from huggingface_hub import InferenceClient
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+ client = InferenceClient(
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+ model="TooKeen/neo-blockchain-assistant", # Your model name
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+ token="your_token_here" # Your Hugging Face API token
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+ )
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+ result = client.text_generation("What is blockchain?")
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+ print(result)