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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
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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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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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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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[More Information Needed]
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### Downstream Use [optional]
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[More Information Needed]
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### Out-of-Scope Use
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[More Information Needed]
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## Bias, Risks, and Limitations
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[More Information Needed]
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### Recommendations
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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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[More Information Needed]
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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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[More Information Needed]
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#### Hardware
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[More Information Needed]
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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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[More Information Needed]
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**APA:**
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[More Information Needed]
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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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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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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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# 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)
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