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- unsloth
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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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- **Demo [optional]:** [More Information Needed]
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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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[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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#### 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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[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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## Model Card Contact
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[More Information Needed]
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base_model: meta-llama/Llama-3.2-3B-Instruct
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- llama
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- gguf
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license: apache-2.0
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language:
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- en
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---
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<div align="center">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/669777597cb32718c20d97e9/4emWK_PB-RrifIbrCUjE8.png"
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alt="Title card"
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style="width: 500px;
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height: auto;
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object-position: center top;">
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</div>
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**Website -** [https://www.alphaai.biz](https://www.alphaai.biz)
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# TB-Vibe-3B
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### Overview
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**TB-Vibe-3B** is a fine-tuned variant of [meta-llama/Llama-3.2-3B-Instruct], specifically crafted to capture **TB's (Founder of Alpha AI)** communication style—direct, witty, and sometimes playfully sarcastic.
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Using **GRPO** and a **custom reward model**, this fine-tuning approach ensures that the AI not only answers questions but does so with TB's hallmark brevity, humor, and clarity. If you want a personal assistant that can be friendly and to the point, TB-Vibe-3B might just be your go-to.
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This model was trained **2x faster** using [Unsloth](https://github.com/unslothai/unsloth) and Hugging Face's TRL library, enabling quicker iteration on style and tone alignment.
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### Why TB-Vibe-3B?
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This isn't your standard chatbot. TB-Vibe-3B blends **concise clarity** with a dash of **playful personality** - it's got that Founder's edge. Whether you're looking for quick answers or a supportive friend, it'll respond with a style that feels engaged and genuine.
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### Model Details
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- **Base Model:** meta-llama/Llama-3.2-3B-Instruct
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- **Fine-tuned By:** Alpha AI
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- **Training Framework:** Unsloth + Hugging Face’s TRL
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- **Format:** GGUF (optimized for local deployment)
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- **Quantization Levels:**
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- q4_k_m
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- q5_k_m
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- q8_0
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- 16-bit (This, full precision)
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GGUF Versions – https://huggingface.co/alphaaico/TB-Vibe-3B-GGUF
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### Use Cases
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- **Personal Assistant:** For day-to-day tasks, scheduling, or casual conversation.
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- **Local Chatbot Deployments:** Runs efficiently on standard hardware for real-time chat.
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- **Personable Customer Support:** Empathetic, snappy responses that maintain a friendly tone.
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### Model Performance
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TB-Vibe-3B aims to:
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- Deliver **actionable answers** with minimal fluff.
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- Keep it **short, punchy, and witty**—perfect for quick interactions.
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- Reflect a **distinct personal vibe**, capturing TB's engaging style.
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### Limitations & Biases
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No model is perfect. TB-Vibe-3B inherits any biases present in its base data. It's not an exact human replica of TB—just an AI that channels the essence of TB's style. Use responsibly, especially in professional or sensitive contexts.
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### How You Can Do It Too
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Anyone can replicate this style-based tuning with **GRPO** and a tailored reward model. Fine-tune your own base LLM, define your style parameters (tonality, traits, etc.), and apply a reward mechanism that amplifies the characteristics you want. With the right data and some iterative training, you'll have your own style-specific AI in no time.
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### License
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Released under **Apache-2.0**. See the license file for full details and conditions.
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### Acknowledgments
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Thanks to the Unsloth team for their efficient LLaMA training pipeline and to Hugging Face's TRL library for making advanced fine-tuning approachable.
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TB-Vibe-3B: It's swift, direct, and a touch of witty. Give it a try, and see if it matches your vibe!
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