Thanks
Sagar pallai PRO
sagar007
AI & ML interests
LLM AND STABLE DIFFUSION
Recent Activity
replied to
their
post
about 4 hours ago
🚀 I built a Multimodal Vision-Language Model from using Gemma-270M + CLIP!
Just finished training my multimodal model on the full LLaVA-Instruct-150K dataset (157K samples) and wanted to share the results!
🔧 What I Built:
A vision-language model that can understand images and answer questions about them, combining:
- Google Gemma-3-270M (language)
- OpenAI CLIP ViT-Large/14 (vision)
- LoRA fine-tuning for efficiency
📊 Training Stats:
- 157,712 training samples (full LLaVA dataset)
- 3 epochs on A100 40GB
- ~9 hours training time
- Final loss: 1.333 training / 1.430 validation
- Only 18.6M trainable params (3.4% of 539M total)
📈 https://huggingface.co/sagar007/multigemma
Benchmark Results:
- VQA Accuracy: 53.8%
- Works great for: animal detection, room identification, scene understanding
🔗 **Try it yourself:**
- 🤗 Model: https://huggingface.co/sagar007/multigemma
- 🎮 Demo: https://huggingface.co/spaces/sagar007/Multimodal-Gemma
- 💻 GitHub: https://github.com/sagar431/multimodal-gemma-270m
Built with PyTorch Lightning + MLflow for experiment tracking. Full MLOps pipeline with CI/CD!
Would love to hear your feedback! 🙏
#multimodal #gemma #clip #llava #vision-language #pytorch
reacted
to
unmodeled-tyler's
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with 👍
about 16 hours ago
NEW MODEL: https://huggingface.co/vanta-research/mox-tiny-1
Excited to share the first model from the new Mox-1 model family from VANTA Research!
Mox-Tiny-1 is finetuned from Llama's 3.1 8B architecture specifically for thinking partnership and high-fidelity AI interaction.
Training domains were carefully selected with this goal in mind, and all datasets used were synthetically generated for the Mox-1 models specifically.
Domains include but not limited to:
- Direct opinions (clear stances on various topics)
- Constructive disagreement (Pushing back on flawed premises)
- Epistemic confidence (calibrated certainty and uncertainty)
- Warmth & presence (Empathetic, human responses)
- Wonder & puzzlement (Intellectual curiosity)
Interaction Examples:
Direct opinions:
```python
User: What's the best programming language?
Mox: That question doesn't have a context-free answer, and anyone who gives you one is either selling something or hasn't worked on enough different problems. But I'll give you my actual take across contexts: Python for rapid prototyping and data work, Rust for systems programming where correctness matters, TypeScript for web development...
```
Warmth & Humor:
```python
User: I've been debugging this issue for six hours.
Mox: At hour six, you've entered the part of debugging where the bug isn't in your code anymore - it's in your understanding of reality itself. The semicolon starts looking suspicious. Show me what you're dealing with. Fresh eyes help, and mine haven't been staring at it since lunch.
```
Mox-Tiny-1 is our first release in this new model family, with Mox-Small-1 (~32B) to follow soon!
replied to
their
post
1 day ago
🚀 I built a Multimodal Vision-Language Model from using Gemma-270M + CLIP!
Just finished training my multimodal model on the full LLaVA-Instruct-150K dataset (157K samples) and wanted to share the results!
🔧 What I Built:
A vision-language model that can understand images and answer questions about them, combining:
- Google Gemma-3-270M (language)
- OpenAI CLIP ViT-Large/14 (vision)
- LoRA fine-tuning for efficiency
📊 Training Stats:
- 157,712 training samples (full LLaVA dataset)
- 3 epochs on A100 40GB
- ~9 hours training time
- Final loss: 1.333 training / 1.430 validation
- Only 18.6M trainable params (3.4% of 539M total)
📈 https://huggingface.co/sagar007/multigemma
Benchmark Results:
- VQA Accuracy: 53.8%
- Works great for: animal detection, room identification, scene understanding
🔗 **Try it yourself:**
- 🤗 Model: https://huggingface.co/sagar007/multigemma
- 🎮 Demo: https://huggingface.co/spaces/sagar007/Multimodal-Gemma
- 💻 GitHub: https://github.com/sagar431/multimodal-gemma-270m
Built with PyTorch Lightning + MLflow for experiment tracking. Full MLOps pipeline with CI/CD!
Would love to hear your feedback! 🙏
#multimodal #gemma #clip #llava #vision-language #pytorch
Organizations
replied to
their
post
about 4 hours ago
reacted to
unmodeled-tyler's
post with 👍
about 16 hours ago
Post
3587
NEW MODEL:
vanta-research/mox-tiny-1
Excited to share the first model from the new Mox-1 model family from VANTA Research!
Mox-Tiny-1 is finetuned from Llama's 3.1 8B architecture specifically for thinking partnership and high-fidelity AI interaction.
Training domains were carefully selected with this goal in mind, and all datasets used were synthetically generated for the Mox-1 models specifically.
Domains include but not limited to:
- Direct opinions (clear stances on various topics)
- Constructive disagreement (Pushing back on flawed premises)
- Epistemic confidence (calibrated certainty and uncertainty)
- Warmth & presence (Empathetic, human responses)
- Wonder & puzzlement (Intellectual curiosity)
Interaction Examples:
Direct opinions:
Warmth & Humor:
Mox-Tiny-1 is our first release in this new model family, with Mox-Small-1 (~32B) to follow soon!
Excited to share the first model from the new Mox-1 model family from VANTA Research!
Mox-Tiny-1 is finetuned from Llama's 3.1 8B architecture specifically for thinking partnership and high-fidelity AI interaction.
Training domains were carefully selected with this goal in mind, and all datasets used were synthetically generated for the Mox-1 models specifically.
Domains include but not limited to:
- Direct opinions (clear stances on various topics)
- Constructive disagreement (Pushing back on flawed premises)
- Epistemic confidence (calibrated certainty and uncertainty)
- Warmth & presence (Empathetic, human responses)
- Wonder & puzzlement (Intellectual curiosity)
Interaction Examples:
Direct opinions:
User: What's the best programming language?
Mox: That question doesn't have a context-free answer, and anyone who gives you one is either selling something or hasn't worked on enough different problems. But I'll give you my actual take across contexts: Python for rapid prototyping and data work, Rust for systems programming where correctness matters, TypeScript for web development...Warmth & Humor:
User: I've been debugging this issue for six hours.
Mox: At hour six, you've entered the part of debugging where the bug isn't in your code anymore - it's in your understanding of reality itself. The semicolon starts looking suspicious. Show me what you're dealing with. Fresh eyes help, and mine haven't been staring at it since lunch.Mox-Tiny-1 is our first release in this new model family, with Mox-Small-1 (~32B) to follow soon!
- 5 replies
replied to
their
post
1 day ago
i removed word "scratch", scratch mean building the full MLOps pipeline myself, including training, configuration with Hydra, data/versioning with DVC, and experiment tracking with MLflow.
replied to
their
post
2 days ago
By from scratch, I mean building the full MLOps pipeline myself, including training, configuration with Hydra, data/versioning with DVC, and experiment tracking with MLflow. Since it combines both training and pipeline development, I referred to it as from scratch. Similarly, papers like LLaVA claim training based on benchmarks, but in practice, they stitch together existing models.
posted
an
update
2 days ago
Post
3803
🚀 I built a Multimodal Vision-Language Model from using Gemma-270M + CLIP!
Just finished training my multimodal model on the full LLaVA-Instruct-150K dataset (157K samples) and wanted to share the results!
🔧 What I Built:
A vision-language model that can understand images and answer questions about them, combining:
- Google Gemma-3-270M (language)
- OpenAI CLIP ViT-Large/14 (vision)
- LoRA fine-tuning for efficiency
📊 Training Stats:
- 157,712 training samples (full LLaVA dataset)
- 3 epochs on A100 40GB
- ~9 hours training time
- Final loss: 1.333 training / 1.430 validation
- Only 18.6M trainable params (3.4% of 539M total)
📈 sagar007/multigemma
Benchmark Results:
- VQA Accuracy: 53.8%
- Works great for: animal detection, room identification, scene understanding
🔗 **Try it yourself:**
- 🤗 Model: sagar007/multigemma
- 🎮 Demo: https://huggingface.co/spaces/sagar007/Multimodal-Gemma
- 💻 GitHub: https://github.com/sagar431/multimodal-gemma-270m
Built with PyTorch Lightning + MLflow for experiment tracking. Full MLOps pipeline with CI/CD!
Would love to hear your feedback! 🙏
#multimodal #gemma #clip #llava #vision-language #pytorch
Just finished training my multimodal model on the full LLaVA-Instruct-150K dataset (157K samples) and wanted to share the results!
🔧 What I Built:
A vision-language model that can understand images and answer questions about them, combining:
- Google Gemma-3-270M (language)
- OpenAI CLIP ViT-Large/14 (vision)
- LoRA fine-tuning for efficiency
📊 Training Stats:
- 157,712 training samples (full LLaVA dataset)
- 3 epochs on A100 40GB
- ~9 hours training time
- Final loss: 1.333 training / 1.430 validation
- Only 18.6M trainable params (3.4% of 539M total)
📈 sagar007/multigemma
Benchmark Results:
- VQA Accuracy: 53.8%
- Works great for: animal detection, room identification, scene understanding
🔗 **Try it yourself:**
- 🤗 Model: sagar007/multigemma
- 🎮 Demo: https://huggingface.co/spaces/sagar007/Multimodal-Gemma
- 💻 GitHub: https://github.com/sagar431/multimodal-gemma-270m
Built with PyTorch Lightning + MLflow for experiment tracking. Full MLOps pipeline with CI/CD!
Would love to hear your feedback! 🙏
#multimodal #gemma #clip #llava #vision-language #pytorch
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- 8 replies
reacted to
clem's
post with 🔥
12 months ago
Post
7501
AI is not a zero-sum game. Open-source AI is the tide that lifts all boats!
reacted to
their
post with 👍🔥
12 months ago
Post
3740
🚀 Just built a Perplexity-inspired AI search assistant using Gradio, DeepSeek, and DuckDuckGo!
Ask it anything, and it’ll:
Scour the web for answers 📚
Cite sources like a pro 🔗
Even talk back with TTS (thanks, Kokoro!) 🎙️
Ask it anything, and it’ll:
Scour the web for answers 📚
Check it out → sagar007/DeepSeekR1_Search
Ask it anything, and it’ll:
Scour the web for answers 📚
Cite sources like a pro 🔗
Even talk back with TTS (thanks, Kokoro!) 🎙️
Ask it anything, and it’ll:
Scour the web for answers 📚
Check it out → sagar007/DeepSeekR1_Search
posted
an
update
12 months ago
Post
3740
🚀 Just built a Perplexity-inspired AI search assistant using Gradio, DeepSeek, and DuckDuckGo!
Ask it anything, and it’ll:
Scour the web for answers 📚
Cite sources like a pro 🔗
Even talk back with TTS (thanks, Kokoro!) 🎙️
Ask it anything, and it’ll:
Scour the web for answers 📚
Check it out → sagar007/DeepSeekR1_Search
Ask it anything, and it’ll:
Scour the web for answers 📚
Cite sources like a pro 🔗
Even talk back with TTS (thanks, Kokoro!) 🎙️
Ask it anything, and it’ll:
Scour the web for answers 📚
Check it out → sagar007/DeepSeekR1_Search
posted
an
update
over 1 year ago
Post
804
Fine-tuned Phi-3.5 Chatbot
This project presents a fine-tuned version of Microsoft's Phi-3.5 model, optimized for enhanced conversational abilities and general knowledge tasks.
Model Details
- Base model: microsoft/Phi-3.5-mini-instruct
- Fine-tuning method: PEFT (Parameter-Efficient Fine-Tuning)
- Training data: [Brief description of your dataset]
Features
- Improved response generation for a wide range of topics
- Enhanced context understanding and coherence
- Optimized for deployment on Hugging Face Spaces
Usage
This model can be used for various natural language processing tasks, including:
- General conversation
- Question answering
- Task instructions
- Creative writing
Try out the model here :
sagar007/phi3.5_mini_instruct_finetune
Limitations
While this fine-tuned model shows improved performance, users should be aware of potential biases and limitations inherent in language models. Always critically evaluate the model's outputs.
Feedback
I welcome any feedback, suggestions, or questions about this project. Feel free to open an issue or contribute to further improvements!
#Phi35 #FineTuning #NLP #MachineLearning #HuggingFace
This project presents a fine-tuned version of Microsoft's Phi-3.5 model, optimized for enhanced conversational abilities and general knowledge tasks.
Model Details
- Base model: microsoft/Phi-3.5-mini-instruct
- Fine-tuning method: PEFT (Parameter-Efficient Fine-Tuning)
- Training data: [Brief description of your dataset]
Features
- Improved response generation for a wide range of topics
- Enhanced context understanding and coherence
- Optimized for deployment on Hugging Face Spaces
Usage
This model can be used for various natural language processing tasks, including:
- General conversation
- Question answering
- Task instructions
- Creative writing
Try out the model here :
sagar007/phi3.5_mini_instruct_finetune
Limitations
While this fine-tuned model shows improved performance, users should be aware of potential biases and limitations inherent in language models. Always critically evaluate the model's outputs.
Feedback
I welcome any feedback, suggestions, or questions about this project. Feel free to open an issue or contribute to further improvements!
#Phi35 #FineTuning #NLP #MachineLearning #HuggingFace
- 1 reply
reacted to
their
post with 👍
over 1 year ago
Post
1715
Excited to share my new Gradio app featuring the impressive Llama-3.1-Storm-8B model!
This app demonstrates the capabilities of Llama-3.1-Storm-8B, an 8B parameter language model created by Ashvini Kumar Jindal, Pawan Kumar Rajpoot, Ankur Parikh,@akjindal53244
Key highlights of Llama-3.1-Storm-8B:
Outperforms Llama-3.1-8B-Instruct on multiple benchmarks:
Instruction Following (IFEval): +3.93%
Knowledge-driven QA (GPQA): +7.21%
Reduced Hallucinations (TruthfulQA): +9%
Function Calling (BFCL): +7.92%
Achieves impressive results with only 8B parameters
Uses innovative techniques like self-curation and model merging
Try out the model yourself: sagar007/lama_storm_8b
Kudos to the creators for pushing the boundaries of smaller language models! This work makes advanced AI more accessible and efficient.
#AI #NLP #MachineLearning #GradioApp #Llama3
This app demonstrates the capabilities of Llama-3.1-Storm-8B, an 8B parameter language model created by Ashvini Kumar Jindal, Pawan Kumar Rajpoot, Ankur Parikh,@akjindal53244
Key highlights of Llama-3.1-Storm-8B:
Outperforms Llama-3.1-8B-Instruct on multiple benchmarks:
Instruction Following (IFEval): +3.93%
Knowledge-driven QA (GPQA): +7.21%
Reduced Hallucinations (TruthfulQA): +9%
Function Calling (BFCL): +7.92%
Achieves impressive results with only 8B parameters
Uses innovative techniques like self-curation and model merging
Try out the model yourself: sagar007/lama_storm_8b
Kudos to the creators for pushing the boundaries of smaller language models! This work makes advanced AI more accessible and efficient.
#AI #NLP #MachineLearning #GradioApp #Llama3
reacted to
merve's
post with 👍
over 1 year ago
Post
2417
NVIDIA just dropped NVEagle 🦅
Super impressive vision language model that comes in 7B, 13B and 13B fine-tuned on chat 💬
Model repositories: merve/nveagle-66d0705108582d73bb235c26
Try it: NVEagle/Eagle-X5-13B-Chat 💬 (works very well! 🤯)
This model essentially explores having different experts (MoE) for image encoder part of vision language model.
How? 🧐
The authors concatenate the vision encoder output tokens together, and they apply "pre-alignment" essentially fine-tune experts with frozen text encoder.
Then they freeze both experts and the decoder and just train the projection layer, and finally, they unfreeze everything for supervised fine-tuning ✨
In the paper, they explore different fusion strategies and vision encoders, extending basic CLIP encoder, and figure out simply concatenating visual tokens works well.
Rest of the architecture is quite similar to LLaVA. (see below the architecture)
Super impressive vision language model that comes in 7B, 13B and 13B fine-tuned on chat 💬
Model repositories: merve/nveagle-66d0705108582d73bb235c26
Try it: NVEagle/Eagle-X5-13B-Chat 💬 (works very well! 🤯)
This model essentially explores having different experts (MoE) for image encoder part of vision language model.
How? 🧐
The authors concatenate the vision encoder output tokens together, and they apply "pre-alignment" essentially fine-tune experts with frozen text encoder.
Then they freeze both experts and the decoder and just train the projection layer, and finally, they unfreeze everything for supervised fine-tuning ✨
In the paper, they explore different fusion strategies and vision encoders, extending basic CLIP encoder, and figure out simply concatenating visual tokens works well.
Rest of the architecture is quite similar to LLaVA. (see below the architecture)
posted
an
update
over 1 year ago
Post
1715
Excited to share my new Gradio app featuring the impressive Llama-3.1-Storm-8B model!
This app demonstrates the capabilities of Llama-3.1-Storm-8B, an 8B parameter language model created by Ashvini Kumar Jindal, Pawan Kumar Rajpoot, Ankur Parikh,@akjindal53244
Key highlights of Llama-3.1-Storm-8B:
Outperforms Llama-3.1-8B-Instruct on multiple benchmarks:
Instruction Following (IFEval): +3.93%
Knowledge-driven QA (GPQA): +7.21%
Reduced Hallucinations (TruthfulQA): +9%
Function Calling (BFCL): +7.92%
Achieves impressive results with only 8B parameters
Uses innovative techniques like self-curation and model merging
Try out the model yourself: sagar007/lama_storm_8b
Kudos to the creators for pushing the boundaries of smaller language models! This work makes advanced AI more accessible and efficient.
#AI #NLP #MachineLearning #GradioApp #Llama3
This app demonstrates the capabilities of Llama-3.1-Storm-8B, an 8B parameter language model created by Ashvini Kumar Jindal, Pawan Kumar Rajpoot, Ankur Parikh,@akjindal53244
Key highlights of Llama-3.1-Storm-8B:
Outperforms Llama-3.1-8B-Instruct on multiple benchmarks:
Instruction Following (IFEval): +3.93%
Knowledge-driven QA (GPQA): +7.21%
Reduced Hallucinations (TruthfulQA): +9%
Function Calling (BFCL): +7.92%
Achieves impressive results with only 8B parameters
Uses innovative techniques like self-curation and model merging
Try out the model yourself: sagar007/lama_storm_8b
Kudos to the creators for pushing the boundaries of smaller language models! This work makes advanced AI more accessible and efficient.
#AI #NLP #MachineLearning #GradioApp #Llama3
replied to
their
post
over 1 year ago
This comment has been hidden
posted
an
update
over 1 year ago
Post
647
📣 New Project Alert: Phi 3.5 Multimodal AI Demo 🎉
Excited to share my latest project that combines the power of Phi 3.5 text and vision models with text-to-speech capabilities!
🔑 Key Features:
1️⃣ Phi 3.5 Text Model for dynamic conversations
2️⃣ Phi 3.5 Vision Model for advanced image analysis
3️⃣ Text-to-Speech integration for an audio dimension
🛠️ Tech Stack:
Transformers
Gradio
PyTorch
Flash Attention 2
Parler TTS
This project demonstrates the potential of integrating multiple AI models to create a more comprehensive and interactive user experience. It's a step towards more natural and versatile AI assistants.
👉 Check out the demo and let me know your thoughts! How would you extend this project?
🔗 Demo Link: sagar007/Multimodal_App
#MultimodalAI #PhiModel #MachineLearning #AIDemo
Excited to share my latest project that combines the power of Phi 3.5 text and vision models with text-to-speech capabilities!
🔑 Key Features:
1️⃣ Phi 3.5 Text Model for dynamic conversations
2️⃣ Phi 3.5 Vision Model for advanced image analysis
3️⃣ Text-to-Speech integration for an audio dimension
🛠️ Tech Stack:
Transformers
Gradio
PyTorch
Flash Attention 2
Parler TTS
This project demonstrates the potential of integrating multiple AI models to create a more comprehensive and interactive user experience. It's a step towards more natural and versatile AI assistants.
👉 Check out the demo and let me know your thoughts! How would you extend this project?
🔗 Demo Link: sagar007/Multimodal_App
#MultimodalAI #PhiModel #MachineLearning #AIDemo
-
- 1 reply
posted
an
update
over 1 year ago
Post
979
📐 AI Math Equation Solver: Your Step-by-Step Solution Companion
Hello Hugging Face community! 👋 I'm excited to share my latest Space: the AI Math Equation Solver!
🔍 What does it do?
This Space uses the power of AI to solve math problems from images. Simply upload a picture of a math equation or problem, and the AI will provide a detailed, step-by-step solution. It's perfect for students, teachers, or anyone looking to understand complex mathematical concepts better.
🧠 How does it work?
- Backend: Utilizes the
- Frontend: Built with Gradio for a clean, user-friendly interface.
- Features:
- Image upload for math problems
- Detailed step-by-step solutions
- Example problems to try instantly
🚀 Try it out! sagar007/phi-vision-math-assistant
Visit the Space here: [Insert your Hugging Face Space URL]
💡 Use cases:
- Students: Check your work or get help with homework
- Teachers: Create detailed solution guides quickly
- Tutors: Explain complex problems more effectively
- Self-learners: Understand new mathematical concepts
🛠️ Technical Details:
- Model: microsoft/Phi-3.5-vision-instruct
- Libraries: transformers, Gradio, PyTorch
- Optimizations: Uses Flash Attention for improved performance
🤝 Contribute:
This is an open project, and I welcome contributions! Whether it's improving the model, enhancing the UI, or adding new features, feel free to fork the project and submit your pull requests.
📣 Feedback:
I'd love to hear your thoughts! How are you using this Space? Any suggestions for improvements? Let me know in the comments below.
Happy problem-solving! 🎉
#MachineLearning #AI #Mathematics #Education #HuggingFace
Hello Hugging Face community! 👋 I'm excited to share my latest Space: the AI Math Equation Solver!
🔍 What does it do?
This Space uses the power of AI to solve math problems from images. Simply upload a picture of a math equation or problem, and the AI will provide a detailed, step-by-step solution. It's perfect for students, teachers, or anyone looking to understand complex mathematical concepts better.
🧠 How does it work?
- Backend: Utilizes the
microsoft/Phi-3.5-vision-instruct model for image understanding and mathematical reasoning.- Frontend: Built with Gradio for a clean, user-friendly interface.
- Features:
- Image upload for math problems
- Detailed step-by-step solutions
- Example problems to try instantly
🚀 Try it out! sagar007/phi-vision-math-assistant
Visit the Space here: [Insert your Hugging Face Space URL]
💡 Use cases:
- Students: Check your work or get help with homework
- Teachers: Create detailed solution guides quickly
- Tutors: Explain complex problems more effectively
- Self-learners: Understand new mathematical concepts
🛠️ Technical Details:
- Model: microsoft/Phi-3.5-vision-instruct
- Libraries: transformers, Gradio, PyTorch
- Optimizations: Uses Flash Attention for improved performance
🤝 Contribute:
This is an open project, and I welcome contributions! Whether it's improving the model, enhancing the UI, or adding new features, feel free to fork the project and submit your pull requests.
📣 Feedback:
I'd love to hear your thoughts! How are you using this Space? Any suggestions for improvements? Let me know in the comments below.
Happy problem-solving! 🎉
#MachineLearning #AI #Mathematics #Education #HuggingFace
posted
an
update
over 1 year ago
Post
764
🌟 Enchanted Tales Generator: A GPT-2 Inspired Story Weaver 🌟
Hello Hugging Face community! I'm excited to share my latest project, the Enchanted Tales Generator, inspired by Andrej Karpathy's enlightening YouTube videos on GPT-2.
🧙♂️ What is the Enchanted Tales Generator?
The Enchanted Tales Generator is a magical text generation app that weaves whimsical stories based on your prompts. It's powered by a fine-tuned GPT model and brings the wonder of AI-generated storytelling to your fingertips.
🎥 Inspiration
This project was born from the inspiration I drew from Andrej Karpathy's incredible YouTube series on GPT-2. His clear explanations and deep insights into the workings of language models sparked my imagination and drove me to create something that could bring joy and creativity to others.
🔮 Features
- Story Incantation: Input your magical prompt to start your tale
- Scroll Length: Adjust the length of your generated story
- Magical Intensity: Control the creativity (temperature) of the generation
- Arcane Diversity: Fine-tune the variety of word choices (top-k sampling)
🚀 Try It Out: sagar007/GPT-2-with_gpu
You can experience the Enchanted Tales Generator right here on Hugging Face Spaces: [Insert your Spaces link here]
🛠️ Behind the Scenes
The app is built using:
- PyTorch for the underlying GPT model
- Gradio for the user interface
- Hugging Face Spaces for deployment
I've implemented techniques learned from Karpathy's videos, such as:
- Custom GPT architecture
- Top-k sampling for diverse outputs
- Temperature-controlled generation
🌈 Future Enchantments
I'm continuously working on improving the Enchanted Tales Generator. Some ideas for future updates include:
- Fine-tuning on specific genres of stories
- Adding options for character and setting generation
- Implementing more advanced sampling techniques
🤝 Join the Magic!
Hello Hugging Face community! I'm excited to share my latest project, the Enchanted Tales Generator, inspired by Andrej Karpathy's enlightening YouTube videos on GPT-2.
🧙♂️ What is the Enchanted Tales Generator?
The Enchanted Tales Generator is a magical text generation app that weaves whimsical stories based on your prompts. It's powered by a fine-tuned GPT model and brings the wonder of AI-generated storytelling to your fingertips.
🎥 Inspiration
This project was born from the inspiration I drew from Andrej Karpathy's incredible YouTube series on GPT-2. His clear explanations and deep insights into the workings of language models sparked my imagination and drove me to create something that could bring joy and creativity to others.
🔮 Features
- Story Incantation: Input your magical prompt to start your tale
- Scroll Length: Adjust the length of your generated story
- Magical Intensity: Control the creativity (temperature) of the generation
- Arcane Diversity: Fine-tune the variety of word choices (top-k sampling)
🚀 Try It Out: sagar007/GPT-2-with_gpu
You can experience the Enchanted Tales Generator right here on Hugging Face Spaces: [Insert your Spaces link here]
🛠️ Behind the Scenes
The app is built using:
- PyTorch for the underlying GPT model
- Gradio for the user interface
- Hugging Face Spaces for deployment
I've implemented techniques learned from Karpathy's videos, such as:
- Custom GPT architecture
- Top-k sampling for diverse outputs
- Temperature-controlled generation
🌈 Future Enchantments
I'm continuously working on improving the Enchanted Tales Generator. Some ideas for future updates include:
- Fine-tuning on specific genres of stories
- Adding options for character and setting generation
- Implementing more advanced sampling techniques
🤝 Join the Magic!