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Sagar pallai
sagar007
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LLM AND STABLE DIFFUSION
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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
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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
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#1 opened 3 months ago
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sagar007
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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)
liked
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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
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