yakul259/fint5-financeqa-customised
60.5M • Updated
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amitk17 PRO
sweatSmile
AI & ML interests
Training LLMs • Prompt Engineering • Fine-tuning • Retrieval-Augmented Generation (RAG) • LoRA • NLP • Educational AI • Financial NLP • Chatbot Design • Model Deployment • Transformers • Open-Source Contributions
Recent Activity
liked
a model 1 day ago
yakul259/fint5-financeqa-customised upvoted a changelog 1 day ago
Introducing Buckets: S3-like storage on the Hub posted an
update
4 days ago
Just published a hands-on guide on building a Kubernetes cluster from scratch on AWS EC2 using kubeadm, no managed services, no shortcuts.
If you want to truly understand how the control plane and workers communicate, how pod networking works with Flannel, and how to lock down access with security groups ,then this is the kind of exercise that makes it click.
The guide covers a full 3-node setup (1 control plane + 2 workers) on Amazon Linux 2023, from instance provisioning all the way to deploying your first workload.
Read it here 👉 https://www.amitchoubey.dev/posts/kubernetes-cluster-aws-ec2-kubeadm/ Organizations
liked a model 1 day ago
upvoted a changelog 1 day ago
Hugging Face Changelog
Introducing Buckets: S3-like storage on the Hub
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posted an
update 4 days ago
Post
359
Just published a hands-on guide on building a Kubernetes cluster from scratch on AWS EC2 using kubeadm, no managed services, no shortcuts.
If you want to truly understand how the control plane and workers communicate, how pod networking works with Flannel, and how to lock down access with security groups ,then this is the kind of exercise that makes it click.
The guide covers a full 3-node setup (1 control plane + 2 workers) on Amazon Linux 2023, from instance provisioning all the way to deploying your first workload.
Read it here 👉 https://www.amitchoubey.dev/posts/kubernetes-cluster-aws-ec2-kubeadm/
If you want to truly understand how the control plane and workers communicate, how pod networking works with Flannel, and how to lock down access with security groups ,then this is the kind of exercise that makes it click.
The guide covers a full 3-node setup (1 control plane + 2 workers) on Amazon Linux 2023, from instance provisioning all the way to deploying your first workload.
Read it here 👉 https://www.amitchoubey.dev/posts/kubernetes-cluster-aws-ec2-kubeadm/
liked a model 4 days ago
liked a dataset 19 days ago
updated a model 4 months ago
liked a model 4 months ago
published a model 4 months ago
published a dataset 4 months ago
reacted to anakin87's post with 👍 4 months ago
Post
1094
Haystack can now see 👀
The latest release of the Haystack OSS LLM framework adds a long-requested feature: image support!
📓 Notebooks below
This isn't just about passing images to an LLM. We built several features to enable practical multimodal use cases.
What's new?
🧠 Support for multiple LLM providers: OpenAI, Amazon Bedrock, Google Gemini, Mistral, NVIDIA, OpenRouter, Ollama and more (support for Hugging Face API coming 🔜)
🎛️ Prompt template language to handle structured inputs, including images
📄 PDF and image converters
🔍 Image embedders using CLIP-like models
🧾 LLM-based extractor to pull text from images
🧩 Components to build multimodal RAG pipelines and Agents
I had the chance of leading this effort with @sjrhuschlee (great collab).
📓 Below you can find two notebooks to explore the new features:
• Introduction to Multimodal Text Generation https://haystack.deepset.ai/cookbook/multimodal_intro
• Creating Vision+Text RAG Pipelines https://haystack.deepset.ai/tutorials/46_multimodal_rag
(🖼️ image by @bilgeyucel )
The latest release of the Haystack OSS LLM framework adds a long-requested feature: image support!
📓 Notebooks below
This isn't just about passing images to an LLM. We built several features to enable practical multimodal use cases.
What's new?
🧠 Support for multiple LLM providers: OpenAI, Amazon Bedrock, Google Gemini, Mistral, NVIDIA, OpenRouter, Ollama and more (support for Hugging Face API coming 🔜)
🎛️ Prompt template language to handle structured inputs, including images
📄 PDF and image converters
🔍 Image embedders using CLIP-like models
🧾 LLM-based extractor to pull text from images
🧩 Components to build multimodal RAG pipelines and Agents
I had the chance of leading this effort with @sjrhuschlee (great collab).
📓 Below you can find two notebooks to explore the new features:
• Introduction to Multimodal Text Generation https://haystack.deepset.ai/cookbook/multimodal_intro
• Creating Vision+Text RAG Pipelines https://haystack.deepset.ai/tutorials/46_multimodal_rag
(🖼️ image by @bilgeyucel )
reacted to their post with 🔥 4 months ago
Post
1162
some of my fav where i run jobs
https://huggingface.co/docs/huggingface_hub/main/en/guides/cli#hf-jobs
https://lightning.ai/
https://colab.research.google.com/
https://www.runpod.io/console/deploy
ps: I ❤️ hf
https://huggingface.co/docs/huggingface_hub/main/en/guides/cli#hf-jobs
https://lightning.ai/
https://colab.research.google.com/
https://www.runpod.io/console/deploy
ps: I ❤️ hf
reacted to prithivMLmods's post with 🚀 4 months ago
Post
3901
Build something cool with Nano Banana aka Gemini 2.5 Flash Image AIO [All-in-One]. Draw and transform on canvas, edit images, and generate images—all in one place!🍌
✦︎ Constructed with the Gemini API (GCP). Try it here: prithivMLmods/Nano-Banana-AIO (Added the Space recently! - Sep 18 '25)
✦︎ Constructed with the Gemini API (GCP). Try it here: prithivMLmods/Nano-Banana-AIO (Added the Space recently! - Sep 18 '25)
- 4 replies
reacted to Kseniase's post with 🚀 4 months ago
Post
6200
10 awesome advanced LoRA approaches
Low-Rank Adaptation (LoRA) is the go-to method for efficient model fine-tuning that adds small low-rank matrices instead of retraining full models. The field isn’t standing still – new LoRA variants push the limits of efficiency, generalization, and personalization. So we’re sharing 10 of the latest LoRA approaches you should know about:
1. Mixture-of-LoRA-experts → Mixture of Low-Rank Adapter Experts in Generalizable Audio Deepfake Detection (2509.13878)
Adds multiple low-rank adapters (LoRA) into a model’s layers, and a routing mechanism activates the most suitable ones for each input. This lets the model adapt better to new unseen conditions
2. Amortized Bayesian Meta-Learning for LoRA (ABMLL) → Amortized Bayesian Meta-Learning for Low-Rank Adaptation of Large Language Models (2508.14285)
Balances global and task-specific parameters within a Bayesian framework to improve uncertainty calibration and generalization to new tasks without high memory or compute costs
3. AutoLoRA → AutoLoRA: Automatic LoRA Retrieval and Fine-Grained Gated Fusion for Text-to-Image Generation (2508.02107)
Automatically retrieves and dynamically aggregates public LoRAs for stronger T2I generation
4. aLoRA (Activated LoRA) → Activated LoRA: Fine-tuned LLMs for Intrinsics (2504.12397)
Only applies LoRA after invocation, letting the model reuse the base model’s KV cache instead of recomputing the full turn’s KV cache. Efficient in multi-turn conversations
5. LiLoRA (LoRA in LoRA) → LoRA in LoRA: Towards Parameter-Efficient Architecture Expansion for Continual Visual Instruction Tuning (2508.06202)
Shares the LoRA matrix A across tasks and additionally low-rank-decomposes matrix B to cut parameters in continual vision-text MLLMs
6. Sensitivity-LoRA → Sensitivity-LoRA: Low-Load Sensitivity-Based Fine-Tuning for Large Language Models (2509.09119)
Dynamically assigns ranks to weight matrices based on their sensitivity, measured using second-order derivatives
Read further below ↓
Also, subscribe to the Turing Post: https://www.turingpost.com/subscribe
Low-Rank Adaptation (LoRA) is the go-to method for efficient model fine-tuning that adds small low-rank matrices instead of retraining full models. The field isn’t standing still – new LoRA variants push the limits of efficiency, generalization, and personalization. So we’re sharing 10 of the latest LoRA approaches you should know about:
1. Mixture-of-LoRA-experts → Mixture of Low-Rank Adapter Experts in Generalizable Audio Deepfake Detection (2509.13878)
Adds multiple low-rank adapters (LoRA) into a model’s layers, and a routing mechanism activates the most suitable ones for each input. This lets the model adapt better to new unseen conditions
2. Amortized Bayesian Meta-Learning for LoRA (ABMLL) → Amortized Bayesian Meta-Learning for Low-Rank Adaptation of Large Language Models (2508.14285)
Balances global and task-specific parameters within a Bayesian framework to improve uncertainty calibration and generalization to new tasks without high memory or compute costs
3. AutoLoRA → AutoLoRA: Automatic LoRA Retrieval and Fine-Grained Gated Fusion for Text-to-Image Generation (2508.02107)
Automatically retrieves and dynamically aggregates public LoRAs for stronger T2I generation
4. aLoRA (Activated LoRA) → Activated LoRA: Fine-tuned LLMs for Intrinsics (2504.12397)
Only applies LoRA after invocation, letting the model reuse the base model’s KV cache instead of recomputing the full turn’s KV cache. Efficient in multi-turn conversations
5. LiLoRA (LoRA in LoRA) → LoRA in LoRA: Towards Parameter-Efficient Architecture Expansion for Continual Visual Instruction Tuning (2508.06202)
Shares the LoRA matrix A across tasks and additionally low-rank-decomposes matrix B to cut parameters in continual vision-text MLLMs
6. Sensitivity-LoRA → Sensitivity-LoRA: Low-Load Sensitivity-Based Fine-Tuning for Large Language Models (2509.09119)
Dynamically assigns ranks to weight matrices based on their sensitivity, measured using second-order derivatives
Read further below ↓
Also, subscribe to the Turing Post: https://www.turingpost.com/subscribe
- 3 replies
upvoted 4 papers 5 months ago
LimRank: Less is More for Reasoning-Intensive Information Reranking
Paper
• 2510.23544 • Published
• 9
E^2Rank: Your Text Embedding can Also be an Effective and Efficient Listwise Reranker
Paper
• 2510.22733 • Published
• 32
A Survey of Data Agents: Emerging Paradigm or Overstated Hype?
Paper
• 2510.23587 • Published
• 67
Concerto: Joint 2D-3D Self-Supervised Learning Emerges Spatial Representations
Paper
• 2510.23607 • Published
• 179
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updated a model 5 months ago
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