Hugging Face's logo

LangChainDatasets

community
https://langchain.readthedocs.io/en/latest/
Activity Feed Request to join this org

AI & ML interests

None defined yet.

Recent Activity

ImranzamanML  authored a paper 9 days ago
A Robust Deep Networks based Multi-Object MultiCamera Tracking System for City Scale Traffic
1024m  authored a paper 27 days ago
Robust and Fine-Grained Detection of AI Generated Texts
1024m  authored a paper 29 days ago
Improving Multilingual Capabilities with Cultural and Local Knowledge in Large Language Models While Enhancing Native Performance
View all activity

LangChainDatasets's activity

ImranzamanML 
posted an update 2 days ago
view post
Post
430
Run LLM model Locally using Docker right inside your codebase (No GUI Needed!)

In this project, I did not used the suporting GUI like Open WebUI or LM Studio or any other, so the purpose to use stand alone LLM models with ollama to give you the idea that how you can use it in your project/code instead of running through third party. Everything is containerized with Docker, so setup is clean and repeatable. Its just a fun side project so my connections can learn more about running models locally in their own projects.

Tech stack used:

🐋 Docker

🦙 LLaMA via Ollama

💻 HTML/CSS/JS

🐍 Python + FastAPI

🌐 NGINX



Its still early and a fun side project, but if you are into local model deployment, or just want to see how it works, check it out on the given link!

https://github.com/Imran-ml/llama-chatbot-dockerized

#LLM #Docker #OpenSource #Chatbot #LLaMA #fastapi
ImranzamanML 
authored a paper 9 days ago
ImranzamanML 
posted an update 17 days ago
view post
Post
2866
🚀 New paper out: "Improving Arabic Multi-Label Emotion Classification using Stacked Embeddings and Hybrid Loss Function"
Improving Arabic Multi-Label Emotion Classification using Stacked Embeddings and Hybrid Loss Function (2410.03979)

In this work, we tackle some major challenges in Arabic multi-label emotion classification especially the issues of class imbalance and label correlation that often hurt model performance, particularly for minority emotions.

Our approach:

Stacked contextual embeddings from fine-tuned ArabicBERT, MarBERT, and AraBERT models.

A meta-learning strategy that builds richer representations.

A hybrid loss function combining class weighting, label correlation matrices, and contrastive learning to better handle class imbalances.

🧠 Model pipeline: stacked embeddings → meta-learner → Bi-LSTM → fully connected network → multi-label classification.

🔍 Extensive experiments show significant improvements across Precision, Recall, F1-Score, Jaccard Accuracy, and Hamming Loss.
🌟 The hybrid loss function in particular helped close the gap between majority and minority classes!

We also performed ablation studies to break down each component’s contribution and the results consistently validated our design choices.

This framework isn't just for Arabic it offers a generalizable path for improving multi-label emotion classification in other low-resource languages and domains.

Big thanks to my co-authors: Muhammad Azeem Aslam, Wang Jun, Nisar Ahmed, Li Yanan, Hu Hongfei, Wang Shiyu, and Xin Liu!

Would love to hear your thoughts on this work! 👇
1024m 
authored a paper 27 days ago
1024m 
authored a paper 29 days ago
ImranzamanML 
posted an update 29 days ago
1024m 
authored a paper about 1 month ago
ImranzamanML 
posted an update about 1 month ago
view post
Post
1577

Llama 4 is here and it's making serious waves!

After diving into the latest benchmark results, it’s clear that Meta’s new Llama 4 lineup (Maverick, Scout, and Behemoth) is no joke.

Here are a few standout highlights🔍:

Llama 4 Maverick hits the sweet spot between cost and performance
- Outperforms GPT-4o in image tasks like ChartQA (90.0 vs 85.7) and DocVQA (94.4 vs 92.8)
- Beats others in MathVista and MMLU Pro too and at a fraction of the cost ($0.19–$0.49 vs $4.38 🤯)

Llama 4 Scout is lean, cost-efficient, and surprisingly capable
- Strong performance across image and language tasks (e.g. ChartQA: 88.8, DocVQA: 94.4)
- More affordable than most competitors and still beats out larger models like Gemini 2.0 Flash-Lite

Llama 4 Behemoth is the heavy hitter.
- Tops the charts in LiveCodeBench (49.4), MATH-500 (95.0), and MMLU Pro (82.2)
- Even edges out Claude 3 Sonnet and Gemini 2 Pro in multiple areas

Meta didn’t just show up, they delivered across multimodal, coding, reasoning, and multilingual benchmarks.

And honestly? Seeing this level of performance, especially at lower inference costs, is a big deal for anyone building on LLMs.

Curious to see how these models do in real-world apps next.

#AI #Meta #Llama4 #LLMs #Benchmarking #MachineLearning #OpenSourceAI #GenerativeAI
  • 1 reply
·
Tonic 
posted an update 2 months ago
view post
Post
1465
🙋🏻‍♂️Hey there folks,

Did you know that you can use ModernBERT to detect model hallucinations ?

Check out the Demo : Tonic/hallucination-test

See here for Medical Context Demo : MultiTransformer/tonic-discharge-guard

check out the model from KRLabs : KRLabsOrg/lettucedect-large-modernbert-en-v1

and the library they kindly open sourced for it : https://github.com/KRLabsOrg/LettuceDetect

👆🏻if you like this topic please contribute code upstream 🚀

  • 2 replies
·
Tonic 
posted an update 2 months ago
view post
Post
795
Powered by KRLabsOrg/lettucedect-large-modernbert-en-v1 from KRLabsOrg.

Detect hallucinations in answers based on context and questions using ModernBERT with 8192-token context support!

### Model Details
- **Model Name**: [lettucedect-large-modernbert-en-v1]( KRLabsOrg/lettucedect-large-modernbert-en-v1)
- **Organization**: [KRLabsOrg]( KRLabsOrg )
- **Github**: [https://github.com/KRLabsOrg/LettuceDetect](https://github.com/KRLabsOrg/LettuceDetect)
- **Architecture**: ModernBERT (Large) with extended context support up to 8192 tokens
- **Task**: Token Classification / Hallucination Detection
- **Training Dataset**: [RagTruth]( wandb/RAGTruth-processed)
- **Language**: English
- **Capabilities**: Detects hallucinated spans in answers, provides confidence scores, and calculates average confidence across detected spans.

LettuceDetect excels at processing long documents to determine if an answer aligns with the provided context, making it a powerful tool for ensuring factual accuracy.
ImranzamanML 
posted an update 3 months ago
view post
Post
3268
Hugging Face just launched the AI Agents Course – a free journey from beginner to expert in AI agents!

- Learn AI Agent fundamentals, use cases and frameworks
- Use top libraries like LangChain & LlamaIndex
- Compete in challenges & earn a certificate
- Hands-on projects & real-world applications

https://huggingface.co/learn/agents-course/unit0/introduction

You can join for a live Q&A on Feb 12 at 5PM CET to learn more about the course here

https://www.youtube.com/live/PopqUt3MGyQ
Tonic 
posted an update 3 months ago
view post
Post
2409
🙋🏻‍♂️hey there folks ,

Goedel's Theorem Prover is now being demo'ed on huggingface : Tonic/Math

give it a try !
Tonic 
posted an update 4 months ago
view post
Post
2996
🙋🏻‍♂️ Hey there folks ,

our team made a game during the @mistral-game-jam and we're trying to win the community award !

try our game out and drop us a ❤️ like basically to vote for us !

Mistral-AI-Game-Jam/TextToSurvive

hope you like it !
Tonic 
posted an update 4 months ago
view post
Post
1921
🙋🏻‍♂️ Hey there folks ,

Facebook AI just released JASCO models that make music stems .

you can try it out here : Tonic/audiocraft

hope you like it
Tonic 
posted an update 4 months ago
view post
Post
2479
🙋🏻‍♂️Hey there folks , Open LLM Europe just released Lucie 7B-Instruct model , a billingual instruct model trained on open data ! You can check out my unofficial demo here while we wait for the official inference api from the group : Tonic/Lucie-7B hope you like it 🚀
Tonic 
posted an update 4 months ago
view post
Post
1736
microsoft just released Phi-4 , check it out here : Tonic/Phi-4

hope you like it :-)
ImranzamanML 
posted an update 5 months ago
view post
Post
715
Deep understanding of (C-index) evaluation measure for better model
Lets start with three patients groups:

Group A
Group B
Group C
For each patient, we will predict risk score (higher score means higher risk of early event).

Step 1: Understanding Concordance Index
The Concordance Index (C-index) evaluate that how well the model ranks survival times.

Understand with sample data:
Group A has 3 patients with actual survival times and predicted risk scores:

Patient Actual Survival Time Predicted Risk Score
P1 5 months 0.8
P2 3 months 0.9
P3 10 months 0.2
Comparable pairs:

(P1, P2): P2 has a shorter survival time and a higher risk score → Concordant ✅
(P1, P3): P3 has a longer survival time and a lower risk score → Concordant ✅
(P2, P3): P3 has a longer survival time and a lower risk score → Concordant ✅
Total pairs = 3
Total concordant pairs = 3

C-index for Group A = Concordant pairs/Total pairs= 3/3 = 1.0

Step 2: Calculate C-index for All Groups
Repeat the process for all groups. For now we can assume:

Group A: C-index = 1.0
Group B: C-index = 0.8
Group C: C-index = 0.6
Step 3: Stratified Concordance Index
The Stratified Concordance Index combines the C-index scores of all groups and focusing on the following:

Average performance across groups (mean of C-indices).
Consistency across groups (low standard deviation of C-indices).
Formula:
Stratified C-index = Mean(C-index scores) - Standard Deviation(C-index scores)

Calculate the mean:
Mean=1.0 + 0.8 + 0.6/3 = 0.8

Calculate the standard deviation:
Standard Deviation= sqrt((1.0-0.8)^2 + (0.8-0.8)^2 + (0.6-0.8)^/3) = 0.16

Stratified C-index:
Stratified C-index = 0.8 - 0.16 = 0.64

Step 4: Interpret the Results
A high Stratified C-index means:

The model predicts well overall (high mean C-index).
Tonic 
posted an update 6 months ago
view post
Post
3611
🙋🏻‍♂️hey there folks,

periodic reminder : if you are experiencing ⚠️500 errors ⚠️ or ⚠️ abnormal spaces behavior on load or launch ⚠️

we have a thread 👉🏻 https://discord.com/channels/879548962464493619/1295847667515129877

if you can record the problem and share it there , or on the forums in your own post , please dont be shy because i'm not sure but i do think it helps 🤗🤗🤗
  • 2 replies
·
Tonic 
posted an update 7 months ago
view post
Post
1193
boomers still pick zenodo.org instead of huggingface ??? absolutely clownish nonsense , my random datasets have 30x more downloads and views than front page zenodos ... gonna write a comparison blog , but yeah... cringe.
  • 1 reply
·
ImranzamanML 
posted an update 7 months ago
view post
Post
735
Easy steps for an effective RAG pipeline with LLM models!
1. Document Embedding & Indexing
We can start with the use of embedding models to vectorize documents, store them in vector databases (Elasticsearch, Pinecone, Weaviate) for efficient retrieval.

2. Smart Querying
Then we can generate query embeddings, retrieve top-K relevant chunks and can apply hybrid search if needed for better precision.

3. Context Management
We can concatenate retrieved chunks, optimize chunk order and keep within token limits to preserve response coherence.

4. Prompt Engineering
Then we can instruct the LLM to leverage retrieved context, using clear instructions to prioritize the provided information.

5. Post-Processing
Finally we can implement response verification, fact-checking and integrate feedback loops to refine the responses.

Happy to connect :)