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reacted to Parveshiiii's post with 👍 27 days ago

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🧠 MathX-5M by XenArcAI — Scalable Math Reasoning for Smarter LLMs

Introducing MathX-5M, a high-quality, instruction-tuned dataset built to supercharge mathematical reasoning in large language models. With 5 million rigorously filtered examples, it spans everything from basic arithmetic to advanced calculus—curated from public sources and enhanced with synthetic data.

🔍 Key Highlights:
- Step-by-step reasoning with verified answers
- Covers algebra, geometry, calculus, logic, and more
- RL-validated correctness and multi-stage filtering
- Ideal for fine-tuning, benchmarking, and educational AI

📂 - XenArcAI/MathX-5M

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liked a model 4 months ago

Qwen/Qwen2.5-VL-7B-Instruct

Image-Text-to-Text • 8B • Updated Apr 6 • 5.81M • • 1.09k

liked a model 5 months ago

ds4sd/SmolDocling-256M-preview

Image-Text-to-Text • 0.3B • Updated May 16 • 91.4k • 1.51k

liked a model 11 months ago

black-forest-labs/FLUX.1-dev

Text-to-Image • Updated Jun 27 • 1.55M • • 11.1k

updated 2 models about 1 year ago

icpro/trained-model-classification-evaluation

Text Classification • 0.3B • Updated May 31, 2024 • 3

edumalin/edumalin-mixtral-indications-and-evaluation-merged

Text Generation • 47B • Updated May 30, 2024 • 2

upvoted a paper about 1 year ago

Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models

Paper • 2405.01535 • Published May 2, 2024 • 124

reacted to victor's post with 🔥 over 1 year ago

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4327

The hype is real: a mysterious gpt2-chatbot model has appeared on the LLM Arena Leaderboard 👀.
It seems to be at least on par with the top performing models (closed and open).

To try it out: https://chat.lmsys.org/ -> then click on the Direct Chat tab and select gpt2-chatbot.

Take your bet, what do you think it is?

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liked 5 models over 1 year ago

MaziyarPanahi/Calme-7B-Instruct-v0.1

Text Generation • 7B • Updated Apr 3, 2024 • 10 • 5

AgentPublic/guillaumetell-7b

Text Generation • 7B • Updated Apr 25, 2024 • 32 • 30

bofenghuang/vigogne-2-7b-instruct

Text Generation • Updated Jul 20, 2023 • 1.83k • 26

almanach/camembert-large

0.3B • Updated 13 days ago • 8.43k • 19

mistralai/Mistral-7B-Instruct-v0.2

Text Generation • 7B • Updated 8 days ago • 1.5M • • 2.89k

liked a Space over 1 year ago

Running on CPU Upgrade

13.4k

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Open LLM Leaderboard

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Track, rank and evaluate open LLMs and chatbots

upvoted an article over 1 year ago

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Article

Fine-tune Llama 3 with ORPO

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Apr 22, 2024

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reacted to tomaarsen's post with 🔥 over 1 year ago

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3206

🚀 Sentence Transformers v2.7.0 is out! Featuring a new loss function, easier Matryoshka model inference & evaluation, CrossEncoder improvements & Intel Gaudi2 Accelerator support. Details:

1️⃣ A new loss function: CachedGISTEmbedLoss
This loss function is a combination of CachedMultipleNegativesRankingLoss and the GISTEmbedLoss, both of which are already excellent. The caching mechanism allows for much higher batch sizes with constant memory usage, which boosts training performance. The GIST part introduces a guide model to guide the in-batch negative sample selection. This prevents false negatives, resulting in a stronger training signal.

2️⃣ Automatic Matryoshka model truncation
Matryoshka models produce embeddings that are still useful after truncation. However, this truncation always had to be done manually, until now! We've added a truncate_dim option to the Sentence Transformer constructor. This also allows truncation when using HuggingFaceEmbeddings from LlamaIndex or LangChain.

3️⃣ Additionally, you can now specify truncate_dim in evaluators to get the performance after truncation. (Hint: it's surprisingly good, even for models not trained with MatryoshkaLoss, and it can speed up e.g. clustering, retrieval, etc.)

4️⃣ CrossEncoder improvements
The CrossEncoder now supports 'push_to_hub' to upload trained reranker models to Hugging Face. Additionally, CrossEncoders now support trust_remote_code to load models with custom modelling code.

5️⃣ Inference on Intel Gaudi2
If you have an Intel Gaudi2 Accelerator, Sentence Transformers now uses it automatically for even faster inference. No changes are necessary to your code, the device is automatically detected!

Check out the release notes for all of the details: https://github.com/UKPLab/sentence-transformers/releases/tag/v2.7.0

I'm very excited for the upcoming releases: I'm making great progress with a notable v3 refactor that should heavily improve the training process for embedding models!

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