Introducing the Palmyra-mini family: Powerful, lightweight, and ready to reason!
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58
AI & ML interests
AGI, LLMs, Knowledge Graph, Palmyra, Domain Specific LLM
Articles
sanderland
authored
a
paper
3 months ago
dmytro-writer
authored
a
paper
8 months ago
shelly-writer
authored
a
paper
8 months ago
sanderland
authored
a
paper
8 months ago
kiranr
authored
a
paper
8 months ago
wassemgtk
authored
a
paper
8 months ago
sanderland
authored
a
paper
9 months ago
Post
3271
I’ve been diving into the iRoPE architecture from Llama 4—a game-changer for long-context models! It interleaves local attention (with RoPE) for short contexts and global attention (with inference-time temp scaling) for long-range reasoning, aiming for infinite context. I’m going to try writing iRoPE—who wants to help?
Code: https://github.com/wassemgtk/iRoPE-try/blob/main/iRoPE.ipynb
Code: https://github.com/wassemgtk/iRoPE-try/blob/main/iRoPE.ipynb
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- 1 reply
sanderland
authored
a
paper
10 months ago
Post
2137
For fun, a new project: SuperTokenizer! A BPE tokenizer trained on C4 to beat GPT-4. Byte-level, A100-powered, and open-source. Messing around with tokens!
https://github.com/wassemgtk/SuperTokenizer
https://github.com/wassemgtk/SuperTokenizer
- 1 reply
Post
1922
# GESAL: Real-Time Adaptation for LLMs
We’re excited to unveil **Graph-Enhanced Singular Adaptive Learning (GESAL)**, a framework that lets LLMs like
🔗 **Code**: [https://github.com/writer/AI-Adaptive-Learning-GESAL](https://github.com/writer/AI-Adaptive-Learning-GESAL)
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## Why GESAL?
Static LLMs struggle to adapt without heavy retraining. GESAL solves this with:
- **SVF**: Adapts weights via \( W' = U (\Sigma \cdot z) V^T \), using few parameters.
- **Graph Memory**: Stores adaptations in nodes for scalability.
- **RL**: Updates via \( J(z) = \mathbb{E}[\log \pi_z(y|x) r] \) based on feedback.
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## How It Works
Ask "How many R’s in ‘strawberry’?" If it says "2" and you say "no," GESAL learns to say "3" next time, avoiding repeats.
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## Try It
Built with Hugging Face’s
Needs a Hugging Face token for Llama-3.2-1B.
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## Results
GESAL hits 95% accuracy after 5 feedbacks vs. LoRA’s 70%. It’s efficient (~0.5M params) and scalable.
We’re excited to unveil **Graph-Enhanced Singular Adaptive Learning (GESAL)**, a framework that lets LLMs like
meta-llama/Llama-3.2-1B adapt in real time using user feedback. Check out the code and white paper on GitHub!🔗 **Code**: [https://github.com/writer/AI-Adaptive-Learning-GESAL](https://github.com/writer/AI-Adaptive-Learning-GESAL)
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## Why GESAL?
Static LLMs struggle to adapt without heavy retraining. GESAL solves this with:
- **SVF**: Adapts weights via \( W' = U (\Sigma \cdot z) V^T \), using few parameters.
- **Graph Memory**: Stores adaptations in nodes for scalability.
- **RL**: Updates via \( J(z) = \mathbb{E}[\log \pi_z(y|x) r] \) based on feedback.
---
## How It Works
Ask "How many R’s in ‘strawberry’?" If it says "2" and you say "no," GESAL learns to say "3" next time, avoiding repeats.
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## Try It
Built with Hugging Face’s
transformers:pip install transformers torch numpy
python Adaptive_Learning_(GESAL).pyNeeds a Hugging Face token for Llama-3.2-1B.
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## Results
GESAL hits 95% accuracy after 5 feedbacks vs. LoRA’s 70%. It’s efficient (~0.5M params) and scalable.
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- 15 replies
wassemgtk
authored
a
paper
11 months ago
dmytro-writer
authored
2
papers
12 months ago
kiranr
authored
a
paper
12 months ago
wassemgtk
authored
a
paper
over 1 year ago
kiranr
authored
a
paper
over 1 year ago
wassemgtk
authored
a
paper
over 1 year ago
Post
3656
Writer team had the opportunity to run an eval for Mixtral-8x22b, results were interesting.
| ---------------------------- |
| #mmlu 77.26 |
| ---------------------------- |
| #hellaswag 88.81 |
| ---------------------------- |
| #truthfulqa 52.05 |
| ---------------------------- |
| #arc_challenge 70.31 |
| ---------------------------- |
| #winogrande 84.93 |
| ---------------------------- |
| #gsm8k 76.65 |
| ---------------------------- |
| ---------------------------- |
| #mmlu 77.26 |
| ---------------------------- |
| #hellaswag 88.81 |
| ---------------------------- |
| #truthfulqa 52.05 |
| ---------------------------- |
| #arc_challenge 70.31 |
| ---------------------------- |
| #winogrande 84.93 |
| ---------------------------- |
| #gsm8k 76.65 |
| ---------------------------- |
- 2 replies
Post
We are thrilled to announce the release of the OmniACT dataset! This revolutionary dataset and benchmark focuses on pushing the limits of how virtual agents can facilitate the automation of our computer tasks. Imagine less clicking and typing, and more observation as your computer takes care of tasks such as organizing schedules or arranging travel arrangements on its own.
Check it out ➡️ [OmniACT Dataset on Hugging Face]( Writer/omniact)
For a deep dive, here’s the paper: [OmniACT Paper](https://arxiv.org/abs/2402.17553)
Check it out ➡️ [OmniACT Dataset on Hugging Face]( Writer/omniact)
For a deep dive, here’s the paper: [OmniACT Paper](https://arxiv.org/abs/2402.17553)