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100
NotebookLM's infographics feature is amazing, it generates poster-type images from any text. Here is one I tried for my new HF article on ellora - https://huggingface.co/blog/codelion/ellora-lora-recipes
Asankhaya Sharma PRO
codelion
AI & ML interests
Creator of OptiLLM, OpenEvolve, Adaptive Classifier, and Ellora. Pioneering a new category in AI infrastructure: inference-time compute for LLMs.
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NotebookLM's infographics feature is amazing, it generates poster-type images from any text. Here is one I tried for my new HF article on ellora - https://huggingface.co/blog/codelion/ellora-lora-recipes
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Perplexity released a dataset (BrowseSafe) and benchmark to catch and prevent malicious prompt-injection instructions in real-time.
We trained a prompt injection classifier on BrowseSafe using adaptive-classifier with ModernBERT-base embeddings.
74.9% F1 on detecting prompt injection in web content.
Model -> adaptive-classifier/browsesafe
Dataset -> perplexity-ai/browsesafe-bench
Repo -> https://github.com/codelion/adaptive-classifier
We trained a prompt injection classifier on BrowseSafe using adaptive-classifier with ModernBERT-base embeddings.
74.9% F1 on detecting prompt injection in web content.
Model -> adaptive-classifier/browsesafe
Dataset -> perplexity-ai/browsesafe-bench
Repo -> https://github.com/codelion/adaptive-classifier
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posted
an
update
3 days ago
Post
2207
Perplexity released a dataset (BrowseSafe) and benchmark to catch and prevent malicious prompt-injection instructions in real-time.
We trained a prompt injection classifier on BrowseSafe using adaptive-classifier with ModernBERT-base embeddings.
74.9% F1 on detecting prompt injection in web content.
Model -> adaptive-classifier/browsesafe
Dataset -> perplexity-ai/browsesafe-bench
Repo -> https://github.com/codelion/adaptive-classifier
We trained a prompt injection classifier on BrowseSafe using adaptive-classifier with ModernBERT-base embeddings.
74.9% F1 on detecting prompt injection in web content.
Model -> adaptive-classifier/browsesafe
Dataset -> perplexity-ai/browsesafe-bench
Repo -> https://github.com/codelion/adaptive-classifier
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4 days ago
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I just published Ellora - 6 production-ready LoRA recipes for enhancing LLMs with specific capabilities. Each recipe costs under $100 to run and includes complete training code, data generation, and evaluation.
The 6 Recipes:
Recipe 1: Accuracy Recovery - Recover 75% of quantization losses with self-distillation
Recipe 2: Reasoning LoRA - Add structured thinking with GRPO (0% to 60% adoption, 75% quality boost)
Recipe 3: Tool Calling - Real execution on actual codebases
Recipe 4: Context Extension - Scale from 32K to 2M tokens (61x increase)
Recipe 5: Secure Code Generation - 97% vulnerability reduction using automated Semgrep analysis
Recipe 6: Execution-Aware World Models - Teaching models runtime behavior
Why Recipes?
Ellora provides methodologies, not frameworks. Use them with your existing tools (PEFT, LoRAX, vLLM, Unsloth, HuggingFace). Each recipe uses self-supervised data generation (Magpie approach) - no expensive human labeling required.
All recipes include Jupyter notebooks you can run immediately with clear success metrics.
GitHub: https://github.com/codelion/ellora
Full Article: https://huggingface.co/blog/codelion/ellora-lora-recipes
Built something with these recipes? I'd love to see what you create!
The 6 Recipes:
Recipe 1: Accuracy Recovery - Recover 75% of quantization losses with self-distillation
Recipe 2: Reasoning LoRA - Add structured thinking with GRPO (0% to 60% adoption, 75% quality boost)
Recipe 3: Tool Calling - Real execution on actual codebases
Recipe 4: Context Extension - Scale from 32K to 2M tokens (61x increase)
Recipe 5: Secure Code Generation - 97% vulnerability reduction using automated Semgrep analysis
Recipe 6: Execution-Aware World Models - Teaching models runtime behavior
Why Recipes?
Ellora provides methodologies, not frameworks. Use them with your existing tools (PEFT, LoRAX, vLLM, Unsloth, HuggingFace). Each recipe uses self-supervised data generation (Magpie approach) - no expensive human labeling required.
All recipes include Jupyter notebooks you can run immediately with clear success metrics.
GitHub: https://github.com/codelion/ellora
Full Article: https://huggingface.co/blog/codelion/ellora-lora-recipes
Built something with these recipes? I'd love to see what you create!
posted
an
update
4 days ago
Post
1553
I just published Ellora - 6 production-ready LoRA recipes for enhancing LLMs with specific capabilities. Each recipe costs under $100 to run and includes complete training code, data generation, and evaluation.
The 6 Recipes:
Recipe 1: Accuracy Recovery - Recover 75% of quantization losses with self-distillation
Recipe 2: Reasoning LoRA - Add structured thinking with GRPO (0% to 60% adoption, 75% quality boost)
Recipe 3: Tool Calling - Real execution on actual codebases
Recipe 4: Context Extension - Scale from 32K to 2M tokens (61x increase)
Recipe 5: Secure Code Generation - 97% vulnerability reduction using automated Semgrep analysis
Recipe 6: Execution-Aware World Models - Teaching models runtime behavior
Why Recipes?
Ellora provides methodologies, not frameworks. Use them with your existing tools (PEFT, LoRAX, vLLM, Unsloth, HuggingFace). Each recipe uses self-supervised data generation (Magpie approach) - no expensive human labeling required.
All recipes include Jupyter notebooks you can run immediately with clear success metrics.
GitHub: https://github.com/codelion/ellora
Full Article: https://huggingface.co/blog/codelion/ellora-lora-recipes
Built something with these recipes? I'd love to see what you create!
The 6 Recipes:
Recipe 1: Accuracy Recovery - Recover 75% of quantization losses with self-distillation
Recipe 2: Reasoning LoRA - Add structured thinking with GRPO (0% to 60% adoption, 75% quality boost)
Recipe 3: Tool Calling - Real execution on actual codebases
Recipe 4: Context Extension - Scale from 32K to 2M tokens (61x increase)
Recipe 5: Secure Code Generation - 97% vulnerability reduction using automated Semgrep analysis
Recipe 6: Execution-Aware World Models - Teaching models runtime behavior
Why Recipes?
Ellora provides methodologies, not frameworks. Use them with your existing tools (PEFT, LoRAX, vLLM, Unsloth, HuggingFace). Each recipe uses self-supervised data generation (Magpie approach) - no expensive human labeling required.
All recipes include Jupyter notebooks you can run immediately with clear success metrics.
GitHub: https://github.com/codelion/ellora
Full Article: https://huggingface.co/blog/codelion/ellora-lora-recipes
Built something with these recipes? I'd love to see what you create!
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17 days ago
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1959
Introducing OpenEvolve Prompt Optimizer - a Space that automatically evolves and optimizes your prompts using OpenEvolve!
This tool uses OpenEvolve to iteratively improve prompts by testing them on real datasets and evolving better versions. No more manual prompt engineering guesswork - let OpenEvolve find the optimal prompts for you.
How it works:
- Enter your initial prompt using {input} as a placeholder for dataset inputs
- Input any HuggingFace dataset name you want to use for optimization
- Specify the dataset split and field names for your use case
- Click Optimize Prompt and the system will validate everything first
- Compare your initial prompt vs the evolved best prompt side-by-side
Try it here: algorithmicsuperintelligence/prompt-optimizer
OpenEvolve GitHub: https://github.com/algorithmicsuperintelligence/openevolve
This tool uses OpenEvolve to iteratively improve prompts by testing them on real datasets and evolving better versions. No more manual prompt engineering guesswork - let OpenEvolve find the optimal prompts for you.
How it works:
- Enter your initial prompt using {input} as a placeholder for dataset inputs
- Input any HuggingFace dataset name you want to use for optimization
- Specify the dataset split and field names for your use case
- Click Optimize Prompt and the system will validate everything first
- Compare your initial prompt vs the evolved best prompt side-by-side
Try it here: algorithmicsuperintelligence/prompt-optimizer
OpenEvolve GitHub: https://github.com/algorithmicsuperintelligence/openevolve
posted
an
update
17 days ago
Post
1959
Introducing OpenEvolve Prompt Optimizer - a Space that automatically evolves and optimizes your prompts using OpenEvolve!
This tool uses OpenEvolve to iteratively improve prompts by testing them on real datasets and evolving better versions. No more manual prompt engineering guesswork - let OpenEvolve find the optimal prompts for you.
How it works:
- Enter your initial prompt using {input} as a placeholder for dataset inputs
- Input any HuggingFace dataset name you want to use for optimization
- Specify the dataset split and field names for your use case
- Click Optimize Prompt and the system will validate everything first
- Compare your initial prompt vs the evolved best prompt side-by-side
Try it here: algorithmicsuperintelligence/prompt-optimizer
OpenEvolve GitHub: https://github.com/algorithmicsuperintelligence/openevolve
This tool uses OpenEvolve to iteratively improve prompts by testing them on real datasets and evolving better versions. No more manual prompt engineering guesswork - let OpenEvolve find the optimal prompts for you.
How it works:
- Enter your initial prompt using {input} as a placeholder for dataset inputs
- Input any HuggingFace dataset name you want to use for optimization
- Specify the dataset split and field names for your use case
- Click Optimize Prompt and the system will validate everything first
- Compare your initial prompt vs the evolved best prompt side-by-side
Try it here: algorithmicsuperintelligence/prompt-optimizer
OpenEvolve GitHub: https://github.com/algorithmicsuperintelligence/openevolve
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22 days ago
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🎯 Introducing Chayan: A Calibrated 4-Model LLM Router Achieving 69% Accuracy on RouterArena
We're excited to share Chayan, a cost-efficient LLM router that intelligently routes queries between 4 models to maximize accuracy while minimizing cost. Chayan just submitted to the RouterArena leaderboard and achieved 69.05% accuracy on the benchmark!
🔗 Model: adaptive-classifier/chayan
🔗 Dataset: RouteWorks/RouterArena
📊 Performance Highlights
Chayan achieves impressive results on the RouterArena benchmark:
• 69.05% accuracy (would rank #1 on current leaderboard)
• $0.333 per 1K queries
• +12.07pp improvement over all-mini baseline (56.98%)
• 99% of perfect 2-model oracle performance at 57% lower cost
Compared to our previous 2-model router (61.43% accuracy), Chayan delivers +7.62pp improvement through smarter 4-model routing.
🧠 How It Works
Chayan uses an Adaptive K-NN classifier with prototype memory to route between 4 models:
• openai/gpt-4o-mini (fast & cheap)
• google/gemini-2.5-flash-lite (balanced)
• google/gemini-2.5-flash (capable)
• openai/gpt-4o (most powerful)
🚀 Getting Started
You can use Chayan directly from HuggingFace:
from adaptive_classifier import AdaptiveClassifier
Load Chayan
router = AdaptiveClassifier.load("adaptive-classifier/chayan")
Route a query
query = "What is the capital of France?"
predictions = router.predict(query, k=4)
Get top model recommendation
best_model = predictions[0][0]
print(f"Recommended model: {best_model}")
Built with the adaptive-classifier library: https://github.com/codelion/adaptive-classifier
We're excited to share Chayan, a cost-efficient LLM router that intelligently routes queries between 4 models to maximize accuracy while minimizing cost. Chayan just submitted to the RouterArena leaderboard and achieved 69.05% accuracy on the benchmark!
🔗 Model: adaptive-classifier/chayan
🔗 Dataset: RouteWorks/RouterArena
📊 Performance Highlights
Chayan achieves impressive results on the RouterArena benchmark:
• 69.05% accuracy (would rank #1 on current leaderboard)
• $0.333 per 1K queries
• +12.07pp improvement over all-mini baseline (56.98%)
• 99% of perfect 2-model oracle performance at 57% lower cost
Compared to our previous 2-model router (61.43% accuracy), Chayan delivers +7.62pp improvement through smarter 4-model routing.
🧠 How It Works
Chayan uses an Adaptive K-NN classifier with prototype memory to route between 4 models:
• openai/gpt-4o-mini (fast & cheap)
• google/gemini-2.5-flash-lite (balanced)
• google/gemini-2.5-flash (capable)
• openai/gpt-4o (most powerful)
🚀 Getting Started
You can use Chayan directly from HuggingFace:
from adaptive_classifier import AdaptiveClassifier
Load Chayan
router = AdaptiveClassifier.load("adaptive-classifier/chayan")
Route a query
query = "What is the capital of France?"
predictions = router.predict(query, k=4)
Get top model recommendation
best_model = predictions[0][0]
print(f"Recommended model: {best_model}")
Built with the adaptive-classifier library: https://github.com/codelion/adaptive-classifier