akahana/en-id-finetuned-4bit
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fahrizalfarid
akahana
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akahana/english-indonesia-wikimatrix-token
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Switching between API Client, browser, and API documentation tools to test and document APIs can harm your flow and leave your docs outdated.
This is what usually happens: While debugging an API in the middle of a sprint, the API Client says that everything's fine, but the docs still show an old version.
So you jump back to the code, find the updated response schema, then go back to the API Client, which gets stuck, forcing you to rerun the tests.
Hours can go by just trying to sync all this up (and that’s if you catch the inconsistencies at all).
The reason? Using disconnected tools for specs, tests, and docs. Doing manual updates, stale docs, and a lot of context switching.
Voiden takes a different approach: Puts specs, tests & docs all in one Markdown file, stored right in the repo.
Everything stays in sync, versioned with Git, and updated in one place, inside your editor.
Download Voiden here: https://voiden.md/download
This is what usually happens: While debugging an API in the middle of a sprint, the API Client says that everything's fine, but the docs still show an old version.
So you jump back to the code, find the updated response schema, then go back to the API Client, which gets stuck, forcing you to rerun the tests.
Hours can go by just trying to sync all this up (and that’s if you catch the inconsistencies at all).
The reason? Using disconnected tools for specs, tests, and docs. Doing manual updates, stale docs, and a lot of context switching.
Voiden takes a different approach: Puts specs, tests & docs all in one Markdown file, stored right in the repo.
Everything stays in sync, versioned with Git, and updated in one place, inside your editor.
Download Voiden here: https://voiden.md/download
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Article
Ellora: Enhancing LLMs with LoRA - Standardized Recipes for Capability Enhancement
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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!
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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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Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA
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