Tiny Court of Everyday Crimes
⚖
5
Multimodal comedy courtroom — text, photo & voice on trial.
Jan Jakubcik
Johnz86
·
AI & ML interests
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Recent Activity
new activity 11 days ago
build-small-hackathon/tiny-court:Link social post in README updated a Space 11 days ago
build-small-hackathon/tiny-court updated a dataset 11 days ago
build-small-hackathon/tiny-court-tracesOrganizations
Link social post in README
#1 opened 11 days ago
by 
Johnz86
updated a
Space 11 days ago
updated a
dataset 11 days ago
published a
dataset 11 days ago
published a
Space 12 days ago
reacted to codelion's post with 🔥 11 months ago
Post
3410
Implemented Test-Time Diffusion Deep Researcher (TTD-DR) in OptiLLM! 🚀
Just shipped a game-changing feature that turns any LLM into a powerful research agent. TTD-DR applies diffusion-inspired techniques to iteratively refine research reports while grounding them in real web sources.
How it works:
• Generates initial draft
• Identifies knowledge gaps
• Searches web for missing info
• Iteratively refines through "denoising" steps
• Produces comprehensive reports with 15-30+ sources
The magic? It works with ANY model so you can choose your favorite open-source models on HF!
Key results:
- 47 complex research queries tested
- Every report backed by real web sources
- Quality rivals human research analysts
- No more hallucinations on current events!
Try it:
pip install optillm
Then use "deep_research-your-model-name" as the model identifier
- Implementation: https://github.com/codelion/optillm/tree/main/optillm/plugins/deep_research
- Paper: https://arxiv.org/abs/2507.16075v1
- Sample reports: https://github.com/codelion/optillm/tree/main/optillm/plugins/deep_research/sample_reports
Special thanks to the TTD-DR paper authors for this brilliant approach!
#research #llm #opensource #inference
Just shipped a game-changing feature that turns any LLM into a powerful research agent. TTD-DR applies diffusion-inspired techniques to iteratively refine research reports while grounding them in real web sources.
How it works:
• Generates initial draft
• Identifies knowledge gaps
• Searches web for missing info
• Iteratively refines through "denoising" steps
• Produces comprehensive reports with 15-30+ sources
The magic? It works with ANY model so you can choose your favorite open-source models on HF!
Key results:
- 47 complex research queries tested
- Every report backed by real web sources
- Quality rivals human research analysts
- No more hallucinations on current events!
Try it:
pip install optillm
Then use "deep_research-your-model-name" as the model identifier
- Implementation: https://github.com/codelion/optillm/tree/main/optillm/plugins/deep_research
- Paper: https://arxiv.org/abs/2507.16075v1
- Sample reports: https://github.com/codelion/optillm/tree/main/optillm/plugins/deep_research/sample_reports
Special thanks to the TTD-DR paper authors for this brilliant approach!
#research #llm #opensource #inference
I finish the test and click submit but i dont get notified about a new certificate
3
#3 opened over 1 year ago
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marlova
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dataset over 1 year ago
upvoted a collection almost 2 years ago
reacted to DavidGF's post with 🔥 about 2 years ago
Post
1811
The kraken has awakened!
A Game-Changer in LLM Flexibility and Performance!
Over the past few weeks, VAGO solutions teamed up with Cognitive Computations and HyperSpace to develop a groundbreaking architecture that redefines flexibility in combining different LLM into one model.
@fernandofernandes , me, @Crystalcareai , @ehartford created the Kraken!
What Can It Do? 🐙
✅ Versatile Architecture: Kraken allows the seamless combination of LLMs with varying sizes, quantizations, and model architectures. It currently supports quantizations in 4-bit, 8-bit, and AWQ, with more on the way. And it runs on Hugging Face Transformers 4.40+
✅ Kraken Router: Utilizing a custom sequence classification model with a context length of 32k tokens, The Kraken Router directs inputs to the most suitable Expert based on their characteristics.
✅ Adaptability: Enhanced input formatting supports the model’s adaptability to diverse conversational contexts.
✅ Extreme Versatility: Easily swap experts within Kraken for your specific use cases without retraining the entire model. For example, if you've built a Kraken for coding in Python you can upgrade your Python model without retraining the router or add a C# model by retraining the router.
✅ Open Source Pipeline: We’re sharing the entire pipeline, including router creation, training, architecture setup, and Kraken inference, on JupyterNotebooks: https://github.com/cognitivecomputations/kraken
Kraken marks the beginning of an exciting new journey in #OpenSource LLM. Why? Because it empowers the open source community in accelerating the catch-up process to proprietary LLMs like #GPT and #Claude 🤩
We proudly introduce the very first 2 Kraken models, that integrates top-tier LLM and Multilingual capabilities:
https://huggingface.co/cognitivecomputations/Kraken
VAGOsolutions/Kraken-Multilingual
Right now it's supported by Hugging Face transformers library. Would love to see the integration into VLM and TGWI!
A Game-Changer in LLM Flexibility and Performance!
Over the past few weeks, VAGO solutions teamed up with Cognitive Computations and HyperSpace to develop a groundbreaking architecture that redefines flexibility in combining different LLM into one model.
@fernandofernandes , me, @Crystalcareai , @ehartford created the Kraken!
What Can It Do? 🐙
✅ Versatile Architecture: Kraken allows the seamless combination of LLMs with varying sizes, quantizations, and model architectures. It currently supports quantizations in 4-bit, 8-bit, and AWQ, with more on the way. And it runs on Hugging Face Transformers 4.40+
✅ Kraken Router: Utilizing a custom sequence classification model with a context length of 32k tokens, The Kraken Router directs inputs to the most suitable Expert based on their characteristics.
✅ Adaptability: Enhanced input formatting supports the model’s adaptability to diverse conversational contexts.
✅ Extreme Versatility: Easily swap experts within Kraken for your specific use cases without retraining the entire model. For example, if you've built a Kraken for coding in Python you can upgrade your Python model without retraining the router or add a C# model by retraining the router.
✅ Open Source Pipeline: We’re sharing the entire pipeline, including router creation, training, architecture setup, and Kraken inference, on JupyterNotebooks: https://github.com/cognitivecomputations/kraken
Kraken marks the beginning of an exciting new journey in #OpenSource LLM. Why? Because it empowers the open source community in accelerating the catch-up process to proprietary LLMs like #GPT and #Claude 🤩
We proudly introduce the very first 2 Kraken models, that integrates top-tier LLM and Multilingual capabilities:
https://huggingface.co/cognitivecomputations/Kraken
VAGOsolutions/Kraken-Multilingual
Right now it's supported by Hugging Face transformers library. Would love to see the integration into VLM and TGWI!