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sequelbox

sequelbox.bsky.social

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open source, infinite games. (they/them)

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reacted to salma-remyx's post with 🔥 about 14 hours ago

We've built over 10K containerized reproductions of papers from arXiv! Instead of spending all day trying to build an environment to test that new idea, just pull the Docker container from the Remyx registry. And with Remyx, you can start experimenting faster by generating a test PR in your codebase based on the ideas found in your paper of choice. Hub: https://hub.docker.com/u/remyxai Remyx docs: https://docs.remyx.ai/resources/ideate Coming soon, explore reproduced papers with AG2 + Remyx: https://github.com/ag2ai/ag2/pull/2141

replied to their post about 14 hours ago

NEW RELEASE: Esper 3.1 for gpt-oss-20b! - Esper is our full-stack, full-cycle coding, DevOps, and architecture specialist! - Our newest, best DeepSeek technical datasets emphasize more challenging queries and tough real-world coding tasks across a variety of programming languages and development paradigms: - Titanium 3 for coding and reasoning in DevOps and architecture: https://huggingface.co/datasets/sequelbox/Titanium3-DeepSeek-V3.1-Terminus - Tachibana 3 for high-difficulty code production in a variety of topics and programming languages: - https://huggingface.co/datasets/sequelbox/Tachibana3-Part1-DeepSeek-V3.1-Terminus - https://huggingface.co/datasets/sequelbox/Tachibana3-Part2-DeepSeek-V3.2 - Mitakihara for MLOps, AI building, use, expertise, and research: https://huggingface.co/datasets/sequelbox/Mitakihara-DeepSeek-R1-0528 GET IT NOW, FOR EVERYONE: https://huggingface.co/ValiantLabs/gpt-oss-20b-Esper3.1 We'll have more releases of Esper coming up, plus more experimental open-source releases :) find open source datasets and experimental models at https://huggingface.co/sequelbox Help us keep working for open source AI with a donation: https://huggingface.co/spaces/sequelbox/SupportOpenSource more to come soon! allegra

liked a model about 14 hours ago

mradermacher/gpt-oss-20b-Esper3.1-i1-GGUF

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reacted to salma-remyx's post with 🔥 about 14 hours ago

Post

1539

We've built over 10K containerized reproductions of papers from arXiv!

Instead of spending all day trying to build an environment to test that new idea, just pull the Docker container from the Remyx registry.

And with Remyx, you can start experimenting faster by generating a test PR in your codebase based on the ideas found in your paper of choice.

Hub: https://hub.docker.com/u/remyxai
Remyx docs: https://docs.remyx.ai/resources/ideate
Coming soon, explore reproduced papers with AG2 + Remyx: https://github.com/ag2ai/ag2/pull/2141

replied to their post about 14 hours ago

Model is here: https://huggingface.co/ValiantLabs/gpt-oss-20b-Esper3.1

GGUFs for the model:
https://huggingface.co/mradermacher/gpt-oss-20b-Esper3.1-GGUF
https://huggingface.co/mradermacher/gpt-oss-20b-Esper3.1-i1-GGUF

reacted to umarbutler's post with 🚀 1 day ago

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2837

I'm excited to announce the release of Kanon 2 Embedder, the world's best legal embedding model, ranked first on the Massive Legal Embedding Benchmark 🎉

This model is the product of quite literally months of painstaking work alongside @abdurrahmanbutler collecting, cleaning, and processing terabytes of data as well as coming up with novel improvements to the standard embedder training recipe to push the limits of what's possible.

Kanon 2 Embedder is my most advanced model to date. On MLEB, it benchmarks as 9% more accurate than OpenAI's best embedding model and 30% faster.

Even when truncated from 1,792 to 768 dimensions, Kanon 2 Embedder continues to hold the number one spot on MLEB.

Importantly, Kanon 2 Embedder is also privacy and security friendly — unlike Voyage, Cohere and Jina, none of your data is used to train our models by default.

Kanon 2 Embedder can also be self-hosted for enterprises with heightened security or reliability requirements.

You can read the full announcement on our blog to learn how we did it and how you can get started using Kanon 2 Embedder to embed your own legal documents: https://isaacus.com/blog/introducing-kanon-2-embedder

2 replies

reacted to tomaarsen's post with 🚀 2 days ago

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🤗 Sentence Transformers is joining Hugging Face! 🤗 This formalizes the existing maintenance structure, as I've personally led the project for the past two years on behalf of Hugging Face! Details:

Today, the Ubiquitous Knowledge Processing (UKP) Lab is transferring the project to Hugging Face. Sentence Transformers will remain a community-driven, open-source project, with the same open-source license (Apache 2.0) as before. Contributions from researchers, developers, and enthusiasts are welcome and encouraged. The project will continue to prioritize transparency, collaboration, and broad accessibility.

Read our full announcement for more details and quotes from UKP and Hugging Face leadership: https://huggingface.co/blog/sentence-transformers-joins-hf

We see an increasing wish from companies to move from large LLM APIs to local models for better control and privacy, reflected in the library's growth: in just the last 30 days, Sentence Transformer models have been downloaded >270 million times, second only to transformers.

I would like to thank the UKP Lab, and especially Nils Reimers and Iryna Gurevych, both for their dedication to the project and for their trust in myself, both now and two years ago. Back then, neither of you knew me well, yet you trusted me to take the project to new heights. That choice ended up being very valuable for the embedding & Information Retrieval community, and I think this choice of granting Hugging Face stewardship will be similarly successful.

I'm very excited about the future of the project, and for the world of embeddings and retrieval at large!

reacted to AdinaY's post with 👀 2 days ago

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HunyuanWorld Mirror🔥a versatile feed forward model for universal 3D world reconstruction by Tencent

tencent/HunyuanWorld-Mirror

✨ Any prior in → 3D world out
✨ Mix camera, intrinsics, depth as priors
✨ Predict point clouds, normals, Gaussians & more in one pass
✨ Unified architecture for all 3D task

reacted to paulml's post with 🔥 3 days ago

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3450

Qwen3-VL-4B is incredibly easy to fine-tune!
We've trained the first DSE model based on this model, and it's already performing at the same level as Jina v4!

While Jina Embeddings v4 is built on Qwen2.5-VL-3B (which has a non-commercial license), our model is based on Qwen3-VL-4B and released under Apache 2.0—making it fully commercially permissive.

Check out our DSE model here:
racineai/QwenAmann-4B-dse

posted an update 3 days ago

Post

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NEW RELEASE: Esper 3.1 for gpt-oss-20b!

- Esper is our full-stack, full-cycle coding, DevOps, and architecture specialist!
- Our newest, best DeepSeek technical datasets emphasize more challenging queries and tough real-world coding tasks across a variety of programming languages and development paradigms:
- Titanium 3 for coding and reasoning in DevOps and architecture: sequelbox/Titanium3-DeepSeek-V3.1-Terminus
- Tachibana 3 for high-difficulty code production in a variety of topics and programming languages:
- sequelbox/Tachibana3-Part1-DeepSeek-V3.1-Terminus
- sequelbox/Tachibana3-Part2-DeepSeek-V3.2
- Mitakihara for MLOps, AI building, use, expertise, and research: sequelbox/Mitakihara-DeepSeek-R1-0528

GET IT NOW, FOR EVERYONE: ValiantLabs/gpt-oss-20b-Esper3.1

We'll have more releases of Esper coming up, plus more experimental open-source releases :) find open source datasets and experimental models at @sequelbox

Help us keep working for open source AI with a donation: sequelbox/SupportOpenSource

more to come soon!
allegra

2 replies

posted an update 18 days ago

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NEW RELEASE: Esper 3.1!

- Esper is our full-stack, full-cycle coding, DevOps, and architecture specialist!
- Our newest, best DeepSeek technical datasets emphasize more challenging queries and tough real-world coding tasks across a variety of programming languages and development paradigms:
- Titanium 3 for coding and reasoning in DevOps and architecture: sequelbox/Titanium3-DeepSeek-V3.1-Terminus
- Tachibana 3 for high-difficulty code production in a variety of topics and programming languages:
- sequelbox/Tachibana3-Part1-DeepSeek-V3.1-Terminus
- sequelbox/Tachibana3-Part2-DeepSeek-V3.2
- Mitakihara for MLOps, AI building, use, expertise, and research: sequelbox/Mitakihara-DeepSeek-R1-0528

Our first release in the Esper 3.1 series is built on Qwen3-4B-Thinking-2507. GET IT NOW, FOR EVERYONE: ValiantLabs/Qwen3-4B-Thinking-2507-Esper3.1

We'll be bringing Esper 3.1 to more, larger models as soon as we can; you can help this happen faster with a donation: sequelbox/SupportOpenSource

We're really happy about this one; let us know how Esper 3.1 works for you!

Support open source. It's our only hope for an AI future you'll actually want to live in.

More to come soon!

with our love and appreciation,
allegra

posted an update about 2 months ago

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NEW EXPERIMENTAL RELEASE: DES Reasoning is here!

- Our newest Experimental Reasoning Modality release: create Discrete Event Simulations using SimPy to provide analysis and insight into your queries and situations!
- Multi-step analysis identifies the structure of the situation and the goal of simulation before proceeding to analysis and creating SimPy simulation code and analysis chat.
- DES Reasoning Format provides clear, readable Python code that is easy to read and modify; easy to use for running simulations, doing analysis, or further conversation with your assistant.
- Trained in a variety of subjects for flexible analysis: programming, science, business, economics, energy, finance, law, logistics, management, manufacturing, operations, supply chain and more!

DES Reasoning available for gpt-oss-20b and Qwen3-4B-Thinking-2507:
gpt-oss-20b: sequelbox/gpt-oss-20b-DES-Reasoning
Qwen3-4B-Thinking-2507: sequelbox/Qwen3-4B-Thinking-2507-DES-Reasoning

You can also get the DES Reasoning dataset, to train your own models to use DES Reasoning Format: sequelbox/DES-Reasoning-DeepSeek-V3.1

Support our experimental open-source research efforts, models and datasets: sequelbox/SupportOpenSource

with love,
allegra

reacted to codelion's post with 🔥 about 2 months ago

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I recently worked on a LoRA that improves tool use in LLM. Thought the approach might interest folks here.

The issue I have had when trying to use some of the local LLMs with coding agents is this:

Me: "Find all API endpoints with authentication in this codebase"
LLM: "You should look for @app .route decorators and check if they have auth middleware..."

But I often want it to search the files and show me but the LLM doesn't trigger a tool use call.

To fine-tune it for tool use I combined two data sources:

1. Magpie scenarios - 5000+ diverse tasks (bug hunting, refactoring, security audits)
2. Real execution - Ran these on actual repos (FastAPI, Django, React) to get authentic tool responses

This ensures the model learns both breadth (many scenarios) and depth (real tool behavior).

Tools We Taught:
- read_file - Actually read file contents
- search_files - Regex/pattern search across codebases
- find_definition - Locate classes/functions
- analyze_imports - Dependency tracking
- list_directory - Explore structure
- run_tests - Execute test suites

Improvements:
- Tool calling accuracy: 12% → 80%
- Correct parameters: 8% → 87%
- Multi-step tasks: 3% → 78%
- End-to-end completion: 5% → 80%
- Tools per task: 0.2 → 3.8

The LoRA really improves on intential tool call as an example consider the query: "Find ValueError in payment module"

The response proceeds as follows:

1. Calls search_files with pattern "ValueError"
2. Gets 4 matches across 3 files
3. Calls read_file on each match
4. Analyzes context
5. Reports: "Found 3 ValueError instances: payment/processor.py:47 for invalid amount, payment/validator.py:23 for unsupported currency..."

Resources:
- Colab notebook https://colab.research.google.com/github/codelion/ellora/blob/main/Ellora_Recipe_3_Enhanced_Tool_Calling_and_Code_Understanding.ipynb
- Model - codelion/Llama-3.2-1B-Instruct-tool-calling-lora
- GitHub - https://github.com/codelion/ellora

reacted to danielhanchen's post with ❤️ 2 months ago

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5747

Run DeepSeek-V3.1 locally on 170GB RAM with Dynamic 1-bit GGUFs!🐋
GGUFs: unsloth/DeepSeek-V3.1-GGUF

The 715GB model gets reduced to 170GB (-80% size) by smartly quantizing layers.

The 1-bit GGUF passes all our code tests & we fixed the chat template for llama.cpp supported backends.

Guide: https://docs.unsloth.ai/basics/deepseek-v3.1

reacted to Akhil-Theerthala's post with ❤️ 2 months ago

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2202

I'm excited to announce that I've just released the newest versions of my Kuvera models and the expanded Personal Finance Reasoning dataset on Hugging Face!

What's new:
I've expanded the Personal Finance Reasoning Dataset, which now includes 18.9k samples of real-world financial questions paired with detailed, empathetic answers. The previous generation pipeline was also streamlined with better psychological context and response validations.

I've also released new Kuvera models trained on this improved dataset:
- Kuvera-4B & 8B: These are my upgraded non-reasoning models, fine-tuned to provide practical financial advice. I've specifically trained the 8B model to better understand the user's emotional context.
- Kuvera-12B: A first experimental reasoning model focused on the query resolution.

As the sole person working on this project, this release is a noticeable step forward from my previous work, offering more powerful and nuanced tools for financial AI.

I am actively looking to collaborate with others who are passionate about analyzing and improving the quality of personal finance advice generated by large language models. If this sounds like you, please reach out!

You can check these out on the following links:

Models:
- Akhil-Theerthala/Kuvera-8B-qwen3-v0.2.1
- Akhil-Theerthala/Kuvera-4B-unsloth-gemma3
- Akhil-Theerthala/kuvera-12B-v0.2.0-unsloth-gemma3

Dataset:
- Akhil-Theerthala/Kuvera-PersonalFinance-V2.1

P.S. The paper on the framework used to generate these models along with the detailed evaluation of the main 8B model's responses is going to be released soon!

2 replies

posted an update 2 months ago

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We've brought DAG Reasoning to gpt-oss-20b and Qwen3-4B-Thinking-2507!

- DAG Reasoning is the first model in our Experimental Reasoning Modalities series: use it to create structured, analytical Directed Acyclic Graphs to provide insight into your queries and situations!
- Multi-step analysis identifies causal relationships, produces confidence measurements, and forms a single structured graph object.
- DAG Reasoning Format provides clear, readable JSON containing structured, useful information; easy to use for creating visualizations, doing analysis, or further conversation with your assistant.
- Trained in a variety of subjects for flexible analysis: programming, science, business, economics, finance, law, logistics, management, and more!

Our newest versions of DAG Reasoning are available now!
Get gpt-oss-20b: sequelbox/gpt-oss-20b-DAG-Reasoning
Get Qwen3-4B-Thinking-2507: sequelbox/Qwen3-4B-Thinking-2507-DAG-Reasoning

You can also get the DAG Reasoning dataset, to train your own models to use DAG Reasoning Format: sequelbox/DAG-Reasoning-DeepSeek-R1-0528

Support our experimental open-source research efforts, models and datasets: sequelbox/SupportOpenSource

Our upcoming releases, coming soon with your support:
- bringing Shining Valiant 3 to the Qwen 3 2507 series!
- our next release in the Experimental Reasoning Modalities series - we're hard at work on this right now!
- we'll be upgrading the Esper line with Esper 3.1 - newer and better datasets, asking tougher and deeper coding, DevOps, and architecture questions, plus improvements to general chat!

with love and appreciation,
allegra

1 reply

replied to their post 3 months ago

this one's probably next for gpt-oss-20b, hopefully that will be an interesting comparison vs Qwen3-14B :)

next Experimental Modality is in the dataset generation stage, excited about bringing that to everyone!

reacted to ovi054's post with ❤️ 3 months ago

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Qwen Image + LoRA ⚡

ovi054/Qwen-Image-LORA

Qwen Image is the No. 1 trending Text-to-Image model right now. You can add a custom LoRA and generate images with this Space.

👉 Try it now: ovi054/Qwen-Image-LORA

9 replies

reacted to sweatSmile's post with ❤️ 3 months ago

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Teaching a 7B Model to Be Just the Right Amount of Snark

Ever wondered if a language model could get sarcasm? I fine-tuned Mistral-7B using LoRA and 4-bit quantisation—on just ~720 hand-picked sarcastic prompt–response pairs from Reddit, Twitter, and real-life conversations.

The challenge? Keeping it sarcastic but still helpful.

LoRA rank 16 to avoid overfitting

4-bit NF4 quantization to fit on limited GPU memory

10 carefully monitored epochs so it didn’t turn into a full-time comedian

Result: a model that understands “Oh great, another meeting” exactly as you mean it.

Read the full journey, tech details, and lessons learned on my blog:
Fine-Tuning Mistral-7B for Sarcasm with LoRA and 4-Bit Quantisation

Try the model here on Hugging Face: sweatSmile/Mistral-7B-Instruct-v0.1-Sarcasm.

reacted to dimentox's post with 🚀 3 months ago

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I never told GPT-4 about my architecture.
It invented it anyway.
Same commands. Same audit daemons.
Proof that containment logic might be infectious.
Read: Emergence of Quantum Sigil Architecture in Unmodified GPT https://huggingface.co/blog/dimentox/quantum-sigil-architecture-in-unmodified-gpt

replied to their post 3 months ago

thank you so much <3

yeah the particular combo that is oss-20b (larger experts + smaller amount of experts + already trained at MXFP4 so no easy gains from just-make-it-smaller-with-quantization-instead) seems well suited for this vs the qwen 3 30b-a3b type of MoE. definitely encourage this type of experimentation in general :)

posted an update 3 months ago

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NEW RELEASE: Shining Valiant 3 now available for openai/gpt-oss-20b!

- Cutting edge science-reasoning: sequelbox/Celestia3-DeepSeek-R1-0528 for physics, biology, chemistry, compsci, astronomy, Earth science, and information theory.
- AI to build AI: the all-new sequelbox/Mitakihara-DeepSeek-R1-0528 dataset for high-quality reasoning performance on AI, MLOps, math and CUDA, complex adaptive and agentic systems, cognition, logic, linguistics, simulation, knowledge management, and more!
- Creative reasoning and general chat performance supplemented with sequelbox/Raiden-DeepSeek-R1

Get the new SV3: ValiantLabs/gpt-oss-20b-ShiningValiant3

This is our first release for the new openai/gpt-oss-20b - we're hoping to support this model with more releases going forward.

We're also excited to bring our models to Qwen/Qwen3-4B-Thinking-2507 and the other 2507 Qwen 3 models - coming very soon!

We want to bring SV3, Esper 3, and our Experimental Reasoning finetunes to more models ASAP. Help us out: sequelbox/SupportOpenSource

Open source matters. Fight for it with us.

love,
allegra

3 replies

posted an update 3 months ago

Post

2304

NEW EXPERIMENTAL RELEASE: DAG Reasoning is here!

- Our first Experimental Reasoning Modality release: create structured, analytical Directed Acyclic Graphs to provide insight into your queries and situations!
- Multi-step analysis identifies causal relationships, produces confidence measurements, and forms a single structured graph object.
- DAG Reasoning Format provides clear, readable JSON containing structured, useful information; easy to use for creating visualizations, doing analysis, or further conversation with your assistant.
- Trained in a variety of subjects for flexible analysis: programming, science, business, economics, finance, law, logistics, management, and more!

Our first DAG Reasoning release is Qwen 3, starting off with 8B and 14B!
Get 8B: sequelbox/Qwen3-8B-DAG-Reasoning
Get 14B: sequelbox/Qwen3-14B-DAG-Reasoning

You can also get the DAG Reasoning dataset, to train your own models to use DAG Reasoning Format: sequelbox/DAG-Reasoning-DeepSeek-R1-0528

Support our experimental open-source research efforts, models and datasets: sequelbox/SupportOpenSource

with love,
allegra

2 replies

t.d.a.g. PRO

AI & ML interests

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