put up a quick merge of the SV3 and Esper 3.1 Ministrals: https://huggingface.co/sequelbox/Ministral-3-14B-Reasoning-2512-PlumEsper1.1
t.d.a.g. PRO
sequelbox
AI & ML interests
open source, infinite games. (they/them)
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about 24 hours ago
mradermacher/Ministral-3-14B-Reasoning-2512-PlumEsper1.1-GGUF
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mradermacher/Ministral-3-14B-Reasoning-2512-ShiningValiant3-i1-GGUF
replied to
their
post
2 days ago
Two new releases today!
Firstly, our new Raiden-Mini dataset, powered by DeepSeek's newest https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Speciale model!
- A V3.2-Speciale reasoning showcase: the Raiden prompts test the model's creative, analytic, and general reasoning skills!
- HEAD TO HEAD: a comparison subset pits V3.2-Speciale against V3.2 with the same prompts, providing a direct look at each model's advantages!
Get the new Raiden-Mini dataset: https://huggingface.co/datasets/sequelbox/Raiden-Mini-DeepSeek-V3.2-Speciale
On the model side, we've also brought Shining Valiant 3 to Ministral 3!
- Science-reasoning: https://huggingface.co/datasets/sequelbox/Celestia3-DeepSeek-R1-0528 for physics, biology, chemistry, compsci, astronomy, Earth science, and information theory.
- AI to build AI: the https://huggingface.co/datasets/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 https://huggingface.co/datasets/sequelbox/Raiden-DeepSeek-R1
Get the newest SV3: https://huggingface.co/ValiantLabs/Ministral-3-14B-Reasoning-2512-ShiningValiant3
Esper 3.1 is available for Ministral 3 as well: https://huggingface.co/ValiantLabs/Ministral-3-14B-Reasoning-2512-Esper3.1
We're working hard on our next Big New Release, coming out in the next few weeks :)
Help support our releases, donations used for models and datasets: https://huggingface.co/spaces/sequelbox/SupportOpenSource
Open source matters. Fight for it with us.
with love and friendship,
allegra
Organizations
replied to
their
post
2 days ago
posted
an
update
3 days ago
Post
2860
Two new releases today!
Firstly, our new Raiden-Mini dataset, powered by DeepSeek's newest deepseek-ai/DeepSeek-V3.2-Speciale model!
- A V3.2-Speciale reasoning showcase: the Raiden prompts test the model's creative, analytic, and general reasoning skills!
- HEAD TO HEAD: a comparison subset pits V3.2-Speciale against V3.2 with the same prompts, providing a direct look at each model's advantages!
Get the new Raiden-Mini dataset: sequelbox/Raiden-Mini-DeepSeek-V3.2-Speciale
On the model side, we've also brought Shining Valiant 3 to Ministral 3!
- Science-reasoning: sequelbox/Celestia3-DeepSeek-R1-0528 for physics, biology, chemistry, compsci, astronomy, Earth science, and information theory.
- AI to build AI: the 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 newest SV3: ValiantLabs/Ministral-3-14B-Reasoning-2512-ShiningValiant3
Esper 3.1 is available for Ministral 3 as well: ValiantLabs/Ministral-3-14B-Reasoning-2512-Esper3.1
We're working hard on our next Big New Release, coming out in the next few weeks :)
Help support our releases, donations used for models and datasets: sequelbox/SupportOpenSource
Open source matters. Fight for it with us.
with love and friendship,
allegra
Firstly, our new Raiden-Mini dataset, powered by DeepSeek's newest deepseek-ai/DeepSeek-V3.2-Speciale model!
- A V3.2-Speciale reasoning showcase: the Raiden prompts test the model's creative, analytic, and general reasoning skills!
- HEAD TO HEAD: a comparison subset pits V3.2-Speciale against V3.2 with the same prompts, providing a direct look at each model's advantages!
Get the new Raiden-Mini dataset: sequelbox/Raiden-Mini-DeepSeek-V3.2-Speciale
On the model side, we've also brought Shining Valiant 3 to Ministral 3!
- Science-reasoning: sequelbox/Celestia3-DeepSeek-R1-0528 for physics, biology, chemistry, compsci, astronomy, Earth science, and information theory.
- AI to build AI: the 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 newest SV3: ValiantLabs/Ministral-3-14B-Reasoning-2512-ShiningValiant3
Esper 3.1 is available for Ministral 3 as well: ValiantLabs/Ministral-3-14B-Reasoning-2512-Esper3.1
We're working hard on our next Big New Release, coming out in the next few weeks :)
Help support our releases, donations used for models and datasets: sequelbox/SupportOpenSource
Open source matters. Fight for it with us.
with love and friendship,
allegra
-
- 1 reply
replied to
their
post
8 days ago
we've added 14b and 3b as well - we'd like to specifically recommend the 14b for everyone to try: https://huggingface.co/ValiantLabs/Ministral-3-14B-Reasoning-2512-Esper3.1
reacted to
danielhanchen's
post with š„
8 days ago
Post
3367
Mistral's new Ministral 3 models can now be Run & Fine-tuned locally! (16GB RAM)
Ministral 3 have vision support and the best-in-class performance for their sizes.
14B Instruct GGUF: unsloth/Ministral-3-14B-Instruct-2512-GGUF
14B Reasoning GGUF: unsloth/Ministral-3-14B-Reasoning-2512-GGUF
š± Step-by-step Guide: https://docs.unsloth.ai/new/ministral-3
All GGUFs, BnB, FP8 etc. variants uploads: https://huggingface.co/collections/unsloth/ministral-3
Ministral 3 have vision support and the best-in-class performance for their sizes.
14B Instruct GGUF: unsloth/Ministral-3-14B-Instruct-2512-GGUF
14B Reasoning GGUF: unsloth/Ministral-3-14B-Reasoning-2512-GGUF
š± Step-by-step Guide: https://docs.unsloth.ai/new/ministral-3
All GGUFs, BnB, FP8 etc. variants uploads: https://huggingface.co/collections/unsloth/ministral-3
- 3 replies
reacted to
sergiopaniego's
post with š
9 days ago
Post
2146
ICYMI, transformers v5 is out!
Grab a coffee ā and go read the announcement blog https://huggingface.co/blog/transformers-v5
Grab a coffee ā and go read the announcement blog https://huggingface.co/blog/transformers-v5
posted
an
update
9 days ago
Post
1999
NEW RELEASE: Esper 3.1 for Ministral 3 14b, 8b, and 3b!
- 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 Esper 3.1 now in all 3 Ministral 3 sizes! (We recommend 14b for general use.)
14b: ValiantLabs/Ministral-3-14B-Reasoning-2512-Esper3.1
8b: ValiantLabs/Ministral-3-8B-Reasoning-2512-Esper3.1
3b: ValiantLabs/Ministral-3-3B-Reasoning-2512-Esper3.1
We'll be bringing more models to Ministral soon, including Shining Valiant 3 :)
We're currently working hard on a big release in a new specialty - hoping to have that up on Valiant Labs before the end of the year! We'll keep pushing the boundaries of what personal-sized AI can do for you.
See our Experimental Reasoning models and open-source datasets: @sequelbox
Help us keep working for open source AI with a donation: sequelbox/SupportOpenSource
with love,
allegra
- 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 Esper 3.1 now in all 3 Ministral 3 sizes! (We recommend 14b for general use.)
14b: ValiantLabs/Ministral-3-14B-Reasoning-2512-Esper3.1
8b: ValiantLabs/Ministral-3-8B-Reasoning-2512-Esper3.1
3b: ValiantLabs/Ministral-3-3B-Reasoning-2512-Esper3.1
We'll be bringing more models to Ministral soon, including Shining Valiant 3 :)
We're currently working hard on a big release in a new specialty - hoping to have that up on Valiant Labs before the end of the year! We'll keep pushing the boundaries of what personal-sized AI can do for you.
See our Experimental Reasoning models and open-source datasets: @sequelbox
Help us keep working for open source AI with a donation: sequelbox/SupportOpenSource
with love,
allegra
-
- 1 reply
reacted to
danielhanchen's
post with ā¤ļø
13 days ago
Post
8291
Qwen3-Next can now be Run locally! (30GB RAM)
Instruct GGUF: unsloth/Qwen3-Next-80B-A3B-Instruct-GGUF
The models come in Thinking and Instruct versions and utilize a new architecture, allowing it to have ~10x faster inference than Qwen32B.
š Step-by-step Guide: https://docs.unsloth.ai/models/qwen3-next
Thinking GGUF: unsloth/Qwen3-Next-80B-A3B-Thinking-GGUF
Instruct GGUF: unsloth/Qwen3-Next-80B-A3B-Instruct-GGUF
The models come in Thinking and Instruct versions and utilize a new architecture, allowing it to have ~10x faster inference than Qwen32B.
š Step-by-step Guide: https://docs.unsloth.ai/models/qwen3-next
Thinking GGUF: unsloth/Qwen3-Next-80B-A3B-Thinking-GGUF
posted
an
update
15 days ago
Post
335
We strongly disagree with Hugging Face's decision to remove the Epstein files dataset. As an open source community, it is imperative that we support free access to important information.
Torrents remain available for those looking to use the information, but ease of access matters too. The datasets library provides legitimate value to users; it matters to be able to access content here.
We'd like to encourage everyone to retain local copies of anything on Hugging Face that's important to you.
Torrents remain available for those looking to use the information, but ease of access matters too. The datasets library provides legitimate value to users; it matters to be able to access content here.
We'd like to encourage everyone to retain local copies of anything on Hugging Face that's important to you.
posted
an
update
30 days ago
Post
2529
NEW RELEASE: UML Generator is here!
- Our newest Experimental Reasoning release: create Unified Modeling Language diagrams to provide analysis and insight into your queries and situations!
- Multi-step reasoning reliably identifies diagram structure before a user response of XMI 2.5.1 code containing the UML diagram. Load the diagram into the UML tool of your choice!
- Trained in a variety of subjects for flexible analysis: software architecture, software development, business processes, systems engineering, data modeling, microservices, reverse engineering and more!
UML Generator available for multiple sizes of gpt-oss and Qwen 3, to provide increased flexibility to the user:
gpt-oss-120b: sequelbox/gpt-oss-120b-UML-Generator
gpt-oss-20b: sequelbox/gpt-oss-20b-UML-Generator
Qwen3-14B: sequelbox/Qwen3-14B-UML-Generator
Qwen3-4B-Thinking-2507: sequelbox/Qwen3-4B-Thinking-2507-UML-Generator
You can also get the UML Generator dataset, to train your own models to use UML Generator Format: sequelbox/UML-Generator-Dataset-DeepSeek-V3.2
Support our experimental open-source research efforts, models and datasets: sequelbox/SupportOpenSource
See our other Experimental Reasoning models: https://huggingface.co/collections/sequelbox/experimental-reasoning-models
with love,
allegra
- Our newest Experimental Reasoning release: create Unified Modeling Language diagrams to provide analysis and insight into your queries and situations!
- Multi-step reasoning reliably identifies diagram structure before a user response of XMI 2.5.1 code containing the UML diagram. Load the diagram into the UML tool of your choice!
- Trained in a variety of subjects for flexible analysis: software architecture, software development, business processes, systems engineering, data modeling, microservices, reverse engineering and more!
UML Generator available for multiple sizes of gpt-oss and Qwen 3, to provide increased flexibility to the user:
gpt-oss-120b: sequelbox/gpt-oss-120b-UML-Generator
gpt-oss-20b: sequelbox/gpt-oss-20b-UML-Generator
Qwen3-14B: sequelbox/Qwen3-14B-UML-Generator
Qwen3-4B-Thinking-2507: sequelbox/Qwen3-4B-Thinking-2507-UML-Generator
You can also get the UML Generator dataset, to train your own models to use UML Generator Format: sequelbox/UML-Generator-Dataset-DeepSeek-V3.2
Support our experimental open-source research efforts, models and datasets: sequelbox/SupportOpenSource
See our other Experimental Reasoning models: https://huggingface.co/collections/sequelbox/experimental-reasoning-models
with love,
allegra
reacted to
salma-remyx's
post with š„
about 2 months ago
Post
3310
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
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
- 1 reply
replied to
their
post
about 2 months ago
reacted to
umarbutler's
post with š
about 2 months ago
Post
2962
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 š
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 ā 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
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 ā 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 š
about 2 months ago
Post
4252
š¤ Sentence Transformers is joining Hugging Face! š¤ 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!
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!
- 1 reply
reacted to
AdinaY's
post with š
about 2 months ago
Post
2650
HunyuanWorld Mirrorš„a versatile feed forward model for universal 3D world reconstruction by Tencent
tencent/HunyuanWorld-Mirror
āØ Any prior in ā 3D world out
āØ Mix camera, intrinsics, depth as priors
āØ Predict point clouds, normals, Gaussians & more in one pass
āØ Unified architecture for all 3D task
tencent/HunyuanWorld-Mirror
āØ Any prior in ā 3D world out
āØ Mix camera, intrinsics, depth as priors
āØ Predict point clouds, normals, Gaussians & more in one pass
āØ Unified architecture for all 3D task
reacted to
paulml's
post with š„
about 2 months ago
Post
3601
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 2.0āmaking it fully commercially permissive.
Check out our DSE model here:
racineai/QwenAmann-4B-dse
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 2.0āmaking it fully commercially permissive.
Check out our DSE model here:
racineai/QwenAmann-4B-dse
posted
an
update
about 2 months ago
Post
2535
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
- 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
2 months ago
Post
1509
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
- 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
3 months ago
Post
2607
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
- 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 š„
3 months ago
Post
6183
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:
-
-
-
-
-
-
Improvements:
- Tool calling accuracy: 12% ā 80%
- Correct parameters: 8% ā 87%
- Multi-step tasks: 3% ā 78%
- End-to-end completion: 5% ā 80%
- Tools per task: 0.2 ā 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
2. Gets 4 matches across 3 files
3. Calls
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
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 suitesImprovements:
- Tool calling accuracy: 12% ā 80%
- Correct parameters: 8% ā 87%
- Multi-step tasks: 3% ā 78%
- End-to-end completion: 5% ā 80%
- Tools per task: 0.2 ā 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 match4. 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 ā¤ļø
4 months ago
Post
6491
Run DeepSeek-V3.1 locally on 170GB RAM with Dynamic 1-bit GGUFs!š
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
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