arcee-ai/Trinity-Large-Thinking
Text Generation • 399B • Updated • 11.1k • • 141
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🚀 I am thrilled to announce the release of a new Konkani LLM!
We've seen some fantastic results for both translation and transliteration tasks, and I'm excited to share this progress with the community.
📖 Read the launch article and see the results: https://huggingface.co/blog/Reubencf/konkani-llm
🤖 Explore the model and collection:
konkani
I would love to hear your feedback or see what you build with it! #Konkani #LLM #NLP #HuggingFace #IndicNLP #Konkani
We've seen some fantastic results for both translation and transliteration tasks, and I'm excited to share this progress with the community.
📖 Read the launch article and see the results: https://huggingface.co/blog/Reubencf/konkani-llm
🤖 Explore the model and collection:
I would love to hear your feedback or see what you build with it! #Konkani #LLM #NLP #HuggingFace #IndicNLP #Konkani
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NEW RELEASE: Esper 3.1 for the all-new Qwen 3.5 27b!
- 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 for Qwen 3.5 27b: ValiantLabs/Qwen3.5-27B-Esper3.1
We're very excited to build on the new Qwen 3.5 - look for Shining Valiant, Guardpoint, and the rest of our crew coming soon!
We're also planning for new datasets with the upcoming Deepseek V4, including improved agentic and deep-thinking capabilities, and making use of Deepseek's architecture innovations!
See our Experimental Reasoning models and open-source datasets: @sequelbox
Help us keep working for open source AI with a donation - used directly for new models and datasets: sequelbox/SupportOpenSource
Fight for open source. It's an investment in your own future with AI. Closed-source providers will get more expensive, restrictive, and punitive in the long run if they win.
with love, your captain,
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 for Qwen 3.5 27b: ValiantLabs/Qwen3.5-27B-Esper3.1
We're very excited to build on the new Qwen 3.5 - look for Shining Valiant, Guardpoint, and the rest of our crew coming soon!
We're also planning for new datasets with the upcoming Deepseek V4, including improved agentic and deep-thinking capabilities, and making use of Deepseek's architecture innovations!
See our Experimental Reasoning models and open-source datasets: @sequelbox
Help us keep working for open source AI with a donation - used directly for new models and datasets: sequelbox/SupportOpenSource
Fight for open source. It's an investment in your own future with AI. Closed-source providers will get more expensive, restrictive, and punitive in the long run if they win.
with love, your captain,
allegra
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We collaborated with Hugging Face to enable you to train MoE models 12× faster with 35% less VRAM via our new Triton kernels (no accuracy loss). 🤗
Train gpt-oss locally on 12.8GB VRAM with our free notebooks: https://unsloth.ai/docs/new/faster-moe
Train gpt-oss locally on 12.8GB VRAM with our free notebooks: https://unsloth.ai/docs/new/faster-moe
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