Ming-V2
Collection
Ming is the multi-modal series of any-to-any models developed by Ant Ling team.
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11 items
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Updated
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33
Duyet Le
duyet
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Ming-V2
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inclusionAI/Ring-2.5-1T
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zai-org/GLM-5
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danielhanchen's
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9 days ago
Post
3689
Qwen releases Qwen3-Coder-Next! 💜 Run the locally on 46GB RAM or less.
Thhe model excels at agentic coding & local use. With 256K context, it delivers similar performance to models with 10-20× more active parameters.
GGUF: unsloth/Qwen3-Coder-Next-GGUF
Guide: https://unsloth.ai/docs/models/qwen3-coder-next
Thhe model excels at agentic coding & local use. With 256K context, it delivers similar performance to models with 10-20× more active parameters.
GGUF: unsloth/Qwen3-Coder-Next-GGUF
Guide: https://unsloth.ai/docs/models/qwen3-coder-next
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2
models
11 days ago
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5
models
24 days ago
stepfun-ai/Step3-VL-10B
Image-Text-to-Text
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10B
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93.7k
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394
mlx-community/GLM-4.7-Flash-4bit
Text Generation
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30B
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33.9k
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unsloth/GLM-4.7-Flash-GGUF
Text Generation
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30B
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387k
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zai-org/GLM-4.7-Flash
Text Generation
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31B
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1.54M
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1.51k
unsloth/GLM-4.7-GGUF
Text Generation
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358B
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72k
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202
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dataset
29 days ago
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Space
about 1 month ago
reacted to
TravisMuhlestein's
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about 1 month ago
Post
2439
Agentic AI doesn’t fail because it lacks intelligence — it fails because it lacks context.
As agents become more autonomous, the real challenge shifts from generation to governance:
understanding when, why, and under what constraints an agent should act.
At GoDaddy, we’ve been treating context as a first-class primitive for agentic systems —
combining identity, intent, permissions, and environment so agents can operate responsibly in production.
Context is what turns automation into judgment.
Without it, autonomy becomes risk.
This post outlines how we’re thinking about the transition from task execution to context-aware agentic systems, and what that means for building AI that can be trusted at scale.
👉 How we build context for agentic AI:
https://www.godaddy.com/resources/news/how-godaddy-builds-context-for-agentic-ai
Curious how others here are modeling context, trust boundaries, and decision constraints in agentic architectures.
As agents become more autonomous, the real challenge shifts from generation to governance:
understanding when, why, and under what constraints an agent should act.
At GoDaddy, we’ve been treating context as a first-class primitive for agentic systems —
combining identity, intent, permissions, and environment so agents can operate responsibly in production.
Context is what turns automation into judgment.
Without it, autonomy becomes risk.
This post outlines how we’re thinking about the transition from task execution to context-aware agentic systems, and what that means for building AI that can be trusted at scale.
👉 How we build context for agentic AI:
https://www.godaddy.com/resources/news/how-godaddy-builds-context-for-agentic-ai
Curious how others here are modeling context, trust boundaries, and decision constraints in agentic architectures.
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