leeloolee/KoTau
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Dokyoon
leeloolee
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ai
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updated a dataset about 3 hours ago
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reacted to DedeProGames's post with š„ 6 days ago
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š Introducing OpenWork ā Deploy agents everywhere.
OpenWork is an open-source AI agent workspace built on top of OpenCode.
Deploy autonomous agents, schedule tasks, manage projects, and let AI work for you ā directly from your terminal.
Features:
- š¤ Deploy autonomous AI agents
- š Schedule recurring tasks
- š§ Persistent memory and workspaces
- š MCP integrations and custom tools
- š„ Agent Inbox and task management
-ā” Support for local and cloud AI models
- š„ļø Interactive terminal interface (TUI)
Your agents. Your models. Your workflow.
Fully open-source. MIT licensed.
ā GitHub: https://github.com/dedeprogames-official/openwork
#OpenSource #AI #AIAgents #OpenWork #OpenCode
OpenWork is an open-source AI agent workspace built on top of OpenCode.
Deploy autonomous agents, schedule tasks, manage projects, and let AI work for you ā directly from your terminal.
Features:
- š¤ Deploy autonomous AI agents
- š Schedule recurring tasks
- š§ Persistent memory and workspaces
- š MCP integrations and custom tools
- š„ Agent Inbox and task management
-ā” Support for local and cloud AI models
- š„ļø Interactive terminal interface (TUI)
Your agents. Your models. Your workflow.
Fully open-source. MIT licensed.
ā GitHub: https://github.com/dedeprogames-official/openwork
#OpenSource #AI #AIAgents #OpenWork #OpenCode
reacted to SeaWolf-AI's post with š„ 7 days ago
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4,113
š Darwin-27B-ZTC-v2 just took #1 on the System One Mosaic Benchmark (S1MB).
S1MB compares 102 models across 137 specialized benchmarks, in three task types: Noul (assess a condition), Choice (select an option), Score (rate on a scale). Ranking is by overall Borda score.
š Top of the board
š„ Darwin ZTC v2 (FINAL-Bench) 89.58
š„ OpenJev-27B 87.50
š„ AutoJev-27B 87.07
4ļøā£ Eikos 27B 85.43
5ļøā£ Jev 1.13 85.05
š Ranks 2 to 5 are all the JEV family (TypeSafe AI's System One model, from ex-OpenAI researchers). S1MB exists to compare these System One judges, so leading it is the headline.
āļø Why a zero-token judge wins here
š¹ It does not generate. It reads the input and typed questions and returns a calibrated distribution in a single forward pass.
š¹ Zero generated tokens, no decoding loop, so latency and cost stay low.
š¹ Holds up out of distribution too: General Noul 96.00, General Choice 99.34.
It is also #1 on the typed-decisions leaderboard (0.743, zero-shot). Same message from both: a deterministic, calibrated judge at one forward pass per call.
š Model: FINAL-Bench/Darwin-27B-ZTC-v2
š Leaderboard: hotchpotch/S1MB-leaderboard
Standings move as new models are added. Numbers reflect the board at the time of writing. š
S1MB compares 102 models across 137 specialized benchmarks, in three task types: Noul (assess a condition), Choice (select an option), Score (rate on a scale). Ranking is by overall Borda score.
š Top of the board
š„ Darwin ZTC v2 (FINAL-Bench) 89.58
š„ OpenJev-27B 87.50
š„ AutoJev-27B 87.07
4ļøā£ Eikos 27B 85.43
5ļøā£ Jev 1.13 85.05
š Ranks 2 to 5 are all the JEV family (TypeSafe AI's System One model, from ex-OpenAI researchers). S1MB exists to compare these System One judges, so leading it is the headline.
āļø Why a zero-token judge wins here
š¹ It does not generate. It reads the input and typed questions and returns a calibrated distribution in a single forward pass.
š¹ Zero generated tokens, no decoding loop, so latency and cost stay low.
š¹ Holds up out of distribution too: General Noul 96.00, General Choice 99.34.
It is also #1 on the typed-decisions leaderboard (0.743, zero-shot). Same message from both: a deterministic, calibrated judge at one forward pass per call.
š Model: FINAL-Bench/Darwin-27B-ZTC-v2
š Leaderboard: hotchpotch/S1MB-leaderboard
Standings move as new models are added. Numbers reflect the board at the time of writing. š
upvoted a collection 19 days ago
reacted to medmekk's post with š 25 days ago
Post
4,383
š Introducing Halo 1.0
Today, we are open-sourcing Halo, the training framework we use to train every model at White Circle.
It comes with:
š§ Full post-training stack: SFT, DPO/KTO/SMPO, reward modeling, GRPO, distillation
š¤ Async multi-turn RL with vLLM/SGLang rollouts and sandboxed tool use
ā” ~2.8Ć TRL throughput on 8Ć B300 (EP+FSDPv2, FA4, fp8/fp4)
š¤ Dense HF models + 15 MoE families (Qwen, GLM, Mistral, DeepSeek-V4ā¦)
š ļø One halo command, prebuilt Docker images, and docs for humans and agents
š» https://github.com/whitecircle/halo
Try it and tell us what you're training
Today, we are open-sourcing Halo, the training framework we use to train every model at White Circle.
It comes with:
š§ Full post-training stack: SFT, DPO/KTO/SMPO, reward modeling, GRPO, distillation
š¤ Async multi-turn RL with vLLM/SGLang rollouts and sandboxed tool use
ā” ~2.8Ć TRL throughput on 8Ć B300 (EP+FSDPv2, FA4, fp8/fp4)
š¤ Dense HF models + 15 MoE families (Qwen, GLM, Mistral, DeepSeek-V4ā¦)
š ļø One halo command, prebuilt Docker images, and docs for humans and agents
š» https://github.com/whitecircle/halo
Try it and tell us what you're training
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