unsloth/Qwen-Image-2.1-Turbo-GGUF
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MadMike
MadMike477
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AI/ML and tech enthusiast, training & fine-tuning models 🦥💚
OSS supporter & Love helping people and always looking to learn, build, and contribute💚
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Recent Activity
liked a model 4 days ago
unsloth/Qwen-Image-2.1-Turbo-GGUF reacted to danielhanchen's post with 🔥 5 days ago
You can now train your own Decision model like Jev locally!
We increased Qwen3.5 0.8B’s aggregate accuracy from 20.7% to 74.3% across 3 decision benchmarks - on just 4GB VRAM.
Turn any LLM like Qwen3.8, Gemma 4 into decision models with our open-source Unsloth repo.
GitHub: https://github.com/unslothai/unsloth
Guide: https://unsloth.ai/docs/basics/train-your-own-decision-model-with-unsloth reacted to danielhanchen's post with ❤️ 5 days ago
You can now train your own Decision model like Jev locally!
We increased Qwen3.5 0.8B’s aggregate accuracy from 20.7% to 74.3% across 3 decision benchmarks - on just 4GB VRAM.
Turn any LLM like Qwen3.8, Gemma 4 into decision models with our open-source Unsloth repo.
GitHub: https://github.com/unslothai/unsloth
Guide: https://unsloth.ai/docs/basics/train-your-own-decision-model-with-unslothOrganizations
liked a
model 4 days ago
reacted to danielhanchen's post with 🔥❤️ 5 days ago
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You can now train your own Decision model like Jev locally!
We increased Qwen3.5 0.8B’s aggregate accuracy from 20.7% to 74.3% across 3 decision benchmarks - on just 4GB VRAM.
Turn any LLM like Qwen3.8, Gemma 4 into decision models with our open-source Unsloth repo.
GitHub: https://github.com/unslothai/unsloth
Guide: https://unsloth.ai/docs/basics/train-your-own-decision-model-with-unsloth
We increased Qwen3.5 0.8B’s aggregate accuracy from 20.7% to 74.3% across 3 decision benchmarks - on just 4GB VRAM.
Turn any LLM like Qwen3.8, Gemma 4 into decision models with our open-source Unsloth repo.
GitHub: https://github.com/unslothai/unsloth
Guide: https://unsloth.ai/docs/basics/train-your-own-decision-model-with-unsloth
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Space 7 days ago
reacted to sergiopaniego's post with ❤️ 7 days ago
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sharing the slides from my talk at Kernel Panic in Madrid
It's basically "The ultimate guide to multi-harness RL" turned slides and condensed into a 20 min talk
FineEnvs/multi-harness-rl-slides
congrats to @Helmcode for the organization!
blog: FineEnvs/multi-harness-rl
It's basically "The ultimate guide to multi-harness RL" turned slides and condensed into a 20 min talk
FineEnvs/multi-harness-rl-slides
congrats to @Helmcode for the organization!
blog: FineEnvs/multi-harness-rl
liked a
Space 7 days ago
reacted to danielhanchen's post with 🚀🔥❤️ 8 days ago
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3887
Google releases EmbeddingGemma 2, a new open embedding model that runs locally on 0.5GB RAM.
The 740M parameter Apache 2.0 model combines a 270M text model with vision (170M) + audio (300M).
Run & train the model via Unsloth.
GGUF: unsloth/embeddinggemma-2-GGUF
Guide: https://unsloth.ai/docs/models/embeddinggemma-2
The 740M parameter Apache 2.0 model combines a 270M text model with vision (170M) + audio (300M).
Run & train the model via Unsloth.
GGUF: unsloth/embeddinggemma-2-GGUF
Guide: https://unsloth.ai/docs/models/embeddinggemma-2
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Welcome RL Environments to the hub
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burtenshaw, AdithyaSK, sergiopaniego, xeophon, ryanmarten, merve, lhoestq, julien-c
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Knowing when to hand off
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New in llama.cpp: Decision Models
ggml-org
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