nvidia/NVIDIA-NemotronLabs-RE-USE
Audio-to-Audio • 9.61M • Updated • 38k • 219
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tencent/Youtu-Parsing-OmniOrganizations
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Last Qwen3.8 Flash update with STQ 2.75bpw v2.0 pack. It moves away from hybrid affine/stq format to pure STQ with +15% quality to match oQ4e. 64GB users can enjoy this while waiting for Qwen 4!
Require Sushi 1.5.1 ro run.
Require Sushi 1.5.1 ro run.
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We’re releasing new Qwen3.8-Flash-Next GGUFs with 15% higher accuracy.
Unsloth Dynamic V3.1 outperforms others by >15% on Terminal Bench, DeepSWE & more benchmarks (256K context).
Run on 32GB+ RAM setups.
GGUF: unsloth/Qwen3.8-Flash-Next-GGUF
Blog: https://unsloth.ai/docs/models/qwen3.8-next
Unsloth Dynamic V3.1 outperforms others by >15% on Terminal Bench, DeepSWE & more benchmarks (256K context).
Run on 32GB+ RAM setups.
GGUF: unsloth/Qwen3.8-Flash-Next-GGUF
Blog: https://unsloth.ai/docs/models/qwen3.8-next
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Quazim0t0/Qwenx86-4B-compact
▶️ See it in action: Quazim0t0/Qwenx86-Debugger . Walk through actual recorded tool calls: the conversation, x86 registers and flags, code at the program counter, RAM and machine output, with all verified blocks on a timeline.
Qwen3.5-4B with a 64-bit x86 computer compiled into its weights: 13 transformer layers ahead of Qwen's own. There are no learned weights in them; all of their weights are computed by a compiler. Qwen invokes the computer through its own tool calls (<tool_call>), and the computer in the weights executes the tool. Calculator, Python, C compiler, notes store, physics/chemistry lookup and Python-docs lookup are all firmware in the checkpoint. This is the compact checkpoint: ~9 GB GPU memory, so it fits on a 16 GB GPU.
Edit: GGUF Release: Quazim0t0/Qwenx86-4B-GGUF
Credits: Qwenputer by Divyansh Agrawal (@divagr1925 , github.com/divagr18/qwenputer, MIT)
▶️ See it in action: Quazim0t0/Qwenx86-Debugger . Walk through actual recorded tool calls: the conversation, x86 registers and flags, code at the program counter, RAM and machine output, with all verified blocks on a timeline.
Qwen3.5-4B with a 64-bit x86 computer compiled into its weights: 13 transformer layers ahead of Qwen's own. There are no learned weights in them; all of their weights are computed by a compiler. Qwen invokes the computer through its own tool calls (<tool_call>), and the computer in the weights executes the tool. Calculator, Python, C compiler, notes store, physics/chemistry lookup and Python-docs lookup are all firmware in the checkpoint. This is the compact checkpoint: ~9 GB GPU memory, so it fits on a 16 GB GPU.
Edit: GGUF Release: Quazim0t0/Qwenx86-4B-GGUF
Credits: Qwenputer by Divyansh Agrawal (@divagr1925 , github.com/divagr18/qwenputer, MIT)
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reacted to jiteshkprasad's post with 👍 11 days ago
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We just released Drishya Tiny, a 212M-parameter video generator trained from scratch on open-domain video, using the frozen Wan2.2 VAE and the Flan-T5-Base text encoder. It turns a text prompt, or a photo plus a prompt, into a 2-second 256×256 clip. The training run was 15 hours on one L40S, and the model’s own path cost about $21 of GPU time.
On VBench (our own sampling and scoring with the official code, not a certified entry) it scores 75.6 on quality, level with VideoCrafter-0.9 (74.9) and close to ModelScope (78.1). On semantic it’s 32.3 against their 65-67, so following the prompt is the weak part. In a controlled test, better captions gave +3.0, and adding 30% photos on top gave about 1.6 more, all of it in what objects are and none in composition. I think at this size, looking real is cheap and understanding the prompt is what costs.
It’s a research release, so the weights, code, logs, the full cost ($57.43 with every failed run) and the negative results are all out. Weights are CC BY-NC 4.0 because the training clips are research-only; the code is Apache-2.0.
And it runs right in your browser with WebGPU, from text or a photo, on your own device. The browser version uses the tiny TAEW2.2 decoder by Ollin Boer Bohan, so its clips are a bit softer, and a clip takes a few seconds on a recent Mac.
Run it in your browser: https://udgamlabs.com/models/drishya-tiny
Space: udgamlabs/Drishya-Tiny
Model: udgamlabs/Drishya-Tiny
Technical report: https://udgamlabs.com/research/drishya-tiny
Write-up: https://udgamlabs.com/blog/drishya-tiny
Code: https://github.com/udgamlabs/drishya-tiny
On VBench (our own sampling and scoring with the official code, not a certified entry) it scores 75.6 on quality, level with VideoCrafter-0.9 (74.9) and close to ModelScope (78.1). On semantic it’s 32.3 against their 65-67, so following the prompt is the weak part. In a controlled test, better captions gave +3.0, and adding 30% photos on top gave about 1.6 more, all of it in what objects are and none in composition. I think at this size, looking real is cheap and understanding the prompt is what costs.
It’s a research release, so the weights, code, logs, the full cost ($57.43 with every failed run) and the negative results are all out. Weights are CC BY-NC 4.0 because the training clips are research-only; the code is Apache-2.0.
And it runs right in your browser with WebGPU, from text or a photo, on your own device. The browser version uses the tiny TAEW2.2 decoder by Ollin Boer Bohan, so its clips are a bit softer, and a clip takes a few seconds on a recent Mac.
Run it in your browser: https://udgamlabs.com/models/drishya-tiny
Space: udgamlabs/Drishya-Tiny
Model: udgamlabs/Drishya-Tiny
Technical report: https://udgamlabs.com/research/drishya-tiny
Write-up: https://udgamlabs.com/blog/drishya-tiny
Code: https://github.com/udgamlabs/drishya-tiny
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Layer-Feedback Transformer (LFT)
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