deepseek-ai/DeepSeek-Math-V2
Text Generation
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Shyam Sunder Kumar
theainerd
AI & ML interests
Natural Language Processing
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deepseek-ai/DeepSeek-Math-V2
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Continuous batching from first principles
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black-forest-labs/FLUX.2-dev
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Continuous batching from first principles
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I run 20 AI coding agents locally on my desktop workstation at 400+ tokens/sec with MiniMax-M2. It’s a Sonnet drop-in replacement in my Cursor, Claude Code, Droid, Kilo and Cline peak at 11k tok/sec input and 433 tok/s output, can generate 1B+ tok/m.All with 196k context window. I'm running it for 6 days now with this config.
Today max performance was stable at 490.2 tokens/sec across 48 concurrent clients and MiniMax M2.
Z8 Fury G5, Xeon 3455, 4xA6K. Aibrix 0.5.0, vLLM 0.11.2,
Today max performance was stable at 490.2 tokens/sec across 48 concurrent clients and MiniMax M2.
Z8 Fury G5, Xeon 3455, 4xA6K. Aibrix 0.5.0, vLLM 0.11.2,
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we've just added several example scripts to TRL showing how to train models with GRPO using some of the new OpenEnv environments
train a model to interact with a browser (🎮 BrowserGym Env), play Wordle (🎮 Wordle Env) and moooore!
TRL (GRPO + vLLM) + OpenEnv! ⚡️
📝 go play with them: https://github.com/huggingface/trl/tree/main/examples/scripts/openenv
📝 examples list: https://huggingface.co/docs/trl/main/en/example_overview#scripts
train a model to interact with a browser (🎮 BrowserGym Env), play Wordle (🎮 Wordle Env) and moooore!
TRL (GRPO + vLLM) + OpenEnv! ⚡️
📝 go play with them: https://github.com/huggingface/trl/tree/main/examples/scripts/openenv
📝 examples list: https://huggingface.co/docs/trl/main/en/example_overview#scripts
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Running large language models efficiently is more than just raw GPU power. The latest guide breaks down the essential math to determine if your LLM workload is compute-bound or memory-bound.
We apply these principles to a real-world example: Qwen's 32B parameter model on the new NVIDIA RTX PRO 6000 Blackwell Edition.
In this guide, you will learn how to:
Calculate your GPU's operational intensity (Ops:Byte Ratio)
Determine your model's arithmetic intensity
Identify whether your workload is memory-bound or compute-bound
Read the full guide here: https://flozi.net/en/guides/ai/llm-inference-math
We apply these principles to a real-world example: Qwen's 32B parameter model on the new NVIDIA RTX PRO 6000 Blackwell Edition.
In this guide, you will learn how to:
Calculate your GPU's operational intensity (Ops:Byte Ratio)
Determine your model's arithmetic intensity
Identify whether your workload is memory-bound or compute-bound
Read the full guide here: https://flozi.net/en/guides/ai/llm-inference-math
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Hindi Speech to Text just crossed 20 million downloads. Grateful for everyone using it.
theainerd/Wav2Vec2-large-xlsr-hindi
theainerd/Wav2Vec2-large-xlsr-hindi
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