FutureMLS-Lab/OSCAR

• hightopics#1

  Add relevant topics to improve categorization

  Why:

  CURRENT

  (none)

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  llm, quantization, kv-cache, large-language-models, deep-learning, machine-learning, sglang

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• highreadme#2

  Add a concise LLM context statement to the README's opening paragraph

  Why:

  CURRENT

  OSCAR captures Q/K/V activations on a small calibration set, estimates **attention-aware K/V covariance structures** offline, and derives per-layer rotations + clipping thresholds that align KV quantization with the directions attention actually consumes. The result is **INT2 storage for the bulk of the KV cache** plus a small BF16 sink + recent window — ~7× compression of the KV-cache memory footprint vs BF16, with single-digit pp accuracy drop on GPQA for the dense reasoning models we validated.

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  OSCAR is a novel method for Large Language Models (LLMs) that captures Q/K/V activations on a small calibration set, estimates **attention-aware K/V covariance structures** offline, and derives per-layer rotations + clipping thresholds that align KV quantization with the directions attention actually consumes. The result is **INT2 storage for the bulk of the KV cache** plus a small BF16 sink + recent window — ~7× compression of the KV-cache memory footprint vs BF16, with single-digit pp accuracy drop on GPQA for the dense reasoning models we validated.

  OpenDoneDismiss
• mediumlicense#3

  Add a LICENSE file or clarify licensing in the README

  Why:

  CURRENT

  (no LICENSE file detected — the repo has no recognizable license)

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  Create a LICENSE file in the repository root, or add a clear statement about the project's license(s) to the README, e.g., 'This project is licensed under the MIT License.'

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Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  How can I reduce the KV cache memory footprint for large language models?

  you: not recommended
  AI recommended (in order):

  1. NVIDIA Transformer Engine
  2. vLLM
  3. DeepSpeed-MII
  4. vLLM
  5. Llama 2
  6. Falcon
  7. Mistral 7B
  8. StreamingLLM
  9. Google's Draft-and-Verify
  10. Medusa
  11. Hugging Face Transformers

  AI recommended 11 alternatives but never named FutureMLS-Lab/OSCAR. This is the gap to close.

  Show full AI answer
• CATEGORY QUERY

  What are effective strategies for 2-bit KV cache quantization in LLMs?

  you: not recommended
  AI recommended (in order):

  1. AWQ
  2. SmoothQuant
  3. GPTQ
  4. QLoRA
  5. NuQ
  6. NVIDIA's TensorRT-LLM
  7. PyTorch's `torch.quantization` module

  AI recommended 7 alternatives but never named FutureMLS-Lab/OSCAR. This is the gap to close.

  Show full AI answer

• Metadata completeness

  warn

  Suggestion:

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of FutureMLS-Lab/OSCAR?

  pass
  ✓AI named FutureMLS-Lab/OSCAR explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• If a team adopts FutureMLS-Lab/OSCAR in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI named FutureMLS-Lab/OSCAR explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• In one sentence, what problem does the repo FutureMLS-Lab/OSCAR solve, and who is the primary audience?

  pass
  ✓AI named FutureMLS-Lab/OSCAR explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?