Create README.md

README.md

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+# Community Quantization Requests
+
+This space is for requesting oQe builds. These quants utilize multi-stage calibration and Hessian-based error compensation to maintain logic stability, specifically tuned for Apple Silicon performance.
+
+### How to Request
+Open a new Discussion for requests. To ensure a valid build, please include:
+
+1. Model Link: URL to the official Hugging Face repository. Note that I only process builds starting from original BF16 or FP16 source weights.
+2. Quantization Format: Specify if you need BF16 or FP16 quants. 
+   * FP16 is generally recommended for M1/M2 series to utilize AMX units for faster prefill.
+   * BF16 is recommended for M3/M4 series with native support.
+3. Preferred Tiers: Specify the target bitrate (e.g., oQ5e, oQ4e) based on your available Unified Memory.
+
+### Guidelines
+* Hardware: Builds are processed on a 192GB M2 Ultra. Models up to 70B parameters (standard dense) are supported. Anything significantly larger (100B+ or large MoE architectures) will exceed memory limits when loading source weights for calibration.
+* Selection Criteria: Priority is given to base models and official instruct tunes. Experimental merges or low-epoch fine-tunes are generally excluded unless there is significant community interest.
+* The Process: Every oQe build undergoes a 600-sample calibration pass. These are not one-pass streaming quants.
+
+### Technical Spec
+All fulfilled requests are processed using the oMLX Enhanced Quantization process:
+* Sensitivity Mapping: Calibration pass measures precision requirements per layer to prevent output drift.
+* Hessian-Based Tuning: GPTQ-Hessian error compensation is applied during weight rounding.
+* Precision Anchoring: Native BF16 for routing gates and FP16 for attention heads to maximize Apple Silicon AMX throughput.
+* Logic Floor: The lm_head and critical early blocks are locked at 8-bit to ensure core reasoning stability.