Add baa.ai model card

README.md

@@ -0,0 +1,68 @@
+---
+library_name: gguf
+tags:
+- gguf
+- quantized
+- llama.cpp
+- mixed-precision
+- glm
+- moe
+license: other
+license_name: glm-open-model-license
+base_model: zai-org/GLM-5.2
+base_model_relation: quantized
+pipeline_tag: text-generation
+---
+
+# GLM-5.2 — 307GB (GGUF)
+
+Mixed-precision quantized version of [zai-org/GLM-5.2](https://huggingface.co/zai-org/GLM-5.2) using a proprietary quantization method by [baa.ai](https://baa.ai).
+
+Per-tensor bit-width allocation via advanced sensitivity analysis and budget-constrained optimisation — no calibration data required. Built at the efficiency knee (best quality-per-GB).
+
+## Metrics
+
+| Metric | Value |
+|--------|-------|
+| **Size** | **307 GB** (7 shards) |
+| Average bits | 3.50 |
+| Format | llama.cpp (GGUF) |
+| Architecture | MoE (256 experts, MLA + sparse attention) |
+
+Routed experts `Q3_K`; attention `Q4_K`; DSA indexer / shared experts `Q6_K`–`Q8_0`; first/last-layer and protected tensors `F16`; output and token-embeddings `Q6_K`.
+
+## Requirements
+
+**llama.cpp build ≥ b9820** is required — GLM-5.2's sparse-attention shared-indexer layout is only handled by recent builds.
+
+## Usage
+
+```bash
+brew install llama.cpp
+
+hf download baa-ai/GLM-5.2-RAM-307GB-GGUF --include "*.gguf" --local-dir ./glm-5.2-ram-307gb
+
+# The model is split into 7 shards — point -m at the first; llama.cpp loads the rest automatically
+llama-cli -m ./glm-5.2-ram-307gb/GLM-5.2-RAM-local-knee-00001-of-00007.gguf -p "Hello!" -n 256 -ngl 99
+```
+
+Or via `llama-server` for an OpenAI-compatible HTTP API:
+
+```bash
+llama-server -m ./glm-5.2-ram-307gb/GLM-5.2-RAM-local-knee-00001-of-00007.gguf --port 8080 -ngl 99 --ctx-size 8192
+```
+
+For fast inference use a host with ≥ ~310 GB RAM/VRAM; otherwise it runs via mmap paging. The chat template is embedded; GLM-5.2 supports a thinking mode. No importance matrix (imatrix) was used — per-tensor sensitivity allocation provides the primary quality signal.
+
+---
+*Quantized by [baa.ai](https://baa.ai)*
+
+---
+
+## Black Sheep AI Products
+
+**[Shepherd](https://baa.ai/shepherd.html)** — Private AI deployment platform that shrinks frontier models by 50-60% through RAM compression, enabling enterprises to run sophisticated AI on single GPU instances or Apple Silicon hardware. Deploy in your VPC with zero data leaving your infrastructure. Includes CI/CD pipeline integration, fleet deployment across Apple Silicon clusters, air-gapped and sovereign deployment support, and multi-format export (MLX, GGUF). Annual cloud costs from ~$2,700 — or run on a Mac Studio for electricity only.
+
+**[Watchman](https://baa.ai/watchman.html)** — Capability audit and governance platform for compressed AI models. Know exactly what your quantized model can do before it goes live. Watchman predicts which capabilities survive compression in minutes — replacing weeks of benchmarking. Includes compliance-ready reporting for regulated industries, quality valley warnings for counterproductive memory allocations, instant regression diagnosis tracing issues to specific tensors, and 22 adversarial security probes scanning for injection, leakage, hallucination, and code vulnerabilities.
+
+Learn more at **[baa.ai](https://baa.ai)** — Sovereign AI.