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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+axquant_plan.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,176 @@
+---
+license: apache-2.0
+library_name: mlx
+base_model: deepseek-ai/DeepSeek-V4-Flash
+base_model_relation: quantized
+pipeline_tag: text-generation
+tags:
+- mlx
+- apple-silicon
+- quantized
+- mixed-precision
+- axquant
+- axq
+- development
+- deepseek-v4
+- 4bit
+- 4-bit
+- mtp
+---
+
+# AX-DeepSeek-V4-Flash-MLX-AXQ-4bit
+
+An **AXQuant (AXQ)** mixed-precision MLX checkpoint for Apple Silicon, converted directly from
+the BF16 source model. The language path is quantized while the multi-token-prediction (MTP) head are preserved at BF16 in the checkpoint (or a bound sidecar when present).
+
+> **Development evidence — not a certified AXQuant release.** This package has conversion and
+> artifact-integrity records, but it does not publish measured quality, long-context, kernel-speed,
+> or MTP-speed evidence. Do not interpret the AXQ product label as a benchmark claim.
+
+
+## Model details
+
+| Property | Value |
+| --- | --- |
+| Base model | [deepseek-ai/DeepSeek-V4-Flash](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash/tree/60d8d70770c6776ff598c94bb586a859a38244f1) |
+| Source revision | `60d8d70770c6776ff598c94bb586a859a38244f1` |
+| Product family | `deepseek-v4` |
+| Source architecture | `DeepseekV4ForCausalLM` (mixture of experts (MoE)); text path optimized |
+| Main-model parameters | 284.33B logical parameters |
+| Quantizer | AXQuant `1.5.1` |
+| Hub budget class | `4bit` |
+| AXQuant base precision class | `4bit` |
+| Planned storage-adjusted BPW | 4.8500 |
+| Measured main-model BPW | 4.5929 |
+| Measured total BPW, including MTP | **4.5874** |
+| Safetensors weight size | 166.83 GB |
+| Approximate complete download | 166.91 GB |
+| Configured maximum context | 1,048,576 tokens; practical limits depend on unified memory |
+| Primary MLX runtime | MLX-LM |
+| AX Engine native execution | Not established; no validated native manifest is included |
+| MTP present | `True` |
+| Vision present | `False` |
+| Audio present | `False` |
+
+This repository contains MLX Safetensors. It does **not** contain PyTorch or GGUF weights.
+
+## Choosing an AXQ pack
+
+AXQ names describe a **storage-budget product class**, not one uniform precision applied to every
+tensor. Protected tensors remain at higher precision, so the exact measured BPW is authoritative.
+In particular, a `6bit`-named mixed plan may retain `4bit` as its base precision while selecting
+6-bit, 8-bit, or BF16 for other tensors to meet an approximately 6-BPW total budget. Protection
+floors can also raise a `4bit`-named pack close to (or above) a `6bit` budget on small or heavily
+protected models. When that collapse happens, AutomatosX does **not** publish a separate
+misleading `4bit` sibling for that base.
+
+
+| Sibling | Intended trade-off |
+| --- | --- |
+| [4bit sibling](https://huggingface.co/AutomatosX/AX-DeepSeek-V4-Flash-MLX-AXQ-4bit) | Lower-storage AXQ budget; check its exact BPW |
+| [6bit sibling](https://huggingface.co/AutomatosX/AX-DeepSeek-V4-Flash-MLX-AXQ-6bit) | Higher average precision near the 6-BPW budget |
+
+See the [AutomatosX MLX model catalog](https://huggingface.co/collections/AutomatosX/automatosx-mlx-model-catalog)
+for related MLX and OptiQ alternatives.
+
+## Download
+
+```bash
+python -m pip install -U huggingface_hub
+hf download AutomatosX/AX-DeepSeek-V4-Flash-MLX-AXQ-4bit --local-dir ./AX-DeepSeek-V4-Flash-MLX-AXQ-4bit
+```
+
+Allow at least 166.91 GB of free disk space. Pin the resulting Hub commit in reproducible
+deployments rather than relying indefinitely on `main`.
+
+## Run with MLX-LM
+
+```bash
+python -m pip install -U mlx-lm
+mlx_lm.generate \
+  --model AutomatosX/AX-DeepSeek-V4-Flash-MLX-AXQ-4bit \
+  --prompt "Explain mixed-precision quantization in three sentences." \
+  --max-tokens 128 \
+  --temp 0.0
+```
+
+MLX-LM compatibility covers standard **text/backbone inference**. It may ignore AXQuant runtime
+metadata and optional sidecars (`vision.safetensors`, `mtp.safetensors`); this command therefore
+does not establish MTP acceleration or vision-language quality. The artifact records MLX
+`0.32.0` and MLX-LM `0.31.3` from conversion.
+
+## AX Engine status
+
+This package does **not** include a validated native `model-manifest.json`, so AX Engine execution
+is not established by this release. The AX Engine fields in `axquant_runtime.json` describe the
+intended compatibility contract, not observed runtime evidence. Use the architecture-specific MLX
+runtime path above. The artifact records AX Engine version
+`6.11.1`, but version discovery alone is not a runtime check.
+
+## Quantization layout
+
+| Main-weight precision | Parameters | Share |
+| --- | ---: | ---: |
+| `4bit` | 281.75B | 96.84% |
+| `8bit` | 529.53M | 0.18% |
+| `bf16` | 8.67B | 2.98% |
+
+- Quantization methods: `affine, bf16`.
+- Group sizes used by quantized assignments: `32, 64`.
+- MTP sidecar: 1575 tensors, 6.61B parameters, 3.59 GB, BF16, F32, F8_E4M3, F8_E8M0, I8.
+- Vision sidecar: not included.
+- Optimization scope: `text-path`.
+- Support tier: `convertible`.
+
+BF16 sidecars, when present, are included in total download size. Their presence does not by itself
+establish MTP acceleration or vision-language quality.
+
+## Evidence and validation status
+
+| Check | Status |
+| --- | --- |
+| Planning evidence | `architecture_prior` |
+| Calibration | none; the allocation is based on architecture priors |
+| Quantizer execution | 33492/33492 recorded module conversions succeeded; 0 fallbacks |
+| AX Engine native manifest | not included |
+| Quality versus BF16 or uniform baselines | Not published; no quality-retention claim |
+| MTP acceptance and speed | not measured; no MTP speedup claim |
+| AX Engine kernel evidence | `unmeasured` |
+| Vision-language quality | Not applicable (no vision tower in this package) |
+| Speech-recognition quality | Not applicable |
+| Long-context quality | 1,048,576-token capacity is config metadata, not a validated claim |
+| Release certification | **Not certified**; formal AXQuant M0-M8 gates are not closed |
+
+## Intended use and limitations
+
+- Intended for local development and evaluation on Apple Silicon with MLX-compatible runtimes.
+- No minimum unified-memory figure is claimed; loadability depends on model size, context length,
+  KV-cache policy, runtime buffers, and other processes using unified memory.
+- Architecture-prior allocation is not measured sensitivity. It must not be presented as measured
+  model quality.
+- MTP may be ignored outside AX Engine and its speedup is unmeasured for this exact checkpoint.
+- The configured context window can require substantially more memory as the KV cache grows.
+- AX Engine execution is not established because this package has no validated native manifest.
+
+- Upstream capabilities, limitations, biases, and responsible-use guidance still apply.
+
+## Provenance and audit files
+
+- [`axquant_manifest.json`](axquant_manifest.json): package identity, byte accounting, runtime
+  contract, software versions, and file checksums.
+- [`axquant_plan.json`](axquant_plan.json): per-tensor precision decisions and planning evidence.
+- [`axquant_quantizer_execution.json`](axquant_quantizer_execution.json): conversion coverage and
+  fallback records.
+- [`axquant_runtime.json`](axquant_runtime.json): declared AX Engine and MLX compatibility metadata; runtime checks remain separate evidence.
+- [`axquant_mtp_sidecar_manifest.json`](axquant_mtp_sidecar_manifest.json): MTP tensor provenance.
+
+All published provenance uses repository-relative paths. Local source paths are stripped before
+publication. The checkpoint was converted from BF16 rather than re-quantized from an OptiQ
+artifact. If an OptiQ repository is published separately, it uses a different quantizer and
+should not be assumed to have identical BPW or quality.
+
+## License
+
+The checkpoint follows the upstream model license where applicable (often Apache License 2.0). See
+the [deepseek-ai/DeepSeek-V4-Flash model card](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash/tree/60d8d70770c6776ff598c94bb586a859a38244f1) for license terms, model
+limitations, and responsible-use guidance.

axquant_manifest.json

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+  "files": [
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