Upload NanoRecon inference assets

.gitattributes

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README.md

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+---
+license: other
+library_name: jax
+tags:
+- nanopore
+- signal-reconstruction
+- audio-codec
+- flax
+- jax
+pipeline_tag: feature-extraction
+---
+
+# NanoRecon
+
+NanoRecon is a Flax/JAX SimVQ-based model for nanopore signal reconstruction. This repository contains inference-ready model weights and the matching architecture config.
+
+## Files
+
+- `flax_model.msgpack`: Flax variables for inference only, containing `params` and mutable `vq` codebook variables.
+- `config.json`: model architecture and signal-shape metadata needed to instantiate the model.
+
+The uploaded package is limited to inference assets and does not include local machine paths or private dataset references.
+
+## Loading Example
+
+```python
+import json
+from pathlib import Path
+
+import jax
+import jax.numpy as jnp
+from flax.core import freeze
+from flax.serialization import from_bytes
+
+from codec.models import build_audio_model
+
+repo_dir = Path("/path/to/NanoRecon")
+cfg = json.loads((repo_dir / "config.json").read_text())
+model = build_audio_model(cfg["model"])
+
+variables = from_bytes(freeze({"params": {}, "vq": {}}), (repo_dir / "flax_model.msgpack").read_bytes())
+
+x = jnp.zeros((1, cfg["input_signal"]["segment_samples"]), dtype=jnp.float32)
+rng = jax.random.PRNGKey(0)
+out = model.apply(
+    variables,
+    x,
+    offset=0,
+    rng=rng,
+    collect_codebook_stats=False,
+)
+reconstructed = out["wave_hat"]
+```
+
+## Notes
+
+This model expects normalized nanopore signal chunks with shape `(batch, samples)`. The exported config records the expected chunk size and sample rate.
+
+## Citation
+
+No citation metadata has been provided yet.

config.json

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+{
+  "architectures": [
+    "SimVQAudioModel"
+  ],
+  "model_type": "nanorecon-simvq-audio-codec",
+  "framework": "flax",
+  "library_name": "jax",
+  "input_signal": {
+    "sample_rate_hz": 5000.0,
+    "segment_samples": 8192,
+    "segment_seconds": 1.6384,
+    "expected_shape": [
+      "batch",
+      "samples"
+    ],
+    "dtype": "float32"
+  },
+  "model": {
+    "variant": "foundation_v1",
+    "enc_channels": [
+      32,
+      64,
+      128,
+      256,
+      512
+    ],
+    "enc_down_strides": [
+      2,
+      2,
+      2,
+      2
+    ],
+    "enc_stage_num_res_blocks": [
+      2,
+      2,
+      3,
+      3
+    ],
+    "enc_kernel_size": 7,
+    "latent_dim": 512,
+    "quantizer_dim": 512,
+    "codebook_size": 65536,
+    "dec_channels": [
+      512
+    ],
+    "decoder_dim": 768,
+    "decoder_intermediate_dim": 2304,
+    "decoder_num_layers": 12,
+    "decoder_pos_net_enabled": true,
+    "decoder_pos_net_dropout": 0.0,
+    "decoder_pos_net_attention_heads": 12,
+    "decoder_pos_net_attention_backend": "jax_cudnn",
+    "istft_n_fft": 512,
+    "istft_hop_length": 16,
+    "istft_sample_rate": 5000,
+    "istft_band_edges_hz": [
+      200,
+      500,
+      1000
+    ],
+    "istft_band_gain_init": [
+      1.0,
+      0.8,
+      0.45,
+      0.12
+    ],
+    "istft_dynamic_gate_scale": 0.5,
+    "residual_correction": {
+      "enabled": true,
+      "alpha_init": 0.0,
+      "alpha_max": 0.1,
+      "hidden_dim": 192
+    },
+    "latent_bilstm_layers": 2,
+    "latent_bilstm_hidden_dim": 256,
+    "cnn_compute_dtype": "fp32",
+    "param_dtype": "fp32",
+    "diveq_sigma2": 0.001,
+    "search_chunk_size": 8192,
+    "quant_conv_kernel_size": 7,
+    "post_quant_conv_kernel_size": 7
+  },
+  "weights": {
+    "format": "flax_msgpack",
+    "file": "flax_model.msgpack",
+    "variables": [
+      "params",
+      "vq"
+    ]
+  }
+}

flax_model.msgpack

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