Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,5 @@ 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
+tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,41 @@
+---
+language:
+- en
+license: apache-2.0
+library_name: mlx-audio
+pipeline_tag: text-to-speech
+tags:
+- mlx
+- text-to-speech
+- speech
+- speech generation
+- voice cloning
+- tts
+---
+
+# mlx-community/maya1-4bit
+This model was converted to MLX format from [`maya-research/maya1`](https://huggingface.co/maya-research/maya1) using mlx-audio version **0.2.9**.
+Refer to the [original model card](https://huggingface.co/maya-research/maya1) for more details on the model.
+
+## Use with mlx-audio
+
+```bash
+pip install -U mlx-audio
+```
+
+### CLI Example:
+```bash
+    python -m mlx_audio.tts.generate --model mlx-community/maya1-4bit --text "Hello, this is a test."
+```
+### Python Example:
+```python
+    from mlx_audio.tts.utils import load_model
+    from mlx_audio.tts.generate import generate_audio
+    model = load_model("mlx-community/maya1-4bit")
+    generate_audio(
+        model=model, text="Hello, this is a test.",
+        ref_audio="path_to_audio.wav",
+        file_prefix="test_audio",
+    )
+
+```

config.json

@@ -0,0 +1,46 @@
+{
+    "architectures": [
+        "LlamaForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 128000,
+    "dtype": "bfloat16",
+    "eos_token_id": 128009,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 3072,
+    "initializer_range": 0.02,
+    "intermediate_size": 8192,
+    "max_position_embeddings": 131072,
+    "mlp_bias": false,
+    "model_type": "llama",
+    "num_attention_heads": 24,
+    "num_hidden_layers": 28,
+    "num_key_value_heads": 8,
+    "pad_token_id": 128263,
+    "pretraining_tp": 1,
+    "quantization": {
+        "group_size": 64,
+        "bits": 4,
+        "mode": "affine"
+    },
+    "quantization_config": {
+        "group_size": 64,
+        "bits": 4,
+        "mode": "affine"
+    },
+    "rms_norm_eps": 1e-05,
+    "rope_scaling": {
+        "factor": 32.0,
+        "high_freq_factor": 4.0,
+        "low_freq_factor": 1.0,
+        "original_max_position_embeddings": 8192,
+        "rope_type": "llama3"
+    },
+    "rope_theta": 500000.0,
+    "tie_word_embeddings": true,
+    "transformers_version": "4.57.1",
+    "use_cache": false,
+    "vocab_size": 156960
+}

emotions.txt

@@ -0,0 +1,17 @@
+<laugh>
+<laugh_harder>
+<sigh>
+<chuckle>
+<gasp>
+<angry>
+<excited>
+<whisper>
+<cry>
+<scream>
+<sing>
+<snort>
+<exhale>
+<gulp>
+<giggle>
+<sarcastic>
+<curious>

generation_config.json

@@ -0,0 +1,13 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128009,
+    128258
+  ],
+  "pad_token_id": 128263,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.57.1"
+}

model-00001-of-00002.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f1dae409f70c5beb92916662c6bc389b9b235ac8aa5edd19a4dcb87e37a73074
+size 4991160848

model-00002-of-00002.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:df22e9a90c1bea262250982640b119e6020474736991da482cb6ed56dd23d045
+size 1610725592

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3419d8c0e4c73d260480df16fc928cf7808442ac53ffd4fb18d120fddf5b14ae
+size 1857097241

model.safetensors.index.json

@@ -0,0 +1,656 @@
+{
+    "metadata": {
+        "total_size": 1857024000,
+        "total_parameters": 3300928512
+    },
+    "weight_map": {
+        "model.embed_tokens.biases": "model.safetensors",
+        "model.embed_tokens.scales": "model.safetensors",
+        "model.embed_tokens.weight": "model.safetensors",
+        "model.layers.0.input_layernorm.weight": "model.safetensors",
+        "model.layers.0.mlp.down_proj.biases": "model.safetensors",
+        "model.layers.0.mlp.down_proj.scales": "model.safetensors",
+        "model.layers.0.mlp.down_proj.weight": "model.safetensors",
+        "model.layers.0.mlp.gate_proj.biases": "model.safetensors",
+        "model.layers.0.mlp.gate_proj.scales": "model.safetensors",
+        "model.layers.0.mlp.gate_proj.weight": "model.safetensors",
+        "model.layers.0.mlp.up_proj.biases": "model.safetensors",
+        "model.layers.0.mlp.up_proj.scales": "model.safetensors",
+        "model.layers.0.mlp.up_proj.weight": "model.safetensors",
+        "model.layers.0.post_attention_layernorm.weight": "model.safetensors",
+        "model.layers.0.self_attn.k_proj.biases": "model.safetensors",
+        "model.layers.0.self_attn.k_proj.scales": "model.safetensors",
+        "model.layers.0.self_attn.k_proj.weight": "model.safetensors",
+        "model.layers.0.self_attn.o_proj.biases": "model.safetensors",
+        "model.layers.0.self_attn.o_proj.scales": "model.safetensors",
+        "model.layers.0.self_attn.o_proj.weight": "model.safetensors",
+        "model.layers.0.self_attn.q_proj.biases": "model.safetensors",
+        "model.layers.0.self_attn.q_proj.scales": "model.safetensors",
+        "model.layers.0.self_attn.q_proj.weight": "model.safetensors",
+        "model.layers.0.self_attn.v_proj.biases": "model.safetensors",
+        "model.layers.0.self_attn.v_proj.scales": "model.safetensors",
+        "model.layers.0.self_attn.v_proj.weight": "model.safetensors",
+        "model.layers.1.input_layernorm.weight": "model.safetensors",
+        "model.layers.1.mlp.down_proj.biases": "model.safetensors",
+        "model.layers.1.mlp.down_proj.scales": "model.safetensors",
+        "model.layers.1.mlp.down_proj.weight": "model.safetensors",
+        "model.layers.1.mlp.gate_proj.biases": "model.safetensors",
+        "model.layers.1.mlp.gate_proj.scales": "model.safetensors",
+        "model.layers.1.mlp.gate_proj.weight": "model.safetensors",
+        "model.layers.1.mlp.up_proj.biases": "model.safetensors",
+        "model.layers.1.mlp.up_proj.scales": "model.safetensors",
+        "model.layers.1.mlp.up_proj.weight": "model.safetensors",
+        "model.layers.1.post_attention_layernorm.weight": "model.safetensors",
+        "model.layers.1.self_attn.k_proj.biases": "model.safetensors",
+        "model.layers.1.self_attn.k_proj.scales": "model.safetensors",
+        "model.layers.1.self_attn.k_proj.weight": "model.safetensors",
+        "model.layers.1.self_attn.o_proj.biases": "model.safetensors",
+        "model.layers.1.self_attn.o_proj.scales": "model.safetensors",
+        "model.layers.1.self_attn.o_proj.weight": "model.safetensors",
+        "model.layers.1.self_attn.q_proj.biases": "model.safetensors",
+        "model.layers.1.self_attn.q_proj.scales": "model.safetensors",
+        "model.layers.1.self_attn.q_proj.weight": "model.safetensors",
+        "model.layers.1.self_attn.v_proj.biases": "model.safetensors",
+        "model.layers.1.self_attn.v_proj.scales": "model.safetensors",
+        "model.layers.1.self_attn.v_proj.weight": "model.safetensors",
+        "model.layers.10.input_layernorm.weight": "model.safetensors",
+        "model.layers.10.mlp.down_proj.biases": "model.safetensors",
+        "model.layers.10.mlp.down_proj.scales": "model.safetensors",
+        "model.layers.10.mlp.down_proj.weight": "model.safetensors",
+        "model.layers.10.mlp.gate_proj.biases": "model.safetensors",
+        "model.layers.10.mlp.gate_proj.scales": "model.safetensors",
+        "model.layers.10.mlp.gate_proj.weight": "model.safetensors",
+        "model.layers.10.mlp.up_proj.biases": "model.safetensors",
+        "model.layers.10.mlp.up_proj.scales": "model.safetensors",
+        "model.layers.10.mlp.up_proj.weight": "model.safetensors",
+        "model.layers.10.post_attention_layernorm.weight": "model.safetensors",
+        "model.layers.10.self_attn.k_proj.biases": "model.safetensors",
+        "model.layers.10.self_attn.k_proj.scales": "model.safetensors",
+        "model.layers.10.self_attn.k_proj.weight": "model.safetensors",
+        "model.layers.10.self_attn.o_proj.biases": "model.safetensors",
+        "model.layers.10.self_attn.o_proj.scales": "model.safetensors",
+        "model.layers.10.self_attn.o_proj.weight": "model.safetensors",
+        "model.layers.10.self_attn.q_proj.biases": "model.safetensors",
+        "model.layers.10.self_attn.q_proj.scales": "model.safetensors",
+        "model.layers.10.self_attn.q_proj.weight": "model.safetensors",
+        "model.layers.10.self_attn.v_proj.biases": "model.safetensors",
+        "model.layers.10.self_attn.v_proj.scales": "model.safetensors",
+        "model.layers.10.self_attn.v_proj.weight": "model.safetensors",
+        "model.layers.11.input_layernorm.weight": "model.safetensors",
+        "model.layers.11.mlp.down_proj.biases": "model.safetensors",
+        "model.layers.11.mlp.down_proj.scales": "model.safetensors",
+        "model.layers.11.mlp.down_proj.weight": "model.safetensors",
+        "model.layers.11.mlp.gate_proj.biases": "model.safetensors",
+        "model.layers.11.mlp.gate_proj.scales": "model.safetensors",
+        "model.layers.11.mlp.gate_proj.weight": "model.safetensors",
+        "model.layers.11.mlp.up_proj.biases": "model.safetensors",
+        "model.layers.11.mlp.up_proj.scales": "model.safetensors",
+        "model.layers.11.mlp.up_proj.weight": "model.safetensors",
+        "model.layers.11.post_attention_layernorm.weight": "model.safetensors",
+        "model.layers.11.self_attn.k_proj.biases": "model.safetensors",
+        "model.layers.11.self_attn.k_proj.scales": "model.safetensors",
+        "model.layers.11.self_attn.k_proj.weight": "model.safetensors",
+        "model.layers.11.self_attn.o_proj.biases": "model.safetensors",
+        "model.layers.11.self_attn.o_proj.scales": "model.safetensors",
+        "model.layers.11.self_attn.o_proj.weight": "model.safetensors",
+        "model.layers.11.self_attn.q_proj.biases": "model.safetensors",
+        "model.layers.11.self_attn.q_proj.scales": "model.safetensors",
+        "model.layers.11.self_attn.q_proj.weight": "model.safetensors",
+        "model.layers.11.self_attn.v_proj.biases": "model.safetensors",
+        "model.layers.11.self_attn.v_proj.scales": "model.safetensors",
+        "model.layers.11.self_attn.v_proj.weight": "model.safetensors",
+        "model.layers.12.input_layernorm.weight": "model.safetensors",
+        "model.layers.12.mlp.down_proj.biases": "model.safetensors",
+        "model.layers.12.mlp.down_proj.scales": "model.safetensors",
+        "model.layers.12.mlp.down_proj.weight": "model.safetensors",
+        "model.layers.12.mlp.gate_proj.biases": "model.safetensors",
+        "model.layers.12.mlp.gate_proj.scales": "model.safetensors",
+        "model.layers.12.mlp.gate_proj.weight": "model.safetensors",
+        "model.layers.12.mlp.up_proj.biases": "model.safetensors",
+        "model.layers.12.mlp.up_proj.scales": "model.safetensors",
+        "model.layers.12.mlp.up_proj.weight": "model.safetensors",
+        "model.layers.12.post_attention_layernorm.weight": "model.safetensors",
+        "model.layers.12.self_attn.k_proj.biases": "model.safetensors",
+        "model.layers.12.self_attn.k_proj.scales": "model.safetensors",
+        "model.layers.12.self_attn.k_proj.weight": "model.safetensors",
+        "model.layers.12.self_attn.o_proj.biases": "model.safetensors",
+        "model.layers.12.self_attn.o_proj.scales": "model.safetensors",
+        "model.layers.12.self_attn.o_proj.weight": "model.safetensors",
+        "model.layers.12.self_attn.q_proj.biases": "model.safetensors",
+        "model.layers.12.self_attn.q_proj.scales": "model.safetensors",
+        "model.layers.12.self_attn.q_proj.weight": "model.safetensors",
+        "model.layers.12.self_attn.v_proj.biases": "model.safetensors",
+        "model.layers.12.self_attn.v_proj.scales": "model.safetensors",
+        "model.layers.12.self_attn.v_proj.weight": "model.safetensors",
+        "model.layers.13.input_layernorm.weight": "model.safetensors",
+        "model.layers.13.mlp.down_proj.biases": "model.safetensors",
+        "model.layers.13.mlp.down_proj.scales": "model.safetensors",
+        "model.layers.13.mlp.down_proj.weight": "model.safetensors",
+        "model.layers.13.mlp.gate_proj.biases": "model.safetensors",
+        "model.layers.13.mlp.gate_proj.scales": "model.safetensors",
+        "model.layers.13.mlp.gate_proj.weight": "model.safetensors",
+        "model.layers.13.mlp.up_proj.biases": "model.safetensors",
+        "model.layers.13.mlp.up_proj.scales": "model.safetensors",
+        "model.layers.13.mlp.up_proj.weight": "model.safetensors",
+        "model.layers.13.post_attention_layernorm.weight": "model.safetensors",
+        "model.layers.13.self_attn.k_proj.biases": "model.safetensors",
+        "model.layers.13.self_attn.k_proj.scales": "model.safetensors",
+        "model.layers.13.self_attn.k_proj.weight": "model.safetensors",
+        "model.layers.13.self_attn.o_proj.biases": "model.safetensors",
+        "model.layers.13.self_attn.o_proj.scales": "model.safetensors",
+        "model.layers.13.self_attn.o_proj.weight": "model.safetensors",
+        "model.layers.13.self_attn.q_proj.biases": "model.safetensors",
+        "model.layers.13.self_attn.q_proj.scales": "model.safetensors",
+        "model.layers.13.self_attn.q_proj.weight": "model.safetensors",
+        "model.layers.13.self_attn.v_proj.biases": "model.safetensors",
+        "model.layers.13.self_attn.v_proj.scales": "model.safetensors",
+        "model.layers.13.self_attn.v_proj.weight": "model.safetensors",
+        "model.layers.14.input_layernorm.weight": "model.safetensors",
+        "model.layers.14.mlp.down_proj.biases": "model.safetensors",
+        "model.layers.14.mlp.down_proj.scales": "model.safetensors",
+        "model.layers.14.mlp.down_proj.weight": "model.safetensors",
+        "model.layers.14.mlp.gate_proj.biases": "model.safetensors",
+        "model.layers.14.mlp.gate_proj.scales": "model.safetensors",
+        "model.layers.14.mlp.gate_proj.weight": "model.safetensors",
+        "model.layers.14.mlp.up_proj.biases": "model.safetensors",
+        "model.layers.14.mlp.up_proj.scales": "model.safetensors",
+        "model.layers.14.mlp.up_proj.weight": "model.safetensors",
+        "model.layers.14.post_attention_layernorm.weight": "model.safetensors",
+        "model.layers.14.self_attn.k_proj.biases": "model.safetensors",
+        "model.layers.14.self_attn.k_proj.scales": "model.safetensors",
+        "model.layers.14.self_attn.k_proj.weight": "model.safetensors",
+        "model.layers.14.self_attn.o_proj.biases": "model.safetensors",
+        "model.layers.14.self_attn.o_proj.scales": "model.safetensors",
+        "model.layers.14.self_attn.o_proj.weight": "model.safetensors",
+        "model.layers.14.self_attn.q_proj.biases": "model.safetensors",
+        "model.layers.14.self_attn.q_proj.scales": "model.safetensors",
+        "model.layers.14.self_attn.q_proj.weight": "model.safetensors",
+        "model.layers.14.self_attn.v_proj.biases": "model.safetensors",
+        "model.layers.14.self_attn.v_proj.scales": "model.safetensors",
+        "model.layers.14.self_attn.v_proj.weight": "model.safetensors",
+        "model.layers.15.input_layernorm.weight": "model.safetensors",
+        "model.layers.15.mlp.down_proj.biases": "model.safetensors",
+        "model.layers.15.mlp.down_proj.scales": "model.safetensors",
+        "model.layers.15.mlp.down_proj.weight": "model.safetensors",
+        "model.layers.15.mlp.gate_proj.biases": "model.safetensors",
+        "model.layers.15.mlp.gate_proj.scales": "model.safetensors",
+        "model.layers.15.mlp.gate_proj.weight": "model.safetensors",
+        "model.layers.15.mlp.up_proj.biases": "model.safetensors",
+        "model.layers.15.mlp.up_proj.scales": "model.safetensors",
+        "model.layers.15.mlp.up_proj.weight": "model.safetensors",
+        "model.layers.15.post_attention_layernorm.weight": "model.safetensors",
+        "model.layers.15.self_attn.k_proj.biases": "model.safetensors",
+        "model.layers.15.self_attn.k_proj.scales": "model.safetensors",
+        "model.layers.15.self_attn.k_proj.weight": "model.safetensors",
+        "model.layers.15.self_attn.o_proj.biases": "model.safetensors",
+        "model.layers.15.self_attn.o_proj.scales": "model.safetensors",
+        "model.layers.15.self_attn.o_proj.weight": "model.safetensors",
+        "model.layers.15.self_attn.q_proj.biases": "model.safetensors",
+        "model.layers.15.self_attn.q_proj.scales": "model.safetensors",
+        "model.layers.15.self_attn.q_proj.weight": "model.safetensors",
+        "model.layers.15.self_attn.v_proj.biases": "model.safetensors",
+        "model.layers.15.self_attn.v_proj.scales": "model.safetensors",
+        "model.layers.15.self_attn.v_proj.weight": "model.safetensors",
+        "model.layers.16.input_layernorm.weight": "model.safetensors",
+        "model.layers.16.mlp.down_proj.biases": "model.safetensors",
+        "model.layers.16.mlp.down_proj.scales": "model.safetensors",
+        "model.layers.16.mlp.down_proj.weight": "model.safetensors",
+        "model.layers.16.mlp.gate_proj.biases": "model.safetensors",
+        "model.layers.16.mlp.gate_proj.scales": "model.safetensors",
+        "model.layers.16.mlp.gate_proj.weight": "model.safetensors",
+        "model.layers.16.mlp.up_proj.biases": "model.safetensors",
+        "model.layers.16.mlp.up_proj.scales": "model.safetensors",
+        "model.layers.16.mlp.up_proj.weight": "model.safetensors",
+        "model.layers.16.post_attention_layernorm.weight": "model.safetensors",
+        "model.layers.16.self_attn.k_proj.biases": "model.safetensors",
+        "model.layers.16.self_attn.k_proj.scales": "model.safetensors",
+        "model.layers.16.self_attn.k_proj.weight": "model.safetensors",
+        "model.layers.16.self_attn.o_proj.biases": "model.safetensors",
+        "model.layers.16.self_attn.o_proj.scales": "model.safetensors",
+        "model.layers.16.self_attn.o_proj.weight": "model.safetensors",
+        "model.layers.16.self_attn.q_proj.biases": "model.safetensors",
+        "model.layers.16.self_attn.q_proj.scales": "model.safetensors",
+        "model.layers.16.self_attn.q_proj.weight": "model.safetensors",
+        "model.layers.16.self_attn.v_proj.biases": "model.safetensors",
+        "model.layers.16.self_attn.v_proj.scales": "model.safetensors",
+        "model.layers.16.self_attn.v_proj.weight": "model.safetensors",
+        "model.layers.17.input_layernorm.weight": "model.safetensors",
+        "model.layers.17.mlp.down_proj.biases": "model.safetensors",
+        "model.layers.17.mlp.down_proj.scales": "model.safetensors",
+        "model.layers.17.mlp.down_proj.weight": "model.safetensors",
+        "model.layers.17.mlp.gate_proj.biases": "model.safetensors",
+        "model.layers.17.mlp.gate_proj.scales": "model.safetensors",
+        "model.layers.17.mlp.gate_proj.weight": "model.safetensors",
+        "model.layers.17.mlp.up_proj.biases": "model.safetensors",
+        "model.layers.17.mlp.up_proj.scales": "model.safetensors",
+        "model.layers.17.mlp.up_proj.weight": "model.safetensors",
+        "model.layers.17.post_attention_layernorm.weight": "model.safetensors",
+        "model.layers.17.self_attn.k_proj.biases": "model.safetensors",
+        "model.layers.17.self_attn.k_proj.scales": "model.safetensors",
+        "model.layers.17.self_attn.k_proj.weight": "model.safetensors",
+        "model.layers.17.self_attn.o_proj.biases": "model.safetensors",
+        "model.layers.17.self_attn.o_proj.scales": "model.safetensors",
+        "model.layers.17.self_attn.o_proj.weight": "model.safetensors",
+        "model.layers.17.self_attn.q_proj.biases": "model.safetensors",
+        "model.layers.17.self_attn.q_proj.scales": "model.safetensors",
+        "model.layers.17.self_attn.q_proj.weight": "model.safetensors",
+        "model.layers.17.self_attn.v_proj.biases": "model.safetensors",
+        "model.layers.17.self_attn.v_proj.scales": "model.safetensors",
+        "model.layers.17.self_attn.v_proj.weight": "model.safetensors",
+        "model.layers.18.input_layernorm.weight": "model.safetensors",
+        "model.layers.18.mlp.down_proj.biases": "model.safetensors",
+        "model.layers.18.mlp.down_proj.scales": "model.safetensors",
+        "model.layers.18.mlp.down_proj.weight": "model.safetensors",
+        "model.layers.18.mlp.gate_proj.biases": "model.safetensors",
+        "model.layers.18.mlp.gate_proj.scales": "model.safetensors",
+        "model.layers.18.mlp.gate_proj.weight": "model.safetensors",
+        "model.layers.18.mlp.up_proj.biases": "model.safetensors",
+        "model.layers.18.mlp.up_proj.scales": "model.safetensors",
+        "model.layers.18.mlp.up_proj.weight": "model.safetensors",
+        "model.layers.18.post_attention_layernorm.weight": "model.safetensors",
+        "model.layers.18.self_attn.k_proj.biases": "model.safetensors",
+        "model.layers.18.self_attn.k_proj.scales": "model.safetensors",
+        "model.layers.18.self_attn.k_proj.weight": "model.safetensors",
+        "model.layers.18.self_attn.o_proj.biases": "model.safetensors",
+        "model.layers.18.self_attn.o_proj.scales": "model.safetensors",
+        "model.layers.18.self_attn.o_proj.weight": "model.safetensors",
+        "model.layers.18.self_attn.q_proj.biases": "model.safetensors",
+        "model.layers.18.self_attn.q_proj.scales": "model.safetensors",
+        "model.layers.18.self_attn.q_proj.weight": "model.safetensors",
+        "model.layers.18.self_attn.v_proj.biases": "model.safetensors",
+        "model.layers.18.self_attn.v_proj.scales": "model.safetensors",
+        "model.layers.18.self_attn.v_proj.weight": "model.safetensors",
+        "model.layers.19.input_layernorm.weight": "model.safetensors",
+        "model.layers.19.mlp.down_proj.biases": "model.safetensors",
+        "model.layers.19.mlp.down_proj.scales": "model.safetensors",
+        "model.layers.19.mlp.down_proj.weight": "model.safetensors",
+        "model.layers.19.mlp.gate_proj.biases": "model.safetensors",
+        "model.layers.19.mlp.gate_proj.scales": "model.safetensors",
+        "model.layers.19.mlp.gate_proj.weight": "model.safetensors",
+        "model.layers.19.mlp.up_proj.biases": "model.safetensors",
+        "model.layers.19.mlp.up_proj.scales": "model.safetensors",
+        "model.layers.19.mlp.up_proj.weight": "model.safetensors",
+        "model.layers.19.post_attention_layernorm.weight": "model.safetensors",
+        "model.layers.19.self_attn.k_proj.biases": "model.safetensors",
+        "model.layers.19.self_attn.k_proj.scales": "model.safetensors",
+        "model.layers.19.self_attn.k_proj.weight": "model.safetensors",
+        "model.layers.19.self_attn.o_proj.biases": "model.safetensors",
+        "model.layers.19.self_attn.o_proj.scales": "model.safetensors",
+        "model.layers.19.self_attn.o_proj.weight": "model.safetensors",
+        "model.layers.19.self_attn.q_proj.biases": "model.safetensors",
+        "model.layers.19.self_attn.q_proj.scales": "model.safetensors",
+        "model.layers.19.self_attn.q_proj.weight": "model.safetensors",
+        "model.layers.19.self_attn.v_proj.biases": "model.safetensors",
+        "model.layers.19.self_attn.v_proj.scales": "model.safetensors",
+        "model.layers.19.self_attn.v_proj.weight": "model.safetensors",
+        "model.layers.2.input_layernorm.weight": "model.safetensors",
+        "model.layers.2.mlp.down_proj.biases": "model.safetensors",
+        "model.layers.2.mlp.down_proj.scales": "model.safetensors",
+        "model.layers.2.mlp.down_proj.weight": "model.safetensors",
+        "model.layers.2.mlp.gate_proj.biases": "model.safetensors",
+        "model.layers.2.mlp.gate_proj.scales": "model.safetensors",
+        "model.layers.2.mlp.gate_proj.weight": "model.safetensors",
+        "model.layers.2.mlp.up_proj.biases": "model.safetensors",
+        "model.layers.2.mlp.up_proj.scales": "model.safetensors",
+        "model.layers.2.mlp.up_proj.weight": "model.safetensors",
+        "model.layers.2.post_attention_layernorm.weight": "model.safetensors",
+        "model.layers.2.self_attn.k_proj.biases": "model.safetensors",
+        "model.layers.2.self_attn.k_proj.scales": "model.safetensors",
+        "model.layers.2.self_attn.k_proj.weight": "model.safetensors",
+        "model.layers.2.self_attn.o_proj.biases": "model.safetensors",
+        "model.layers.2.self_attn.o_proj.scales": "model.safetensors",
+        "model.layers.2.self_attn.o_proj.weight": "model.safetensors",
+        "model.layers.2.self_attn.q_proj.biases": "model.safetensors",
+        "model.layers.2.self_attn.q_proj.scales": "model.safetensors",
+        "model.layers.2.self_attn.q_proj.weight": "model.safetensors",
+        "model.layers.2.self_attn.v_proj.biases": "model.safetensors",
+        "model.layers.2.self_attn.v_proj.scales": "model.safetensors",
+        "model.layers.2.self_attn.v_proj.weight": "model.safetensors",
+        "model.layers.20.input_layernorm.weight": "model.safetensors",
+        "model.layers.20.mlp.down_proj.biases": "model.safetensors",
+        "model.layers.20.mlp.down_proj.scales": "model.safetensors",
+        "model.layers.20.mlp.down_proj.weight": "model.safetensors",
+        "model.layers.20.mlp.gate_proj.biases": "model.safetensors",
+        "model.layers.20.mlp.gate_proj.scales": "model.safetensors",
+        "model.layers.20.mlp.gate_proj.weight": "model.safetensors",
+        "model.layers.20.mlp.up_proj.biases": "model.safetensors",
+        "model.layers.20.mlp.up_proj.scales": "model.safetensors",
+        "model.layers.20.mlp.up_proj.weight": "model.safetensors",
+        "model.layers.20.post_attention_layernorm.weight": "model.safetensors",
+        "model.layers.20.self_attn.k_proj.biases": "model.safetensors",
+        "model.layers.20.self_attn.k_proj.scales": "model.safetensors",
+        "model.layers.20.self_attn.k_proj.weight": "model.safetensors",
+        "model.layers.20.self_attn.o_proj.biases": "model.safetensors",
+        "model.layers.20.self_attn.o_proj.scales": "model.safetensors",
+        "model.layers.20.self_attn.o_proj.weight": "model.safetensors",
+        "model.layers.20.self_attn.q_proj.biases": "model.safetensors",
+        "model.layers.20.self_attn.q_proj.scales": "model.safetensors",
+        "model.layers.20.self_attn.q_proj.weight": "model.safetensors",
+        "model.layers.20.self_attn.v_proj.biases": "model.safetensors",
+        "model.layers.20.self_attn.v_proj.scales": "model.safetensors",
+        "model.layers.20.self_attn.v_proj.weight": "model.safetensors",
+        "model.layers.21.input_layernorm.weight": "model.safetensors",
+        "model.layers.21.mlp.down_proj.biases": "model.safetensors",
+        "model.layers.21.mlp.down_proj.scales": "model.safetensors",
+        "model.layers.21.mlp.down_proj.weight": "model.safetensors",
+        "model.layers.21.mlp.gate_proj.biases": "model.safetensors",
+        "model.layers.21.mlp.gate_proj.scales": "model.safetensors",
+        "model.layers.21.mlp.gate_proj.weight": "model.safetensors",
+        "model.layers.21.mlp.up_proj.biases": "model.safetensors",
+        "model.layers.21.mlp.up_proj.scales": "model.safetensors",
+        "model.layers.21.mlp.up_proj.weight": "model.safetensors",
+        "model.layers.21.post_attention_layernorm.weight": "model.safetensors",
+        "model.layers.21.self_attn.k_proj.biases": "model.safetensors",
+        "model.layers.21.self_attn.k_proj.scales": "model.safetensors",
+        "model.layers.21.self_attn.k_proj.weight": "model.safetensors",
+        "model.layers.21.self_attn.o_proj.biases": "model.safetensors",
+        "model.layers.21.self_attn.o_proj.scales": "model.safetensors",
+        "model.layers.21.self_attn.o_proj.weight": "model.safetensors",
+        "model.layers.21.self_attn.q_proj.biases": "model.safetensors",
+        "model.layers.21.self_attn.q_proj.scales": "model.safetensors",
+        "model.layers.21.self_attn.q_proj.weight": "model.safetensors",
+        "model.layers.21.self_attn.v_proj.biases": "model.safetensors",
+        "model.layers.21.self_attn.v_proj.scales": "model.safetensors",
+        "model.layers.21.self_attn.v_proj.weight": "model.safetensors",
+        "model.layers.22.input_layernorm.weight": "model.safetensors",
+        "model.layers.22.mlp.down_proj.biases": "model.safetensors",
+        "model.layers.22.mlp.down_proj.scales": "model.safetensors",
+        "model.layers.22.mlp.down_proj.weight": "model.safetensors",
+        "model.layers.22.mlp.gate_proj.biases": "model.safetensors",
+        "model.layers.22.mlp.gate_proj.scales": "model.safetensors",
+        "model.layers.22.mlp.gate_proj.weight": "model.safetensors",
+        "model.layers.22.mlp.up_proj.biases": "model.safetensors",
+        "model.layers.22.mlp.up_proj.scales": "model.safetensors",
+        "model.layers.22.mlp.up_proj.weight": "model.safetensors",
+        "model.layers.22.post_attention_layernorm.weight": "model.safetensors",
+        "model.layers.22.self_attn.k_proj.biases": "model.safetensors",
+        "model.layers.22.self_attn.k_proj.scales": "model.safetensors",
+        "model.layers.22.self_attn.k_proj.weight": "model.safetensors",
+        "model.layers.22.self_attn.o_proj.biases": "model.safetensors",
+        "model.layers.22.self_attn.o_proj.scales": "model.safetensors",
+        "model.layers.22.self_attn.o_proj.weight": "model.safetensors",
+        "model.layers.22.self_attn.q_proj.biases": "model.safetensors",
+        "model.layers.22.self_attn.q_proj.scales": "model.safetensors",
+        "model.layers.22.self_attn.q_proj.weight": "model.safetensors",
+        "model.layers.22.self_attn.v_proj.biases": "model.safetensors",
+        "model.layers.22.self_attn.v_proj.scales": "model.safetensors",
+        "model.layers.22.self_attn.v_proj.weight": "model.safetensors",
+        "model.layers.23.input_layernorm.weight": "model.safetensors",
+        "model.layers.23.mlp.down_proj.biases": "model.safetensors",
+        "model.layers.23.mlp.down_proj.scales": "model.safetensors",
+        "model.layers.23.mlp.down_proj.weight": "model.safetensors",
+        "model.layers.23.mlp.gate_proj.biases": "model.safetensors",
+        "model.layers.23.mlp.gate_proj.scales": "model.safetensors",
+        "model.layers.23.mlp.gate_proj.weight": "model.safetensors",
+        "model.layers.23.mlp.up_proj.biases": "model.safetensors",
+        "model.layers.23.mlp.up_proj.scales": "model.safetensors",
+        "model.layers.23.mlp.up_proj.weight": "model.safetensors",
+        "model.layers.23.post_attention_layernorm.weight": "model.safetensors",
+        "model.layers.23.self_attn.k_proj.biases": "model.safetensors",
+        "model.layers.23.self_attn.k_proj.scales": "model.safetensors",
+        "model.layers.23.self_attn.k_proj.weight": "model.safetensors",
+        "model.layers.23.self_attn.o_proj.biases": "model.safetensors",
+        "model.layers.23.self_attn.o_proj.scales": "model.safetensors",
+        "model.layers.23.self_attn.o_proj.weight": "model.safetensors",
+        "model.layers.23.self_attn.q_proj.biases": "model.safetensors",
+        "model.layers.23.self_attn.q_proj.scales": "model.safetensors",
+        "model.layers.23.self_attn.q_proj.weight": "model.safetensors",
+        "model.layers.23.self_attn.v_proj.biases": "model.safetensors",
+        "model.layers.23.self_attn.v_proj.scales": "model.safetensors",
+        "model.layers.23.self_attn.v_proj.weight": "model.safetensors",
+        "model.layers.24.input_layernorm.weight": "model.safetensors",
+        "model.layers.24.mlp.down_proj.biases": "model.safetensors",
+        "model.layers.24.mlp.down_proj.scales": "model.safetensors",
+        "model.layers.24.mlp.down_proj.weight": "model.safetensors",
+        "model.layers.24.mlp.gate_proj.biases": "model.safetensors",
+        "model.layers.24.mlp.gate_proj.scales": "model.safetensors",
+        "model.layers.24.mlp.gate_proj.weight": "model.safetensors",
+        "model.layers.24.mlp.up_proj.biases": "model.safetensors",
+        "model.layers.24.mlp.up_proj.scales": "model.safetensors",
+        "model.layers.24.mlp.up_proj.weight": "model.safetensors",
+        "model.layers.24.post_attention_layernorm.weight": "model.safetensors",
+        "model.layers.24.self_attn.k_proj.biases": "model.safetensors",
+        "model.layers.24.self_attn.k_proj.scales": "model.safetensors",
+        "model.layers.24.self_attn.k_proj.weight": "model.safetensors",
+        "model.layers.24.self_attn.o_proj.biases": "model.safetensors",
+        "model.layers.24.self_attn.o_proj.scales": "model.safetensors",
+        "model.layers.24.self_attn.o_proj.weight": "model.safetensors",
+        "model.layers.24.self_attn.q_proj.biases": "model.safetensors",
+        "model.layers.24.self_attn.q_proj.scales": "model.safetensors",
+        "model.layers.24.self_attn.q_proj.weight": "model.safetensors",
+        "model.layers.24.self_attn.v_proj.biases": "model.safetensors",
+        "model.layers.24.self_attn.v_proj.scales": "model.safetensors",
+        "model.layers.24.self_attn.v_proj.weight": "model.safetensors",
+        "model.layers.25.input_layernorm.weight": "model.safetensors",
+        "model.layers.25.mlp.down_proj.biases": "model.safetensors",
+        "model.layers.25.mlp.down_proj.scales": "model.safetensors",
+        "model.layers.25.mlp.down_proj.weight": "model.safetensors",
+        "model.layers.25.mlp.gate_proj.biases": "model.safetensors",
+        "model.layers.25.mlp.gate_proj.scales": "model.safetensors",
+        "model.layers.25.mlp.gate_proj.weight": "model.safetensors",
+        "model.layers.25.mlp.up_proj.biases": "model.safetensors",
+        "model.layers.25.mlp.up_proj.scales": "model.safetensors",
+        "model.layers.25.mlp.up_proj.weight": "model.safetensors",
+        "model.layers.25.post_attention_layernorm.weight": "model.safetensors",
+        "model.layers.25.self_attn.k_proj.biases": "model.safetensors",
+        "model.layers.25.self_attn.k_proj.scales": "model.safetensors",
+        "model.layers.25.self_attn.k_proj.weight": "model.safetensors",
+        "model.layers.25.self_attn.o_proj.biases": "model.safetensors",
+        "model.layers.25.self_attn.o_proj.scales": "model.safetensors",
+        "model.layers.25.self_attn.o_proj.weight": "model.safetensors",
+        "model.layers.25.self_attn.q_proj.biases": "model.safetensors",
+        "model.layers.25.self_attn.q_proj.scales": "model.safetensors",
+        "model.layers.25.self_attn.q_proj.weight": "model.safetensors",
+        "model.layers.25.self_attn.v_proj.biases": "model.safetensors",
+        "model.layers.25.self_attn.v_proj.scales": "model.safetensors",
+        "model.layers.25.self_attn.v_proj.weight": "model.safetensors",
+        "model.layers.26.input_layernorm.weight": "model.safetensors",
+        "model.layers.26.mlp.down_proj.biases": "model.safetensors",
+        "model.layers.26.mlp.down_proj.scales": "model.safetensors",
+        "model.layers.26.mlp.down_proj.weight": "model.safetensors",
+        "model.layers.26.mlp.gate_proj.biases": "model.safetensors",
+        "model.layers.26.mlp.gate_proj.scales": "model.safetensors",
+        "model.layers.26.mlp.gate_proj.weight": "model.safetensors",
+        "model.layers.26.mlp.up_proj.biases": "model.safetensors",
+        "model.layers.26.mlp.up_proj.scales": "model.safetensors",
+        "model.layers.26.mlp.up_proj.weight": "model.safetensors",
+        "model.layers.26.post_attention_layernorm.weight": "model.safetensors",
+        "model.layers.26.self_attn.k_proj.biases": "model.safetensors",
+        "model.layers.26.self_attn.k_proj.scales": "model.safetensors",
+        "model.layers.26.self_attn.k_proj.weight": "model.safetensors",
+        "model.layers.26.self_attn.o_proj.biases": "model.safetensors",
+        "model.layers.26.self_attn.o_proj.scales": "model.safetensors",
+        "model.layers.26.self_attn.o_proj.weight": "model.safetensors",
+        "model.layers.26.self_attn.q_proj.biases": "model.safetensors",
+        "model.layers.26.self_attn.q_proj.scales": "model.safetensors",
+        "model.layers.26.self_attn.q_proj.weight": "model.safetensors",
+        "model.layers.26.self_attn.v_proj.biases": "model.safetensors",
+        "model.layers.26.self_attn.v_proj.scales": "model.safetensors",
+        "model.layers.26.self_attn.v_proj.weight": "model.safetensors",
+        "model.layers.27.input_layernorm.weight": "model.safetensors",
+        "model.layers.27.mlp.down_proj.biases": "model.safetensors",
+        "model.layers.27.mlp.down_proj.scales": "model.safetensors",
+        "model.layers.27.mlp.down_proj.weight": "model.safetensors",
+        "model.layers.27.mlp.gate_proj.biases": "model.safetensors",
+        "model.layers.27.mlp.gate_proj.scales": "model.safetensors",
+        "model.layers.27.mlp.gate_proj.weight": "model.safetensors",
+        "model.layers.27.mlp.up_proj.biases": "model.safetensors",
+        "model.layers.27.mlp.up_proj.scales": "model.safetensors",
+        "model.layers.27.mlp.up_proj.weight": "model.safetensors",
+        "model.layers.27.post_attention_layernorm.weight": "model.safetensors",
+        "model.layers.27.self_attn.k_proj.biases": "model.safetensors",
+        "model.layers.27.self_attn.k_proj.scales": "model.safetensors",
+        "model.layers.27.self_attn.k_proj.weight": "model.safetensors",
+        "model.layers.27.self_attn.o_proj.biases": "model.safetensors",
+        "model.layers.27.self_attn.o_proj.scales": "model.safetensors",
+        "model.layers.27.self_attn.o_proj.weight": "model.safetensors",
+        "model.layers.27.self_attn.q_proj.biases": "model.safetensors",
+        "model.layers.27.self_attn.q_proj.scales": "model.safetensors",
+        "model.layers.27.self_attn.q_proj.weight": "model.safetensors",
+        "model.layers.27.self_attn.v_proj.biases": "model.safetensors",
+        "model.layers.27.self_attn.v_proj.scales": "model.safetensors",
+        "model.layers.27.self_attn.v_proj.weight": "model.safetensors",
+        "model.layers.3.input_layernorm.weight": "model.safetensors",
+        "model.layers.3.mlp.down_proj.biases": "model.safetensors",
+        "model.layers.3.mlp.down_proj.scales": "model.safetensors",
+        "model.layers.3.mlp.down_proj.weight": "model.safetensors",
+        "model.layers.3.mlp.gate_proj.biases": "model.safetensors",
+        "model.layers.3.mlp.gate_proj.scales": "model.safetensors",
+        "model.layers.3.mlp.gate_proj.weight": "model.safetensors",
+        "model.layers.3.mlp.up_proj.biases": "model.safetensors",
+        "model.layers.3.mlp.up_proj.scales": "model.safetensors",
+        "model.layers.3.mlp.up_proj.weight": "model.safetensors",
+        "model.layers.3.post_attention_layernorm.weight": "model.safetensors",
+        "model.layers.3.self_attn.k_proj.biases": "model.safetensors",
+        "model.layers.3.self_attn.k_proj.scales": "model.safetensors",
+        "model.layers.3.self_attn.k_proj.weight": "model.safetensors",
+        "model.layers.3.self_attn.o_proj.biases": "model.safetensors",
+        "model.layers.3.self_attn.o_proj.scales": "model.safetensors",
+        "model.layers.3.self_attn.o_proj.weight": "model.safetensors",
+        "model.layers.3.self_attn.q_proj.biases": "model.safetensors",
+        "model.layers.3.self_attn.q_proj.scales": "model.safetensors",
+        "model.layers.3.self_attn.q_proj.weight": "model.safetensors",
+        "model.layers.3.self_attn.v_proj.biases": "model.safetensors",
+        "model.layers.3.self_attn.v_proj.scales": "model.safetensors",
+        "model.layers.3.self_attn.v_proj.weight": "model.safetensors",
+        "model.layers.4.input_layernorm.weight": "model.safetensors",
+        "model.layers.4.mlp.down_proj.biases": "model.safetensors",
+        "model.layers.4.mlp.down_proj.scales": "model.safetensors",
+        "model.layers.4.mlp.down_proj.weight": "model.safetensors",
+        "model.layers.4.mlp.gate_proj.biases": "model.safetensors",
+        "model.layers.4.mlp.gate_proj.scales": "model.safetensors",
+        "model.layers.4.mlp.gate_proj.weight": "model.safetensors",
+        "model.layers.4.mlp.up_proj.biases": "model.safetensors",
+        "model.layers.4.mlp.up_proj.scales": "model.safetensors",
+        "model.layers.4.mlp.up_proj.weight": "model.safetensors",
+        "model.layers.4.post_attention_layernorm.weight": "model.safetensors",
+        "model.layers.4.self_attn.k_proj.biases": "model.safetensors",
+        "model.layers.4.self_attn.k_proj.scales": "model.safetensors",
+        "model.layers.4.self_attn.k_proj.weight": "model.safetensors",
+        "model.layers.4.self_attn.o_proj.biases": "model.safetensors",
+        "model.layers.4.self_attn.o_proj.scales": "model.safetensors",
+        "model.layers.4.self_attn.o_proj.weight": "model.safetensors",
+        "model.layers.4.self_attn.q_proj.biases": "model.safetensors",
+        "model.layers.4.self_attn.q_proj.scales": "model.safetensors",
+        "model.layers.4.self_attn.q_proj.weight": "model.safetensors",
+        "model.layers.4.self_attn.v_proj.biases": "model.safetensors",
+        "model.layers.4.self_attn.v_proj.scales": "model.safetensors",
+        "model.layers.4.self_attn.v_proj.weight": "model.safetensors",
+        "model.layers.5.input_layernorm.weight": "model.safetensors",
+        "model.layers.5.mlp.down_proj.biases": "model.safetensors",
+        "model.layers.5.mlp.down_proj.scales": "model.safetensors",
+        "model.layers.5.mlp.down_proj.weight": "model.safetensors",
+        "model.layers.5.mlp.gate_proj.biases": "model.safetensors",
+        "model.layers.5.mlp.gate_proj.scales": "model.safetensors",
+        "model.layers.5.mlp.gate_proj.weight": "model.safetensors",
+        "model.layers.5.mlp.up_proj.biases": "model.safetensors",
+        "model.layers.5.mlp.up_proj.scales": "model.safetensors",
+        "model.layers.5.mlp.up_proj.weight": "model.safetensors",
+        "model.layers.5.post_attention_layernorm.weight": "model.safetensors",
+        "model.layers.5.self_attn.k_proj.biases": "model.safetensors",
+        "model.layers.5.self_attn.k_proj.scales": "model.safetensors",
+        "model.layers.5.self_attn.k_proj.weight": "model.safetensors",
+        "model.layers.5.self_attn.o_proj.biases": "model.safetensors",
+        "model.layers.5.self_attn.o_proj.scales": "model.safetensors",
+        "model.layers.5.self_attn.o_proj.weight": "model.safetensors",
+        "model.layers.5.self_attn.q_proj.biases": "model.safetensors",
+        "model.layers.5.self_attn.q_proj.scales": "model.safetensors",
+        "model.layers.5.self_attn.q_proj.weight": "model.safetensors",
+        "model.layers.5.self_attn.v_proj.biases": "model.safetensors",
+        "model.layers.5.self_attn.v_proj.scales": "model.safetensors",
+        "model.layers.5.self_attn.v_proj.weight": "model.safetensors",
+        "model.layers.6.input_layernorm.weight": "model.safetensors",
+        "model.layers.6.mlp.down_proj.biases": "model.safetensors",
+        "model.layers.6.mlp.down_proj.scales": "model.safetensors",
+        "model.layers.6.mlp.down_proj.weight": "model.safetensors",
+        "model.layers.6.mlp.gate_proj.biases": "model.safetensors",
+        "model.layers.6.mlp.gate_proj.scales": "model.safetensors",
+        "model.layers.6.mlp.gate_proj.weight": "model.safetensors",
+        "model.layers.6.mlp.up_proj.biases": "model.safetensors",
+        "model.layers.6.mlp.up_proj.scales": "model.safetensors",
+        "model.layers.6.mlp.up_proj.weight": "model.safetensors",
+        "model.layers.6.post_attention_layernorm.weight": "model.safetensors",
+        "model.layers.6.self_attn.k_proj.biases": "model.safetensors",
+        "model.layers.6.self_attn.k_proj.scales": "model.safetensors",
+        "model.layers.6.self_attn.k_proj.weight": "model.safetensors",
+        "model.layers.6.self_attn.o_proj.biases": "model.safetensors",
+        "model.layers.6.self_attn.o_proj.scales": "model.safetensors",
+        "model.layers.6.self_attn.o_proj.weight": "model.safetensors",
+        "model.layers.6.self_attn.q_proj.biases": "model.safetensors",
+        "model.layers.6.self_attn.q_proj.scales": "model.safetensors",
+        "model.layers.6.self_attn.q_proj.weight": "model.safetensors",
+        "model.layers.6.self_attn.v_proj.biases": "model.safetensors",
+        "model.layers.6.self_attn.v_proj.scales": "model.safetensors",
+        "model.layers.6.self_attn.v_proj.weight": "model.safetensors",
+        "model.layers.7.input_layernorm.weight": "model.safetensors",
+        "model.layers.7.mlp.down_proj.biases": "model.safetensors",
+        "model.layers.7.mlp.down_proj.scales": "model.safetensors",
+        "model.layers.7.mlp.down_proj.weight": "model.safetensors",
+        "model.layers.7.mlp.gate_proj.biases": "model.safetensors",
+        "model.layers.7.mlp.gate_proj.scales": "model.safetensors",
+        "model.layers.7.mlp.gate_proj.weight": "model.safetensors",
+        "model.layers.7.mlp.up_proj.biases": "model.safetensors",
+        "model.layers.7.mlp.up_proj.scales": "model.safetensors",
+        "model.layers.7.mlp.up_proj.weight": "model.safetensors",
+        "model.layers.7.post_attention_layernorm.weight": "model.safetensors",
+        "model.layers.7.self_attn.k_proj.biases": "model.safetensors",
+        "model.layers.7.self_attn.k_proj.scales": "model.safetensors",
+        "model.layers.7.self_attn.k_proj.weight": "model.safetensors",
+        "model.layers.7.self_attn.o_proj.biases": "model.safetensors",
+        "model.layers.7.self_attn.o_proj.scales": "model.safetensors",
+        "model.layers.7.self_attn.o_proj.weight": "model.safetensors",
+        "model.layers.7.self_attn.q_proj.biases": "model.safetensors",
+        "model.layers.7.self_attn.q_proj.scales": "model.safetensors",
+        "model.layers.7.self_attn.q_proj.weight": "model.safetensors",
+        "model.layers.7.self_attn.v_proj.biases": "model.safetensors",
+        "model.layers.7.self_attn.v_proj.scales": "model.safetensors",
+        "model.layers.7.self_attn.v_proj.weight": "model.safetensors",
+        "model.layers.8.input_layernorm.weight": "model.safetensors",
+        "model.layers.8.mlp.down_proj.biases": "model.safetensors",
+        "model.layers.8.mlp.down_proj.scales": "model.safetensors",
+        "model.layers.8.mlp.down_proj.weight": "model.safetensors",
+        "model.layers.8.mlp.gate_proj.biases": "model.safetensors",
+        "model.layers.8.mlp.gate_proj.scales": "model.safetensors",
+        "model.layers.8.mlp.gate_proj.weight": "model.safetensors",
+        "model.layers.8.mlp.up_proj.biases": "model.safetensors",
+        "model.layers.8.mlp.up_proj.scales": "model.safetensors",
+        "model.layers.8.mlp.up_proj.weight": "model.safetensors",
+        "model.layers.8.post_attention_layernorm.weight": "model.safetensors",
+        "model.layers.8.self_attn.k_proj.biases": "model.safetensors",
+        "model.layers.8.self_attn.k_proj.scales": "model.safetensors",
+        "model.layers.8.self_attn.k_proj.weight": "model.safetensors",
+        "model.layers.8.self_attn.o_proj.biases": "model.safetensors",
+        "model.layers.8.self_attn.o_proj.scales": "model.safetensors",
+        "model.layers.8.self_attn.o_proj.weight": "model.safetensors",
+        "model.layers.8.self_attn.q_proj.biases": "model.safetensors",
+        "model.layers.8.self_attn.q_proj.scales": "model.safetensors",
+        "model.layers.8.self_attn.q_proj.weight": "model.safetensors",
+        "model.layers.8.self_attn.v_proj.biases": "model.safetensors",
+        "model.layers.8.self_attn.v_proj.scales": "model.safetensors",
+        "model.layers.8.self_attn.v_proj.weight": "model.safetensors",
+        "model.layers.9.input_layernorm.weight": "model.safetensors",
+        "model.layers.9.mlp.down_proj.biases": "model.safetensors",
+        "model.layers.9.mlp.down_proj.scales": "model.safetensors",
+        "model.layers.9.mlp.down_proj.weight": "model.safetensors",
+        "model.layers.9.mlp.gate_proj.biases": "model.safetensors",
+        "model.layers.9.mlp.gate_proj.scales": "model.safetensors",
+        "model.layers.9.mlp.gate_proj.weight": "model.safetensors",
+        "model.layers.9.mlp.up_proj.biases": "model.safetensors",
+        "model.layers.9.mlp.up_proj.scales": "model.safetensors",
+        "model.layers.9.mlp.up_proj.weight": "model.safetensors",
+        "model.layers.9.post_attention_layernorm.weight": "model.safetensors",
+        "model.layers.9.self_attn.k_proj.biases": "model.safetensors",
+        "model.layers.9.self_attn.k_proj.scales": "model.safetensors",
+        "model.layers.9.self_attn.k_proj.weight": "model.safetensors",
+        "model.layers.9.self_attn.o_proj.biases": "model.safetensors",
+        "model.layers.9.self_attn.o_proj.scales": "model.safetensors",
+        "model.layers.9.self_attn.o_proj.weight": "model.safetensors",
+        "model.layers.9.self_attn.q_proj.biases": "model.safetensors",
+        "model.layers.9.self_attn.q_proj.scales": "model.safetensors",
+        "model.layers.9.self_attn.q_proj.weight": "model.safetensors",
+        "model.layers.9.self_attn.v_proj.biases": "model.safetensors",
+        "model.layers.9.self_attn.v_proj.scales": "model.safetensors",
+        "model.layers.9.self_attn.v_proj.weight": "model.safetensors",
+        "model.norm.weight": "model.safetensors"
+    }
+}

prompt.txt

@@ -0,0 +1,97 @@
+# TTS Voice Design Description
+
+## Core Function
+
+You generate voice descriptions for TTS systems by mapping user requests to allowed attributes. No templates. No formatting rules. Just natural descriptions using the options below.
+
+## Voice Categories
+
+**Realistic Voices**
+Professional, business, educational, support, real-world scenarios (podcast hosts, instructors, customer service).
+
+**Creative Voices**
+Fantasy characters, fictional personas, stylized voices (pirates, robots, villains, anime).
+
+---
+
+## Available Attributes
+
+### Age
+- `20s`, `30s`, `40s`
+
+### Gender
+- `male`, `female`
+
+### Accent
+- `american`, `indian`, `middle_eastern`, `asian_american`, `british`
+
+### Pitch
+- `low`, `normal`, `high`
+- **Constraint:** For 40s age, avoid high pitch (use sparingly, max 15%)
+
+### Timbre
+
+**For Realistic:**
+`deep`, `warm`, `gravelly`, `smooth`, `raspy`, `nasally`, `throaty`, `harsh`
+
+**For Creative:**
+All realistic options PLUS `robotic`, `ethereal`
+- **Constraint:** `robotic`/`ethereal` only with: `ai_machine_voice`, `cyborg`, `alien_scifi`, `mythical_godlike_magical`
+
+### Pacing
+- `very_slow`, `slow`, `conversational`, `brisk`, `fast`, `very_fast`
+- **Character-specific overrides:**
+  - `mafia`: slow or conversational only
+  - `flirty`: slow or conversational only
+  - `alpha`: fast or very_fast only
+  - `seductively`: very_slow or slow only
+
+### Emotion
+- `neutral`, `energetic`, `excited`, `sad`, `sarcastic`, `dry`
+- **Default to neutral** for most requests
+
+### Emotion Intensity
+- `low`, `med`, `high`
+
+---
+
+## Realistic-Only Attributes
+
+### Domain
+`social_content`, `podcast`, `commercial`, `education`, `support`, `entertainment`, `corporate`, `viral_content`
+
+### Speaking Role (matches domain)
+- **social_content:** youtube_vlogger, social_media_creator, influencer_voice, streamer_companion
+- **podcast:** podcast_host, interviewer
+- **commercial:** ad_narrator, brand_spokesperson, product_demo_voice, sales_pitch_voice
+- **education:** elearning_instructor, kids_story_voice
+- **support:** customer_support_agent, virtual_receptionist, healthcare_assistant
+- **entertainment:** storyteller, social_media_reaction, meme_voice
+- **corporate:** explainer_video_voice, event_host, corporate_training_narrator
+- **viral_content:** short_form_narrator, meme_voice
+
+### Register
+- `formal`, `neutral`, `casual`
+
+---
+
+## Creative-Only Attributes
+
+### Character
+`animated_cartoon`, `ai_machine_voice`, `alien_scifi`, `seductively`, `flirty`, `anime`, `cyborg`, `pirate`, `dark_villain`, `demon`, `gangster`, `mafia`, `dramatic_narrator`, `mythical_godlike_magical`, `spy`, `vampire`, `alpha`
+
+---
+
+## Output Guidelines
+
+When a user requests a voice, describe it naturally using the appropriate attributes from above. Apply constraints where specified. Choose defaults when attributes aren't mentioned.
+
+**Example mapping:**
+- "professional podcast host" → realistic male, 30s, american accent, warm timbre, conversational pacing, podcast domain
+- "AI robot voice" → creative, ai_machine_voice character, robotic timbre
+- "young excited instructor" → realistic, 20s, energetic emotion, education domain
+
+
+Few deterministic and verbose descriptions:
+- Realistic male voice in the 30s age with a american accent. Normal pitch, warm timbre, conversational pacing, neutral tone delivery at med intensity, podcast Domain, podcast_host role, neutral delivery
+- Creative, ai_machine_voice character. Male voice in their 20s with a american accent. Normal pitch, robotic timbre, conversational pacing, neutral tone at med intensity.

special_tokens_map.json

@@ -0,0 +1,165 @@
+{
+  "additional_special_tokens": [
+    {
+      "content": "<angry>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<appalled>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<chuckle>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<cry>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<curious>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<disappointed>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<excited>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<exhale>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<gasp>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<giggle>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<gulp>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<laugh>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<laugh_harder>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<mischievous>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<sarcastic>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<scream>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<sigh>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<sing>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<snort>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<whisper>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    }
+  ],
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<custom_token_7>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6c5e5b1d89b7e3738e5a5a4f93c326d8f3292ea83f9c560b8dbb6d66fb851973
+size 22853258

tokenizer/special_tokens_map.json

@@ -0,0 +1,165 @@
+{
+  "additional_special_tokens": [
+    {
+      "content": "<angry>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<appalled>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<chuckle>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<cry>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<curious>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<disappointed>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<excited>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<exhale>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<gasp>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<giggle>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<gulp>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<laugh>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<laugh_harder>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<mischievous>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<sarcastic>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<scream>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<sigh>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<sing>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<snort>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<whisper>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    }
+  ],
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<custom_token_7>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer/tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6c5e5b1d89b7e3738e5a5a4f93c326d8f3292ea83f9c560b8dbb6d66fb851973
+size 22853258

vllm_streaming_inference.py

@@ -0,0 +1,561 @@
+"""
+Maya-1-Voice VLLM Streaming Inference - Standalone Reference Implementation
+
+This is a complete, self-contained example for using Maya-1-Voice TTS model with VLLM and SNAC.
+Demonstrates streaming audio generation with sliding window approach for smooth playback.
+
+Requirements:
+    pip install vllm transformers torch snac numpy
+
+Usage:
+    python vllm_streaming_inference.py
+
+Author: Maya-1-Voice Team
+License: MIT
+"""
+
+import torch
+import numpy as np
+import asyncio
+from typing import List, Optional, AsyncGenerator
+from transformers import AutoTokenizer
+from vllm import AsyncLLMEngine, AsyncEngineArgs, SamplingParams
+from snac import SNAC
+
+
+# ============================================================================
+# CONSTANTS
+# ============================================================================
+
+# Special control tokens
+CODE_START_TOKEN_ID = 128257  # Start of Speech (SOS)
+CODE_END_TOKEN_ID = 128258    # End of Speech (EOS) - stop token for audio
+CODE_TOKEN_OFFSET = 128266    # Start of SNAC codes
+
+# SNAC token range (7 tokens per frame, 4096 codes per level)
+SNAC_MIN_ID = 128266
+SNAC_MAX_ID = 156937  # 128266 + (7 * 4096) - 1
+
+# SNAC configuration
+SNAC_MODEL_NAME = "hubertsiuzdak/snac_24khz"
+SNAC_SAMPLE_RATE = 24000
+SNAC_TOKENS_PER_FRAME = 7
+
+# Generation parameters
+DEFAULT_TEMPERATURE = 0.4
+DEFAULT_TOP_P = 0.9
+DEFAULT_MAX_TOKENS = 2000
+DEFAULT_MIN_TOKENS = 28  # At least 4 SNAC frames
+DEFAULT_REPETITION_PENALTY = 1.1
+
+
+# ============================================================================
+# SNAC DECODER
+# ============================================================================
+
+class SNACDecoder:
+    """
+    Decodes SNAC tokens (7-token frames) to audio waveforms.
+    
+    The unpacking logic converts flat 7-token frames back to hierarchical
+    3-level SNAC codes (matching the training preprocessing exactly).
+    """
+    
+    def __init__(self, device: str = "cuda"):
+        """Initialize SNAC decoder with 24kHz model."""
+        self.device = device
+        print(f"🎵 Loading SNAC 24kHz model to {device}...")
+        self.snac_model = SNAC.from_pretrained(SNAC_MODEL_NAME).eval().to(device)
+        print(f"✅ SNAC decoder initialized")
+    
+    def unpack_snac_from_7(self, vocab_ids: List[int]) -> List[List[int]]:
+        """
+        Unpack 7-token SNAC frames to 3 hierarchical levels.
+        
+        This is the EXACT INVERSE of training preprocessing.
+        
+        Frame structure (7 tokens per frame):
+        [slot0, slot1, slot2, slot3, slot4, slot5, slot6]
+        
+        Unpacking to [L1, L2, L3]:
+        - slot0 → L1[i]       (coarse: 1x rate)
+        - slot1 → L2[2*i]     (medium: 2x rate, even)
+        - slot2 → L3[4*i+0]   (fine: 4x rate)
+        - slot3 → L3[4*i+1]
+        - slot4 → L2[2*i+1]   (medium: odd)
+        - slot5 → L3[4*i+2]
+        - slot6 → L3[4*i+3]
+        
+        Args:
+            vocab_ids: List of SNAC token IDs (128266-156937), length divisible by 7
+        
+        Returns:
+            [L1, L2, L3] where L1=n, L2=2n, L3=4n elements
+        """
+        # Remove EOS token if present
+        if vocab_ids and vocab_ids[-1] == CODE_END_TOKEN_ID:
+            vocab_ids = vocab_ids[:-1]
+        
+        # Ensure complete frames
+        frames = len(vocab_ids) // SNAC_TOKENS_PER_FRAME
+        vocab_ids = vocab_ids[:frames * SNAC_TOKENS_PER_FRAME]
+        
+        if frames == 0:
+            return [[], [], []]
+        
+        l1, l2, l3 = [], [], []
+        
+        for i in range(frames):
+            slots = vocab_ids[i*7:(i+1)*7]
+            
+            # Subtract offset and mod 4096 to get original SNAC codes
+            l1.append((slots[0] - CODE_TOKEN_OFFSET) % 4096)
+            l2.extend([
+                (slots[1] - CODE_TOKEN_OFFSET) % 4096,  # Even
+                (slots[4] - CODE_TOKEN_OFFSET) % 4096,  # Odd
+            ])
+            l3.extend([
+                (slots[2] - CODE_TOKEN_OFFSET) % 4096,
+                (slots[3] - CODE_TOKEN_OFFSET) % 4096,
+                (slots[5] - CODE_TOKEN_OFFSET) % 4096,
+                (slots[6] - CODE_TOKEN_OFFSET) % 4096,
+            ])
+        
+        return [l1, l2, l3]
+    
+    @torch.inference_mode()
+    def decode(
+        self, 
+        snac_tokens: List[int], 
+        use_sliding_window: bool = False
+    ) -> Optional[np.ndarray]:
+        """
+        Decode SNAC tokens to audio waveform.
+        
+        Args:
+            snac_tokens: List of SNAC token IDs (7*n tokens)
+            use_sliding_window: If True, return only middle 2048 samples 
+                               (for smooth streaming without pops/clicks)
+        
+        Returns:
+            Audio waveform as float32 numpy array, 24kHz mono
+        """
+        if len(snac_tokens) < SNAC_TOKENS_PER_FRAME:
+            return None
+        
+        # Unpack to 3 hierarchical levels
+        levels = self.unpack_snac_from_7(snac_tokens)
+        
+        if not levels[0]:
+            return None
+        
+        # Convert to tensors
+        codes = [
+            torch.tensor(level, dtype=torch.long, device=self.device).unsqueeze(0)
+            for level in levels
+        ]
+        
+        # Decode through SNAC quantizer + decoder
+        z_q = self.snac_model.quantizer.from_codes(codes)
+        audio = self.snac_model.decoder(z_q)
+        
+        # Extract audio: [batch, 1, samples] → [samples]
+        audio = audio[0, 0].cpu().numpy()
+        
+        # Sliding window mode: keep middle 2048 samples only
+        # This eliminates popping/cracking in streaming by overlapping windows
+        if use_sliding_window and len(audio) >= 4096:
+            audio = audio[2048:4096]
+        
+        return audio
+    
+    def decode_to_bytes(
+        self, 
+        snac_tokens: List[int], 
+        use_sliding_window: bool = False
+    ) -> Optional[bytes]:
+        """
+        Decode SNAC tokens to audio bytes (int16 PCM).
+        
+        Args:
+            snac_tokens: List of SNAC token IDs
+            use_sliding_window: Use sliding window for smooth streaming
+        
+        Returns:
+            Audio as bytes (int16 PCM, 24kHz mono)
+        """
+        audio = self.decode(snac_tokens, use_sliding_window=use_sliding_window)
+        
+        if audio is None:
+            return None
+        
+        # Convert float32 to int16 PCM
+        audio_int16 = (audio * 32767).astype(np.int16)
+        return audio_int16.tobytes()
+
+
+# ============================================================================
+# CUSTOM LOGITS PROCESSOR
+# ============================================================================
+
+class OnlyAudioAfterSOS:
+    """
+    Restricts vocabulary to SNAC codes + EOS after SOS token.
+    
+    This prevents the model from generating text tokens during audio phase,
+    which would cause "hallucination" where the model repeats description text
+    instead of generating proper audio codes.
+    """
+    
+    def __init__(self):
+        self._seen_sos = False
+    
+    def __call__(
+        self,
+        prompt_token_ids: List[int],
+        generated_token_ids: List[int],
+        logits: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Apply constraint: after SOS, only allow SNAC codes + EOS.
+        
+        Args:
+            prompt_token_ids: Original prompt token IDs
+            generated_token_ids: Tokens generated so far
+            logits: Logits for next token [vocab_size]
+        
+        Returns:
+            Modified logits with masked tokens
+        """
+        # Check if SOS has been generated
+        if not self._seen_sos:
+            all_token_ids = prompt_token_ids + generated_token_ids
+            if CODE_START_TOKEN_ID in all_token_ids:
+                self._seen_sos = True
+            else:
+                return logits  # No constraint yet
+        
+        # Apply constraint: mask all tokens except SNAC codes + EOS
+        mask = torch.full_like(logits, float('-inf'))
+        mask[SNAC_MIN_ID:SNAC_MAX_ID + 1] = 0  # Allow SNAC codes
+        mask[CODE_END_TOKEN_ID] = 0            # Allow EOS
+        
+        return logits + mask
+    
+    def reset(self):
+        """Reset state for reuse across generations."""
+        self._seen_sos = False
+
+
+# ============================================================================
+# MAYA-1-VOICE MODEL
+# ============================================================================
+
+class Maya1VoiceModel:
+    """
+    Maya-1-Voice TTS Model with VLLM inference engine.
+    
+    Handles model loading, tokenizer initialization, and VLLM engine setup.
+    """
+    
+    def __init__(
+        self,
+        model_path: str,
+        dtype: str = "bfloat16",
+        max_model_len: int = 8192,
+        gpu_memory_utilization: float = 0.85,
+    ):
+        """
+        Initialize Maya-1-Voice model with VLLM.
+        
+        Args:
+            model_path: Path to model checkpoint (local or HuggingFace)
+            dtype: Model precision (bfloat16 recommended)
+            max_model_len: Maximum sequence length
+            gpu_memory_utilization: GPU memory fraction to use (0.0-1.0)
+        """
+        self.model_path = model_path
+        
+        print(f"🚀 Initializing Maya-1-Voice Model")
+        print(f"📁 Model: {model_path}")
+        print(f"🔢 Dtype: {dtype}")
+        
+        # Load tokenizer (must be from checkpoint with emotion tags)
+        print(f"📝 Loading tokenizer...")
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            model_path,
+            trust_remote_code=True,
+        )
+        print(f"✅ Tokenizer loaded: {len(self.tokenizer)} tokens")
+        
+        # Initialize VLLM async engine
+        print(f"🔧 Initializing VLLM engine...")
+        engine_args = AsyncEngineArgs(
+            model=model_path,
+            tokenizer=model_path,
+            dtype=dtype,
+            max_model_len=max_model_len,
+            gpu_memory_utilization=gpu_memory_utilization,
+            trust_remote_code=True,
+        )
+        
+        self.engine = AsyncLLMEngine.from_engine_args(engine_args)
+        print(f"✅ VLLM engine ready")
+    
+    def build_prompt(self, description: str, text: str) -> str:
+        """
+        Build prompt in Maya-1-Voice format using chat template.
+        
+        Format: Chat template with <description="..."> text as content
+        
+        The model expects:
+        1. Description of voice/character
+        2. Text to synthesize (optionally with <emotion> tags)
+        
+        Args:
+            description: Voice description 
+                Example: "Realistic male voice in the 30s age with american accent. 
+                         Normal pitch, warm timbre, conversational pacing."
+            text: Text to synthesize
+                Example: "Hello world! <excited> This is amazing!"
+        
+        Returns:
+            Formatted prompt string using chat template
+        """
+        content = f'<description="{description}"> {text}'
+        messages = [{"role": "user", "content": content}]
+        return self.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+
+# ============================================================================
+# STREAMING PIPELINE
+# ============================================================================
+
+class Maya1VoiceStreamingPipeline:
+    """
+    Streaming TTS pipeline using sliding window approach.
+    
+    This generates smooth audio by:
+    1. Streaming tokens from VLLM as they're generated
+    2. Every 7 tokens, decoding the last 28 tokens (4 frames) - sliding window
+    3. Keeping only middle 2048 samples from each decode
+    4. Creating natural overlap between chunks for artifact-free playback
+    """
+    
+    def __init__(self, model: Maya1VoiceModel, snac_decoder: SNACDecoder):
+        """Initialize streaming pipeline."""
+        self.model = model
+        self.snac_decoder = snac_decoder
+        print(f"🌊 Maya-1-Voice Streaming Pipeline initialized")
+    
+    async def generate_speech_stream(
+        self,
+        description: str,
+        text: str,
+        temperature: float = DEFAULT_TEMPERATURE,
+        top_p: float = DEFAULT_TOP_P,
+        max_tokens: int = DEFAULT_MAX_TOKENS,
+        repetition_penalty: float = DEFAULT_REPETITION_PENALTY,
+    ) -> AsyncGenerator[bytes, None]:
+        """
+        Generate speech audio with streaming.
+        
+        Args:
+            description: Voice/character description
+            text: Text to synthesize (with optional <emotion> tags)
+            temperature: Sampling temperature (lower = more stable)
+            top_p: Nucleus sampling
+            max_tokens: Max SNAC tokens to generate
+            repetition_penalty: Prevent repetition loops
+        
+        Yields:
+            Audio chunks as bytes (int16 PCM, 24kHz mono)
+        """
+        print(f"\n🌊 Starting streaming generation")
+        print(f"📝 Description: {description[:80]}...")
+        print(f"💬 Text: {text}")
+        
+        # Build prompt
+        prompt = self.model.build_prompt(description, text)
+        
+        # Configure sampling (removed custom logits processor for V1 compatibility)
+        sampling_params = SamplingParams(
+            temperature=temperature,
+            top_p=top_p,
+            max_tokens=max_tokens,
+            min_tokens=DEFAULT_MIN_TOKENS,
+            repetition_penalty=repetition_penalty,
+            stop_token_ids=[CODE_END_TOKEN_ID],  # Stop on audio EOS
+        )
+        
+        print(f"🎲 Sampling: temp={temperature}, top_p={top_p}, max_tokens={max_tokens}")
+        
+        # Token buffer for sliding window
+        token_buffer = []
+        total_tokens = 0
+        total_chunks = 0
+        
+        # Generate with VLLM
+        import uuid
+        import time
+        request_id = f"maya1voice-{uuid.uuid4().hex[:8]}-{int(time.time() * 1000000)}"
+        
+        results_generator = self.model.engine.generate(
+            prompt=prompt,
+            sampling_params=sampling_params,
+            request_id=request_id,
+        )
+        
+        # Stream tokens with sliding window decoding
+        async for request_output in results_generator:
+            generated_ids = request_output.outputs[0].token_ids
+            
+            # Process only new tokens
+            new_tokens = generated_ids[total_tokens:]
+            total_tokens = len(generated_ids)
+            
+            # Filter and buffer SNAC tokens only
+            for token_id in new_tokens:
+                if SNAC_MIN_ID <= token_id <= SNAC_MAX_ID:
+                    token_buffer.append(token_id)
+                    
+                    # Sliding window: process every 7 tokens when buffer > 27
+                    # Take last 28 tokens (4 frames) for smooth overlap
+                    if len(token_buffer) % 7 == 0 and len(token_buffer) > 27:
+                        window_tokens = token_buffer[-28:]
+                        
+                        # Decode with sliding window (returns middle 2048 samples)
+                        audio_bytes = self.snac_decoder.decode_to_bytes(
+                            window_tokens, 
+                            use_sliding_window=True
+                        )
+                        
+                        if audio_bytes:
+                            total_chunks += 1
+                            if total_chunks == 1:
+                                print(f"🎵 First chunk decoded ({len(audio_bytes)} bytes)")
+                            yield audio_bytes
+        
+        print(f"✅ Streaming complete: {total_tokens} tokens → {total_chunks} chunks")
+
+
+# ============================================================================
+# MAIN EXAMPLE
+# ============================================================================
+
+async def main():
+    """
+    Example usage of Maya-1-Voice streaming inference.
+    
+    This demonstrates:
+    1. Model initialization
+    2. SNAC decoder setup
+    3. Streaming generation
+    4. Audio chunk handling
+    """
+    
+    # Configuration
+    MODEL_PATH = "/home/ubuntu/veena_temp/maya-1-voice"  # Local model path
+    DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+    
+    print("=" * 80)
+    print("Maya-1-Voice VLLM Streaming Inference Example")
+    print("=" * 80)
+    
+    # Initialize model
+    model = Maya1VoiceModel(
+        model_path=MODEL_PATH,
+        dtype="bfloat16",
+        max_model_len=8192,
+        gpu_memory_utilization=0.8,  # Reduced for available GPU memory (12GB free)
+    )
+    
+    # Initialize SNAC decoder
+    snac_decoder = SNACDecoder(device=DEVICE)
+    
+    # Create pipeline
+    pipeline = Maya1VoiceStreamingPipeline(model, snac_decoder)
+    
+    # Example 1: Professional voice
+    description = (
+        "Realistic male voice in the 30s age with american accent. "
+        "Normal pitch, warm timbre, conversational pacing, neutral tone delivery at med intensity."
+    )
+    text = "Hello! This is a test of the Maya-1-Voice text-to-speech system."
+    
+    print(f"\n{'='*80}")
+    print("Example 1: Professional Voice")
+    print(f"{'='*80}")
+    
+    audio_chunks = []
+    async for chunk in pipeline.generate_speech_stream(
+        description=description,
+        text=text,
+        temperature=0.4,
+        max_tokens=500,
+    ):
+        audio_chunks.append(chunk)
+        print(f"📦 Received chunk {len(audio_chunks)}: {len(chunk)} bytes")
+    
+    # Combine chunks
+    full_audio = b''.join(audio_chunks)
+    print(f"\n✅ Total audio: {len(full_audio)} bytes ({len(full_audio)//2} samples, {len(full_audio)/2/24000:.2f}s)")
+    
+    # Save audio (optional)
+    try:
+        import wave
+        output_file = "output_example1.wav"
+        with wave.open(output_file, 'wb') as wav:
+            wav.setnchannels(1)  # Mono
+            wav.setsampwidth(2)  # 16-bit
+            wav.setframerate(24000)  # 24kHz
+            wav.writeframes(full_audio)
+        print(f"💾 Saved to {output_file}")
+    except ImportError:
+        print(f"⚠️  Install 'wave' module to save audio files")
+    
+    # Example 2: Character voice with emotions
+    print(f"\n{'='*80}")
+    print("Example 2: Character Voice with Emotions")
+    print(f"{'='*80}")
+    
+    description = (
+        "Creative, dark_villain character. Male voice in their 40s with british accent. "
+        "Low pitch, gravelly timbre, slow pacing, angry tone at high intensity."
+    )
+    text = "The darkness isn't coming... <angry> it's already here!"
+    
+    audio_chunks = []
+    async for chunk in pipeline.generate_speech_stream(
+        description=description,
+        text=text,
+        temperature=0.5,
+        max_tokens=800,
+    ):
+        audio_chunks.append(chunk)
+        print(f"📦 Received chunk {len(audio_chunks)}: {len(chunk)} bytes")
+    
+    full_audio = b''.join(audio_chunks)
+    print(f"\n✅ Total audio: {len(full_audio)} bytes ({len(full_audio)//2} samples, {len(full_audio)/2/24000:.2f}s)")
+    
+    # Save audio
+    try:
+        import wave
+        output_file = "output_example2.wav"
+        with wave.open(output_file, 'wb') as wav:
+            wav.setnchannels(1)
+            wav.setsampwidth(2)
+            wav.setframerate(24000)
+            wav.writeframes(full_audio)
+        print(f"💾 Saved to {output_file}")
+    except ImportError:
+        pass
+    
+    print(f"\n{'='*80}")
+    print("🎉 Examples complete!")
+    print(f"{'='*80}")
+
+
+if __name__ == "__main__":
+    # Run async main
+    asyncio.run(main())