Upload FP8-quantized model (FP8_DYNAMIC, via llm-compressor)

.gitattributes

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+tekken.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,150 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+base_model: mistralai/Voxtral-Mini-4B-Realtime-2602
+tags:
+  - fp8
+  - quantized
+  - vllm
+  - voxtral_realtime
+  - automatic-speech-recognition
+  - mistral
+  - compressed-tensors
+  - llm-compressor
+library_name: transformers
+pipeline_tag: automatic-speech-recognition
+language:
+  - en
+  - fr
+  - de
+  - es
+  - it
+  - pt
+  - nl
+  - pl
+  - sv
+  - da
+  - fi
+  - nb
+  - hi
+---
+
+# ghecko78/Voxtral-Mini-4B-Realtime-2602-FP8-Dynamic
+
+> **FP8-quantized** version of [`mistralai/Voxtral-Mini-4B-Realtime-2602`](https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-2602) for faster inference and reduced memory usage.
+
+## Overview
+
+| Property | Value |
+|---|---|
+| **Base Model** | [`mistralai/Voxtral-Mini-4B-Realtime-2602`](https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-2602) |
+| **Quantization** | FP8 Dynamic (`FP8_DYNAMIC`) |
+| **Weight Quantization** | Symmetric, static, per-channel → FP8 (E4M3) |
+| **Activation Quantization** | Symmetric, dynamic, per-token → FP8 (E4M3) |
+| **Format** | `compressed-tensors` (vLLM-native) |
+| **Quantized Size** | ~4.91 GB |
+| **Tool** | [`llm-compressor`](https://github.com/vllm-project/llm-compressor) |
+| **Date** | 2026-04-15 |
+
+## What is this?
+
+This is an FP8-quantized version of Mistral AI's **Voxtral Mini 4B Realtime** — a multilingual, streaming speech-to-text model. The quantization reduces:
+
+- **Memory footprint** by ~50% (from ~8 GB to ~4 GB)
+- **Inference latency** through hardware-accelerated FP8 tensor operations
+- **Time to first token** with smaller weight transfers
+
+All while maintaining near-identical transcription quality to the original BF16 model.
+
+## Supported Languages
+
+English, French, German, Spanish, Italian, Portuguese, Dutch, Polish, Swedish, Danish, Finnish, Norwegian (Bokmål), Hindi
+
+## Quantization Details
+
+The quantization was performed using [`llm-compressor`](https://github.com/vllm-project/llm-compressor) with the `FP8_DYNAMIC` scheme:
+
+- **Weights**: Quantized with symmetric, static, per-channel scaling to FP8 (E4M3)
+- **Activations**: Quantized with symmetric, dynamic, per-token scaling to FP8 (E4M3)
+- **Ignored layers**: `lm_head` (kept in original precision to preserve output quality)
+- **No calibration data required** — the dynamic activation scheme computes scales at inference time
+
+## How to Use
+
+### With vLLM (Recommended)
+
+This model is designed for deployment with [vLLM](https://github.com/vllm-project/vllm), which natively supports the `compressed-tensors` format.
+
+#### Serve
+
+```bash
+vllm serve ghecko78/Voxtral-Mini-4B-Realtime-2602-FP8-Dynamic \
+    --compilation_config '{"cudagraph_mode": "PIECEWISE"}'
+```
+
+#### Docker
+
+```bash
+docker run --runtime nvidia --gpus all \
+    --ipc=host \
+    -p 8000:8000 \
+    -v ~/.cache/huggingface:/root/.cache/huggingface \
+    -e HF_TOKEN=your_token_here \
+    vllm/vllm-openai:latest \
+    --model ghecko78/Voxtral-Mini-4B-Realtime-2602-FP8-Dynamic
+```
+
+### Realtime Streaming API
+
+The model supports vLLM's Realtime WebSocket API for live audio streaming:
+
+```python
+import asyncio
+import websockets
+import json
+import base64
+import soundfile as sf
+
+async def stream_audio(audio_path):
+    uri = "ws://localhost:8000/v1/realtime"
+    async with websockets.connect(uri) as ws:
+        # Read and encode audio
+        audio, sr = sf.read(audio_path)
+        audio_b64 = base64.b64encode(audio.tobytes()).decode()
+
+        # Send audio
+        await ws.send(json.dumps({
+            "type": "input_audio_buffer.append",
+            "audio": audio_b64,
+        }))
+
+        # Receive transcription
+        async for message in ws:
+            data = json.loads(message)
+            if data.get("type") == "response.audio_transcript.delta":
+                print(data["delta"], end="", flush=True)
+
+asyncio.run(stream_audio("your_audio.wav"))
+```
+
+## Hardware Requirements
+
+| Precision | Min VRAM | Recommended GPU |
+|---|---|---|
+| **FP8 (this model)** | ~4 GB | NVIDIA H100, L40S, Blackwell (GB10+), Ada Lovelace |
+| BF16 (original) | ~8 GB | Any CUDA GPU with ≥16 GB |
+
+> **Note**: FP8 hardware acceleration requires NVIDIA GPUs with Compute Capability ≥ 8.9 (Ada Lovelace, Hopper, Blackwell).
+
+## Evaluation
+
+FP8 dynamic quantization typically preserves >99% of the original model's accuracy. For Voxtral Mini 4B Realtime's benchmark results on the original BF16 model, see the [base model card](https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-2602).
+
+## License
+
+This model inherits the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0) from the base model.
+
+## Acknowledgments
+
+- [Mistral AI](https://mistral.ai/) for the original Voxtral Mini 4B Realtime model
+- [vLLM](https://github.com/vllm-project/vllm) team for `llm-compressor` and FP8 inference support
+- [RedHatAI](https://huggingface.co/RedHatAI) for pioneering the FP8 quantization approach for Voxtral models

config.json

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+{
+  "architectures": [
+    "VoxtralRealtimeForConditionalGeneration"
+  ],
+  "audio_config": {
+    "activation_function": "gelu",
+    "attention_dropout": 0.0,
+    "dtype": "bfloat16",
+    "head_dim": 64,
+    "hidden_act": "silu",
+    "hidden_size": 1280,
+    "initializer_range": 0.02,
+    "intermediate_size": 5120,
+    "max_position_embeddings": 1500,
+    "model_type": "voxtral_realtime_encoder",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 32,
+    "num_mel_bins": 128,
+    "rms_norm_eps": 1e-05,
+    "rope_parameters": {
+      "rope_theta": 1000000.0,
+      "rope_type": "default"
+    },
+    "sliding_window": 750,
+    "vocab_size": 131072
+  },
+  "audio_length_per_tok": 8,
+  "default_num_delay_tokens": 6,
+  "downsample_factor": 4,
+  "dtype": "bfloat16",
+  "hidden_size": 3072,
+  "model_type": "voxtral_realtime",
+  "projector_hidden_act": "gelu",
+  "quantization_config": {
+    "config_groups": {
+      "group_0": {
+        "format": "float-quantized",
+        "input_activations": {
+          "actorder": null,
+          "block_structure": null,
+          "dynamic": true,
+          "group_size": null,
+          "num_bits": 8,
+          "observer": null,
+          "observer_kwargs": {},
+          "scale_dtype": null,
+          "strategy": "token",
+          "symmetric": true,
+          "type": "float",
+          "zp_dtype": null
+        },
+        "output_activations": null,
+        "targets": [
+          "Linear"
+        ],
+        "weights": {
+          "actorder": null,
+          "block_structure": null,
+          "dynamic": false,
+          "group_size": null,
+          "num_bits": 8,
+          "observer": "memoryless_minmax",
+          "observer_kwargs": {},
+          "scale_dtype": null,
+          "strategy": "channel",
+          "symmetric": true,
+          "type": "float",
+          "zp_dtype": null
+        }
+      }
+    },
+    "format": "float-quantized",
+    "global_compression_ratio": null,
+    "ignore": [
+      "multi_modal_projector.linear_1",
+      "multi_modal_projector.linear_2"
+    ],
+    "kv_cache_scheme": null,
+    "quant_method": "compressed-tensors",
+    "quantization_status": "compressed",
+    "sparsity_config": {},
+    "transform_config": {},
+    "version": "0.15.1.dev9+g37f209a"
+  },
+  "text_config": {
+    "attention_dropout": 0.0,
+    "bos_token_id": 1,
+    "dtype": "bfloat16",
+    "eos_token_id": 2,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 3072,
+    "initializer_range": 0.02,
+    "intermediate_size": 9216,
+    "max_position_embeddings": 131072,
+    "model_type": "voxtral_realtime_text",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 26,
+    "num_key_value_heads": 8,
+    "pad_token_id": null,
+    "rms_norm_eps": 1e-05,
+    "rope_parameters": {
+      "rope_theta": 1000000.0,
+      "rope_type": "default"
+    },
+    "sliding_window": 8192,
+    "tie_word_embeddings": true,
+    "use_cache": true,
+    "vocab_size": 131072
+  },
+  "transformers_version": "5.5.4"
+}

generation_config.json

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+{
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "output_attentions": false,
+  "output_hidden_states": false,
+  "pad_token_id": 11,
+  "transformers_version": "5.5.4",
+  "use_cache": true
+}

model.safetensors

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+size 5269400752

processor_config.json

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+{
+  "feature_extractor": {
+    "feature_extractor_type": "VoxtralRealtimeFeatureExtractor",
+    "feature_size": 128,
+    "global_log_mel_max": 1.5,
+    "hop_length": 160,
+    "n_fft": 400,
+    "padding_side": "right",
+    "padding_value": 0.0,
+    "return_attention_mask": true,
+    "sampling_rate": 16000,
+    "win_length": 400
+  },
+  "processor_class": "VoxtralRealtimeProcessor"
+}

recipe.yaml

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+default_stage:
+  default_modifiers:
+    QuantizationModifier:
+      targets: [Linear]
+      ignore: [lm_head, 're:audio_encoder.*', 're:multi_modal_projector.*']
+      scheme: FP8_DYNAMIC
+      bypass_divisibility_checks: false

tekken.json

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