Initial quantized checkpoint + model card

.gitattributes

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

LICENSE

@@ -0,0 +1,10 @@
+Modified MIT License 
+
+Attribution notice: 2025 - Browser Use Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of the weights of this model and associated documentation files (the “Model”), to deal in the Model without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Model, and to permit persons to whom the Model is furnished to do so, subject to the following conditions: 
+
+1. The above attribution notice and this permission notice shall be included in all copies or substantial portions of the Model. 
+2. You are not authorized to exercise any rights under this license if the global consolidated annual revenue of your company (or that of your employer) exceeds $1 million (or its equivalent in another currency) for the preceding month. This restriction in (b) applies to the Model and any derivatives, modifications, or combined works based on it, whether provided by Browser Use or by a third party. You may contact Browser Use (support@browser-use.com) to request a commercial license, which Browser Use Inc. may grant you at its sole discretion, or choose to use the Model on Browser Use hosted services available at https://browser-use.com/.
+
+THE MODEL IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL BROWSER USE BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE MODEL OR THE USE OR OTHER DEALINGS IN THE MODEL.

README.md

@@ -0,0 +1,161 @@
+---
+base_model: browser-use/bu-30b-a3b-preview
+base_model_relation: quantized
+license: other
+license_name: modified-mit-browser-use
+license_link: https://huggingface.co/browser-use/bu-30b-a3b-preview/blob/main/LICENSE
+tags:
+  - nvfp4
+  - awq
+  - modelopt
+  - browser-use
+  - agent
+  - vision-language
+  - moe
+  - quantized
+pipeline_tag: image-text-to-text
+library_name: transformers
+---
+
+# bu-30b-a3b-preview NVFP4-AWQ (LITE)
+
+A 4-bit NVFP4 + AWQ-lite quantization of
+[browser-use/bu-30b-a3b-preview](https://huggingface.co/browser-use/bu-30b-a3b-preview) — the 30B Qwen3-VL-MoE browser-agent model — produced with
+[NVIDIA TensorRT-Model-Optimizer](https://github.com/NVIDIA/TensorRT-Model-Optimizer)
+v0.43.
+
+**What's notable about this quant**
+
+This is (as of upload) the first **NVFP4_AWQ** quantization of any browser-agent VLM on the Hub, and the first NVFP4 quant of this model with documented calibration provenance. Existing NVFP4 / INT4-AWQ quants of `bu-30b-a3b-preview` either lack calibration data disclosure or calibrate against generic text corpora; this one was calibrated **on-distribution**, using 602 real multimodal browser-use trajectories generated by the full-precision model itself.
+
+The calibration-data argument is the load-bearing claim of this quant — it's documented in detail below.
+
+## Why NVFP4 for this model
+
+- **Native acceleration on Blackwell.** RTX 5090, PRO 6000, B100/B200, GB10 all have native FP4 tensor cores (sm_100+). On Blackwell-class hardware NVFP4 weights execute at ~2× the throughput of FP8.
+- **Memory.** ~17 GB vs ~58 GB at BF16. Fits comfortably on a single RTX 5090 (32 GB) with headroom for the 32K-token context window.
+- **Accuracy-preserving 4-bit format.** NVFP4's two-level scales (FP8 E4M3 block scales at block size 16, plus FP32 per-tensor scale) substantially outperform naive INT4 in accuracy, and AWQ's activation-aware per-channel scaling protects the weight channels that matter most.
+
+## Quantization Recipe
+
+**Base config**: `NVFP4_AWQ_LITE_CFG` from `modelopt.torch.quantization.config`.
+
+**Module-scoped exclusions (kept at BF16 precision)**:
+
+| Module pattern | Reason |
+|---|---|
+| `*visual*` | Vision encoder (ViT tower) is small relative to MoE decoder; disproportionate accuracy loss for minimal memory savings. Standard practice. |
+| `*mlp.gate.*` | MoE router — tiny logit perturbations cascade into expert misrouting. Already excluded in `NVFP4_AWQ_LITE_CFG`. |
+| `*lm_head*` | Output projection. Already excluded. |
+| `*router*`, `*block_sparse_moe.gate*` | Generic router patterns (covers Mixtral-style MoE architectures). Already excluded. |
+
+All 128 MoE experts (`model.language_model.layers.*.mlp.experts.*`) and attention matrices are quantized to NVFP4 weights + NVFP4 activations (W4A4). The `model.visual.*` ViT tower (depth 27, hidden 1152) stays in BF16.
+
+## Calibration Data
+
+**602 samples** of real browser-use agent trajectories:
+
+| Category (BU_Bench V1) | Tasks | Samples | Weight (rationale) |
+|---|---|---|---|
+| GAIA | 8 | ~200 | Research + reasoning — dominant agent workload |
+| OM2W2 | 6 | ~150 | Open-ended info gathering |
+| BrowseComp | 5 | ~130 | Cross-source comparison |
+| WebBenchREAD | 5 | ~80 | Clean DOM activations |
+| InteractionTests | 1 | ~15 | Signal floor for form/interaction regime |
+
+**Collection process:**
+
+1. Full-precision bu-30b-a3b-preview served via vLLM 0.17 at `--dtype bfloat16`.
+2. 3 parallel `browser-use` v0.12.6 agents with `enable_planning=True` and `use_vision=True` ran 25 tasks sampled from the official [browser-use/benchmark](https://github.com/browser-use/benchmark) BU_Bench V1 set.
+3. Per-category step caps: 40 for GAIA/OM2W2/BrowseComp, 25 for WebBenchREAD/InteractionTests.
+4. A proxy between the agents and vLLM captured every `/v1/chat/completions` request payload (including image parts) to JSONL.
+5. Samples with total tokens < 1000 (keepalive/error artifacts, 3) or blank screenshots (variance < 150, 16) were filtered out.
+
+**Sample-level statistics** (staged calibration, 602 samples, Qwen3-VL tokenizer + true vision-token expansion):
+
+| Metric | Value |
+|---|---|
+| Total tokens | min=3, p25=11.2K, median=13.4K, p75=15.8K, p90=18.1K, max=35.4K |
+| 8-16K bucket | 439 samples (73%) |
+| 16-32K bucket | 144 samples (24%) |
+| 32K+ samples | 6 (long-context tail) |
+| Samples with screenshot | 93.6% |
+| Non-degenerate screenshots | 97.2% |
+| DOM element count (median / max) | 136 / 941 |
+
+The calibration distribution was committed to **before** running the analyzer on the exploratory data — weights reflect the target user population (researchers and educators running a local agent), not post-hoc curve-fitting to whatever tasks happened to look interesting.
+
+## Serving
+
+### ⚠ vLLM support
+
+As of **vLLM 0.19.1 / main**, the `ModelOpt` quantization loader does **not** accept `quant_algo: NVFP4_AWQ` — the supported list is only `['FP8', 'FP8_PER_CHANNEL_PER_TOKEN', 'FP8_PB_WO', 'NVFP4', 'MXFP8', 'MIXED_PRECISION']`. Renaming the algo to plain `NVFP4` would load but produce mathematically wrong inference because the 18,480 `pre_quant_scale` tensors that carry AWQ's per-channel activation rescaling would not be applied.
+
+If you want a vLLM-loadable variant, use the sibling repo **[`Code4me2/bu-30b-a3b-preview-NVFP4`](https://huggingface.co/Code4me2/bu-30b-a3b-preview-NVFP4)** (plain NVFP4, no AWQ, slightly lower accuracy but same memory footprint).
+
+### TensorRT-LLM (recommended)
+
+This format is produced by and natively supported by [NVIDIA TensorRT-Model-Optimizer](https://github.com/NVIDIA/TensorRT-Model-Optimizer) + TensorRT-LLM. Build an NVFP4 engine:
+
+```bash
+trtllm-build --checkpoint_dir Code4me2/bu-30b-a3b-preview-NVFP4-AWQ \
+    --quant_format nvfp4 \
+    --max_seq_len 32768
+```
+
+See the [TRT-LLM NVFP4 guide](https://nvidia.github.io/TensorRT-LLM/reference/precision.html) for more details.
+
+### SGLang
+
+SGLang's ModelOpt integration supports NVFP4_AWQ when built against the matching ModelOpt version — consult their docs for the current status.
+
+## Intended Use
+
+This model is a drop-in replacement for `bu-30b-a3b-preview` within the
+[browser-use](https://github.com/browser-use/browser-use) library. It is
+trained/tuned specifically for browser-use's indexed-DOM + structured-action
+format. Using it outside that flow (or with a different harness / freeform
+CDP scripting) will produce substantially worse results than the
+quantization accuracy alone would suggest.
+
+## Evaluation
+
+_Evaluation numbers (MMLU, GSM8K, MM-Bench, BU_Bench V1 subset) will be
+added after running against BF16 baseline. See methodology below._
+
+Planned eval suite:
+- MMLU (general knowledge, 5-shot)
+- GSM8K (math reasoning, 0-shot chain-of-thought)
+- MM-Bench (vision-language, 0-shot)
+- BU_Bench V1 held-out tasks (agent-specific, using the same browser-use harness)
+
+## Reproduction
+
+- Base model: `browser-use/bu-30b-a3b-preview`
+- Quantization tool: `nvidia-modelopt==0.43.0`
+- Quantization config: `NVFP4_AWQ_LITE_CFG` with `*visual*` excluded (ViT stays BF16); router (`*mlp.gate.*`) already excluded by the config default
+- Calibration samples: 512 / 602 (shuffled, seed=42). 6 samples above 32K tokens skipped (aligned with `--max-model-len`)
+- Host: single RTX PRO 6000 Blackwell, 98GB
+- Calibration wall time: ~14h (70 min cache activation stats + 12h AWQ scale search + 10 min export)
+
+### ModelOpt patch for Qwen3-VL-MoE support
+
+ModelOpt 0.43 does not natively know how to export quantized checkpoints for `Qwen3VLMoeForConditionalGeneration`. Three patches were required (included in the model repo as `modelopt_patch.py`):
+
+1. `get_expert_linear_names()` in `layer_utils.py` — recognize `Qwen3VLMoe*` and return `[gate_proj, up_proj, down_proj]`
+2. `get_experts_list()` in `layer_utils.py` — recognize `qwen3vlmoe*` model_type
+3. `_export_transformers_checkpoint()` in `unified_export_hf.py` — wrap the `QuantQwen3VLMoeTextExperts` container with a transparent iterable proxy so the existing iterable dispatch walks the un-BMM'd per-expert `ModuleList`s, while `__call__` and attribute access still delegate to the real experts module for the internal dummy forward pass
+
+Reference code + calibration harness: [GitHub link TBD]
+
+## Attribution & License
+
+Derived from [`browser-use/bu-30b-a3b-preview`](https://huggingface.co/browser-use/bu-30b-a3b-preview), which is distributed under a **Modified MIT License** by Browser Use Inc. with a commercial-use restriction: **use is not permitted for organizations whose annual consolidated revenue exceeds USD 1 million for the preceding month**. That restriction propagates to this derivative. Commercial users above the revenue threshold must obtain a license from Browser Use Inc. (`support@browser-use.com`) or use Browser Use's hosted services.
+
+The original LICENSE file is included alongside the weights.
+
+## Acknowledgements
+
+- **Browser Use** for the base model and the open benchmark suite
+- **NVIDIA Model Optimizer** for the NVFP4_AWQ calibration tooling
+- **Qwen team** for the Qwen3-VL-MoE architecture

added_tokens.json

@@ -0,0 +1,28 @@
+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

@@ -0,0 +1,120 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {%- if messages[0].content is string %}
+            {{- messages[0].content }}
+        {%- else %}
+            {%- for content in messages[0].content %}
+                {%- if 'text' in content %}
+                    {{- content.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {{- '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' }}
+        {%- if messages[0].content is string %}
+            {{- messages[0].content }}
+        {%- else %}
+            {%- for content in messages[0].content %}
+                {%- if 'text' in content %}
+                    {{- content.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set image_count = namespace(value=0) %}
+{%- set video_count = namespace(value=0) %}
+{%- for message in messages %}
+    {%- if message.role == "user" %}
+        {{- '<|im_start|>' + message.role + '\n' }}
+        {%- if message.content is string %}
+            {{- message.content }}
+        {%- else %}
+            {%- for content in message.content %}
+                {%- if content.type == 'image' or 'image' in content or 'image_url' in content %}
+                    {%- set image_count.value = image_count.value + 1 %}
+                    {%- if add_vision_id %}Picture {{ image_count.value }}: {% endif -%}
+                    <|vision_start|><|image_pad|><|vision_end|>
+                {%- elif content.type == 'video' or 'video' in content %}
+                    {%- set video_count.value = video_count.value + 1 %}
+                    {%- if add_vision_id %}Video {{ video_count.value }}: {% endif -%}
+                    <|vision_start|><|video_pad|><|vision_end|>
+                {%- elif 'text' in content %}
+                    {{- content.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role + '\n' }}
+        {%- if message.content is string %}
+            {{- message.content }}
+        {%- else %}
+            {%- for content_item in message.content %}
+                {%- if 'text' in content_item %}
+                    {{- content_item.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and message.content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {%- if message.content is string %}
+            {{- message.content }}
+        {%- else %}
+            {%- for content in message.content %}
+                {%- if content.type == 'image' or 'image' in content or 'image_url' in content %}
+                    {%- set image_count.value = image_count.value + 1 %}
+                    {%- if add_vision_id %}Picture {{ image_count.value }}: {% endif -%}
+                    <|vision_start|><|image_pad|><|vision_end|>
+                {%- elif content.type == 'video' or 'video' in content %}
+                    {%- set video_count.value = video_count.value + 1 %}
+                    {%- if add_vision_id %}Video {{ video_count.value }}: {% endif -%}
+                    <|vision_start|><|video_pad|><|vision_end|>
+                {%- elif 'text' in content %}
+                    {{- content.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

@@ -0,0 +1,155 @@
+{
+    "architectures": [
+        "Qwen3VLMoeForConditionalGeneration"
+    ],
+    "dtype": "bfloat16",
+    "hidden_size": 2048,
+    "image_token_id": 151655,
+    "model_type": "qwen3_vl_moe",
+    "pad_token_id": 151643,
+    "text_config": {
+        "attention_bias": false,
+        "attention_dropout": 0.0,
+        "bos_token_id": 151643,
+        "decoder_sparse_step": 1,
+        "dtype": "bfloat16",
+        "eos_token_id": 151645,
+        "head_dim": 128,
+        "hidden_act": "silu",
+        "hidden_size": 2048,
+        "initializer_range": 0.02,
+        "intermediate_size": 6144,
+        "max_position_embeddings": 262144,
+        "mlp_only_layers": [],
+        "model_type": "qwen3_vl_moe_text",
+        "moe_intermediate_size": 768,
+        "norm_topk_prob": true,
+        "num_attention_heads": 32,
+        "num_experts": 128,
+        "num_experts_per_tok": 8,
+        "num_hidden_layers": 48,
+        "num_key_value_heads": 4,
+        "pad_token_id": 151643,
+        "rms_norm_eps": 1e-06,
+        "rope_scaling": {
+            "mrope_interleaved": true,
+            "mrope_section": [
+                24,
+                20,
+                20
+            ],
+            "rope_type": "default"
+        },
+        "rope_theta": 5000000,
+        "router_aux_loss_coef": 0.001,
+        "use_cache": true,
+        "vocab_size": 151936
+    },
+    "tie_word_embeddings": false,
+    "transformers_version": "4.57.6",
+    "video_token_id": 151656,
+    "vision_config": {
+        "deepstack_visual_indexes": [
+            8,
+            16,
+            24
+        ],
+        "depth": 27,
+        "dtype": "bfloat16",
+        "hidden_act": "gelu_pytorch_tanh",
+        "hidden_size": 1152,
+        "in_channels": 3,
+        "initializer_range": 0.02,
+        "intermediate_size": 4304,
+        "model_type": "qwen3_vl_moe",
+        "num_heads": 16,
+        "num_position_embeddings": 2304,
+        "out_hidden_size": 2048,
+        "pad_token_id": 151643,
+        "patch_size": 16,
+        "spatial_merge_size": 2,
+        "temporal_patch_size": 2
+    },
+    "vision_end_token_id": 151653,
+    "vision_start_token_id": 151652,
+    "vocab_size": 151936,
+    "quantization_config": {
+        "config_groups": {
+            "group_0": {
+                "input_activations": {
+                    "dynamic": false,
+                    "num_bits": 4,
+                    "type": "float",
+                    "group_size": 16
+                },
+                "weights": {
+                    "dynamic": false,
+                    "num_bits": 4,
+                    "type": "float",
+                    "group_size": 16
+                },
+                "targets": [
+                    "Linear"
+                ]
+            }
+        },
+        "ignore": [
+            "lm_head",
+            "model.language_model.layers.0.mlp.gate",
+            "model.language_model.layers.1.mlp.gate",
+            "model.language_model.layers.10.mlp.gate",
+            "model.language_model.layers.11.mlp.gate",
+            "model.language_model.layers.12.mlp.gate",
+            "model.language_model.layers.13.mlp.gate",
+            "model.language_model.layers.14.mlp.gate",
+            "model.language_model.layers.15.mlp.gate",
+            "model.language_model.layers.16.mlp.gate",
+            "model.language_model.layers.17.mlp.gate",
+            "model.language_model.layers.18.mlp.gate",
+            "model.language_model.layers.19.mlp.gate",
+            "model.language_model.layers.2.mlp.gate",
+            "model.language_model.layers.20.mlp.gate",
+            "model.language_model.layers.21.mlp.gate",
+            "model.language_model.layers.22.mlp.gate",
+            "model.language_model.layers.23.mlp.gate",
+            "model.language_model.layers.24.mlp.gate",
+            "model.language_model.layers.25.mlp.gate",
+            "model.language_model.layers.26.mlp.gate",
+            "model.language_model.layers.27.mlp.gate",
+            "model.language_model.layers.28.mlp.gate",
+            "model.language_model.layers.29.mlp.gate",
+            "model.language_model.layers.3.mlp.gate",
+            "model.language_model.layers.30.mlp.gate",
+            "model.language_model.layers.31.mlp.gate",
+            "model.language_model.layers.32.mlp.gate",
+            "model.language_model.layers.33.mlp.gate",
+            "model.language_model.layers.34.mlp.gate",
+            "model.language_model.layers.35.mlp.gate",
+            "model.language_model.layers.36.mlp.gate",
+            "model.language_model.layers.37.mlp.gate",
+            "model.language_model.layers.38.mlp.gate",
+            "model.language_model.layers.39.mlp.gate",
+            "model.language_model.layers.4.mlp.gate",
+            "model.language_model.layers.40.mlp.gate",
+            "model.language_model.layers.41.mlp.gate",
+            "model.language_model.layers.42.mlp.gate",
+            "model.language_model.layers.43.mlp.gate",
+            "model.language_model.layers.44.mlp.gate",
+            "model.language_model.layers.45.mlp.gate",
+            "model.language_model.layers.46.mlp.gate",
+            "model.language_model.layers.47.mlp.gate",
+            "model.language_model.layers.5.mlp.gate",
+            "model.language_model.layers.6.mlp.gate",
+            "model.language_model.layers.7.mlp.gate",
+            "model.language_model.layers.8.mlp.gate",
+            "model.language_model.layers.9.mlp.gate",
+            "model.visual*"
+        ],
+        "quant_algo": "NVFP4",
+        "producer": {
+            "name": "modelopt",
+            "version": "0.43.0"
+        },
+        "quant_method": "modelopt"
+    }
+}

generation_config.json

@@ -0,0 +1,7 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "pad_token_id": 151643,
+  "transformers_version": "4.57.6"
+}

hf_quant_config.json

@@ -0,0 +1,63 @@
+{
+    "producer": {
+        "name": "modelopt",
+        "version": "0.43.0"
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