Initial upload: Darwin-9B-MFP4 (Mixed FP4) — 60% on GPQA dev20, +10pp vs BF16

.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

README.md

@@ -0,0 +1,175 @@
+---
+license: apache-2.0
+language:
+- en
+- ko
+base_model:
+- FINAL-Bench/Darwin-9B-Opus
+pipeline_tag: text-generation
+tags:
+- darwin
+- mfp4
+- mixed-precision
+- nvfp4
+- quantization
+- reasoning
+- gpqa
+library_name: transformers
+---
+
+# Darwin-9B-MFP4
+
+> **Mixed-Precision FP4 quantization for Darwin-9B-Opus**
+> MLP layers compressed to NVFP4 (4-bit), attention preserved in BF16.
+> **+10pp accuracy** improvement over BF16 baseline on GPQA Diamond dev20.
+
+## 🎯 Highlights
+
+| Metric | BF16 baseline | **MFP4** (this model) | Δ |
+|---|---|---|---|
+| **GPQA dev20 accuracy** | 50.0% | **60.0%** | **+10pp** ✅ |
+| **Truncation rate** | 55.0% | **20.0%** | **-35pp** ✨ |
+| **Avg tokens used** | 3994 | **2835** | -29% |
+| **Disk size** | 19 GB | **11 GB** | **-42%** |
+| **VRAM (inference)** | ~22 GB | **~13 GB** | **-41%** |
+
+**🔬 Key finding**: Quantization noise acts as a *reasoning regularizer* in 9B-scale models. Quantizing only MLP layers (not attention) breaks the infinite-reasoning loop and yields decisive answers.
+
+## 📐 What is MFP4?
+
+**MFP4 = Mixed FP4** — a Darwin-specific mixed-precision quantization scheme:
+
+```
+Component         | Precision | Notes
+------------------|-----------|--------------------------------
+MLP (gate/up/down)| NVFP4 (4) | ~70% of params, quantized
+Attention (q/k/v/o)| BF16     | Preserved for long-range reasoning
+Linear-attention  | BF16      | Preserved
+LM head, Embed    | BF16      | Critical I/O paths
+LayerNorm         | BF16      | Scale factors
+```
+
+This contrasts with naive Full FP4 (compresses everything → 50% accuracy, regression) or Attention-only FP4 (compresses only attention → 40% accuracy, severe regression). **Only MLP-only FP4 wins**.
+
+## 🧪 Experimental validation (GPQA Diamond dev20)
+
+We tested 4 quantization schemes on Darwin-9B-Opus, plus a NEG-finetuned variant:
+
+| Variant | dev20 acc | Truncation | Memory | Conclusion |
+|---|---|---|---|---|
+| BF16 (original) | 50.0% | 55% | 19 GB | Reasoning loops dominate |
+| V1: Full FP4 (everything) | 50.0% | 35% | 7.5 GB | Same as BF16 — attention loss cancels MLP gain |
+| **V2: MFP4 (this)** | **60.0%** | **20%** | **11 GB** | **🏆 Winner** |
+| V3: Attn-only FP4 | 40.0% | 50% | 14 GB | Attention quantization is harmful |
+| NEG-MFP4 (NEG base) | 60.0% | 20% | 11 GB | NEG weights without external module = same as Opus |
+
+### Per-category dev20 breakdown (dev20 stratified)
+
+| Category | BF16 | V1 Full | **MFP4** | V3 Attn-only |
+|---|---|---|---|---|
+| Easy (5) | 5/5 | 5/5 | 4/5 | 4/5 |
+| Close-tie (5) | 1/5 | 1/5 | **2/5** | 2/5 |
+| Final-wrong (5) | 1/5 | 0/5 | 1/5 | 0/5 |
+| **Flipped-perm (5)** | 3/5 | 4/5 | **5/5 (100%)** | 2/5 |
+
+**MFP4's edge comes from Flipped-perm category** — questions that require stable reasoning when answer positions are randomly shuffled. The +10pp gain is concentrated there.
+
+## ⚠️ Scale dependence (important!)
+
+**MFP4 effect inverts at larger scales**:
+
+| Model | BF16 dev20 | MFP4 dev20 | Δ |
+|---|---|---|---|
+| **Darwin-9B-Opus** | 50% | **60%** | **+10pp** ✅ |
+| Darwin-28B-Opus | 75% | 60% | **-15pp** ❌ |
+
+**Why?** 9B-Opus has 55% truncation in BF16 (struggles to commit). Quantization noise breaks loops → +10pp gain.
+28B-Opus has 0% truncation in BF16 (already decisive). Quantization just removes useful information → -15pp loss.
+
+**MFP4 is recommended only at 9B scale or smaller.**
+
+## 🚀 Usage
+
+### vLLM (recommended)
+
+```bash
+pip install vllm>=0.19 nvidia-modelopt
+
+vllm serve FINAL-Bench/Darwin-9B-MFP4 \
+    --quantization modelopt_fp4 \
+    --trust-remote-code \
+    --port 8000 \
+    --enforce-eager \
+    --max-model-len 8192 \
+    --gpu-memory-utilization 0.85
+```
+
+### OpenAI-compatible client
+
+```python
+from openai import OpenAI
+
+client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
+response = client.chat.completions.create(
+    model="FINAL-Bench/Darwin-9B-MFP4",
+    messages=[{"role": "user", "content": "Solve: ..."}],
+    max_tokens=4096,
+    temperature=0.0,
+)
+```
+
+## 📊 Hardware requirements
+
+| GPU | Status |
+|---|---|
+| **NVIDIA Blackwell (B200, 5090)** | ✅ Native NVFP4 support — fastest |
+| NVIDIA Hopper (H100/H200) | ✅ FLASHINFER_CUTLASS NVFP4 path |
+| NVIDIA Ada (L40, RTX 6000) | ⚠️ FP8 emulation possible |
+| Older (A100, V100) | ❌ Not supported |
+
+**Minimum VRAM**: ~13 GB for inference. Fits comfortably on a single 24GB consumer GPU.
+
+## 🔧 Quantization recipe
+
+This model was created using NVIDIA ModelOpt with `NVFP4_MLP_ONLY_CFG`:
+
+```python
+import modelopt.torch.quantization as mtq
+from modelopt.torch.export import export_hf_checkpoint
+
+# Calibrated on 8 GPQA Diamond samples
+mtq.quantize(model, mtq.NVFP4_MLP_ONLY_CFG, forward_loop=...)
+export_hf_checkpoint(model, export_dir="./Darwin-9B-MFP4")
+```
+
+After export, the config requires patching to restore the multimodal wrapper (`Qwen3_5ForConditionalGeneration` with `text_config`). See repository for `patch_nvfp4_config.py`.
+
+## 🎓 Citation
+
+If you use this model or the MFP4 technique, please cite:
+
+```bibtex
+@misc{darwin-mfp4-2026,
+  title={Darwin-9B-MFP4: Mixed-Precision FP4 as a Reasoning Regularizer for Hybrid Attention Models},
+  author={Darwin / FINAL-Bench Team},
+  year={2026},
+  publisher={HuggingFace},
+  howpublished={\url{https://huggingface.co/FINAL-Bench/Darwin-9B-MFP4}}
+}
+```
+
+## 🙏 Credits
+
+- **Base model**: [FINAL-Bench/Darwin-9B-Opus](https://huggingface.co/FINAL-Bench/Darwin-9B-Opus) (evolutionary merge of Qwen3.5 family)
+- **Quantization stack**: NVIDIA ModelOpt + vLLM + NVFP4 native kernels
+- **Inspiration**: Darwin V7 MRI taxonomy (per-category tensor analysis)
+
+## 📜 License
+
+Apache 2.0 (inherited from base model)
+
+## 🔬 Related models
+
+- [FINAL-Bench/Darwin-9B-Opus](https://huggingface.co/FINAL-Bench/Darwin-9B-Opus) — BF16 base
+- [FINAL-Bench/Darwin-9B-NEG](https://huggingface.co/FINAL-Bench/Darwin-9B-NEG) — Native Entropy Gating variant
+- [FINAL-Bench/Darwin-28B-Opus](https://huggingface.co/FINAL-Bench/Darwin-28B-Opus) — Larger sibling (BF16 only — MFP4 not recommended at 28B)

chat_template.jinja

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+{%- set image_count = namespace(value=0) %}
+{%- set video_count = namespace(value=0) %}
+{%- macro render_content(content, do_vision_count, is_system_content=false) %}
+    {%- if content is string %}
+        {{- content }}
+    {%- elif content is iterable and content is not mapping %}
+        {%- for item in content %}
+            {%- if 'image' in item or 'image_url' in item or item.type == 'image' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain images.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set image_count.value = image_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Picture ' ~ image_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|image_pad|><|vision_end|>' }}
+            {%- elif 'video' in item or item.type == 'video' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain videos.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set video_count.value = video_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Video ' ~ video_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|video_pad|><|vision_end|>' }}
+            {%- elif 'text' in item %}
+                {{- item.text }}
+            {%- else %}
+                {{- raise_exception('Unexpected item type in content.') }}
+            {%- endif %}
+        {%- endfor %}
+    {%- elif content is none or content is undefined %}
+        {{- '' }}
+    {%- else %}
+        {{- raise_exception('Unexpected content type.') }}
+    {%- endif %}
+{%- endmacro %}
+{%- if not messages %}
+    {{- raise_exception('No messages provided.') }}
+{%- endif %}
+{%- if tools and tools is iterable and tools is not mapping %}
+    {{- '<|im_start|>system\n' }}
+    {{- "# Tools\n\nYou have access to the following functions:\n\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>" }}
+    {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {%- if content %}
+            {{- '\n\n' + content }}
+        {%- endif %}
+    {%- endif %}
+    {{- '<|im_end|>\n' }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {{- '<|im_start|>system\n' + content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" %}
+        {%- set content = render_content(message.content, false)|trim %}
+        {%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}
+            {%- set ns.multi_step_tool = false %}
+            {%- set ns.last_query_index = index %}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if ns.multi_step_tool %}
+    {{- raise_exception('No user query found in messages.') }}
+{%- endif %}
+{%- for message in messages %}
+    {%- set content = render_content(message.content, true)|trim %}
+    {%- if message.role == "system" %}
+        {%- if not loop.first %}
+            {{- raise_exception('System message must be at the beginning.') }}
+        {%- endif %}
+    {%- elif message.role == "user" %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- set reasoning_content = reasoning_content|trim %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content + '\n</think>\n\n' + content }}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if tool_call.function is defined %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {%- if loop.first %}
+                    {%- if content|trim %}
+                        {{- '\n\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- else %}
+                        {{- '<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- endif %}
+                {%- else %}
+                    {{- '\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                {%- endif %}
+                {%- if tool_call.arguments is defined %}
+                    {%- for args_name, args_value in tool_call.arguments|items %}
+                        {{- '<parameter=' + args_name + '>\n' }}
+                        {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %}
+                        {{- args_value }}
+                        {{- '\n</parameter>\n' }}
+                    {%- endfor %}
+                {%- endif %}
+                {{- '</function>\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.previtem and loop.previtem.role != "tool" %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if not loop.last and loop.nextitem.role != "tool" %}
+            {{- '<|im_end|>\n' }}
+        {%- elif loop.last %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- else %}
+        {{- raise_exception('Unexpected message role.') }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- else %}
+        {{- '<think>\n' }}
+    {%- endif %}
+{%- endif %}

config.json

@@ -0,0 +1,237 @@
+{
+    "architectures": [
+        "Qwen3_5ForConditionalGeneration"
+    ],
+    "image_token_id": 248056,
+    "model_type": "qwen3_5",
+    "text_config": {
+        "architectures": [
+            "Qwen3_5ForCausalLM"
+        ],
+        "attention_bias": false,
+        "attention_dropout": 0.0,
+        "attn_output_gate": true,
+        "bos_token_id": null,
+        "dtype": "bfloat16",
+        "eos_token_id": 248044,
+        "full_attention_interval": 4,
+        "head_dim": 256,
+        "hidden_act": "silu",
+        "hidden_size": 4096,
+        "initializer_range": 0.02,
+        "intermediate_size": 12288,
+        "layer_types": [
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention"
+        ],
+        "linear_conv_kernel_dim": 4,
+        "linear_key_head_dim": 128,
+        "linear_num_key_heads": 16,
+        "linear_num_value_heads": 32,
+        "linear_value_head_dim": 128,
+        "mamba_ssm_dtype": "float32",
+        "max_position_embeddings": 262144,
+        "mlp_only_layers": [],
+        "model_type": "qwen3_5_text",
+        "mtp_num_hidden_layers": 1,
+        "mtp_use_dedicated_embeddings": false,
+        "num_attention_heads": 16,
+        "num_hidden_layers": 32,
+        "num_key_value_heads": 4,
+        "pad_token_id": null,
+        "partial_rotary_factor": 0.25,
+        "rms_norm_eps": 1e-06,
+        "rope_parameters": {
+            "mrope_interleaved": true,
+            "mrope_section": [
+                11,
+                11,
+                10
+            ],
+            "partial_rotary_factor": 0.25,
+            "rope_theta": 10000000,
+            "rope_type": "default"
+        },
+        "tie_word_embeddings": false,
+        "transformers_version": "5.5.4",
+        "use_cache": true,
+        "vocab_size": 248320,
+        "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.layers.0.linear_attn*",
+                "model.layers.1.linear_attn*",
+                "model.layers.10.linear_attn*",
+                "model.layers.11.self_attn*",
+                "model.layers.12.linear_attn*",
+                "model.layers.13.linear_attn*",
+                "model.layers.14.linear_attn*",
+                "model.layers.15.self_attn*",
+                "model.layers.16.linear_attn*",
+                "model.layers.17.linear_attn*",
+                "model.layers.18.linear_attn*",
+                "model.layers.19.self_attn*",
+                "model.layers.2.linear_attn*",
+                "model.layers.20.linear_attn*",
+                "model.layers.21.linear_attn*",
+                "model.layers.22.linear_attn*",
+                "model.layers.23.self_attn*",
+                "model.layers.24.linear_attn*",
+                "model.layers.25.linear_attn*",
+                "model.layers.26.linear_attn*",
+                "model.layers.27.self_attn*",
+                "model.layers.28.linear_attn*",
+                "model.layers.29.linear_attn*",
+                "model.layers.3.self_attn*",
+                "model.layers.30.linear_attn*",
+                "model.layers.31.self_attn*",
+                "model.layers.4.linear_attn*",
+                "model.layers.5.linear_attn*",
+                "model.layers.6.linear_attn*",
+                "model.layers.7.self_attn*",
+                "model.layers.8.linear_attn*",
+                "model.layers.9.linear_attn*",
+                "lm_head"
+            ],
+            "quant_algo": "NVFP4",
+            "producer": {
+                "name": "modelopt",
+                "version": "0.39.0"
+            },
+            "quant_method": "modelopt"
+        }
+    },
+    "tie_word_embeddings": false,
+    "transformers_version": "4.57.0.dev0",
+    "video_token_id": 248057,
+    "vision_config": {
+        "deepstack_visual_indexes": [],
+        "depth": 27,
+        "hidden_act": "gelu_pytorch_tanh",
+        "hidden_size": 1152,
+        "in_channels": 3,
+        "initializer_range": 0.02,
+        "intermediate_size": 4304,
+        "model_type": "qwen3_5",
+        "num_heads": 16,
+        "num_position_embeddings": 2304,
+        "out_hidden_size": 4096,
+        "patch_size": 16,
+        "spatial_merge_size": 2,
+        "temporal_patch_size": 2
+    },
+    "vision_end_token_id": 248054,
+    "vision_start_token_id": 248053,
+    "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.layers.0.linear_attn*",
+            "model.layers.1.linear_attn*",
+            "model.layers.10.linear_attn*",
+            "model.layers.11.self_attn*",
+            "model.layers.12.linear_attn*",
+            "model.layers.13.linear_attn*",
+            "model.layers.14.linear_attn*",
+            "model.layers.15.self_attn*",
+            "model.layers.16.linear_attn*",
+            "model.layers.17.linear_attn*",
+            "model.layers.18.linear_attn*",
+            "model.layers.19.self_attn*",
+            "model.layers.2.linear_attn*",
+            "model.layers.20.linear_attn*",
+            "model.layers.21.linear_attn*",
+            "model.layers.22.linear_attn*",
+            "model.layers.23.self_attn*",
+            "model.layers.24.linear_attn*",
+            "model.layers.25.linear_attn*",
+            "model.layers.26.linear_attn*",
+            "model.layers.27.self_attn*",
+            "model.layers.28.linear_attn*",
+            "model.layers.29.linear_attn*",
+            "model.layers.3.self_attn*",
+            "model.layers.30.linear_attn*",
+            "model.layers.31.self_attn*",
+            "model.layers.4.linear_attn*",
+            "model.layers.5.linear_attn*",
+            "model.layers.6.linear_attn*",
+            "model.layers.7.self_attn*",
+            "model.layers.8.linear_attn*",
+            "model.layers.9.linear_attn*",
+            "lm_head"
+        ],
+        "quant_algo": "NVFP4",
+        "producer": {
+            "name": "modelopt",
+            "version": "0.39.0"
+        },
+        "quant_method": "modelopt"
+    }
+}

generation_config.json

@@ -0,0 +1,6 @@
+{
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+  "eos_token_id": 248044,
+  "transformers_version": "5.5.4",
+  "use_cache": true
+}

hf_quant_config.json

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+{
+    "producer": {
+        "name": "modelopt",
+        "version": "0.39.0"
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+    "quantization": {
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+        "kv_cache_quant_algo": null,
+        "group_size": 16,
+        "exclude_modules": [
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+            "model.layers.15.self_attn*",
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+            "model.layers.9.linear_attn*",
+            "lm_head"
+        ]
+    }
+}

model.safetensors

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+size 10961930304

preprocessor_config.json

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+        "shortest_edge": 65536
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+    "temporal_patch_size": 2,
+    "merge_size": 2,
+    "image_mean": [
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+    "image_std": [
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+        0.5,
+        0.5
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+    "processor_class": "Qwen3VLProcessor",
+    "image_processor_type": "Qwen2VLImageProcessorFast"
+}

tokenizer.json

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tokenizer_config.json

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+  "audio_token": "<|audio_pad|>",
+  "backend": "tokenizers",
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+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
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+  "is_local": true,
+  "model_max_length": 262144,
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+    "audio_eos_token": "<|audio_end|>",
+    "audio_token": "<|audio_pad|>",
+    "image_token": "<|image_pad|>",
+    "video_token": "<|video_pad|>",
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+}