Publish AX-Clef-Flash-9B-CUDA-AXQ-NVFP4-W4A16

.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

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NOTICE

@@ -0,0 +1,10 @@
+This directory is a quantized CUDA derivative of Cloudflare/clef-flash.
+
+Changes from the upstream BF16 release:
+- Eligible language attention and MLP weights were quantized to NVFP4 W4A16
+  by AXQuant (round-to-nearest, block 16, no AWQ).
+- Vision, norms, token embeddings, and the LM head stay at source precision.
+- joint_head.safetensors is the original BF16 joint schema head, copied unchanged.
+- Activations are not quantized.
+
+The upstream work is Apache-2.0. See LICENSE (Copyright 2026 Alibaba Cloud).

README.md

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+---
+library_name: transformers
+pipeline_tag: text-generation
+base_model: Cloudflare/clef-flash
+license: apache-2.0
+tags:
+- axquant
+- cuda
+- nvfp4
+- clef
+- decision-model
+---
+
+# AX-Clef-Flash-9B-CUDA-AXQ-NVFP4-W4A16
+
+Native AXQuant RTN NVFP4 W4A16 development checkpoint. Eligible language attention and MLP matrices use E2M1 weights, E4M3FN block scales of 16, and an FP32 inverse global scale. Activations stay BF16. There is no AWQ and no activation quantization. The checkpoint uses the public compressed-tensors NVFP4 layout.
+
+Source: [Cloudflare/clef-flash](https://huggingface.co/Cloudflare/clef-flash) at `8b2e5fd17c09fd49bc2880b805d5323ec7ff4ff3`. Serialization backend: `torch-cuda`.
+
+## Decision model
+
+Clef is not a chat model. A request is a state plus a schema of typed questions (`noul`, `choice`, `score`). One prefill returns a probability for every allowed option. `joint_head.safetensors` is the original BF16 joint schema head, copied unchanged, with `joint_schema_model.py` and `joint_head_config.json`. Text generation does not run that head.
+
+## Precision
+
+Vision, norms, token embeddings, and the LM head stay at source precision. The joint schema head is not part of the language index and is not quantized. This package is development evidence. It is not a Decision Index certificate and it does not claim parity with the upstream BF16 checkpoint.
+
+The upstream license is Apache-2.0. See LICENSE and NOTICE.

axquant_cuda_manifest.json

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+  ],
+  "runtime_verified": false,
+  "schema_version": "axquant.cuda-pack.v1",
+  "status": "development"
+}

chat_template.jinja

@@ -0,0 +1,154 @@
+{%- 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 +1,435 @@
+{
+  "architectures": [
+    "Qwen3_5ForConditionalGeneration"
+  ],
+  "dtype": "bfloat16",
+  "image_token_id": 248056,
+  "model_type": "qwen3_5",
+  "quantization_config": {
+    "config_groups": {
+      "nvfp4": {
+        "format": "nvfp4-pack-quantized",
+        "input_activations": null,
+        "output_activations": null,
+        "targets": [
+          "Linear"
+        ],
+        "weights": {
+          "dynamic": false,
+          "group_size": 16,
+          "num_bits": 4,
+          "scale_dtype": "float8_e4m3fn",
+          "strategy": "tensor_group",
+          "symmetric": true,
+          "type": "float"
+        }
+      }
+    },
+    "format": "nvfp4-pack-quantized",
+    "ignore": [
+      "lm_head",
+      "model.language_model.embed_tokens",
+      "model.language_model.layers.0.input_layernorm",
+      "model.language_model.layers.0.linear_attn.conv1d",
+      "model.language_model.layers.0.linear_attn.norm",
+      "model.language_model.layers.0.post_attention_layernorm",
+      "model.language_model.layers.1.input_layernorm",
+      "model.language_model.layers.1.linear_attn.conv1d",
+      "model.language_model.layers.1.linear_attn.norm",
+      "model.language_model.layers.1.post_attention_layernorm",
+      "model.language_model.layers.10.input_layernorm",
+      "model.language_model.layers.10.linear_attn.conv1d",
+      "model.language_model.layers.10.linear_attn.norm",
+      "model.language_model.layers.10.post_attention_layernorm",
+      "model.language_model.layers.11.input_layernorm",
+      "model.language_model.layers.11.post_attention_layernorm",
+      "model.language_model.layers.11.self_attn.k_norm",
+      "model.language_model.layers.11.self_attn.q_norm",
+      "model.language_model.layers.12.input_layernorm",
+      "model.language_model.layers.12.linear_attn.conv1d",
+      "model.language_model.layers.12.linear_attn.norm",
+      "model.language_model.layers.12.post_attention_layernorm",
+      "model.language_model.layers.13.input_layernorm",
+      "model.language_model.layers.13.linear_attn.conv1d",
+      "model.language_model.layers.13.linear_attn.norm",
+      "model.language_model.layers.13.post_attention_layernorm",
+      "model.language_model.layers.14.input_layernorm",
+      "model.language_model.layers.14.linear_attn.conv1d",
+      "model.language_model.layers.14.linear_attn.norm",
+      "model.language_model.layers.14.post_attention_layernorm",
+      "model.language_model.layers.15.input_layernorm",
+      "model.language_model.layers.15.post_attention_layernorm",
+      "model.language_model.layers.15.self_attn.k_norm",
+      "model.language_model.layers.15.self_attn.q_norm",
+      "model.language_model.layers.16.input_layernorm",
+      "model.language_model.layers.16.linear_attn.conv1d",
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+      "model.language_model.layers.16.post_attention_layernorm",
+      "model.language_model.layers.17.input_layernorm",
+      "model.language_model.layers.17.linear_attn.conv1d",
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+      "model.language_model.layers.18.input_layernorm",
+      "model.language_model.layers.18.linear_attn.conv1d",
+      "model.language_model.layers.18.linear_attn.norm",
+      "model.language_model.layers.18.post_attention_layernorm",
+      "model.language_model.layers.19.input_layernorm",
+      "model.language_model.layers.19.post_attention_layernorm",
+      "model.language_model.layers.19.self_attn.k_norm",
+      "model.language_model.layers.19.self_attn.q_norm",
+      "model.language_model.layers.2.input_layernorm",
+      "model.language_model.layers.2.linear_attn.conv1d",
+      "model.language_model.layers.2.linear_attn.norm",
+      "model.language_model.layers.2.post_attention_layernorm",
+      "model.language_model.layers.20.input_layernorm",
+      "model.language_model.layers.20.linear_attn.conv1d",
+      "model.language_model.layers.20.linear_attn.norm",
+      "model.language_model.layers.20.post_attention_layernorm",
+      "model.language_model.layers.21.input_layernorm",
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+      "model.language_model.layers.21.post_attention_layernorm",
+      "model.language_model.layers.22.input_layernorm",
+      "model.language_model.layers.22.linear_attn.conv1d",
+      "model.language_model.layers.22.linear_attn.norm",
+      "model.language_model.layers.22.post_attention_layernorm",
+      "model.language_model.layers.23.input_layernorm",
+      "model.language_model.layers.23.post_attention_layernorm",
+      "model.language_model.layers.23.self_attn.k_norm",
+      "model.language_model.layers.23.self_attn.q_norm",
+      "model.language_model.layers.24.input_layernorm",
+      "model.language_model.layers.24.linear_attn.conv1d",
+      "model.language_model.layers.24.linear_attn.norm",
+      "model.language_model.layers.24.post_attention_layernorm",
+      "model.language_model.layers.25.input_layernorm",
+      "model.language_model.layers.25.linear_attn.conv1d",
+      "model.language_model.layers.25.linear_attn.norm",
+      "model.language_model.layers.25.post_attention_layernorm",
+      "model.language_model.layers.26.input_layernorm",
+      "model.language_model.layers.26.linear_attn.conv1d",
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+    "mlp_only_layers": [],
+    "model_type": "qwen3_5_text",
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+  "torch_dtype": "bfloat16",
+  "transformers_version": "5.10.2",
+  "video_token_id": 248057,
+  "vision_config": {
+    "deepstack_visual_indexes": [],
+    "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_5_vision",
+    "num_heads": 16,
+    "num_position_embeddings": 2304,
+    "out_hidden_size": 4096,
+    "patch_size": 16,
+    "spatial_merge_size": 2,
+    "temporal_patch_size": 2
+  },
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+  "vision_start_token_id": 248053
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "eos_token_id": 248044,
+  "transformers_version": "5.10.2",
+  "use_cache": true
+}

joint_head.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:19cdcec8c81dc9212be320fff47462ab342fbc1278be4368fb3da71241cf5ba0
+size 243538016

joint_head_config.json

@@ -0,0 +1,8 @@
+{
+  "hidden_size": 4096,
+  "width": 1024,
+  "routing_layers": 2,
+  "layers": 4,
+  "heads": 16,
+  "feedforward": 4096
+}

joint_schema_model.py

@@ -0,0 +1,576 @@
+"""Clef: a multimodal Qwen backbone with a joint schema head for typed decisions.
+
+A record provides a ``state`` (any JSON value), optional ``images`` and ``videos``,
+and ``questions``. Each question has a ``type`` (``noul``, ``choice``, or ``score``),
+``instructions``, and, for ``choice`` and ``score``, ``criteria`` describing the
+allowed options. The model returns one logit per allowed option for every question.
+``systemone`` answers a Jev/SystemOne ``/v1/systemone`` request body with the same
+response body.
+"""
+
+from __future__ import annotations
+
+import json
+import math
+from dataclasses import dataclass
+from dataclasses import field as dataclass_field
+from pathlib import Path
+from typing import Any
+
+import torch
+import torch.nn.functional as functional
+
+
+SYSTEM_PROMPT = (
+    "Read the complete state and schema. Decide every field jointly. Each answer "
+    "must be exactly one of that field's allowed options."
+)
+IMAGE_PLACEHOLDER = "<|vision_start|><|image_pad|><|vision_end|>"
+VIDEO_PLACEHOLDER = "<|vision_start|><|video_pad|><|vision_end|>"
+MEDIA_BATCH_KEYS = ("pixel_values", "image_grid_thw", "pixel_values_videos", "video_grid_thw")
+MEDIA_TOKEN_KEYS = ("mm_token_type_ids",)
+QUESTION_TYPES = {"noul": 0, "choice": 1, "score": 2}
+
+
+def render(value: Any) -> str:
+    if isinstance(value, str):
+        return value
+    return json.dumps(
+        value,
+        ensure_ascii=False,
+        separators=(",", ":"),
+        sort_keys=True,
+    )
+
+
+def question_options(question: dict[str, Any]) -> list[tuple[str, Any]]:
+    question_type = str(question["type"])
+    if question_type == "noul":
+        criteria = {
+            "true": "The proposition is true or the answer is yes.",
+            "false": "The proposition is false or the answer is no.",
+        }
+        criteria.update(question.get("criteria") or {})
+        return [(key, criteria[key]) for key in ("true", "false")]
+    if question_type == "choice":
+        return sorted((str(key), value) for key, value in question["criteria"].items())
+    return [(str(index), value) for index, value in enumerate(question["criteria"])]
+
+
+@dataclass(frozen=True)
+class EncodedQuestion:
+    question_id: str
+    question_type: int
+    question_span: tuple[int, int]
+    option_spans: tuple[tuple[int, int], ...]
+    option_ids: tuple[str, ...]
+
+
+@dataclass(frozen=True)
+class EncodedRecord:
+    input_ids: tuple[int, ...]
+    questions: tuple[EncodedQuestion, ...]
+    record_id: str
+    media: dict[str, Any] | None = dataclass_field(default=None, compare=False, repr=False)
+
+
+def _tokens(tokenizer: Any, text: str) -> list[int]:
+    return tokenizer(text, add_special_tokens=False).input_ids
+
+
+def _encode_media(processor: Any, record: dict[str, Any]) -> tuple[list[int], dict[str, Any] | None]:
+    images = list(record.get("images") or [])
+    videos = list(record.get("videos") or [])
+    if not images and not videos:
+        return [], None
+    if processor is None:
+        raise ValueError("records with images or videos require a processor")
+    text = IMAGE_PLACEHOLDER * len(images) + VIDEO_PLACEHOLDER * len(videos) + "\n"
+    encoded = processor(
+        text=[text],
+        images=images or None,
+        videos=videos or None,
+        return_tensors="pt",
+        **(record.get("media_kwargs") or {}),
+    )
+    media = {key: encoded[key] for key in MEDIA_BATCH_KEYS if key in encoded}
+    for key in MEDIA_TOKEN_KEYS:
+        if key in encoded:
+            media[key] = encoded[key][0].tolist()
+    return encoded["input_ids"][0].tolist(), media
+
+
+def encode_record(
+    tokenizer: Any,
+    record: dict[str, Any],
+    max_length: int = 16384,
+    max_state_tokens: int | None = None,
+    processor: Any | None = None,
+) -> EncodedRecord:
+    schema_ids = _tokens(tokenizer, "\n\nSCHEMA FIELDS:\n")
+    questions: list[EncodedQuestion] = []
+    for question_index, (question_id, question) in enumerate(record["questions"].items()):
+        schema_ids.extend(
+            _tokens(
+                tokenizer,
+                f"\nFIELD {question_index + 1}\nID: {question_id}\nTYPE: {question['type']}\nINSTRUCTION: ",
+            )
+        )
+        question_start = len(schema_ids)
+        instructions = question.get("instructions")
+        if instructions is None or instructions == "":
+            instructions = str(question_id)
+        schema_ids.extend(_tokens(tokenizer, render(instructions)))
+        question_end = len(schema_ids)
+        schema_ids.extend(_tokens(tokenizer, "\nALLOWED OPTIONS:\n"))
+
+        option_spans: list[tuple[int, int]] = []
+        option_ids: list[str] = []
+        for option_index, (option_id, description) in enumerate(question_options(question)):
+            schema_ids.extend(_tokens(tokenizer, f"OPTION {option_index + 1}: "))
+            option_start = len(schema_ids)
+            semantics = {"option_id": option_id}
+            if description is not None:
+                semantics["description"] = description
+            schema_ids.extend(_tokens(tokenizer, render(semantics)))
+            option_spans.append((option_start, len(schema_ids)))
+            option_ids.append(option_id)
+            schema_ids.extend(_tokens(tokenizer, "\n"))
+        schema_ids.extend(_tokens(tokenizer, "END FIELD\n"))
+        questions.append(
+            EncodedQuestion(
+                question_id=str(question_id),
+                question_type=QUESTION_TYPES[str(question["type"])],
+                question_span=(question_start, question_end),
+                option_spans=tuple(option_spans),
+                option_ids=tuple(option_ids),
+            )
+        )
+
+    prefix_ids = _tokens(
+        tokenizer,
+        f"<|im_start|>system\n{SYSTEM_PROMPT}<|im_end|>\n<|im_start|>user\nSTATE:\n",
+    )
+    suffix_ids = _tokens(
+        tokenizer,
+        "\n<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\nJOINT SCHEMA DECISIONS:",
+    )
+    media_ids, media = _encode_media(processor, record)
+    if media is not None:
+        media["token_offset"] = len(prefix_ids)
+        prefix_ids = prefix_ids + media_ids
+    state_ids = _tokens(tokenizer, render(record["state"]))
+    if max_state_tokens is not None:
+        state_ids = state_ids[:max_state_tokens]
+    fixed_length = len(prefix_ids) + len(schema_ids) + len(suffix_ids)
+    if fixed_length > max_length:
+        raise ValueError(
+            f"schema requires {fixed_length} tokens before state; maximum is {max_length}"
+        )
+    state_ids = state_ids[: max_length - fixed_length]
+    schema_offset = len(prefix_ids) + len(state_ids)
+    shifted_questions = tuple(
+        EncodedQuestion(
+            question_id=question.question_id,
+            question_type=question.question_type,
+            question_span=(
+                question.question_span[0] + schema_offset,
+                question.question_span[1] + schema_offset,
+            ),
+            option_spans=tuple(
+                (start + schema_offset, end + schema_offset)
+                for start, end in question.option_spans
+            ),
+            option_ids=question.option_ids,
+        )
+        for question in questions
+    )
+    input_ids = tuple(prefix_ids + state_ids + schema_ids + suffix_ids)
+    if not input_ids or not shifted_questions:
+        raise ValueError("record produced no model input or questions")
+    return EncodedRecord(
+        input_ids=input_ids,
+        questions=shifted_questions,
+        record_id=str(record.get("id", "unknown")),
+        media=media,
+    )
+
+
+def collate_records(
+    records: list[EncodedRecord],
+    pad_token_id: int,
+    device: torch.device,
+) -> dict[str, Any]:
+    maximum_length = max(len(record.input_ids) for record in records)
+    input_ids = torch.full(
+        (len(records), maximum_length),
+        pad_token_id,
+        dtype=torch.long,
+        device=device,
+    )
+    attention_mask = torch.zeros(
+        (len(records), maximum_length),
+        dtype=torch.long,
+        device=device,
+    )
+    for index, record in enumerate(records):
+        length = len(record.input_ids)
+        input_ids[index, :length] = torch.tensor(record.input_ids, device=device)
+        attention_mask[index, :length] = 1
+    media: dict[str, torch.Tensor] = {}
+    for key in MEDIA_BATCH_KEYS:
+        values = [record.media[key] for record in records if record.media and key in record.media]
+        if values:
+            media[key] = torch.cat(values, dim=0).to(device)
+    for key in MEDIA_TOKEN_KEYS:
+        if any(record.media and key in record.media for record in records):
+            token_values = torch.zeros((len(records), maximum_length), dtype=torch.long, device=device)
+            for index, record in enumerate(records):
+                if record.media and key in record.media:
+                    offset = record.media["token_offset"]
+                    values = torch.tensor(record.media[key], dtype=torch.long, device=device)
+                    token_values[index, offset : offset + len(values)] = values
+            media[key] = token_values
+    return {
+        "input_ids": input_ids,
+        "attention_mask": attention_mask,
+        "records": records,
+        "media": media,
+    }
+
+
+class EvidenceRoutingLayer(torch.nn.Module):
+    def __init__(
+        self,
+        width: int,
+        heads: int,
+        feedforward: int,
+        dropout: float = 0.0,
+    ) -> None:
+        super().__init__()
+        self.query_norm = torch.nn.LayerNorm(width)
+        self.memory_norm = torch.nn.LayerNorm(width)
+        self.attention = torch.nn.MultiheadAttention(
+            width,
+            heads,
+            dropout=dropout,
+            batch_first=True,
+        )
+        self.attention_dropout = torch.nn.Dropout(dropout)
+        self.feedforward_norm = torch.nn.LayerNorm(width)
+        self.feedforward = torch.nn.Sequential(
+            torch.nn.Linear(width, feedforward),
+            torch.nn.GELU(),
+            torch.nn.Dropout(dropout),
+            torch.nn.Linear(feedforward, width),
+            torch.nn.Dropout(dropout),
+        )
+
+    def forward(self, queries: torch.Tensor, memory: torch.Tensor) -> torch.Tensor:
+        normalized_queries = self.query_norm(queries)
+        routed, _ = self.attention(
+            normalized_queries,
+            self.memory_norm(memory),
+            self.memory_norm(memory),
+            need_weights=False,
+        )
+        queries = queries + self.attention_dropout(routed)
+        return queries + self.feedforward(self.feedforward_norm(queries))
+
+
+class JointSchemaHead(torch.nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        width: int,
+        routing_layers: int,
+        layers: int,
+        heads: int,
+        feedforward: int,
+        dropout: float = 0.0,
+    ) -> None:
+        super().__init__()
+        self.hidden_norm = torch.nn.LayerNorm(hidden_size)
+        self.memory_projection = torch.nn.Linear(hidden_size, width, bias=False)
+        self.question_projection = torch.nn.Linear(hidden_size, width, bias=False)
+        self.option_question_projection = torch.nn.Linear(hidden_size, width, bias=False)
+        self.global_projection = torch.nn.Linear(hidden_size, width, bias=False)
+        self.option_context_projection = torch.nn.Linear(hidden_size, width, bias=False)
+        self.option_lexical_projection = torch.nn.Linear(hidden_size, width, bias=False)
+        self.type_embedding = torch.nn.Embedding(3, width)
+        self.evidence_layers = torch.nn.ModuleList(
+            [
+                EvidenceRoutingLayer(
+                    width=width,
+                    heads=heads,
+                    feedforward=feedforward,
+                    dropout=dropout,
+                )
+                for _ in range(routing_layers)
+            ]
+        )
+        self.option_summary_norm = torch.nn.LayerNorm(width)
+        self.layers = torch.nn.ModuleList(
+            [
+                torch.nn.TransformerDecoderLayer(
+                    d_model=width,
+                    nhead=heads,
+                    dim_feedforward=feedforward,
+                    dropout=dropout,
+                    activation="gelu",
+                    batch_first=True,
+                    norm_first=True,
+                )
+                for _ in range(layers)
+            ]
+        )
+        self.field_norm = torch.nn.LayerNorm(width)
+        self.option_norm = torch.nn.LayerNorm(width)
+        self.residual_scorer = torch.nn.Sequential(
+            torch.nn.Linear(width * 4, width),
+            torch.nn.GELU(),
+            torch.nn.Dropout(dropout),
+            torch.nn.Linear(width, 1),
+        )
+        self.prior_logit_scale = torch.nn.Parameter(torch.zeros(()))
+        self.joint_logit_scale = torch.nn.Parameter(torch.zeros(()))
+        self.residual_gate = torch.nn.Parameter(torch.zeros(()))
+
+    @staticmethod
+    def _mean_span(values: torch.Tensor, span: tuple[int, int]) -> torch.Tensor:
+        start, end = span
+        return values[start:end].mean(dim=0)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        input_ids: torch.Tensor,
+        attention_mask: torch.Tensor,
+        records: list[EncodedRecord],
+        output_embedding_weight: torch.Tensor,
+    ) -> list[list[torch.Tensor]]:
+        results: list[list[torch.Tensor]] = []
+        normalized_hidden = self.hidden_norm(hidden_states)
+        for batch_index, record in enumerate(records):
+            sequence_length = int(attention_mask[batch_index].sum().item())
+            sequence_hidden = normalized_hidden[batch_index, :sequence_length]
+            memory = self.memory_projection(sequence_hidden).unsqueeze(0)
+            global_vector = sequence_hidden[-1]
+            question_vectors = torch.stack(
+                [
+                    self._mean_span(sequence_hidden, question.question_span)
+                    for question in record.questions
+                ]
+            )
+            type_ids = torch.tensor(
+                [question.question_type for question in record.questions],
+                device=hidden_states.device,
+            )
+            option_contexts: list[torch.Tensor] = []
+            lexical_options: list[torch.Tensor] = []
+            option_counts = []
+            for question in record.questions:
+                context_vectors = torch.stack(
+                    [
+                        self._mean_span(sequence_hidden, span)
+                        for span in question.option_spans
+                    ]
+                )
+                lexical_vectors = []
+                for start, end in question.option_spans:
+                    token_ids = input_ids[batch_index, start:end]
+                    lexical_vectors.append(output_embedding_weight[token_ids].mean(dim=0))
+                lexical = torch.stack(lexical_vectors)
+                option_contexts.append(context_vectors)
+                lexical_options.append(lexical)
+                option_counts.append(len(question.option_spans))
+
+            option_queries = []
+            for question_index, (context_vectors, lexical) in enumerate(
+                zip(option_contexts, lexical_options)
+            ):
+                option_queries.append(
+                    self.option_context_projection(context_vectors)
+                    + self.option_lexical_projection(lexical)
+                    + self.option_question_projection(
+                        question_vectors[question_index]
+                    ).unsqueeze(0)
+                )
+            routed_options = torch.cat(option_queries, dim=0).unsqueeze(0)
+            for layer in self.evidence_layers:
+                routed_options = layer(routed_options, memory)
+            routed_options = routed_options[0]
+            split_options = list(torch.split(routed_options, option_counts, dim=0))
+
+            base_fields = self.question_projection(question_vectors)
+            option_summaries = []
+            for field, options in zip(base_fields, split_options):
+                routing_weights = torch.softmax(
+                    torch.matmul(options, field) / math.sqrt(options.shape[-1]),
+                    dim=0,
+                )
+                option_summaries.append(
+                    torch.sum(routing_weights.unsqueeze(-1) * options, dim=0)
+                )
+            fields = (
+                base_fields
+                + self.option_summary_norm(torch.stack(option_summaries))
+                + self.global_projection(global_vector).unsqueeze(0)
+                + self.type_embedding(type_ids)
+            )
+            fields = fields.unsqueeze(0)
+            for layer in self.layers:
+                fields = layer(fields, memory)
+            fields = self.field_norm(fields[0])
+
+            record_logits: list[torch.Tensor] = []
+            for field, question, lexical, routed in zip(
+                fields,
+                record.questions,
+                lexical_options,
+                split_options,
+            ):
+                anchor = functional.normalize(
+                    question_vectors[len(record_logits)] + global_vector,
+                    dim=-1,
+                )
+                lexical_anchor = functional.normalize(lexical, dim=-1)
+                prior_scale = self.prior_logit_scale.clamp(max=math.log(100.0)).exp()
+                prior = prior_scale * torch.matmul(lexical_anchor, anchor)
+                options = self.option_norm(routed)
+                repeated_field = field.unsqueeze(0).expand_as(options)
+                cosine = functional.cosine_similarity(repeated_field, options, dim=-1)
+                features = torch.cat(
+                    [
+                        repeated_field,
+                        options,
+                        repeated_field * options,
+                        torch.abs(repeated_field - options),
+                    ],
+                    dim=-1,
+                )
+                residual = self.residual_scorer(features).squeeze(-1)
+                joint_scale = self.joint_logit_scale.clamp(max=math.log(100.0)).exp()
+                joint = joint_scale * cosine + residual
+                record_logits.append(
+                    prior + torch.sigmoid(self.residual_gate) * joint
+                )
+            results.append(record_logits)
+        return results
+
+
+class ClefModel(torch.nn.Module):
+    def __init__(self, language_model: Any, head: JointSchemaHead) -> None:
+        super().__init__()
+        self.language_model = language_model
+        self.head = head
+
+    def forward(self, batch: dict[str, Any]) -> list[list[torch.Tensor]]:
+        base_model = (
+            self.language_model.get_base_model()
+            if hasattr(self.language_model, "get_base_model")
+            else self.language_model
+        )
+        media = batch.get("media") or {}
+        text_model = base_model.model
+        if not media and hasattr(text_model, "language_model"):
+            text_model = text_model.language_model
+        outputs = text_model(
+            input_ids=batch["input_ids"],
+            attention_mask=batch["attention_mask"],
+            use_cache=False,
+            return_dict=True,
+            **media,
+        )
+        return self.head(
+            outputs.last_hidden_state,
+            batch["input_ids"],
+            batch["attention_mask"],
+            batch["records"],
+            base_model.get_output_embeddings().weight,
+        )
+
+
+def load_release_model(
+    model_path: str | Path,
+    device: str | torch.device = "cuda",
+    dtype: torch.dtype = torch.bfloat16,
+    **from_pretrained_kwargs: Any,
+) -> tuple[ClefModel, Any]:
+    """Load a Clef release (merged backbone, joint schema head, and processor)."""
+    from huggingface_hub import snapshot_download
+    from safetensors.torch import load_file
+    from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration
+
+    path = Path(model_path)
+    if not path.is_dir():
+        path = Path(snapshot_download(str(model_path)))
+    backbone = Qwen3_5ForConditionalGeneration.from_pretrained(
+        path,
+        dtype=dtype,
+        device_map={"": str(device)},
+        **from_pretrained_kwargs,
+    )
+    backbone.config.use_cache = False
+    head_config = json.loads((path / "joint_head_config.json").read_text())
+    head = JointSchemaHead(**head_config)
+    head.load_state_dict(load_file(path / "joint_head.safetensors"), strict=True)
+    head = head.to(device=device, dtype=dtype)
+    processor = AutoProcessor.from_pretrained(path)
+    return ClefModel(backbone, head).eval(), processor
+
+
+def systemone_answer(question: dict[str, Any], probabilities: dict[str, float]) -> dict[str, Any]:
+    """Convert per-option probabilities for one question into a SystemOne answer."""
+    if question["type"] == "noul":
+        return {"type": "noul", "noul": round(probabilities["true"], 4)}
+    if question["type"] == "choice":
+        options = [str(option) for option in question["criteria"]]
+        choice = max(options, key=probabilities.__getitem__)
+        return {
+            "type": "choice",
+            "choice": choice,
+            "confidence": round(probabilities[choice], 4),
+            "probabilities": {option: round(probabilities[option], 4) for option in options},
+        }
+    levels = [str(index) for index in range(len(question["criteria"]))]
+    return {
+        "type": "score",
+        "score": round(sum(index * probabilities[level] for index, level in enumerate(levels)), 4),
+        "confidence": round(max(probabilities[level] for level in levels), 4),
+        "legend": dict(zip(levels, question["criteria"])),
+        "probabilities": {level: round(probabilities[level], 4) for level in levels},
+    }
+
+
+@torch.inference_mode()
+def systemone(model: ClefModel, processor: Any, request: dict[str, Any], max_length: int = 16384) -> dict[str, Any]:
+    """Answer a Jev/SystemOne ``/v1/systemone`` request body with a SystemOne response body.
+
+    The request has ``model``, ``state``, and ``questions``, plus optional ``images`` and ``videos``.
+    """
+    questions = request.get("questions")
+    if not isinstance(request.get("model"), str) or "state" not in request:
+        raise ValueError("model and state are required")
+    if not isinstance(questions, dict) or not questions:
+        raise ValueError("at least one question is required")
+    for question_id, question in questions.items():
+        if question.get("type") not in QUESTION_TYPES:
+            raise ValueError(f"{question_id}: type must be noul, choice, or score")
+        if question["type"] != "noul" and not question.get("criteria"):
+            raise ValueError(f"{question_id}: criteria must not be empty")
+    encoded = encode_record(processor.tokenizer, request, max_length=max_length, processor=processor)
+    device = next(model.parameters()).device
+    logits = model(collate_records([encoded], processor.tokenizer.pad_token_id, device))[0]
+    answers = {
+        question.question_id: systemone_answer(
+            questions[question.question_id],
+            dict(zip(question.option_ids, question_logits.float().softmax(-1).tolist())),
+        )
+        for question, question_logits in zip(encoded.questions, logits)
+    }
+    return {
+        "model": request["model"],
+        "answers": answers,
+        "usage": {"input_tokens": len(encoded.input_ids), "output_tokens": 0},
+    }

model-00001-of-00004.safetensors

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processor_config.json

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+{
+  "image_processor": {
+    "do_convert_rgb": true,
+    "do_normalize": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "image_mean": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "image_processor_type": "Qwen2VLImageProcessor",
+    "image_std": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "merge_size": 2,
+    "patch_size": 16,
+    "resample": 3,
+    "rescale_factor": 0.00392156862745098,
+    "size": {
+      "longest_edge": 16777216,
+      "shortest_edge": 65536
+    },
+    "temporal_patch_size": 2
+  },
+  "processor_class": "Qwen3VLProcessor",
+  "video_processor": {
+    "do_convert_rgb": true,
+    "do_normalize": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "do_sample_frames": true,
+    "fps": 2,
+    "image_mean": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "image_std": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "max_frames": 768,
+    "merge_size": 2,
+    "min_frames": 4,
+    "patch_size": 16,
+    "resample": 3,
+    "rescale_factor": 0.00392156862745098,
+    "return_metadata": false,
+    "size": {
+      "longest_edge": 25165824,
+      "shortest_edge": 4096
+    },
+    "temporal_patch_size": 2,
+    "video_processor_type": "Qwen3VLVideoProcessor"
+  }
+}

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+size 19989325

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "audio_bos_token": "<|audio_start|>",
+  "audio_eos_token": "<|audio_end|>",
+  "audio_token": "<|audio_pad|>",
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "image_token": "<|image_pad|>",
+  "model_max_length": 262144,
+  "model_specific_special_tokens": {
+    "audio_bos_token": "<|audio_start|>",
+    "audio_eos_token": "<|audio_end|>",
+    "audio_token": "<|audio_pad|>",
+    "image_token": "<|image_pad|>",
+    "video_token": "<|video_pad|>",
+    "vision_bos_token": "<|vision_start|>",
+    "vision_eos_token": "<|vision_end|>"
+  },
+  "pad_token": "<|endoftext|>",
+  "pretokenize_regex": "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+|\\p{N}| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null,
+  "video_token": "<|video_pad|>",
+  "vision_bos_token": "<|vision_start|>",
+  "vision_eos_token": "<|vision_end|>"
+}