Add teaser, inference assets, and working UserRepGen example

Preserve upstream Qwen asset license; include the training prompt and validated Transformers inference path.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/teaser.png filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

LICENSE.Qwen

@@ -0,0 +1,202 @@
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README.md

@@ -5,6 +5,7 @@ language:
 base_model:
 - Qwen/Qwen3.5-4B
 pipeline_tag: text-generation
+library_name: transformers
 tags:
 - user-representation
 - user-simulation
@@ -14,6 +15,10 @@ tags:
 
 # UserRepGen-4B
 
+<p align="center">
+  <img src="./assets/teaser.png" alt="UserRepGen generates task and behavior representations from conversations to condition a separate user simulator." width="100%">
+</p>
+
 UserRepGen-4B is a fine-tuned Qwen3.5-4B language model that generates a
 structured, natural-language user representation from a multi-turn
 user-assistant conversation. Each representation contains a **task description**
@@ -76,29 +81,128 @@ The intended workflow is:
    and task-specific leakage.
 5. Pass the reviewed representation to a separate LLM-based user simulator.
 
-The following illustrates the output structure. It is not a measured model
-output:
+### Quickstart
+
+The following example uses Hugging Face Transformers. The conversation is a
+newly written, simplified example inspired by an Excel-help conversation in
+WildChat; it is not an original user transcript.
+
+```python
+import json
+from pathlib import Path
+
+import torch
+from huggingface_hub import hf_hub_download
+from transformers import AutoModelForImageTextToText, AutoTokenizer
+
+model_id = "microsoft/UserRepGen-4B"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+if device == "cpu":
+    torch.set_num_threads(8)
+
+conversation = [
+    {"role": "user", "content": "I need an Excel formula to count dates in A2:A100 before today plus B1."},
+    {"role": "assistant", "content": 'Try =COUNTIF(A2:A100,"<"&TODAY()+B1).'},
+    {"role": "user", "content": "That didn't work. The dates come from another formula."},
+    {"role": "assistant", "content": "Are those results actual Excel dates, or text that looks like dates?"},
+    {"role": "user", "content": "They're text. Some cells are empty too."},
+    {"role": "assistant", "content": "Convert the nonempty text values with DATEVALUE, then count the resulting dates."},
+    {"role": "user", "content": "Can you show the steps using a helper column?"},
+    {"role": "assistant", "content": 'In C2 enter =IF(A2="","",DATEVALUE(A2)), fill down to C100, then use =COUNTIFS(C2:C100,"<"&TODAY()+B1,C2:C100,">0").'},
+]
+
+template = Path(
+    hf_hub_download(model_id, "representation_prompt.txt")
+).read_text(encoding="utf-8").strip()
+history = "\n".join(f"[{m['role']}]: {m['content']}" for m in conversation)
+prompt = template.format(conversation_history=history)
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+inputs = tokenizer.apply_chat_template(
+    [{"role": "user", "content": prompt}],
+    tokenize=True,
+    add_generation_prompt=True,
+    enable_thinking=False,
+    return_tensors="pt",
+    return_dict=True,
+)
+prompt_length = inputs["input_ids"].shape[1]
+if prompt_length > 8192:
+    raise ValueError("The formatted prompt exceeds the 8,192-token limit.")
+
+model = AutoModelForImageTextToText.from_pretrained(
+    model_id,
+    dtype=torch.float32 if device == "cpu" else torch.bfloat16,
+    device_map=device,
+).eval()
+
+with torch.inference_mode():
+    output = model.generate(
+        **inputs.to(device),
+        max_new_tokens=1024,
+        do_sample=False,
+        temperature=None,
+        top_p=None,
+        top_k=None,
+        eos_token_id=tokenizer.eos_token_id,
+        pad_token_id=tokenizer.pad_token_id,
+    )
+
+text = tokenizer.decode(output[0, prompt_length:], skip_special_tokens=True).strip()
+representation = json.loads(text)
+if (
+    not isinstance(representation, dict)
+    or set(representation) != {"task", "user_behavior"}
+    or not all(isinstance(value, str) and value.strip() for value in representation.values())
+):
+    raise ValueError("Expected nonempty task and user_behavior strings.")
+print(json.dumps(representation, indent=2))
+```
+
+The [standalone example script](./example.py) runs the same conversation and
+also accepts a JSON list of `role`/`content` messages:
 
-```json
-{
-  "task": "Get help planning a vegetarian dinner for four people within 45 minutes.",
-  "user_behavior": "In the source conversation, the user makes a brief initial request, provides constraints incrementally when asked, and asks for concrete next steps."
-}
+```bash
+hf download microsoft/UserRepGen-4B example.py --local-dir userrepgen-example
+python userrepgen-example/example.py --device cuda
+# To use your own authorized conversation:
+python userrepgen-example/example.py --device cuda --conversation conversation.json
 ```
 
-### Inference requirements and release status
+Use `--device cpu` when a suitable GPU is not available. The script accepts
+`--model /path/to/local/model` for an already downloaded model directory.
+While the repository is private, authenticate with `hf auth login` using an
+account with read access.
+
+The example uses greedy decoding for a simple smoke test, not the sampling
+settings used for the paper's evaluations. Invalid JSON or missing/empty fields
+raise an error rather than silently repairing the model's output.
+
+### Inference requirements and checkpoint format
 
 The reported inference setup uses Python, PyTorch, and a Qwen3.5-compatible
 runtime such as Hugging Face Transformers or vLLM. The model was hosted with
 vLLM on NVIDIA H100, A100, RTX A6000, and B200 GPUs. Other hardware and operating
-systems, including CPU-only and mobile devices, have not been tested. No
-optimized or device-specific UserRepGen-4B formats are currently available.
-
-**The repository currently contains the model weights only, alongside Git
-attributes. The model configuration, tokenizer/chat-template assets, and exact
-inference prompt are not yet included.** The weights alone are not a complete
-`from_pretrained` package. A runnable loading and generation example must be
-validated with those assets before being added here.
+systems were not evaluated in the research. A Linux CPU smoke test with
+Transformers 5.2.0, PyTorch 2.11.0, and Accelerate 1.10.1 successfully loaded the
+checkpoint and generated valid JSON for a local WildChat conversation. This is
+a functional check, not a CPU performance benchmark. CPU inference uses
+float32 and requires substantially more memory than the approximately 9.1 GB
+weight file. No optimized or device-specific formats are currently available.
+
+The checkpoint retains the Qwen3.5 conditional-generation architecture, so the
+example uses `AutoModelForImageTextToText`, even though UserRepGen-4B was
+fine-tuned and evaluated for **text-only** inputs. Image and video use are not
+supported by this release.
+
+Configuration and tokenizer/chat-template assets come from
+[Qwen3.5-4B revision `851bf6e`](https://huggingface.co/Qwen/Qwen3.5-4B/tree/851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a).
+All 723 stored tensor names and shapes were checked against that configuration;
+the output head shares the input-embedding weights. The upstream assets retain
+their [Apache-2.0 license](./LICENSE.Qwen). The
+[representation prompt](./representation_prompt.txt) and conversation formatting
+match the training pipeline. The outer chat message has the `user` role, and
+`enable_thinking=False` disables the backbone's reasoning mode.
 
 The training limits were 8,192 prompt tokens and 1,024 completion tokens.
 Extended-context use beyond the 9,216-token training sequence length is not

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 +1,104 @@
+{
+    "architectures": [
+        "Qwen3_5ForConditionalGeneration"
+    ],
+    "image_token_id": 248056,
+    "model_type": "qwen3_5",
+    "text_config": {
+        "attention_bias": false,
+        "attention_dropout": 0.0,
+        "attn_output_gate": true,
+        "dtype": "bfloat16",
+        "eos_token_id": 248044,
+        "full_attention_interval": 4,
+        "head_dim": 256,
+        "hidden_act": "silu",
+        "hidden_size": 2560,
+        "initializer_range": 0.02,
+        "intermediate_size": 9216,
+        "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,
+        "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,
+        "rms_norm_eps": 1e-06,
+        "tie_word_embeddings": true,
+        "use_cache": true,
+        "vocab_size": 248320,
+        "mamba_ssm_dtype": "float32",
+        "rope_parameters": {
+            "mrope_interleaved": true,
+            "mrope_section": [
+                11,
+                11,
+                10
+            ],
+            "rope_type": "default",
+            "rope_theta": 10000000,
+            "partial_rotary_factor": 0.25
+        }
+    },
+    "tie_word_embeddings": true,
+    "transformers_version": "4.57.0.dev0",
+    "video_token_id": 248057,
+    "vision_config": {
+        "deepstack_visual_indexes": [],
+        "depth": 24,
+        "hidden_act": "gelu_pytorch_tanh",
+        "hidden_size": 1024,
+        "in_channels": 3,
+        "initializer_range": 0.02,
+        "intermediate_size": 4096,
+        "model_type": "qwen3_5",
+        "num_heads": 16,
+        "num_position_embeddings": 2304,
+        "out_hidden_size": 2560,
+        "patch_size": 16,
+        "spatial_merge_size": 2,
+        "temporal_patch_size": 2
+    },
+    "vision_end_token_id": 248054,
+    "vision_start_token_id": 248053
+}

example.py

@@ -0,0 +1,140 @@
+"""Generate a task and behavior representation from a conversation."""
+
+import argparse
+import json
+import sys
+import time
+from pathlib import Path
+
+import torch
+from huggingface_hub import hf_hub_download
+from transformers import AutoModelForImageTextToText, AutoTokenizer
+
+
+# An authored, simplified example inspired by an Excel-help WildChat conversation.
+EXAMPLE_CONVERSATION = [
+    {
+        "role": "user",
+        "content": "I need an Excel formula to count dates in A2:A100 before today plus B1.",
+    },
+    {
+        "role": "assistant",
+        "content": 'Try =COUNTIF(A2:A100,"<"&TODAY()+B1).',
+    },
+    {
+        "role": "user",
+        "content": "That didn't work. The dates come from another formula.",
+    },
+    {
+        "role": "assistant",
+        "content": "Are those results actual Excel dates, or text that looks like dates?",
+    },
+    {"role": "user", "content": "They're text. Some cells are empty too."},
+    {
+        "role": "assistant",
+        "content": "Convert the nonempty text values with DATEVALUE, then count the resulting dates.",
+    },
+    {
+        "role": "user",
+        "content": "Can you show the steps using a helper column?",
+    },
+    {
+        "role": "assistant",
+        "content": 'In C2 enter =IF(A2="","",DATEVALUE(A2)), fill down to C100, then use =COUNTIFS(C2:C100,"<"&TODAY()+B1,C2:C100,">0").',
+    },
+]
+
+
+def format_prompt(conversation: list[dict[str, str]], template: str) -> str:
+    if not isinstance(conversation, list) or not conversation:
+        raise ValueError("The conversation must be a nonempty list of messages.")
+    for message in conversation:
+        if not isinstance(message, dict):
+            raise ValueError("Each message must be an object with role and content.")
+        if message.get("role") not in ("user", "assistant"):
+            raise ValueError("Message roles must be 'user' or 'assistant'.")
+        if not isinstance(message.get("content"), str) or not message["content"].strip():
+            raise ValueError("Each message must contain nonempty text.")
+    history = "\n".join(f"[{m['role']}]: {m['content']}" for m in conversation)
+    return template.format(conversation_history=history)
+
+
+def parse_representation(text: str) -> dict[str, str]:
+    representation = json.loads(text)
+    if not isinstance(representation, dict) or set(representation) != {"task", "user_behavior"}:
+        raise ValueError(f"Expected exactly task and user_behavior fields; received: {text}")
+    if not all(isinstance(value, str) and value.strip() for value in representation.values()):
+        raise ValueError(f"Both representation fields must be nonempty strings; received: {text}")
+    return representation
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--model", default="microsoft/UserRepGen-4B", help="Hub ID or local model directory.")
+    parser.add_argument("--conversation", type=Path, help="JSON list of role/content messages; otherwise use the built-in example.")
+    parser.add_argument("--device", choices=["cpu", "cuda"], default="cuda" if torch.cuda.is_available() else "cpu")
+    args = parser.parse_args()
+
+    conversation = (
+        json.loads(args.conversation.read_text(encoding="utf-8"))
+        if args.conversation is not None
+        else EXAMPLE_CONVERSATION
+    )
+    if Path(args.model).is_dir():
+        prompt_path = Path(args.model) / "representation_prompt.txt"
+    else:
+        prompt_path = Path(hf_hub_download(args.model, "representation_prompt.txt"))
+    prompt = format_prompt(conversation, prompt_path.read_text(encoding="utf-8").strip())
+
+    tokenizer = AutoTokenizer.from_pretrained(args.model)
+    inputs = tokenizer.apply_chat_template(
+        [{"role": "user", "content": prompt}],
+        tokenize=True,
+        add_generation_prompt=True,
+        enable_thinking=False,
+        return_tensors="pt",
+        return_dict=True,
+    )
+    prompt_tokens = inputs["input_ids"].shape[1]
+    if prompt_tokens > 8192:
+        raise ValueError(f"Prompt has {prompt_tokens} tokens; the supported maximum is 8192.")
+
+    if args.device == "cpu":
+        torch.set_num_threads(8)
+    print(f"Loading {args.model} on {args.device}; prompt: {prompt_tokens} tokens.", file=sys.stderr, flush=True)
+    model, loading_info = AutoModelForImageTextToText.from_pretrained(
+        args.model,
+        dtype=torch.float32 if args.device == "cpu" else torch.bfloat16,
+        device_map=args.device,
+        output_loading_info=True,
+    )
+    for key in ("missing_keys", "unexpected_keys", "mismatched_keys", "error_msgs"):
+        if loading_info.get(key):
+            raise RuntimeError(f"Checkpoint loading failed ({key}): {loading_info[key]}")
+    model.eval()
+    inputs = inputs.to(args.device)
+    started = time.monotonic()
+    print("Generating representation...", file=sys.stderr, flush=True)
+    with torch.inference_mode():
+        output = model.generate(
+            **inputs,
+            max_new_tokens=1024,
+            do_sample=False,
+            temperature=None,
+            top_p=None,
+            top_k=None,
+            eos_token_id=tokenizer.eos_token_id,
+            pad_token_id=tokenizer.pad_token_id,
+        )
+    generated_ids = output[0, prompt_tokens:]
+    text = tokenizer.decode(generated_ids, skip_special_tokens=True).strip()
+    representation = parse_representation(text)
+    print(json.dumps(representation, indent=2, ensure_ascii=False))
+    print(
+        f"Validated JSON; generated {len(generated_ids)} tokens in {time.monotonic() - started:.1f}s.",
+        file=sys.stderr,
+    )
+
+
+if __name__ == "__main__":
+    main()

representation_prompt.txt

@@ -0,0 +1,10 @@
+You are an expert at analyzing user behavior in conversations between a user and an AI assistant. Given the conversation below, your task is to produce a representation of the user.
+
+Treat the conversation as evidence about the user, and use it to infer how they generally behave and how they would typically act in other conversations. Focus on stable, general tendencies such as: how they phrase and specify requests, how they decompose goals across turns, how they respond to and give feedback to the assistant, how and when they provide context, their communication style, the dialog acts they use, and their degree and depth of engagement. Keep this description general: do not include topic-specific details or content from the conversation, do not quote or closely paraphrase full user messages, and do not refer to specific moments in this conversation (e.g. "here", "in this conversation", "when they asked about X"). Short distinctive words or phrases may be quoted sparingly when they are useful examples of the user's communication style. Describe their patterns in your own general terms instead.
+
+Separately, you will also describe the task the user is trying to accomplish. Here you may include topic-specific details about what the user is doing in the conversation.
+
+Conversation:
+{conversation_history}
+
+Output a single valid JSON object of the form {{"task": "...", "user_behavior": "..."}}. Do not wrap the output in markdown or add any other text.

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5f9e4d4901a92b997e463c1f46055088b6cca5ca61a6522d1b9f64c4bb81cb42
+size 12807982

tokenizer_config.json

@@ -0,0 +1,305 @@
+{
+    "add_prefix_space": false,
+    "added_tokens_decoder": {
+        "248044": {
+            "content": "<|endoftext|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": true
+        },
+        "248045": {
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+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": true
+        },
+        "248046": {
+            "content": "<|im_end|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": true
+        },
+        "248047": {
+            "content": "<|object_ref_start|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": true
+        },
+        "248048": {
+            "content": "<|object_ref_end|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": true
+        },
+        "248049": {
+            "content": "<|box_start|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": true
+        },
+        "248050": {
+            "content": "<|box_end|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": true
+        },
+        "248051": {
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+            "special": true
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+        "248059": {
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+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "248064": {
+            "content": "<|repo_name|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "248065": {
+            "content": "<|file_sep|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "248066": {
+            "content": "<tool_response>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "248067": {
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+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
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+        },
+        "248068": {
+            "content": "<think>",
+            "lstrip": false,
+            "normalized": false,
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+        },
+        "248069": {
+            "content": "</think>",
+            "lstrip": false,
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+        },
+        "248070": {
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+            "special": true
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+    },
+    "additional_special_tokens": [
+        "<|im_start|>",
+        "<|im_end|>",
+        "<|object_ref_start|>",
+        "<|object_ref_end|>",
+        "<|box_start|>",
+        "<|box_end|>",
+        "<|quad_start|>",
+        "<|quad_end|>",
+        "<|vision_start|>",
+        "<|vision_end|>",
+        "<|vision_pad|>",
+        "<|image_pad|>",
+        "<|video_pad|>"
+    ],
+    "bos_token": null,
+    "chat_template": "{%- set image_count = namespace(value=0) %}\n{%- set video_count = namespace(value=0) %}\n{%- macro render_content(content, do_vision_count, is_system_content=false) %}\n    {%- if content is string %}\n        {{- content }}\n    {%- elif content is iterable and content is not mapping %}\n        {%- for item in content %}\n            {%- if 'image' in item or 'image_url' in item or item.type == 'image' %}\n                {%- if is_system_content %}\n                    {{- raise_exception('System message cannot contain images.') }}\n                {%- endif %}\n                {%- if do_vision_count %}\n                    {%- set image_count.value = image_count.value + 1 %}\n                {%- endif %}\n                {%- if add_vision_id %}\n                    {{- 'Picture ' ~ image_count.value ~ ': ' }}\n                {%- endif %}\n                {{- '<|vision_start|><|image_pad|><|vision_end|>' }}\n            {%- elif 'video' in item or item.type == 'video' %}\n                {%- if is_system_content %}\n                    {{- raise_exception('System message cannot contain videos.') }}\n                {%- endif %}\n                {%- if do_vision_count %}\n                    {%- set video_count.value = video_count.value + 1 %}\n                {%- endif %}\n                {%- if add_vision_id %}\n                    {{- 'Video ' ~ video_count.value ~ ': ' }}\n                {%- endif %}\n                {{- '<|vision_start|><|video_pad|><|vision_end|>' }}\n            {%- elif 'text' in item %}\n                {{- item.text }}\n            {%- else %}\n                {{- raise_exception('Unexpected item type in content.') }}\n            {%- endif %}\n        {%- endfor %}\n    {%- elif content is none or content is undefined %}\n        {{- '' }}\n    {%- else %}\n        {{- raise_exception('Unexpected content type.') }}\n    {%- endif %}\n{%- endmacro %}\n{%- if not messages %}\n    {{- raise_exception('No messages provided.') }}\n{%- endif %}\n{%- if tools and tools is iterable and tools is not mapping %}\n    {{- '<|im_start|>system\\n' }}\n    {{- \"# Tools\\n\\nYou have access to the following functions:\\n\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\" }}\n    {{- '\\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>' }}\n    {%- if messages[0].role == 'system' %}\n        {%- set content = render_content(messages[0].content, false, true)|trim %}\n        {%- if content %}\n            {{- '\\n\\n' + content }}\n        {%- endif %}\n    {%- endif %}\n    {{- '<|im_end|>\\n' }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {%- set content = render_content(messages[0].content, false, true)|trim %}\n        {{- '<|im_start|>system\\n' + content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- if ns.multi_step_tool and message.role == \"user\" %}\n        {%- set content = render_content(message.content, false)|trim %}\n        {%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}\n            {%- set ns.multi_step_tool = false %}\n            {%- set ns.last_query_index = index %}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if ns.multi_step_tool %}\n    {{- raise_exception('No user query found in messages.') }}\n{%- endif %}\n{%- for message in messages %}\n    {%- set content = render_content(message.content, true)|trim %}\n    {%- if message.role == \"system\" %}\n        {%- if not loop.first %}\n            {{- raise_exception('System message must be at the beginning.') }}\n        {%- endif %}\n    {%- elif message.role == \"user\" %}\n        {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {%- set reasoning_content = '' %}\n        {%- if message.reasoning_content is string %}\n            {%- set reasoning_content = message.reasoning_content %}\n        {%- else %}\n            {%- if '</think>' in content %}\n                {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n                {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n            {%- endif %}\n        {%- endif %}\n        {%- set reasoning_content = reasoning_content|trim %}\n        {%- if loop.index0 > ns.last_query_index %}\n            {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content + '\\n</think>\\n\\n' + content }}\n        {%- else %}\n            {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- endif %}\n        {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if tool_call.function is defined %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {%- if loop.first %}\n                    {%- if content|trim %}\n                        {{- '\\n\\n<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n                    {%- else %}\n                        {{- '<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n                    {%- endif %}\n                {%- else %}\n                    {{- '\\n<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n                {%- endif %}\n                {%- if tool_call.arguments is defined %}\n                    {%- for args_name, args_value in tool_call.arguments|items %}\n                        {{- '<parameter=' + args_name + '>\\n' }}\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 %}\n                        {{- args_value }}\n                        {{- '\\n</parameter>\\n' }}\n                    {%- endfor %}\n                {%- endif %}\n                {{- '</function>\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.previtem and loop.previtem.role != \"tool\" %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- content }}\n        {{- '\\n</tool_response>' }}\n        {%- if not loop.last and loop.nextitem.role != \"tool\" %}\n            {{- '<|im_end|>\\n' }}\n        {%- elif loop.last %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- else %}\n        {{- raise_exception('Unexpected message role.') }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n    {%- if enable_thinking is defined and enable_thinking is false %}\n        {{- '<think>\\n\\n</think>\\n\\n' }}\n    {%- else %}\n        {{- '<think>\\n' }}\n    {%- endif %}\n{%- endif %}",
+    "clean_up_tokenization_spaces": false,
+    "eos_token": "<|im_end|>",
+    "errors": "replace",
+    "model_max_length": 262144,
+    "pad_token": "<|endoftext|>",
+    "split_special_tokens": false,
+    "tokenizer_class": "Qwen2Tokenizer",
+    "unk_token": null,
+    "add_bos_token": false,
+    "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+",
+    "extra_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|>"
+    }
+}