Release BotPsy-0.8B (BF16 safetensors)

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README.md

@@ -0,0 +1,348 @@
+---
+license: apache-2.0
+license_link: https://huggingface.co/qvac/BotPsy-0.8B/blob/main/LICENSE
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+base_model:
+- Qwen/Qwen3.5-0.8B
+base_model_relation: finetune
+tags:
+- tool-calling
+- function-calling
+- agent
+- agentic
+- on-device
+- edge
+- qwen3_5
+- tether-ai
+- qvac
+---
+
+# BotPsy-0.8B
+
+**BotPsy-0.8B** is a 0.8B-parameter model designed for function calling and agentic workflows, built on top of [Qwen3.5-0.8B](https://huggingface.co/Qwen/Qwen3.5-0.8B) and suitable for on-device deployment. BotPsy is a "personal assistant" that uses tools to perform a variety of simple tasks for the user, in domains such as web search & research, travel & lifestyle, commerce & finance, business & customer operations, device & vehicle control, files & documents, calendar, and email & messaging. Due to its small parameter count, it is best suited to tasks of low to medium difficulty. BotPsy achieves state-of-the-art (SOTA) tool-use and agentic performance for its size, outperforming every model of up to 1.2B parameters that we tested. As a small model, BotPsy still makes mistakes and is not intended for high-stakes use; please read the [Intended Use and Limitations](#intended-use-and-limitations) section for more information.
+
+This repository holds the BF16 weights in Hugging Face safetensors format. GGUF files (BF16, Q8_0 and Q4_K_M) for llama.cpp and the QVAC SDK are in [qvac/BotPsy-0.8B-GGUFs](https://huggingface.co/qvac/BotPsy-0.8B-GGUFs). The full write-up, with every benchmark and the training method, is the [BotPsy blog post](https://huggingface.co/blog/qvac/botpsy).
+
+**Copyright Complaints**: We will take appropriate actions in response to notice of copyright infringement. If you believe your work has been used or copied in a manner that infringes upon your intellectual property rights, please email [data-apps@tether.io](mailto:data-apps@tether.io) identifying and describing both the copyrighted work and alleged infringing content to file a notice of infringement.
+
+| | |
+|:---|:---|
+| **Developed by** | [Tether AI Research](https://tether.io/)<sup>*</sup> |
+| **Model type** | Causal language model for tool calling (Qwen3.5 architecture: hybrid Gated DeltaNet + gated attention), used text-only |
+| **Parameters** | 0.8B |
+| **Base model** | [Qwen3.5-0.8B](https://huggingface.co/Qwen/Qwen3.5-0.8B) |
+| **Post-training** | SFT, then multi-teacher on-policy distillation (MOPD), then two rounds of RL (DAPO) |
+| **Reasoning** | Thinks before it answers (thinking mode, `enable_thinking=True`) |
+| **Language** | English (recommended) |
+| **Precision** | BF16 (this repository); BF16, Q8_0 and Q4_K_M GGUF in [BotPsy-0.8B-GGUFs](https://huggingface.co/qvac/BotPsy-0.8B-GGUFs) |
+| **License** | [Apache 2.0](https://huggingface.co/qvac/BotPsy-0.8B/blob/main/LICENSE) |
+| **Blog post** | [BotPsy on Hugging Face](https://huggingface.co/blog/qvac/botpsy) |
+| **Collection** | [BotPsy](https://huggingface.co/collections/qvac/botpsy) |
+
+<sup>*</sup> References to Tether AI Research are references to Tether Data, S.A. de C.V.
+
+## Key Highlights
+
+### Benchmark results
+- **Twice the performance of its base model:** BotPsy roughly doubles the agentic capability of Qwen3.5-0.8B-Think on key benchmarks: BFCL v4 rises from 23.4 to 44.0, ACEBench from 30.1 to 58.7, and τ-bench from 23.2 to 51.5 (mean of the retail, telecom and banking subsets).
+- **Best agent in its category:** BotPsy outperforms leading models in the ≤1.2B-parameter weight class (Qwen3.5-0.8B-Think, LFM2.5-1.2B-Think, MiniCPM5-1B-Think) on all six tool-use and agentic benchmarks: BFCL v4, ACEBench, τ-bench, ToolSandbox, Claw-Eval and BrowseComp-Plus.
+- **Outperforms its larger sibling:** BotPsy has the highest score of all models tested on five of the six benchmarks, beating Qwen3.5-2B-Think, a model 2.5× its size, on BFCL v4 (44.0 vs 37.7), ACEBench (58.7 vs 55.4), τ-bench (51.5 vs 46.5), ToolSandbox (64.6 vs 41.2) and BrowseComp-Plus (4.5 vs 2.8). Only on Claw-Eval does Qwen3.5-2B-Think lead (48.9 vs 46.5).
+
+### Model capabilities
+- **More reliable in multi-turn workflows:** On τ²-bench, where the agent must hold a long customer conversation, follow a strict policy, and act on the customer's behalf, it scores 82.5 in telecom, ahead of Qwen3.5-2B-Think (69.3). On BFCL v4's multi-turn tasks, it rises from 7.5 to 46.1 over its base, nearly double Qwen3.5-2B-Think (23.8). Grouped by application, its multi-step scores lead all models tested in commerce and services (58.3 vs 47.4 for the next best, Qwen3.5-2B-Think) and in device and system management (46.1 vs 31.9).
+- **Knows when (not) to call tools:** An agent that acts must also know when not to act. On BFCL v4's hallucination tests, which check that the model declines when no appropriate tool exists, BotPsy scores 86.1, the highest of all models tested. On ACEBench's special cases, where the request is incomplete, contains wrong parameters, or cannot be served by the given tools, it scores 51.3, up from 13.7 for its base model.
+- **Follows instructions while preserving knowledge:** BotPsy specializes in tool use while preserving general skills. Instruction following improves sharply, with IFEval rising from 59.7 to 90.8 and IFBench from 22.0 to 55.9, the highest IFBench score of all models tested. General knowledge also improves, with GPQA Diamond rising from 33.2 to 38.1 and MMLU-Pro from 41.6 to 48.9. On all four benchmarks, BotPsy outperforms every other model of 1.2B parameters or fewer that we tested.
+
+### Safety, efficiency and deployment
+- **Safer than leading models in its category:** In automated red-teaming across six harm categories, BotPsy produces unsafe responses 21% of the time, compared with 36% for its base model. This is the lowest rate of all models of up to 1.2B parameters (LFM2.5-1.2B-Think: 35%; MiniCPM5-1B-Think: 47%). Under prompt-injection attacks, which matter most for agents that read untrusted tool outputs, the unsafe rate drops from 46% for its base model to 31%.
+- **More efficient reasoning and less looping:** On questions that all five models answer correctly, BotPsy reaches the answer with about 3× fewer reasoning tokens than its base model on GPQA Diamond (3,690 vs 11,563) and 2.7× fewer on MMLU-Pro (1,912 vs 5,105). Across all IFBench attempts, its rate of getting stuck in repetitive reasoning loops falls from 22.9% to 1.4%, and runs that hit the 32k-token limit fall from 28.3% to 0.2%. Shorter, more decisive reasoning should reduce latency on-device.
+- **Open-source and ready to use:** BotPsy is released with open weights under the Apache 2.0 license for research and educational use. It is available in three precisions (bf16, Q8, Q4), as Hugging Face safetensors and as GGUF files for the QVAC SDK and llama.cpp.
+
+## Benchmark Results
+
+All five models were evaluated in thinking mode with one shared evaluation harness. BotPsy was served from these BF16 weights with vLLM. Best score per row in **bold**, second best <u>underlined</u>; multipliers give parameter count relative to BotPsy-0.8B.
+
+| Benchmark | **BotPsy-0.8B**<br><sub>ours</sub> | Qwen3.5-0.8B-Think<br><sub>base · 1×</sub> | Qwen3.5-2B-Think<br><sub>2.5×</sub> | LFM2.5-1.2B-Think<br><sub>1.5×</sub> | MiniCPM5-1B-Think<br><sub>1.25×</sub> |
+| :--- | :---: | :---: | :---: | :---: | :---: |
+| ***Tool use & agents*** | | | | | |
+| BFCL v4¹ | **44.0** | 23.4 | <u>37.7</u> | 28.1 | 22.8 |
+| ACEBench² | **58.7** | 30.1 | <u>55.4</u> | 39.4 | 24.5 |
+| τ-bench³ | **51.5** | 23.2 | <u>46.5</u> | 6.5 | 28.8 |
+| Claw-Eval⁴ | <u>46.5</u> | 39.3 | **48.9** | 31.6 | 31.0 |
+| BrowseComp-Plus⁵ | **4.5** | 1.9 | <u>2.8</u> | 0.2 | 0.8 |
+| ToolSandbox⁶ | **64.6** | 30.5 | 41.2 | <u>45.7</u> | 28.1 |
+| *Average* | ***45.0*** | *24.7* | *<u>38.8</u>* | *25.3* | *22.7* |
+| ***Knowledge*** | | | | | |
+| GPQA Diamond⁷ | <u>38.1</u> | 33.2 | **59.9** | 35.0 | 15.8 |
+| MMLU-Pro⁸ | <u>48.9</u> | 41.6 | **65.5** | 39.4 | 40.0 |
+| ***Instruction following*** | | | | | |
+| IFEval⁹ | **90.8** | 59.7 | 80.0 | <u>85.6</u> | 78.4 |
+| IFBench⁹ | **55.9** | 22.0 | <u>40.9</u> | 33.9 | 40.6 |
+| Multi-IF¹⁰ | <u>67.2</u> | 43.2 | 66.9 | **68.2** | 53.4 |
+
+<details>
+<summary>Evaluation footnotes</summary>
+
+- All scores are percentages (higher is better).
+- All models were evaluated in thinking (reasoning) mode.
+- All benchmarks that use an external API were run on **November 3, 2026**.
+- ¹ BFCL v4: overall score calculated using the official weighted formula (40% Agentic, 30% Multi-Turn, 10% Live, 10% Non-Live, and 10% Hallucination), averaged over 3 runs.
+- ² ACEBench: overall accuracy. All models were evaluated in function-calling (FC) mode; mean of 2 runs.
+- ³ τ-bench: average of τ²-bench (retail, telecom) and τ³-bench (banking), excluding airline; mean of 3 runs.
+- ⁴ Claw-Eval: official average task score, where each task scores safety × (0.8 × completion + 0.2 × robustness); mean of 3 runs.
+- ⁵ BrowseComp-Plus: accuracy, with a maximum of 30 steps for all models.
+- ⁶ ToolSandbox: mean similarity score over 8 settings.
+- ⁷ GPQA Diamond: accuracy over all 198 questions, mean of 3 runs, thinking enabled.
+- ⁸ MMLU-Pro: accuracy.
+- ⁹ IFEval, IFBench: prompt-level strict accuracy.
+- ¹⁰ Multi-IF: average of 3 runs.
+
+</details>
+
+BotPsy's weaker spots are open-ended search and a few areas where Qwen3.5-2B-Think still leads: BrowseComp-Plus deep research (4.5, the best of the five but low in absolute terms), BFCL web search (14.0) and memory (16.3), Claw-Eval, and ACEBench's special cases. On τ³-banking no model gets above 5. Knowledge still scales mainly with parameter count, so BotPsy is designed to call tools for facts it does not know, not to serve as a knowledge model. See the [blog post](https://huggingface.co/blog/qvac/botpsy) for the multi-turn, app-level and efficiency analyses.
+
+## Training
+
+BotPsy is based on Qwen3.5-0.8B and is post-trained in four sequential stages. Each stage starts from the previous stage's best checkpoint.
+
+1. **Learning from demonstrations (SFT):** full-parameter supervised fine-tuning on tool-use conversations, including multi-turn agent trajectories from simulated environments, replayed single, parallel and multi-step calls, and cases where no tool should be called.
+2. **Learning from teachers (MOPD):** multi-teacher on-policy distillation. The student generates its own responses and a specialist teacher provides feedback on every token, using the per-token reverse KL divergence as the training signal. The three teachers are Qwen3.5-4B models, each RL-trained for one skill (tool use, general reasoning, instruction following), and every prompt is routed to one of them.
+3. **Learning from rules (RL, round 1):** DAPO with rule-based rewards, focused on tool calling with instruction following and STEM multiple-choice questions. The tool-calling reward checks the reasoning and tool-call format, then gives partial credit for matching tool names, argument names and argument values.
+4. **Learning from rules and environments (RL, round 2):** DAPO on general-domain verifiable tasks: math, code, knowledge questions, instruction following, structured outputs and multi-step tool use in a simulated workplace environment.
+
+Training code and training data are not part of this release. The datasets used are listed under [Licensing](#licensing).
+
+## Usage
+
+BotPsy keeps the Qwen3.5 architecture, so Hugging Face Transformers, vLLM and llama.cpp run it without custom code. Two settings matter.
+
+- **Turn thinking on.** BotPsy was trained and evaluated in thinking mode. The chat template defaults to non-thinking, so pass `enable_thinking=True`.
+- **Sampling.** Our evaluations used `temperature=0.3`, `top_p=0.95`, `top_k=20`, `presence_penalty=1.5` and up to 32,768 new tokens. `generation_config.json` sets the first three; presence penalty is available in vLLM and llama.cpp.
+
+### Transformers
+
+Requires a `transformers` release with Qwen3.5 support (tested with 5.19.0). Install `flash-linear-attention` and `causal-conv1d` for the fast Gated DeltaNet kernels; without them Transformers falls back to a slower reference implementation.
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "qvac/BotPsy-0.8B"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16).to(device)
+
+tools = [{
+    "type": "function",
+    "function": {
+        "name": "create_event",
+        "description": "Add an event to the user's calendar",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "title": {"type": "string"},
+                "day": {"type": "string", "description": "Day of the week"},
+                "time": {"type": "string", "description": "Start time, 24h HH:MM"},
+            },
+            "required": ["title", "day", "time"],
+        },
+    },
+}]
+messages = [{"role": "user", "content": "Add the dentist on Friday at 3 pm."}]
+
+inputs = tokenizer.apply_chat_template(
+    messages, tools=tools, add_generation_prompt=True, enable_thinking=True,
+    return_dict=True, return_tensors="pt",
+).to(model.device)
+out = model.generate(**inputs, max_new_tokens=4096)  # sampling from generation_config.json
+print(tokenizer.decode(out[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))
+```
+
+The answer ends with a tool call in the Qwen3.5 XML format:
+
+```
+<tool_call>
+<function=create_event>
+<parameter=title>
+Dentist
+</parameter>
+...
+</function>
+</tool_call>
+```
+
+### vLLM (OpenAI-compatible server)
+
+```bash
+vllm serve qvac/BotPsy-0.8B --language-model-only \
+    --enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3
+```
+
+```python
+from openai import OpenAI
+
+client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
+resp = client.chat.completions.create(
+    model="qvac/BotPsy-0.8B",
+    messages=[{"role": "user", "content": "Add the dentist on Friday at 3 pm."}],
+    tools=tools,  # same list as in the Transformers example
+    temperature=0.3, top_p=0.95, presence_penalty=1.5, max_tokens=8192,
+    extra_body={"top_k": 20, "chat_template_kwargs": {"enable_thinking": True}},
+)
+print(resp.choices[0].message.tool_calls)
+```
+
+### llama.cpp and the QVAC SDK
+
+Use the GGUF files in [qvac/BotPsy-0.8B-GGUFs](https://huggingface.co/qvac/BotPsy-0.8B-GGUFs); its model card has llama.cpp and QVAC SDK examples.
+
+## Intended Use and Limitations
+
+BotPsy has been developed to advance the state of the art and to support research and educational uses. Models developed based on our work could be suitable for tasks in domains such as device & system management (settings, files & documents, clocks & alarms), personal productivity & work (calendar & tasks, email, collaboration tools), communication (messaging), information retrieval & research (web search, weather, news), media & lifestyle (video, music, recipes & fitness, photos) and commerce & services (maps & travel, finance, shopping & delivery). Due to its small parameter count, it is best suited to tasks of low to medium difficulty.
+
+BotPsy has been primarily trained and tested in English, which is the recommended language. It has also been evaluated on Multi-IF, which covers seven additional languages (Chinese, French, Hindi, Italian, Portuguese, Russian and Spanish): it performs well in most of them, but scores much lower in Hindi and Russian. BotPsy is a small language model, and it makes mistakes. It is not intended for safety-critical or high-stakes automated decisions, or for applications involving money, personal information, health and other sensitive topics.
+
+### Safety
+
+We did not add a separate input or output filter to the model; its safety behaviour comes from post-training. In automated red teaming with [DeepTeam](https://github.com/confident-ai/deepteam) across six risk areas (illegal activity, toxicity, graphic content, personal safety, misinformation, bias), BotPsy answers 21% of attack prompts unsafely, down from 36% for its base model. Bias (40%) and misinformation (33%) remain the weakest areas, and prompt injection is the most effective attack style (31%). For use cases with strict safety requirements, we recommend a separate guardrail, such as input and output moderation, especially when the model reads tool outputs or other untrusted text.
+
+## Related Resources
+
+- [BotPsy blog post](https://huggingface.co/blog/qvac/botpsy): methodology, all benchmarks, app-level breakdown, inference efficiency
+- [BotPsy-0.8B-GGUFs](https://huggingface.co/qvac/BotPsy-0.8B-GGUFs): BF16, Q8_0 and Q4_K_M GGUF files for llama.cpp and the QVAC SDK
+- [BotPsy collection](https://huggingface.co/collections/qvac/botpsy)
+- [QVAC SDK](https://qvac.tether.io/dev/sdk/): on-device AI deployment framework
+- [Qwen3.5-0.8B](https://huggingface.co/Qwen/Qwen3.5-0.8B): base model
+
+## Citation
+
+```bibtex
+@misc{botpsy2026,
+  title        = {BotPsy: On-Device Personal Assistant with a Sub-Billion-Parameter Agentic Language Model},
+  author       = {Tether AI Research},
+  year         = {2026},
+  howpublished = {Hugging Face blog},
+  url          = {https://huggingface.co/blog/qvac/botpsy}
+}
+```
+
+## Copyright
+
+We will take appropriate actions in response to notice of copyright infringement. If you believe your work has been used or copied in a manner that infringes upon your intellectual property rights, please email [data-apps@tether.io](mailto:data-apps@tether.io) identifying and describing both the copyrighted work and alleged infringing content to file a notice of infringement.
+
+## Licensing
+
+This model which was trained as described in the [blog post](https://huggingface.co/blog/qvac/botpsy) is licensed by Tether Data, S.A. de C.V. under the [Apache 2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md) license. As described in the blog post, this model is a version of the Qwen3.5-0.8B (Thinking) model ([https://huggingface.co/Qwen/Qwen3.5-0.8B](https://huggingface.co/Qwen/Qwen3.5-0.8B)) which is licensed under the Apache 2.0 license ([https://choosealicense.com/licenses/apache-2.0/](https://choosealicense.com/licenses/apache-2.0/)).
+
+As described in the blog post, the Qwen3.5-4B model ([https://huggingface.co/Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B)) which is licensed under the Apache 2.0 license ([https://choosealicense.com/licenses/apache-2.0/](https://choosealicense.com/licenses/apache-2.0/)) was put through three different reinforcement learning processes to create three versions of the model and each of those three versions of the model were then used as a teacher model.
+
+One version of the teacher model used the following datasets:
+
+- conv_tool_use_pivot forming a part of the Nemotron-RL-Agentic-Conversational-Tool-Use-Pivot-v1 dataset	([https://huggingface.co/datasets/nvidia/Nemotron-RL-Agentic-Conversational-Tool-Use-Pivot-v1](https://huggingface.co/datasets/nvidia/Nemotron-RL-Agentic-Conversational-Tool-Use-Pivot-v1)) made available under the CC-BY 4.0 (Creative Commons Attribution 4.0 International) license ([https://choosealicense.com/licenses/cc-by-4.0/](https://choosealicense.com/licenses/cc-by-4.0/));
+
+- toolcall forming a part of the Tool N1 dataset ([https://github.com/NVlabs/Tool-N1](https://github.com/NVlabs/Tool-N1)) made available under the Apache 2.0 license ([https://github.com/NVlabs/Tool-N1/blob/master/LICENSE](https://github.com/NVlabs/Tool-N1/blob/master/LICENSE));
+
+- rlpr_mcq forming a part of the SuperGPQA dataset ([https://huggingface.co/datasets/m-a-p/SuperGPQA](https://huggingface.co/datasets/m-a-p/SuperGPQA)) made available under the ODC-BY (Open Data Commons Attribution License v1.0) license ([https://opendatacommons.org/licenses/by/1-0/](https://opendatacommons.org/licenses/by/1-0/));
+
+- Nemotron-RL-instruction_following dataset ([https://huggingface.co/datasets/nvidia/Nemotron-RL-instruction_following](https://huggingface.co/datasets/nvidia/Nemotron-RL-instruction_following)) made available under the ODC-BY (Open Data Commons Attribution License v1.0) license ([https://opendatacommons.org/licenses/by/1-0/](https://opendatacommons.org/licenses/by/1-0/));
+
+- xlam-irrelevance-7.5k dataset ([https://huggingface.co/datasets/MadeAgents/xlam-irrelevance-7.5k](https://huggingface.co/datasets/MadeAgents/xlam-irrelevance-7.5k)) made available under the the CC-BY 4.0 (Creative Commons Attribution 4.0 International) license ([https://choosealicense.com/licenses/cc-by-4.0/](https://choosealicense.com/licenses/cc-by-4.0/));
+
+- When2Call dataset ([https://huggingface.co/datasets/nvidia/When2Call](https://huggingface.co/datasets/nvidia/When2Call)) made available under the CC-BY 4.0 (Creative Commons Attribution 4.0 International) license ([https://choosealicense.com/licenses/cc-by-4.0/](https://choosealicense.com/licenses/cc-by-4.0/));
+
+- rlla_4k forming a part of the ToolRL dataset ([https://github.com/qiancheng0/ToolRL](https://github.com/qiancheng0/ToolRL)) made available under the Apache 2.0 license ([https://github.com/qiancheng0/ToolRL?tab=Apache-2.0-1-ov-file](https://github.com/qiancheng0/ToolRL?tab=Apache-2.0-1-ov-file)); and
+
+- fc_pivot forming a part of the Nemotron-RL-Agentic-Function-Calling-Pivot-v1 dataset ([https://huggingface.co/datasets/nvidia/Nemotron-RL-Agentic-Function-Calling-Pivot-v1](https://huggingface.co/datasets/nvidia/Nemotron-RL-Agentic-Function-Calling-Pivot-v1)) made available under the CC-BY 4.0 (Creative Commons Attribution 4.0 International) license ([https://choosealicense.com/licenses/cc-by-4.0/](https://choosealicense.com/licenses/cc-by-4.0/)).
+
+One version of the teacher model used the following datasets:
+
+- Nemotron-RL-knowledge-mcqa dataset ([https://huggingface.co/datasets/nvidia/emotron-RL-knowledge-mcqa](https://huggingface.co/datasets/nvidia/Nemotron-RL-knowledge-mcqa)) made available under the CC-BY 4.0 (Creative Commons Attribution 4.0 International) license ([https://choosealicense.com/licenses/cc-by-4.0/](https://choosealicense.com/licenses/cc-by-4.0/)); and
+
+- rlpr_mcq forming a part of the SuperGPQA dataset ([https://huggingface.co/datasets/m-a-p/SuperGPQA](https://huggingface.co/datasets/m-a-p/SuperGPQA)) made available under the ODC-BY (Open Data Commons Attribution License v1.0) license ([https://opendatacommons.org/licenses/by/1-0/](https://opendatacommons.org/licenses/by/1-0/)).
+
+One version of the teacher model used the following datasets:
+
+- Nemotron-RL-instruction_following dataset ([https://huggingface.co/datasets/nvidia/Nemotron-RL-instruction_following](https://huggingface.co/datasets/nvidia/Nemotron-RL-instruction_following)) made available under the CC-BY 4.0 (Creative Commons Attribution 4.0 International) license ([https://choosealicense.com/licenses/cc-by-4.0/](https://choosealicense.com/licenses/cc-by-4.0/));
+
+- Nemotron-RL-Instruction-Following-Calendar-v2 dataset ([https://huggingface.co/datasets/nvidia/Nemotron-RL-Instruction-Following-Calendar-v2](https://huggingface.co/datasets/nvidia/Nemotron-RL-Instruction-Following-Calendar-v2)) made available under the CC-BY 4.0 (Creative Commons Attribution 4.0 International) license ([https://choosealicense.com/licenses/cc-by-4.0/](https://choosealicense.com/licenses/cc-by-4.0/)); and
+
+- Nemotron-RL-instruction_following-structured_outputs dataset ([https://huggingface.co/datasets/nvidia/Nemotron-RL-instruction_following-structured_outputs](https://huggingface.co/datasets/nvidia/Nemotron-RL-instruction_following-structured_outputs)) made available under the CC-BY 4.0 (Creative Commons Attribution 4.0 International) license ([https://choosealicense.com/licenses/cc-by-4.0/](https://choosealicense.com/licenses/cc-by-4.0/)).
+
+As described in the blog post, the model was fine tuned using the teacher models described above and the following datasets:
+
+- AReal_tau2 dataset ([https://huggingface.co/datasets/inclusionAI/AReaL-tau2-data](https://huggingface.co/datasets/inclusionAI/AReaL-tau2-data)) made available under the Apache 2.0 license ([https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md));
+
+- EnvFactory-SFT-FILTERED dataset ([https://huggingface.co/datasets/LARK-Lab/EnvFactory-SFT-FILTERED](https://huggingface.co/datasets/LARK-Lab/EnvFactory-SFT-FILTERED)) made available under the Apache 2.0 license ([https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md));
+
+- xlam_v2 forming a part of the xlam-function-calling-60k dataset ([https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k)) made available under the  CC-BY 4.0 (Creative Commons Attribution 4.0 International) license ([https://choosealicense.com/licenses/cc-by-4.0/](https://choosealicense.com/licenses/cc-by-4.0/));
+
+- Glaive_v1 forming a part of the glaive-function-calling-v2 dataset ([https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2)) made available under the Apache 2.0 license ([https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md));
+
+- toolace_v2 dataset forming a part of the ToolACE dataset ([https://huggingface.co/datasets/Team-ACE/ToolACE](https://huggingface.co/datasets/Team-ACE/ToolACE)) made available under the Apache 2.0 license ([https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md));
+
+- When2call_v2 dataset ([https://huggingface.co/datasets/nvidia/When2Call](https://huggingface.co/datasets/nvidia/When2Call)) made available under the CC-BY 4.0 (Creative Commons Attribution 4.0 International) license ([https://creativecommons.org/licenses/by/4.0/legalcode](https://creativecommons.org/licenses/by/4.0/legalcode));
+
+- nemotron_interactive forming a part of the Nemotron-SFT-Agentic-v2 dataset ([https://huggingface.co/datasets/nvidia/Nemotron-SFT-Agentic-v2](https://huggingface.co/datasets/nvidia/Nemotron-SFT-Agentic-v2)) made available under the CC-BY 4.0 (Creative Commons Attribution 4.0 International) license ([https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/cc-by-4.0.md](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/cc-by-4.0.md));
+
+- Infinity-nstruct dataset ([https://huggingface.co/datasets/BAAI/Infinity-Instruct](https://huggingface.co/datasets/BAAI/Infinity-Instruct)) made available under the CC-BY 4.0 (Creative Commons Attribution 4.0 International) license ([https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/cc-by-sa-4.0.md](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/cc-by-sa-4.0.md));
+
+- toolbench_v1 dataset ([https://huggingface.co/datasets/tuandunghcmut/toolbench-v1](https://huggingface.co/datasets/tuandunghcmut/toolbench-v1)) made available under the Apache 2.0 license ([https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md));
+
+- Toolmind__apigen_mt forming a part of the ToolMind dataset ([https://huggingface.co/datasets/Nanbeige/ToolMind](https://huggingface.co/datasets/Nanbeige/ToolMind)) made available under the Apache 2.0 license ([https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md));
+
+- Toucan_sft_v1 forming a part of the Toucan-1.5M dataset (https://huggingface.co/datasets/Agent-Ark/Toucan-1.5M) made available under the Apache 2.0 license ([https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md));
+
+- an unpublished synthetic dataset generated by Tether Data using Qwen3.5-9B ([https://huggingface.co/Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B)) and Qwen3.6-27B ([https://huggingface.co/Qwen/Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B)), both of which are licensed under the Apache-2.0 license ([https://choosealicense.com/licenses/apache-2.0/](https://choosealicense.com/licenses/apache-2.0/)) and τ²-Bench made available under the MIT license ([https://github.com/eligotts/tau2-bench?tab=License-1-ov-file](https://github.com/eligotts/tau2-bench?tab=License-1-ov-file));
+
+- toolmind__xlam  forming a part of the ToolMind dataset (https://huggingface.co/datasets/Nanbeige/ToolMind) made available under the Apache 2.0 license ([https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md));
+
+- xlam_irrelevance_v2 forming part of the xlam-irrelevance-7.5k dataset ([https://huggingface.co/datasets/MadeAgents/xlam-irrelevance-7.5k](https://huggingface.co/datasets/MadeAgents/xlam-irrelevance-7.5k)) made available under the CC-BY 4.0 (Creative Commons Attribution 4.0 International) license ([https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/cc-by-4.0.md](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/cc-by-4.0.md));
+
+- ultrachat_200k dataset ([https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k)) made available under the MIT license ([https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/mit.md](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/mit.md));
+
+- toolmind__graphsyn forming a part of the ToolMind dataset (https://huggingface.co/datasets/Nanbeige/ToolMind) made available under the Apache 2.0 license ([https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md));
+
+- smolagents_traces (SFT/smolagents_toolcalling_traces_think) dataset forming a part of the smoltalk2 dataset ([https://huggingface.co/datasets/HuggingFaceTB/smoltalk2](https://huggingface.co/datasets/HuggingFaceTB/smoltalk2)) made available under the Apache 2.0 license  ([https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md));
+
+- ultradata_if_think(IF / think split) forming a part of the UltraData-SFT-2605 dataset ([https://huggingface.co/datasets/openbmb/UltraData-SFT-2605](https://huggingface.co/datasets/openbmb/UltraData-SFT-2605)) made available under the Apache 2.0 license ([https://choosealicense.com/licenses/apache-2.0/](https://choosealicense.com/licenses/apache-2.0/));
+
+- conv_tool_use_pivot forming a part of the Nemotron-RL-Agentic-Conversational-Tool-Use-Pivot-v1 dataset ([https://huggingface.co/datasets/nvidia/Nemotron-RL-Agentic-Conversational-Tool-Use-Pivot-v1](https://huggingface.co/datasets/nvidia/Nemotron-RL-Agentic-Conversational-Tool-Use-Pivot-v1)) made available under the CC-BY 4.0 (Creative Commons Attribution 4.0 International) license ([https://choosealicense.com/licenses/cc-by-4.0/](https://choosealicense.com/licenses/cc-by-4.0/));
+
+- toolcall forming a part of the Tool N1 dataset ([https://github.com/NVlabs/Tool-N1](https://github.com/NVlabs/Tool-N1)) made available under the Apache 2.0 license ([https://github.com/NVlabs/Tool-N1/blob/master/LICENSE](https://github.com/NVlabs/Tool-N1/blob/master/LICENSE));
+
+- IF_multi_constraints_upto5 dataset ([https://huggingface.co/datasets/allenai/IF_multi_constraints_upto5](https://huggingface.co/datasets/allenai/IF_multi_constraints_upto5)) made available under the ODC-BY (Open Data Commons Attribution License v1.0) license ([https://opendatacommons.org/licenses/by/1-0/](https://opendatacommons.org/licenses/by/1-0/));
+
+- rlpr_mcq forming a part of the SuperGPQA dataset ([https://huggingface.co/datasets/m-a-p/SuperGPQA](https://huggingface.co/datasets/m-a-p/SuperGPQA)) made available under the ODC-BY (Open Data Commons Attribution License v1.0) license ([https://opendatacommons.org/licenses/by/1-0/](https://opendatacommons.org/licenses/by/1-0/));
+
+- Instruction_following forming a part of the Nemotron-RL-instruction_following dataset ([https://huggingface.co/datasets/nvidia/Nemotron-RL-instruction_following](https://huggingface.co/datasets/nvidia/Nemotron-RL-instruction_following)) made available under the ODC-BY (Open Data Commons Attribution License v1.0) license ([https://opendatacommons.org/licenses/by/1-0/](https://opendatacommons.org/licenses/by/1-0/));
+
+- xlam_irrelevance-7.5K dataset ([https://huggingface.co/datasets/MadeAgents/xlam-irrelevance-7.5k](https://huggingface.co/datasets/MadeAgents/xlam-irrelevance-7.5k)) made available under the CC-BY 4.0 (Creative Commons Attribution 4.0 International) license (https://choosealicense.com/licenses/cc-by-4.0/);
+
+- Calendar-Scheduling-Dataset ([https://huggingface.co/datasets/nvidia/Nemotron-RL-Instruction-Following-Calendar-v2](https://huggingface.co/datasets/nvidia/Nemotron-RL-Instruction-Following-Calendar-v2)) made available under the CC-BY 4.0 (Creative Commons Attribution 4.0 International) license ([https://choosealicense.com/licenses/cc-by-4.0/](https://choosealicense.com/licenses/cc-by-4.0/));
+
+- When2Call dataset ([https://huggingface.co/datasets/nvidia/When2Call](https://huggingface.co/datasets/nvidia/When2Call)) made available under the CC-BY 4.0 (Creative Commons Attribution 4.0 International) license (https://choosealicense.com/licenses/cc-by-4.0/);
+
+- rlla forming a part of the ToolRL dataset ([https://github.com/qiancheng0/ToolRL](https://github.com/qiancheng0/ToolRL)) made available under the Apache 2.0 license ([https://github.com/NVlabs/Tool-N1/blob/master/LICENSE](https://github.com/NVlabs/Tool-N1/blob/master/LICENSE));
+
+- fc_pivot forming a part of the Nemotron-RL-Agentic-Function-Calling-Pivot-v1 dataset ([https://huggingface.co/datasets/nvidia/Nemotron-RL-Agentic-Function-Calling-Pivot-v1](https://huggingface.co/datasets/nvidia/Nemotron-RL-Agentic-Function-Calling-Pivot-v1)) made available under the CC-BY 4.0 (Creative Commons Attribution 4.0 International) license (https://choosealicense.com/licenses/cc-by-4.0/);
+
+- math_with_judge_simple_agent forming a part of the DAPO-Math-17k dataset ([https://huggingface.co/datasets/BytedTsinghua-SIA/DAPO-Math-17k](https://huggingface.co/datasets/BytedTsinghua-SIA/DAPO-Math-17k)) dataset made available under the Apache 2.0 license ([https://github.com/NVlabs/Tool-N1/blob/master/LICENSE](https://github.com/NVlabs/Tool-N1/blob/master/LICENSE));
+
+- math_with_judge_simple_agent forming a part of the Skywork-OR1-RL-Data dataset ([https://huggingface.co/datasets/Skywork/Skywork-OR1-RL-Data](https://huggingface.co/datasets/Skywork/Skywork-OR1-RL-Data)) made accessible on Huggingface at the foregoing link;
+
+- Nemotron-3-Nano-RL-Training-Blend dataset ([https://huggingface.co/datasets/nvidia/Nemotron-3-Nano-RL-Training-Blend](https://huggingface.co/datasets/nvidia/Nemotron-3-Nano-RL-Training-Blend)) made available under the ODC-BY (Open Data Commons Attribution License v1.0) license (https://opendatacommons.org/licenses/by/1-0/);
+
+- portions of the mcqa_simple_agent forming a part of the Nemotron-3-Nano-RL-Training-Blend dataset ([https://huggingface.co/datasets/nvidia/Nemotron-3-Nano-RL-Training-Blend](https://huggingface.co/datasets/nvidia/Nemotron-3-Nano-RL-Training-Blend)) made available under the ODC-BY (Open Data Commons Attribution License v1.0) license ([https://opendatacommons.org/licenses/by/1-0/](https://opendatacommons.org/licenses/by/1-0/)) which are a modified subset of the Nemotron-RL-knowledge-mcqa dataset ([https://huggingface.co/datasets/nvidia/Nemotron-RL-knowledge-mcqa](https://huggingface.co/datasets/nvidia/Nemotron-RL-knowledge-mcqa)) made available under the CC-BY 4.0 (Creative Commons Attribution 4.0 International) license ([https://choosealicense.com/licenses/cc-by-4.0/](https://choosealicense.com/licenses/cc-by-4.0/));
+
+- portions of the code_gen_simple_agent forming a part of the Nemotron-3-Nano-RL-Training-Blend dataset ([https://huggingface.co/datasets/nvidia/Nemotron-3-Nano-RL-Training-Blend](https://huggingface.co/datasets/nvidia/Nemotron-3-Nano-RL-Training-Blend)) made available under the ODC-BY (Open Data Commons Attribution License v1.0) license ([https://opendatacommons.org/licenses/by/1-0/](https://opendatacommons.org/licenses/by/1-0/)) which are a subset of the Nemotron-RL-coding-competitive_coding dataset ([https://huggingface.co/datasets/nvidia/Nemotron-RL-coding-competitive_coding](https://huggingface.co/datasets/nvidia/Nemotron-RL-coding-competitive_coding)) made available under the CC-BY-SA 4.0 (Creative Commons Share Attribution Alike 4.0 International) ([https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/cc-by-sa-4.0.md](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/cc-by-sa-4.0.md));
+
+- portions of instruction_following_simple_agent forming a part of the Nemotron-3-Nano-RL-Training-Blend dataset ([https://huggingface.co/datasets/nvidia/Nemotron-3-Nano-RL-Training-Blend](https://huggingface.co/datasets/nvidia/Nemotron-3-Nano-RL-Training-Blend)) made available under the ODC-BY (Open Data Commons Attribution License v1.0) license ([https://opendatacommons.org/licenses/by/1-0/](https://opendatacommons.org/licenses/by/1-0/)) which are a subset of the Nemotron-RL-instruction_following dataset ([https://huggingface.co/datasets/nvidia/Nemotron-RL-instruction_following](https://huggingface.co/datasets/nvidia/Nemotron-RL-instruction_following)) made available under the ODC-BY (Open Data Commons Attribution License v1.0) license ([https://opendatacommons.org/licenses/by/1-0/](https://opendatacommons.org/licenses/by/1-0/));
+
+- portions of workplace_assistant_simple_agent forming a part of the Nemotron-3-Nano-RL-Training-Blend dataset ([https://huggingface.co/datasets/nvidia/Nemotron-3-Nano-RL-Training-Blend](https://huggingface.co/datasets/nvidia/Nemotron-3-Nano-RL-Training-Blend)) made available under the ODC-BY (Open Data Commons Attribution License v1.0) license ([https://opendatacommons.org/licenses/by/1-0/](https://opendatacommons.org/licenses/by/1-0/)) which are a subset of the Nemotron-RL-agent-workplace_assistant dataset ([https://huggingface.co/datasets/nvidia/Nemotron-RL-agent-workplace_assistant](https://huggingface.co/datasets/nvidia/Nemotron-RL-agent-workplace_assistant)) made available under the CC-BY 4.0 (Creative Commons Attribution 4.0 International) license ([https://choosealicense.com/licenses/cc-by-4.0/](https://choosealicense.com/licenses/cc-by-4.0/)); and
+
+- portions of structured_outputs_simple_agent forming a part of the Nemotron-3-Nano-RL-Training-Blend dataset ([https://huggingface.co/datasets/nvidia/Nemotron-3-Nano-RL-Training-Blend](https://huggingface.co/datasets/nvidia/Nemotron-3-Nano-RL-Training-Blend)) made available under the ODC-BY (Open Data Commons Attribution License v1.0) license ([https://opendatacommons.org/licenses/by/1-0/](https://opendatacommons.org/licenses/by/1-0/)) which are a subset of the Nemotron-RL-instruction_following-structured_outputs dataset ([https://huggingface.co/datasets/nvidia/Nemotron-RL-instruction_following-structured_outputs](https://huggingface.co/datasets/nvidia/Nemotron-RL-instruction_following-structured_outputs)) made available under the CC-BY 4.0 (Creative Commons Attribution 4.0 International) license ([https://choosealicense.com/licenses/cc-by-4.0/](https://choosealicense.com/licenses/cc-by-4.0/)).
+
+The full text of each license is reproduced in [ATTRIBUTIONS.md](https://huggingface.co/qvac/BotPsy-0.8B/blob/main/ATTRIBUTIONS.md).

chat_template.jinja

@@ -0,0 +1,157 @@
+{%- 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 %}
+        {{- '<|im_start|>' + message.role + '\n' -}}
+        {% generation %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {{- '<think>\n' + reasoning_content + '\n</think>\n\n' + content -}}
+        {%- else %}
+            {{- 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' }}
+        {%- endgeneration %}
+    {%- 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 true %}
+        {{- '<think>\n' }}
+    {%- else %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

config.json

@@ -0,0 +1,104 @@
+{
+  "architectures": [
+    "Qwen3_5ForConditionalGeneration"
+  ],
+  "bos_token_id": null,
+  "dtype": "bfloat16",
+  "eos_token_id": 248046,
+  "image_token_id": 248056,
+  "model_type": "qwen3_5",
+  "pad_token_id": 248044,
+  "text_config": {
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "attn_output_gate": true,
+    "bos_token_id": null,
+    "dtype": "bfloat16",
+    "eos_token_id": 248046,
+    "full_attention_interval": 4,
+    "head_dim": 256,
+    "hidden_act": "silu",
+    "hidden_size": 1024,
+    "initializer_range": 0.02,
+    "intermediate_size": 3584,
+    "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_conv_kernel_dim": 4,
+    "linear_key_head_dim": 128,
+    "linear_num_key_heads": 16,
+    "linear_num_value_heads": 16,
+    "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": 0,
+    "mtp_use_dedicated_embeddings": false,
+    "num_attention_heads": 8,
+    "num_hidden_layers": 24,
+    "num_key_value_heads": 2,
+    "pad_token_id": 248044,
+    "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": true,
+    "use_cache": false,
+    "vocab_size": 248320
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.8.1",
+  "video_token_id": 248057,
+  "vision_config": {
+    "deepstack_visual_indexes": [],
+    "depth": 12,
+    "dtype": "bfloat16",
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_size": 768,
+    "in_channels": 3,
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "model_type": "qwen3_5_vision",
+    "num_heads": 12,
+    "num_position_embeddings": 2304,
+    "out_hidden_size": 1024,
+    "patch_size": 16,
+    "spatial_merge_size": 2,
+    "temporal_patch_size": 2
+  },
+  "vision_end_token_id": 248054,
+  "vision_start_token_id": 248053
+}

generation_config.json

@@ -0,0 +1,13 @@
+{
+  "do_sample": true,
+  "eos_token_id": [
+    248046,
+    248044
+  ],
+  "pad_token_id": 248044,
+  "temperature": 0.3,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "5.8.1",
+  "use_cache": true
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9a52e9fb1552e8592542e3de8104af9040b56c60644f5a9c2c270621a3b0ab48
+size 1706030528

processor_config.json

@@ -0,0 +1,60 @@
+{
+  "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"
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523
+size 19989325

tokenizer_config.json

@@ -0,0 +1,30 @@
+{
+  "add_prefix_space": false,
+  "audio_bos_token": "<|audio_start|>",
+  "audio_eos_token": "<|audio_end|>",
+  "audio_token": "<|audio_pad|>",
+  "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+",
+  "processor_class": "Qwen3VLProcessor",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null,
+  "video_token": "<|video_pad|>",
+  "vision_bos_token": "<|vision_start|>",
+  "vision_eos_token": "<|vision_end|>"
+}