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LICENSE

@@ -0,0 +1,20 @@
+Creative Commons Attribution-NonCommercial 4.0 International
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+
+Copyright (c) 2026 Frontis AI and the OpenMLE contributors.
+
+Except for third-party material identified in NOTICE, the Frontis-MA1 model weights, configuration changes, and documentation distributed in this repository are licensed under the Creative Commons Attribution-NonCommercial 4.0 International Public License.
+
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LICENSE-UPSTREAM-APACHE-2.0

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NOTICE

@@ -0,0 +1,8 @@
+Frontis-MA1-30B
+Copyright 2026 Frontis AI and the OpenMLE contributors.
+
+This model is derived from Qwen3-30B-A3B-Thinking-2507, which is Copyright 2025 Alibaba Cloud and licensed under the Apache License 2.0.
+
+Frontis-MA1-30B contains modifications produced through execution-grounded supervised fine-tuning and reinforcement learning. See README.md for the model lineage, intended use, evaluation scope, and limitations.
+
+Original Frontis-MA1 material is released under CC BY-NC 4.0 as stated in LICENSE. The upstream Apache License 2.0 text is preserved in LICENSE-UPSTREAM-APACHE-2.0; that upstream notice is not replaced by the Frontis-MA1 license.

README.md

@@ -1,3 +1,280 @@
 ---
 license: cc-by-nc-4.0
+base_model: Qwen/Qwen3-30B-A3B-Thinking-2507
+base_model_relation: finetune
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+  - openmle
+  - frontis-ma1
+  - machine-learning-engineering
+  - autoresearch
+  - agent
+  - coding
+  - qwen3
+  - moe
+  - post-training
 ---
+
+<p align="center">
+  <img src="assets/frontis-logo.png" width="360" alt="Frontis AI">
+</p>
+
+<h1 align="center">Frontis-MA1-30B</h1>
+
+<p align="center">
+  <b>An execution-grounded model for evolutionary machine learning engineering and AutoResearch</b>
+</p>
+
+<p align="center">
+  <a href="#paper-and-citation">📄 Paper</a>
+  &nbsp;•&nbsp;
+  <a href="https://rc314159-creator.github.io/OpenRSI/">🌐 Project</a>
+  &nbsp;•&nbsp;
+  <a href="https://github.com/rc314159-creator/OpenRSI">💻 Code</a>
+  &nbsp;•&nbsp;
+  <a href="https://huggingface.co/FrontisAI">🤗 Models</a>
+  &nbsp;•&nbsp;
+  <a href="https://huggingface.co/datasets/FrontisAI/OpenMLE-Tasks">🧩 Tasks</a>
+  &nbsp;•&nbsp;
+  <a href="https://huggingface.co/datasets/FrontisAI/OpenMLE-SFT-Traces">📚 SFT Traces</a>
+</p>
+
+## Introduction
+
+**Frontis-MA1-30B** is the companion 30B open-weight research model released with **OpenMLE**, an execution-grounded system for studying meta-evolution in machine learning engineering (MLE).
+
+Starting from [Qwen3-30B-A3B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507), we apply execution-grounded supervised fine-tuning and reinforcement learning so that the model learns four reusable program-transformation operators:
+
+- **Draft** — create a complete initial solution;
+- **Improve** — refine a parent program using its score and execution evidence;
+- **Debug** — repair invalid or failing code;
+- **Crossover** — recombine useful elements from two parent solutions.
+
+At inference time, OpenMLE-Evo composes these operators into a long-horizon search loop. Frontis-MA1 is therefore both a product of the OpenMLE training stack and the model that drives its evolutionary search.
+
+## Highlights
+
+- **Execution-grounded post-training.** SFT examples and RL rewards are constructed from programs executed in isolated MLE sandboxes.
+- **Trainable evolutionary operators.** Draft, Improve, Debug, and Crossover provide a shared action space across training and test-time search.
+- **Linked research release.** The model card connects the weights to the OpenMLE training code, search protocol, task environments, and evaluation boundary.
+- **Strong controlled model gain.** Under the same OpenMLE-Evo harness on MLE-Bench Lite, Frontis-MA1-30B improves Medal Average from **34.85% to 53.03%** and Human Rank from **0.5573 to 0.7055** over its base model.
+
+> The reported scores measure a **model–harness system**, not standalone one-shot generation. Reproduction requires the same OpenMLE-Evo search configuration, task environments, sandbox budget, and evaluation protocol.
+
+## Model family
+
+| Model | Base model | Release format | Status |
+| --- | --- | --- | --- |
+| **Frontis-MA1-30B** | Qwen3-30B-A3B-Thinking-2507 | BF16 Transformers | This repository |
+| [Frontis-MA1-30B-GGUF](https://huggingface.co/FrontisAI/Frontis-MA1-30B-GGUF) | Frontis-MA1-30B | Q4_K_M GGUF | Local deployment |
+| [Frontis-MA1-35B](https://huggingface.co/FrontisAI/Frontis-MA1-35B) | Qwen3.6-35B-A3B | BF16 Transformers | Companion release |
+
+## Model details
+
+| Field | Value |
+| --- | --- |
+| Model family | Frontis-MA1 |
+| Architecture | Mixture-of-Experts causal language model |
+| Parameters | 30.5B total, 3.3B activated |
+| Layers | 48 |
+| Experts | 128 total, 8 activated per token |
+| Weight precision | BF16 |
+| Native configuration context | 262,144 tokens |
+| Post-training SFT cutoff | 32,768 tokens |
+| License | CC BY-NC 4.0 |
+
+The 262,144-token context length is inherited from the base-model configuration. OpenMLE post-training used a 32,768-token SFT cutoff; substantially longer contexts have not yet been validated to the same standard as the reported experiments.
+
+## Training
+
+Frontis-MA1-30B uses full-parameter supervised fine-tuning followed by reinforcement learning from executable task feedback.
+
+### Supervised fine-tuning
+
+- 26,259 released execution-grounded examples: 17,245 full responses and 9,014 trajectory steps;
+- full-solution and trajectory-step supervision;
+- BF16 full-parameter training on 8 NVIDIA H200 GPUs;
+- global batch size 128;
+- learning rate `3e-5` with cosine decay and 0.1 warmup fraction;
+- three epochs;
+- Qwen3-compatible thinking-loss masking.
+
+### Reinforcement learning
+
+- online generation and evaluation in isolated task sandboxes;
+- operator mixture: Draft 0.50, Improve 0.17, Debug 0.17, Crossover 0.16;
+- 16 prompts per rollout and 16 samples per prompt;
+- maximum response length 24,576 tokens;
+- GSPO with execution-grounded reward post-processing;
+- Adam optimizer with learning rate `1e-6`.
+
+See the paper and [OpenRSI code](https://github.com/rc314159-creator/OpenRSI) for the complete data-construction, training, search, and evaluation protocol.
+
+<p align="center">
+  <img src="assets/paper-figure-openmle-framework.png" width="95%" alt="Paper framework figure: OpenMLE training and inference workflow">
+</p>
+
+<p align="center"><sub>
+Paper framework figure. Frontis-MA1 learns the same four atomic operators used by OpenMLE-Evo, first from executable SFT rollouts and then from online RL execution feedback.
+</sub></p>
+
+## Benchmark results
+
+The headline numbers are **model–harness system results**, not standalone one-shot scores. The controlled comparison holds the OpenMLE-Evo harness fixed and changes only the model from the Qwen base to Frontis-MA1-30B.
+
+### MLE-Bench Lite protocol
+
+| Item | Setting |
+| --- | --- |
+| Benchmark | Official 22-task MLE-Bench Lite split |
+| Independent runs | 3 per OpenMLE-Evo configuration, unless stated otherwise |
+| Sandbox budget | 12 hours on 0.5 NVIDIA RTX 4090 equivalent per task |
+| Equivalent compute | 6 RTX 4090 GPU-hours per task |
+| Execution | Generated programs are run and scored in isolated task sandboxes |
+| OpenMLE-Evo-Max | MLE-Bench-disjoint priors plus asynchronous multi-GPU search |
+| Budget comparison | OpenMLE-Evo-Max keeps the same total sandbox-compute budget |
+
+The paper reports:
+
+- **Valid Rate:** mean number of the 22 tasks with a valid submission;
+- **Medal Average:** mean fraction of tasks receiving any Kaggle medal;
+- **Human Rank:** mean fraction of human leaderboard participants surpassed by the selected submission.
+
+All three metrics are higher-is-better.
+
+### Main results from the paper
+
+<p align="center">
+  <img src="assets/paper-figure-main-results.png" width="98%" alt="Paper main figure: model-harness results on MLE-Bench Lite">
+</p>
+
+<p align="center"><sub>
+Current paper main-results figure. The left panel shows all completed model–harness results on MLE-Bench Lite; the right panel compares model size with Medal Average. Orange denotes Frontis-MA1 with OpenMLE-Evo, cyan denotes other models with OpenMLE-Evo, hatching denotes OpenMLE-Evo-Max, and gray denotes general-purpose coding harnesses. The paper highlights the primary 35B model in this figure and reports the companion 30B results in its main table.
+</sub></p>
+
+### Controlled 30B comparison
+
+| Model | Harness | Valid Rate ↑ | Medal Average ↑ | Human Rank ↑ |
+| --- | --- | ---: | ---: | ---: |
+| Qwen3-30B-A3B-Thinking-2507 | OpenMLE-Evo | 17.33/22 | 34.85% | 0.5573 |
+| **Frontis-MA1-30B** | OpenMLE-Evo | **21.67/22** | **53.03%** | **0.7055** |
+| **Frontis-MA1-30B** | OpenMLE-Evo-Max | **22.00/22** | **66.67%** | **0.8053** |
+
+| Comparison | Δ Valid Rate | Δ Medal Average | Δ Human Rank | What it measures |
+| --- | ---: | ---: | ---: | --- |
+| Frontis-MA1-30B vs. Qwen base, OpenMLE-Evo fixed | +4.34 tasks | +18.18 pp | +0.1482 | Post-training gain under a fixed harness |
+| OpenMLE-Evo-Max vs. OpenMLE-Evo, Frontis-MA1-30B fixed | +0.33 task | +13.64 pp | +0.0998 | Additional system-level search gain |
+
+The fixed-harness comparison is the primary evidence for model improvement. OpenMLE-Evo-Max also changes the search system, so its result must be read as an end-to-end model–harness score rather than a pure model gain. These experiments do not establish a standalone ranking outside the OpenMLE evaluation protocol.
+
+The paper does not provide a 30B-only result figure, so this model card keeps the original paper values as a searchable table rather than introducing a derived visualization.
+
+### Public-weight model comparison under OpenMLE-Evo
+
+The following rows use the same OpenMLE-Evo harness, benchmark split, and sandbox-compute budget. This is the closest available comparison with strong public-weight models, although model scale, pretraining data, and architecture still differ.
+
+| Public-weight model | Valid Rate ↑ | Medal Average ↑ | Human Rank ↑ |
+| --- | ---: | ---: | ---: |
+| [Kimi K2.6](https://huggingface.co/moonshotai/Kimi-K2.6) | 21.67/22 | **66.67%** | 0.7859 |
+| [GLM-5.2](https://huggingface.co/zai-org/GLM-5.2) | 19.67/22 | 62.12% | 0.7069 |
+| [Frontis-MA1-35B](https://huggingface.co/FrontisAI/Frontis-MA1-35B) | 21.67/22 | 60.61% | 0.7647 |
+| [MiniMax M3](https://huggingface.co/MiniMaxAI/MiniMax-M3) | **22.00/22** | 59.09% | **0.7994** |
+| **Frontis-MA1-30B** | 21.67/22 | 53.03% | 0.7055 |
+| [DeepSeek-V4-Flash](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash) | 21.33/22 | 51.52% | 0.6957 |
+| [Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) | 19.67/22 | 39.39% | 0.5828 |
+| [Qwen3-30B-A3B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507) | 17.33/22 | 34.85% | 0.5573 |
+
+These are model–harness results rather than standalone one-shot model scores. The full paper figure above additionally includes OpenMLE-Evo-Max and general-purpose coding-agent systems, whose harnesses are not directly interchangeable with this fixed-harness table.
+
+## Usage
+
+### Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "FrontisAI/Frontis-MA1-30B"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype="auto",
+    device_map="auto",
+)
+
+messages = [
+    {
+        "role": "user",
+        "content": (
+            "Build a strong tabular classification baseline and explain "
+            "the validation design."
+        ),
+    }
+]
+
+inputs = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    tokenize=True,
+    return_dict=True,
+    return_tensors="pt",
+).to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=2048)
+completion = outputs[0, inputs["input_ids"].shape[1]:]
+print(tokenizer.decode(completion, skip_special_tokens=True))
+```
+
+### vLLM
+
+```bash
+vllm serve FrontisAI/Frontis-MA1-30B \
+  --served-model-name Frontis-MA1-30B \
+  --tensor-parallel-size 8 \
+  --max-model-len 32768 \
+  --enable-reasoning \
+  --reasoning-parser deepseek_r1
+```
+
+## Intended use
+
+Frontis-MA1-30B is intended for:
+
+- research on machine learning engineering and AutoResearch agents;
+- generation and iterative improvement of ML experiment code;
+- execution-grounded program search;
+- use with OpenMLE-Evo or compatible sandboxed harnesses.
+
+Generated code may be incorrect, insecure, destructive, or expensive to execute. Run it inside an isolated environment with explicit CPU, memory, GPU, network, filesystem, and time limits.
+
+## Limitations
+
+- The main evaluation centers on MLE-Bench Lite and does not establish general performance across all software-engineering or scientific-research tasks.
+- The paper results depend on an external evolutionary search harness and sandbox feedback.
+- Execution reward does not fully measure maintainability, robustness, security, or responsible data handling.
+- Context lengths beyond the post-training cutoff require additional validation.
+- Outputs may contain reasoning traces and should be handled according to downstream requirements.
+
+## Release artifacts
+
+This repository is the canonical BF16 Transformers release. The [Frontis-MA1-30B-GGUF](https://huggingface.co/FrontisAI/Frontis-MA1-30B-GGUF) repository provides the Q4_K_M local-deployment derivative.
+
+<a id="paper"></a>
+## Paper and citation
+
+**OpenMLE: Scaling Evolutionary MLE Agents toward Recursive Self-Improvement**
+
+The public preprint URL and archival BibTeX will be added when the paper record is available.
+
+## License
+
+Original Frontis-MA1 material is released under [CC BY-NC 4.0](LICENSE) for attribution-required, non-commercial use. Commercial use is not granted.
+
+The upstream Qwen Apache License 2.0 notice is preserved in [LICENSE-UPSTREAM-APACHE-2.0](LICENSE-UPSTREAM-APACHE-2.0) and [NOTICE](NOTICE).
+
+The license in this repository applies to the model weights, configuration files, and model documentation distributed here. Training and evaluation datasets and third-party software remain subject to their respective terms.
+
+## Acknowledgements
+
+We thank the Qwen team and the open-source communities behind Transformers, SLIME, Ray, Megatron-LM, SGLang, MLE-Bench, and the broader executable MLE research ecosystem.

config.json

@@ -0,0 +1,38 @@
+{
+  "architectures": [
+    "Qwen3MoeForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "decoder_sparse_step": 1,
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 6144,
+  "max_position_embeddings": 262144,
+  "max_window_layers": 48,
+  "mlp_only_layers": [],
+  "model_type": "qwen3_moe",
+  "moe_intermediate_size": 768,
+  "norm_topk_prob": true,
+  "num_attention_heads": 32,
+  "num_experts": 128,
+  "num_experts_per_tok": 8,
+  "num_hidden_layers": 48,
+  "num_key_value_heads": 4,
+  "output_router_logits": false,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000000,
+  "router_aux_loss_coef": 0.001,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

config_1m.json

@@ -0,0 +1,45 @@
+{
+  "architectures": [
+    "Qwen3MoeForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "decoder_sparse_step": 1,
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 6144,
+  "max_position_embeddings": 262144,
+  "max_window_layers": 48,
+  "mlp_only_layers": [],
+  "model_type": "qwen3_moe",
+  "moe_intermediate_size": 768,
+  "norm_topk_prob": true,
+  "num_attention_heads": 32,
+  "num_experts": 128,
+  "num_experts_per_tok": 8,
+  "num_hidden_layers": 48,
+  "num_key_value_heads": 4,
+  "output_router_logits": false,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000000,
+  "router_aux_loss_coef": 0.001,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936,
+  "dual_chunk_attention_config":{
+    "chunk_size": 131072,
+    "local_size": 4096,
+    "original_max_position_embeddings": 131072,
+    "sparse_attention_enabled": true,
+    "sparse_attention_config": [{"0": ["vertical_and_slash", 17408, 23552, 0], "1": ["vertical_and_slash", 19456, 21504, 0], "2": ["vertical_and_slash", 17408, 23552, 0], "3": ["vertical_and_slash", 19456, 21504, 48], "4": ["vertical_and_slash", 17408, 23552, 966], "5": ["vertical_and_slash", 17408, 23552, 0], "6": ["vertical_and_slash", 17408, 23552, 0], "7": ["vertical_and_slash", 17408, 23552, 0], "8": ["vertical_and_slash", 17408, 23552, 2], "9": ["vertical_and_slash", 17408, 23552, 0], "10": ["vertical_and_slash", 17408, 23552, 0], "11": ["vertical_and_slash", 17408, 23552, 33], "12": ["vertical_and_slash", 17408, 23552, 2], "13": ["vertical_and_slash", 17408, 23552, 0], "14": ["vertical_and_slash", 17408, 23552, 0], "15": ["vertical_and_slash", 17408, 23552, 0], "16": ["vertical_and_slash", 17408, 23552, 6], "17": ["vertical_and_slash", 17408, 23552, 0], "18": ["vertical_and_slash", 17408, 23552, 0], "19": ["vertical_and_slash", 17408, 23552, 152], "20": ["vertical_and_slash", 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"31": ["vertical_and_slash", 21504, 19456, 1617133]}]
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+    "temperature": 0.6,
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+    "add_prefix_space": false,
+    "added_tokens_decoder": {
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+            "content": "<|endoftext|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": true
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+            "normalized": false,
+            "rstrip": false,
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+            "lstrip": false,
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+            "single_word": false,
+            "special": false
+        },
+        "151658": {
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+            "special": false
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+            "lstrip": false,
+            "normalized": false,
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+        },
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+            "special": false
+        },
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+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
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+            "content": "<|repo_name|>",
+            "lstrip": false,
+            "normalized": false,
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+            "single_word": false,
+            "special": false
+        },
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+            "content": "<|file_sep|>",
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+            "normalized": false,
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+            "single_word": false,
+            "special": false
+        },
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+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
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+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "151667": {
+            "content": "<think>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "151668": {
+            "content": "</think>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        }
+    },
+    "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": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {{- messages[0].content + '\\n\\n' }}\n    {%- endif %}\n    {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0].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\" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n        {%- set ns.multi_step_tool = false %}\n        {%- set ns.last_query_index = index %}\n    {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n    {%- if message.content is string %}\n        {%- set content = message.content %}\n    {%- else %}\n        {%- set content = '' %}\n    {%- endif %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\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        {%- if loop.index0 > ns.last_query_index %}\n            {%- if loop.last or (not loop.last and reasoning_content) %}\n                {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n            {%- else %}\n                {{- '<|im_start|>' + message.role + '\\n' + content }}\n            {%- endif %}\n        {%- else %}\n            {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- endif %}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n<think>\\n' }}\n{%- endif %}",
+    "clean_up_tokenization_spaces": false,
+    "eos_token": "<|im_end|>",
+    "errors": "replace",
+    "model_max_length": 1010000,
+    "pad_token": "<|endoftext|>",
+    "split_special_tokens": false,
+    "tokenizer_class": "Qwen2Tokenizer",
+    "unk_token": null,
+    "add_bos_token": false
+}