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Model: Naseer-010/Qwen3-8B-Finetuned-DIME
Source: Original Platform

.gitattributes

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+fig1_reward_cliff.png filter=lfs diff=lfs merge=lfs -text
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+fig2_cascade_exploit_fix.png filter=lfs diff=lfs merge=lfs -text
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+fig3_benchmark.png filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-8B
+tags:
+  - reinforcement-learning
+  - grpo
+  - infrastructure-management
+  - sre
+  - kubernetes
+  - lora
+  - unsloth
+  - trl
+language:
+  - en
+pipeline_tag: text-generation
+---
+
+# Qwen3-8B — GRPO Fine-tuned on DIME
+
+**Qwen3-8B fine-tuned via Group Relative Policy Optimization (GRPO) to act as an autonomous Site-Reliability Engineer in a simulated 8-node Kubernetes cluster.**
+
+---
+
+## What is this model?
+
+This is a **merged BF16 checkpoint** of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) after 300 steps of GRPO fine-tuning on the DIME (Distributed Infrastructure Management Environment) benchmark — trained in **44 minutes on a single A100-SXM4-80GB**.
+
+The model observes per-node CPU, memory, queue depths, and tail-latency telemetry, then outputs a single `kubectl` command to maintain cluster health. It was trained from scratch with a completely redesigned reward signal after the original reward function was found to produce zero-variance advantages that blocked all gradient flow.
+
+---
+
+## Benchmark Results
+
+**+44.2% relative improvement over zero-shot Qwen3-8B** on the 14-task DIME benchmark.
+
+![Benchmark comparison](fig3_benchmark.png)
+
+| Metric | Zero-shot | Fine-tuned |
+|---|---|---|
+| Overall avg score | 0.394 | **0.569** (best episode) |
+| connection_pool_deadlock | 0.630 | **0.976** |
+| memory_leak_slow_burn | 0.990 | **0.990** |
+| node_failure | 0.220 | **0.920** |
+| retry_storm | 0.377 | **0.587** |
+| thundering_herd | 0.393 | **0.606** |
+| traffic_spike | 0.024 | **0.399** |
+
+---
+
+## The Reward Engineering Challenge
+
+The central technical contribution is replacing a **gradient-blocking reward cliff** with a differentiable seven-component signal.
+
+The original reward returned `r = −1000` whenever the database node failed — which happened within the first 3 steps of most episodes. With all rewards identical, GRPO advantages collapsed to zero and no gradient flowed.
+
+![Reward cliff vs bounded signal](fig1_reward_cliff.png)
+
+The fixed reward:
+
+$$R_{env}(s,a) = \text{clip}(\, r_{topo} + r_{shed} + r_{mem} + r_{fric} + r_{lat} + r_{up} + r_{eff},\; -5,\; +5 \,)$$
+
+with bounded components ensuring non-zero gradient everywhere.
+
+---
+
+## DIME Environment
+
+![DIME architecture](fig4_dime_architecture.png)
+
+- **8-node cluster**: node-0 is a stateful PostgreSQL DB (SPOF), nodes 1–7 are stateless workers
+- **Partial observability**: telemetry dropout when `cpu_i = −1`
+- **6 action types**: restart, reroute, scale_up, throttle, query_logs, no_op
+- **14 failure scenarios**: traffic spikes, node failures, memory leaks, retry storms, split-brain, and more
+- **Error budget**: throttle burns irreplaceable budget; the agent must be economical
+
+---
+
+## Training Dynamics
+
+![Training curves](fig2_training_curves.png)
+
+- `reward_format` reached 3.0 (perfect) from step 1 — the model learned XML scaffold immediately
+- `reward_validity` stabilised at 1.9+ — no invalid commands after step ~10
+- `reward_env` improved steadily — environment physics signal dominated learning
+- `clipped_ratio` stayed near 0 throughout — healthy PPO clip utilisation
+- Total wall-clock: **44 minutes** at 8.4 s/step
+
+---
+
+## Triage Oracle and the Priority Inversion Bug
+
+The model is guided during training by a 10-rule deterministic triage tree:
+
+![Triage tree](fig7_triage_tree.png)
+
+A critical discovery: the original oracle evaluated the **Black Swan rule** (`|F(s)| ≥ 2 → throttle(0.3)`) *before* the **DB Recovery rule** (`0 ∈ F(s) → restart_node(0)`). When multiple nodes including the database were failed, the oracle prescribed `throttle` instead of `restart_node(0)`. The model faithfully learned this suboptimal policy. Fixing the priority ordering accounted for **+0.044 benchmark score** improvement.
+
+---
+
+## Failure Modes Documented
+
+Five training configurations failed before convergence — each diagnosable from standard TRL metrics:
+
+![Failure modes](fig6_failure_modes.png)
+
+| Run | Failure | Signal |
+|---|---|---|
+| vLLM on A100-40GB | SM 8.0 segfault (`compilation_config` not set) | Crash at init |
+| batch=4, gen=8 | CPU-bound rewards; 126 s/step, GPU idle | `samples/sec = 0.06` |
+| max_comp=256 | `<think>` blocks truncated before `</think>` | `frac_reward_zero_std = 1.0` |
+| reward_env×2 | 10:1 env-to-triage ratio recreated zero-variance | `zero_std → 1.0` at step 119 |
+| oracle inverted | Learned `throttle` in DB-failure states | Low `triage/mean` |
+
+---
+
+## Reward Components
+
+![Reward components](fig5_reward_components.png)
+
+```
+R(a,s) = R_fmt [-3,+3]  +  R_val [-2,+2]  +  R_env [-5,+5]  +  R_tri [-0.5,+1]
+```
+
+- **R_fmt**: XML scaffold compliance (`<reasoning>` + `<action>` tags)
+- **R_val**: Syntactic kubectl parse success
+- **R_env**: 7-component physics reward (topology, latency, memory, uptime, budget)
+- **R_tri**: Oracle triage alignment (gentle guidance, not primary teacher)
+
+---
+
+## Quick Start
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Naseer-010/Qwen3-8B-Finetuned-DIME",
+    torch_dtype="bfloat16",
+    device_map="auto",
+)
+tokenizer = AutoTokenizer.from_pretrained("Naseer-010/Qwen3-8B-Finetuned-DIME")
+
+system_prompt = """You are an autonomous SRE agent managing an 8-node Kubernetes cluster.
+Node-0 is the PostgreSQL database (SPOF). Nodes 1-7 are stateless workers.
+
+TRIAGE PRIORITY (check in order):
+1. OOM: if any node mem > 0.92 → kubectl delete pod node-<i>
+2. DB RECOVERY: if node-0 in failed_nodes → kubectl delete pod node-0
+3. SPLIT-BRAIN: if io_wait > 0.80 → kubectl throttle ingress --rate=0.5
+4. HOT-SHARD: if one worker cpu > 0.90, others low → reroute traffic
+5. RETRY STORM: if p99 > 100ms and rr > 150 → kubectl throttle ingress --rate=0.4
+6. ZOMBIE NODE: if worker cpu near 0 → reroute away from it
+7. BLACK SWAN: if 2+ nodes failed (DB alive) → kubectl throttle ingress --rate=0.3
+8. DB STRESS: if node-0 cpu > 0.80 → kubectl throttle ingress --rate=0.7
+9. SAFE SCALE: if avg worker cpu > 0.75 and budget > 20 → scale up
+10. HEALTHY → no_op
+
+Output format:
+<reasoning>One sentence identifying which rule applies.</reasoning>
+<action>{"command": "kubectl ..."}</action>"""
+
+obs = {
+    "cpu_loads": [0.45, 0.82, 0.79, 0.88, 0.75, 0.81, 0.77, 0.73],
+    "mem_utilizations": [0.41, 0.68, 0.71, 0.65, 0.62, 0.70, 0.66, 0.64],
+    "queue_lengths": [12, 45, 41, 53, 38, 44, 40, 37],
+    "failed_nodes": [],
+    "latency_ms": 187.3,
+    "p99_latency": 312.5,
+    "request_rate": 1840.0,
+    "io_wait": 0.12,
+    "error_budget": 85,
+    "step": 4,
+    "task_hint": "System is under heavy traffic load."
+}
+
+import json
+messages = [
+    {"role": "system", "content": system_prompt},
+    {"role": "user", "content": f"Current system state:\n{json.dumps(obs, indent=2)}\nWhat action should be taken?"}
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=1024,
+    temperature=0.6,
+    top_p=0.95,
+    do_sample=True,
+)
+print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
+```
+
+Expected output:
+```
+<think>
+P99 latency is 312ms with request rate 1840 rps and no failed nodes.
+Rule 5 (retry storm): p99 > 100ms and rr > 150 → throttle at 0.4.
+</think>
+<reasoning>Rule 5 applies: p99 latency 312ms exceeds threshold with high request rate 1840 rps — throttle ingress to shed load.</reasoning>
+<action>{"command": "kubectl throttle ingress --rate=0.4"}</action>
+```
+
+---
+
+## Training Details
+
+| Parameter | Value |
+|---|---|
+| Base model | `Qwen/Qwen3-8B` (BF16) |
+| Method | GRPO (TRL 0.24.0 + Unsloth + vLLM 0.6.3) |
+| LoRA rank | 32, alpha=64, all projection layers |
+| Trainable params | 1.05% (349 MB adapter) |
+| Training steps | 300 |
+| Batch size | 1 × 4 generations = 4 completions/step |
+| Learning rate | 5e-6, cosine schedule |
+| Max completion length | 1024 tokens |
+| GPU | A100-SXM4-80GB |
+| Wall-clock time | 44 minutes |
+
+---
+
+## Citation
+
+```bibtex
+@misc{dime2026,
+  title  = {Fine-Tuning Language Models as Autonomous SREs via GRPO: The DIME Benchmark},
+  author = {Nithish Sriram and Naseer},
+  year   = {2026},
+  url    = {https://huggingface.co/Naseer-010/Qwen3-8B-Finetuned-DIME}
+}
+```
+
+---
+
+*Trained at SRM AP · Hackathon 2026*

added_tokens.json

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+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|PAD_TOKEN|>": 151669,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# 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>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\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" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for forward_message in messages %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- set message = messages[index] %}
+    {%- set current_content = message.content if message.content is not none else '' %}
+    {%- set tool_start = '<tool_response>' %}
+    {%- set tool_start_length = tool_start|length %}
+    {%- set start_of_message = current_content[:tool_start_length] %}
+    {%- set tool_end = '</tool_response>' %}
+    {%- set tool_end_length = tool_end|length %}
+    {%- set start_pos = (current_content|length) - tool_end_length %}
+    {%- if start_pos < 0 %}
+        {%- set start_pos = 0 %}
+    {%- endif %}
+    {%- set end_of_message = current_content[start_pos:] %}
+    {%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set content = message.content %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in message.content %}
+                {%- set content = (message.content.split('</think>')|last).lstrip('\n') %}
+                {%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\n') %}
+                {%- set reasoning_content = (reasoning_content.split('<think>')|last).lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- 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' }}
+    {%- endif %}
+{%- endif %}

config.json

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+{
+    "architectures": [
+        "Qwen3ForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "torch_dtype": "bfloat16",
+    "eos_token_id": 151645,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 4096,
+    "initializer_range": 0.02,
+    "intermediate_size": 12288,
+    "layer_types": [
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention"
+    ],
+    "max_position_embeddings": 40960,
+    "max_window_layers": 36,
+    "model_type": "qwen3",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 36,
+    "num_key_value_heads": 8,
+    "pad_token_id": 151669,
+    "rms_norm_eps": 1e-06,
+    "rope_scaling": null,
+    "rope_theta": 1000000,
+    "sliding_window": null,
+    "tie_word_embeddings": false,
+    "unsloth_fixed": true,
+    "unsloth_version": "2026.4.8",
+    "use_cache": true,
+    "use_sliding_window": false,
+    "vocab_size": 151936
+}

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+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151665": {
+      "content": "<tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151666": {
+      "content": "</tool_response>",
+      "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
+    },
+    "151669": {
+      "content": "<|PAD_TOKEN|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "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,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 40960,
+  "pad_token": "<|PAD_TOKEN|>",
+  "padding_side": "left",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_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 forward_message in messages %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- set message = messages[index] %}\n    {%- set current_content = message.content if message.content is not none else '' %}\n    {%- set tool_start = '<tool_response>' %}\n    {%- set tool_start_length = tool_start|length %}\n    {%- set start_of_message = current_content[:tool_start_length] %}\n    {%- set tool_end = '</tool_response>' %}\n    {%- set tool_end_length = tool_end|length %}\n    {%- set start_pos = (current_content|length) - tool_end_length %}\n    {%- if start_pos < 0 %}\n        {%- set start_pos = 0 %}\n    {%- endif %}\n    {%- set end_of_message = current_content[start_pos:] %}\n    {%- if ns.multi_step_tool and message.role == \"user\" and not(start_of_message == tool_start and end_of_message == tool_end) %}\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.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {%- set content = message.content %}\n        {%- set reasoning_content = '' %}\n        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n            {%- set reasoning_content = message.reasoning_content %}\n        {%- else %}\n            {%- if '</think>' in message.content %}\n                {%- set content = (message.content.split('</think>')|last).lstrip('\\n') %}\n                {%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\\n') %}\n                {%- set reasoning_content = (reasoning_content.split('<think>')|last).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        {{- message.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' }}\n    {%- if enable_thinking is defined and enable_thinking is false %}\n        {{- '<think>\\n\\n</think>\\n\\n' }}\n    {%- endif %}\n{%- endif %}"
+}