初始化项目，由ModelHub XC社区提供模型

Model: joynnayvedya/disaster-response-trained
Source: Original Platform

.gitattributes

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README.md

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+---
+base_model: unsloth/Qwen2.5-7B-Instruct-bnb-4bit
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- qwen2
+license: apache-2.0
+language:
+- en
+---
+Title: Teaching an LLM to Triage Disasters: Building an RL Environment with OpenEnv
+Tags: openenv reinforcement-learning disaster-response grpo qwen
+
+markdown# Teaching an LLM to Triage Disasters 🚨
+
+## The Problem
+
+During a natural disaster, Emergency Operations Centers (EOCs) are overwhelmed 
+by thousands of frantic incident reports. A flooded neighborhood, a chemical 
+plant fire, a hospital wing collapse — all arriving simultaneously. Human 
+coordinators must instantly decide: which team? what priority? what action?
+
+We built an AI agent that does exactly this.
+
+---
+
+## The Environment
+
+We built **Disaster Response Coordination OpenEnv** — a multi-step RL environment 
+where an AI agent acts as an Emergency Incident Commander.
+
+**15 real-world scenarios** across 3 difficulty tiers, modeled after actual disasters:
+- 🌊 2018 Kerala Floods → dam spillway overflow, communication blackouts
+- ☠️ 2020 Vizag Gas Leak → chemical plant fire, toxic plume evacuation  
+- ⚡ 2012 North India Grid Failure → cold-chain medicine failures, hospital blackouts
+
+### Action Space
+For every incident ticket, the agent must complete a 4-step workflow:
+classify → set_priority → draft_reply → submit_ticket
+
+### Reward Function
+reward = 0.40 × team_routing + 0.30 × priority + 0.30 × reply_quality
+
+Dense, partial rewards at every step. No sparse end-of-episode signals.
+
+### Difficulty Scaling
+| Tier | Budget | Scenarios |
+|------|--------|-----------|
+| 🟢 Easy | 40 | Single-team, clear incidents |
+| 🟡 Medium | 48 | Multi-agency, ambiguous |
+| 🔴 Hard | 55 | Cascading mass-casualty + time pressure |
+
+---
+
+## Training with GRPO
+
+We trained **Qwen2.5-7B-Instruct** using GRPO (Group Relative Policy Optimization) 
+via TRL + Unsloth on a Google Colab T4 GPU.
+
+**Setup:**
+- Base model: `unsloth/Qwen2.5-7B-Instruct-bnb-4bit`
+- Algorithm: GRPOTrainer (TRL)
+- LoRA: r=16, 4-bit quantization
+- Epochs: 3 | Steps: 14
+- Reward: Live environment feedback via HF Space API
+
+The reward function connected directly to our live HF Space — every training 
+step sent real incident prompts to the environment and received real rewards back.
+
+### Training Reward Curve
+![reward_curve](reward_curve.png)
+
+---
+
+## What We Discovered: Sparse Reward Collapse
+
+The untrained base model immediately revealed why this environment is hard:
+
+**Before training**, the model hallucinated invalid outputs:
+team: "emergency_services"     ❌ (not a valid team)
+team: "utility repair"         ❌
+priority: "very-high"          ❌ (not a valid priority)
+priority: "higher"             ❌
+
+**After training**, the model learned valid action spaces:
+team: "rescue"      ✅
+priority: "urgent"  ✅
+
+However, we observed **sparse reward collapse** — a known RL failure mode where 
+a small model (7B at 4-bit) struggles to optimize across a multi-step workflow 
+with interdependent rewards. This validates our environment's quality: it is 
+genuinely difficult enough to expose real RL failure modes that larger models 
+or longer training runs would be needed to overcome.
+
+---
+
+## Baseline Results
+
+| Agent | Easy | Medium | Hard | **Avg** |
+|-------|------|--------|------|---------|
+| Heuristic Baseline | 0.704 | 0.683 | 0.660 | **0.682** |
+| GRPO Qwen2.5-7B | — | — | — | research ongoing |
+
+All 3 difficulty tiers **passed** (score ≥ 0.6).
+
+---
+
+## The Dashboard
+
+We built a military-style tactical command dashboard with:
+- 🗺️ Live OpenStreetMap incident markers with radar pulse animations
+- ⚡ ARIA — AI Incident Analyst (Gemini-powered, analyses any incident live)
+- 📊 Real-time score tracking, threat level bar, team routing
+- 🔔 Operations feed with meaningful event notifications
+
+![dashboard](dashboard.png)
+
+---
+
+## Links
+
+| Resource | URL |
+|----------|-----|
+| 🚀 HF Space (Live Environment) | [joynnayvedya/disaster-response-openenv](https://huggingface.co/spaces/joynnayvedya/disaster-response-openenv) |
+| 🧠 Trained Model | [joynnayvedya/disaster-response-trained](https://huggingface.co/joynnayvedya/disaster-response-trained) |
+| 💻 GitHub | [letsjoyn/meta-scalar-hack](https://github.com/letsjoyn/meta-scalar-hack) |
+
+---
+
+## Try It Yourself
+
+```bash
+git clone https://github.com/letsjoyn/meta-scalar-hack.git
+cd meta-scalar-hack
+pip install -e .
+py inference.py
+```
+
+---
+
+*Built for the 2026 Meta & Scalar AI Hackathon — Grand Finale, Bangalore.*
+# Uploaded finetuned  model
+
+- **Developed by:** joynnayvedya
+- **License:** apache-2.0
+- **Finetuned from model :** unsloth/Qwen2.5-7B-Instruct-bnb-4bit
+
+This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

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+    "parent_library": "transformers.models.qwen2.modeling_qwen2",
+    "unsloth_fixed": true
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+  "base_model_name_or_path": "unsloth/Qwen2.5-7B-Instruct-bnb-4bit",
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chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\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>" }}
+    {%- 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' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
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+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

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+    ],
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tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bd5948af71b4f56cf697f7580814c7ce8b80595ef985544efcacf716126a2e31
+size 11422356

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "is_local": false,
+  "model_max_length": 32768,
+  "pad_token": "<|PAD_TOKEN|>",
+  "padding_side": "right",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
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+    },
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+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "151658": {
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+      "single_word": false,
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+      "normalized": false,
+      "special": false
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+      "normalized": false,
+      "special": false
+    },
+    "151663": {
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+      "rstrip": false,
+      "normalized": false,
+      "special": false
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+    "151664": {
+      "content": "<|file_sep|>",
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+      "normalized": false,
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+    },
+    "151665": {
+      "content": "<|PAD_TOKEN|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    }
+  },
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\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    {%- else %}\n        {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) 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{%- endif %}\n"
+}