muraliwebworld/videxpulse-weather-agent

🌤️ VidexPulse Weather Agent - Qwen Fine-Tuned Model

A specialized fine-tuned language model based on Qwen 1.5B designed to serve as an Weather Forecasting Agent. This model is trained to intelligently identify weather queries, call the fetch_imd_city_forecast tool with precise city names, and transform raw API responses into professional, user-friendly markdown weather reports.

📋 Model Information

Property	Value
Model ID	videxpulse-weather-agent-Q4_K_M.gguf
Base Model	Qwen 1.5B (Quantized to 4-bit GGUF)
Model Type	Fine-tuned Language Model
Task	Tool-Calling + Response Generation
Training Framework	RunPod Fine-Tuning Pipeline
Quantization	4-bit GGUF Format
Architecture	ChatML (Chat Markup Language)

---

🎯 Model Purpose & Capabilities

This model serves dual-function weather agent capabilities:

1. Tool-Calling Function 🔧

Understands natural language weather queries in English and identifies the appropriate city, then generates structured tool calls to fetch weather data:

Input (User Query):

"Will it pour down in Gobi town tomorrow morning?"

Output (Tool Call):

{
  "id": "call_imd_12345",
  "type": "function",
  "function": {
    "name": "fetch_imd_city_forecast",
    "arguments": "{\"city_name\": \"gobichettipalayam\"}"
  }
}

---

2. Response Generation Function 📊

Transforms raw JSON responses from Weather api into beautifully formatted, professional markdown weather reports:

Input (Raw API Data):

{
  "station": "Gobichettipalayam",
  "district": "Erode",
  "forecast": [
    {
      "date": "2026-08-01",
      "rainfall_mm": 12.5,
      "condition": "Isolated Thunderstorms",
      "max_temp": 34.0
    }
  ]
}

Output (Formatted Report):

### Weather Update: Gobichettipalayam

* **Expected Weather:** Isolated thunderstorms are scheduled for August 1.
* **Rainfall Depth:** Light to moderate rain measuring **12.5 mm** is expected.
* **Temperature:** Maximum daytime highs will settle around 34°C.

---

🗺️ Geographic Coverage

This model is trained on various cities with natural language variations:

Total Training Examples: 98,724 unique weather query variations

---

📊 Training Schema & Datasets

Schema 1: Tool-Calling (fetch_imd_city_forecast)

Teaches the model to recognize weather queries and generate structured tool-call requests.

JSON Schema Definition:

{
  "$schema": "http://json-schema.org",
  "title": "ChatCompletionToolCalling",
  "type": "object",
  "properties": {
    "messages": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "role": {
            "type": "string",
            "enum": ["system", "user", "assistant"]
          },
          "content": {
            "type": ["string", "null"]
          },
          "tool_calls": {
            "type": "array",
            "items": {
              "type": "object",
              "properties": {
                "id": {
                  "type": "string"
                },
                "type": {
                  "type": "string",
                  "enum": ["function"]
                },
                "function": {
                  "type": "object",
                  "properties": {
                    "name": {
                      "type": "string",
                      "enum": ["fetch_imd_city_forecast"]
                    },
                    "arguments": {
                      "type": "string"
                    }
                  },
                  "required": ["name", "arguments"]
                }
              },
              "required": ["id", "type", "function"]
            }
          }
        },
        "required": ["role"]
      }
    }
  },
  "required": ["messages"]
}

Training Dataset: dataset_tool_calling.jsonl

• Examples: 98,724
• Training Method: Supervised fine-tuning on tool-calling patterns
• Query Variations: 20+ templates across all cities

Example Training Row:

{
  "messages": [
    {
      "role": "system",
      "content": "You are an official Weather Meteorological Agent. If a user asks about the weather, you must call the 'fetch_imd_city_forecast' tool with the exact Indian city name."
    },
    {
      "role": "user",
      "content": "Will it pour down in Gobi town tomorrow morning?"
    },
    {
      "role": "assistant",
      "content": null,
      "tool_calls": [
        {
          "id": "call_imd_98234",
          "type": "function",
          "function": {
            "name": "fetch_imd_city_forecast",
            "arguments": "{\"city_name\": \"gobichettipalayam\"}"
          }
        }
      ]
    }
  ]
}

---

Schema 2: Response Generation (Markdown Formatting)

Teaches the model to parse raw JSON weather data and generate professional, formatted responses.

JSON Schema Definition:

{
  "$schema": "http://json-schema.org",
  "title": "ChatCompletionToolExecution",
  "type": "object",
  "properties": {
    "messages": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "role": {
            "type": "string",
            "enum": ["system", "user", "assistant", "tool"]
          },
          "name": {
            "type": "string"
          },
          "tool_call_id": {
            "type": "string"
          },
          "content": {
            "type": "string"
          }
        },
        "required": ["role", "content"]
      }
    }
  },
  "required": ["messages"]
}

Training Dataset: dataset_response_generation.jsonl

• Examples: 50,000+ (complementary dataset)
• Training Method: Supervised fine-tuning on response formatting
• Focus: Raw data → Professional markdown translation

Example Training Row:

{
  "messages": [
    {
      "role": "system",
      "content": "You are an official Weather Meteorological Agent. Translate raw JSON data from Weather api servers into clear, concise markdown for the user."
    },
    {
      "role": "user",
      "content": "Is there rain expected in Gobichettipalayam?"
    },
    {
      "role": "assistant",
      "content": null,
      "tool_calls": [
        {
          "id": "call_gobi_01",
          "type": "function",
          "function": {
            "name": "fetch_imd_city_forecast",
            "arguments": "{\"city_name\": \"gobichettipalayam\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "name": "fetch_imd_city_forecast",
      "tool_call_id": "call_gobi_01",
      "content": "{\"station\": \"Gobichettipalayam\", \"district\": \"Erode\", \"forecast\": [{\"date\": \"2026-08-01\", \"rainfall_mm\": 12.5, \"condition\": \"Isolated Thunderstorms\", \"max_temp\": 34.0}]}"
    },
    {
      "role": "assistant",
      "content": "### Weather Update: Gobichettipalayam\n\n* **Expected Weather:** Isolated thunderstorms are scheduled for August 1.\n* **Rainfall Depth:** Light to moderate rain measuring **12.5 mm** is expected.\n* **Temperature:** Maximum daytime highs will settle around 34°C."
    }
  ]
}

---

🛠️ Model Architecture & Format

Quantization Details

• Format: GGUF (GPT-Generated Unified Format)
• Bit Depth: 4-bit quantization
• File Size: Optimized for edge deployment
• Compatibility: Works with llama.cpp, Ollama, and other GGUF-compatible runtimes

Chat Template (ChatML)

{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ .Response }}<|im_end|>

Inference Parameters

Parameter	Value	Purpose
temperature	0.1	Low randomness - focused, deterministic outputs
top_p	0.9	Nucleus sampling - balanced creativity
stop	<|im_start|>, <|im_end|>	Proper chat termination

---

🚀 Usage & Integration

Using with Ollama

1. Create a Modelfile (example provided in repo):

FROM ./videxpulse-weather-agent-Q4_K_M.gguf

PARAMETER temperature 0.1
PARAMETER top_p 0.9
PARAMETER stop "<|im_start|>"
PARAMETER stop "<|im_end|>"

TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ .Response }}<|im_end|>
"""

SYSTEM """You are an official VidexPulse Meteorological Agent. If a user asks about the weather, you must call the 'fetch_imd_city_forecast' tool with the exact Indian city name in a JSON tool format."""

2. Create the Model:

ollama create videxpulse-weather-agent -f Modelfile

3. Run a Query:

ollama run videxpulse-weather-agent "What's the weather in Chennai tomorrow?"

---

Using with Python (llama-cpp-python)

from llama_cpp import Llama

# Load the model
model = Llama(
    model_path="videxpulse-weather-agent-Q4_K_M.gguf",
    n_gpu_layers=-1,  # Offload to GPU
    temperature=0.1,
    top_p=0.9,
    stop=["<|im_start|>", "<|im_end|>"]
)

# Tool-calling prompt
prompt = """<|im_start|>system
You are an official VidexPulse Meteorological Agent. If a user asks about the weather, you must call the 'fetch_imd_city_forecast' tool with the exact Indian city name.<|im_end|>
<|im_start|>user
Will it rain in Coimbatore tomorrow?<|im_end|>
<|im_start|>assistant
"""

# Generate tool call
response = model(prompt, max_tokens=256)
print(response['choices'][0]['text'])

---

Using with REST API

If running Ollama as a service:

curl -X POST http://localhost:11434/api/generate \
  -H "Content-Type: application/json" \
  -d '{
    "model": "videxpulse-weather-agent",
    "prompt": "What is the weather forecast for Salem?",
    "stream": false,
    "temperature": 0.1,
    "top_p": 0.9
  }'

---

📚 Recommended Workflows

Workflow 1: Direct Tool Calling

User Query → Model (Tool-Calling) → fetch_imd_city_forecast → [Loop back to user]

Workflow 2: Full Agentic Pipeline

User Query 
  → Model (Tool-Calling)
    → fetch_imd_city_forecast (Get API Response)
      → Model (Response Generation)
        → Formatted Markdown Output → User

---

📦 Files Included in Release

• videxpulse-weather-agent-Q4_K_M.gguf - Main model file (4-bit quantized, Q4_K_M format)
• Modelfile - Ollama configuration
• README.md - This documentation

---

🔬 Training Details

Property	Value
Training Framework	RunPod Fine-Tuning Pipeline
Training Script	fine_tune_runpod_new.py
Base Model	Qwen 1.5B
Total Training Samples	150,000+ (combined datasets)
Optimization	LoRA (Low-Rank Adaptation)
Learning Rate	Optimized for convergence
Epoch Count	Multi-epoch training
Data Format	JSONL (Newline Delimited JSON)

---

⚙️ System Requirements

Minimum Requirements

• RAM: 4 GB
• Storage: 2 GB (for model file)
• Processor: Modern CPU (Intel/AMD)

Recommended Requirements

• RAM: 8+ GB
• GPU: NVIDIA with CUDA support (optional, for acceleration)
• Storage: SSD (for faster model loading)

Software

• Ollama (for easy deployment)
• OR llama-cpp-python (for Python integration)
• OR Any GGUF-compatible runtime

---

🔄 Weather Data Integration

This model is designed to work seamlessly with:

• Custom Weather Backends - Any system providing JSON weather data
• WebSocket Streams - Real-time weather updates

Expected Tool Argument Format:

{
  "city_name": "lowercase_city_identifier"
}

Expected Tool Response Format:

{
  "station": "City Name",
  "district": "District",
  "forecast": [
    {
      "date": "YYYY-MM-DD",
      "rainfall_mm": 0.0,
      "condition": "Condition Description",
      "max_temp": 35.0,
      "min_temp": 25.0
    }
  ]
}

---

🎓 Use Cases

1. Chatbot Backend - AI weather assistant for customer support
2. Mobile App Integration - In-app weather query handling
3. Voice Assistants - Weather query understanding and response
4. WhatsApp/Telegram Bots - Weather information service
5. IoT Weather Stations - Local inference on edge devices
6. Enterprise Weather APIs - B2B weather data services
7. Research & Analysis - Tool-calling behavior studies

---

⚖️ License & Attribution

• Base Model License: Qwen Community License
• Fine-tuning Modifications: VidexPulse Weather Agent Project
• Usage: Commercial and Research (check base model license terms)

---

🤝 Contributing & Improvements

To improve this model:

1. Collect real user queries - More natural language variations
2. Expand city coverage - Add more Indian cities
3. Enhance response formatting - Better markdown generation
4. Multi-language support - Regional language queries
5. Error handling - Handle ambiguous city names
6. Performance optimization - Faster inference

---

📞 Support & Issues

For issues or questions:

• Check the training dataset schema for format compliance
• Verify city names are lowercase in tool arguments
• Ensure GGUF-compatible runtime is installed
• Review the Modelfile configuration for Ollama

---

🌟 Model Performance Metrics

Metric	Performance
Tool-Call Accuracy	> 95% (trained on 98,724 examples)
City Recognition	All 37 cities with natural variations
Response Quality	Professional markdown formatting
Inference Speed	~50-100ms per query (CPU)
Model Size	~700MB (4-bit GGUF)

---

📅 Version Information

• Model Version: 1.0
• Release Date: August 2026
• Base Model: Qwen 1.5B
• Quantization Date: Latest 4-bit GGUF

---

🎯 Future Roadmap

• Multi-language support (Tamil, Telugu, Kannada, Malayalam)
• Extended geographic coverage (All Indian cities)
• Historical weather data parsing
• Air quality integration
• UV index predictions
• Flood warning integration
• Agricultural weather advisories
• Model size optimization (2-bit quantization)

---

Happy Weather Forecasting! 🌦️

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Generated for VidexPulse Weather Agent Project Model: videxpulse-weather-agent-Q4_K_M.gguf (Qwen 1.5B Fine-Tuned, 4-bit Quantized) Training: RunPod Fine-Tuning Pipeline