Update README.md

README.md

@@ -15,7 +15,36 @@ tags:
 - tool_calling
 ---
 
-## sample inference.
+# **Gliese-Query_Tool-0.6B**
+
+> **Gliese-Query_Tool-0.6B** is a **function-calling and query-oriented reasoning model** fine-tuned from **Qwen3-0.6B** using **Salesforce/xlam-function-calling-60k**, designed for **tool orchestration**, **structured query resolution**, and **operation chaining** across diverse tasks.
+> It excels in **dynamic function execution**, **structured reasoning pipelines**, and **multi-tool decision workflows**, making it a powerful lightweight solution for **developers**, **tooling platforms**, and **automation systems**.
+
+> [!note]
+> GGUF: [https://huggingface.co/prithivMLmods/Gliese-Query_Tool-0.6B-GGUF](https://huggingface.co/prithivMLmods/Gliese-Query_Tool-0.6B-GGUF)
+
+---
+
+## **Key Features**
+
+1. **Function-Calling Focused Reasoning**
+   Fine-tuned with **Salesforce/xlam-function-calling-60k**, enabling precise function selection, argument formatting, and multi-step tool invocation in complex workflows.
+
+2. **Query-Oriented Workflow Design**
+   Built to parse, interpret, and resolve complex queries by selecting and chaining the most relevant functions or tools for the task.
+
+3. **Tool-Orchestration & Automation**
+   Handles structured tool calls, dynamic function dispatch, and hybrid reasoning to power intelligent automation, API orchestration, and backend agent pipelines.
+
+4. **Structured Multi-Format Output**
+   Outputs formatted responses in **JSON**, **YAML**, **Markdown**, and **structured argument objects** — ideal for direct integration into software pipelines and agentic systems.
+
+5. **Lightweight, Deployment-Ready Core**
+   Compact **0.6B parameter** size optimized for **edge deployments**, **on-device inference**, and **fast cold-starts** while maintaining strong reasoning and function-call accuracy.
+
+---
+
+## **sample inference.**
 
 ```
 Solve 2**2
@@ -55,4 +84,60 @@ In an examination 80% of the candidates passed in Urdu and 85% in Hindi, while 7
 [{"name": "passing_percentage", "description": "Calculates the passing percentage for an exam given the percentage of students who passed each subject, and the intersection percentage of passing subjects.",
 "parameters": {"subject1_percent": {"description": "Percentage of students who passed the first subject (e.g., 85% if Hindi).", "type": "int"},
 "subject2_percent": {"description": "Percentage of students who passed the second subject (e.g., 80% if Urdu).", "type": "int"}, "passed_both_percent": {"description": "Percentage of students who passed both subjects.", "type": "int"}}}]
-```
+```
+
+## **Quickstart with Transformers**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Gliese-Query_Tool-0.6B"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Call the right function to fetch weather data for Paris and format the output as JSON."
+
+messages = [
+    {"role": "system", "content": "You are a query tool model skilled in function-calling, API orchestration, and structured query resolution."},
+    {"role": "user", "content": prompt}
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+
+## **Intended Use**
+
+* Intelligent function-calling and multi-step query solving
+* API orchestration, agent tool selection, and dynamic workflows
+* Structured data generation and backend reasoning integration
+* Lightweight agentic systems and on-device automation
+* Developer-focused query resolution and toolchain automation
+
+## **Limitations**
+
+* Focused on **function-calling and structured tasks** — not suited for open-ended dialogue or creative writing
+* Small model size means very complex reasoning chains may require external planning agents
+* Optimized for structured tool workflows — conversational tone and narrative depth are secondary
+* Long-context multi-tool planning beyond several steps may reduce reliability