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--- |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- trl |
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- text-generation-inference |
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- agent |
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- tool |
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- web-agent |
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--- |
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# **Dorado-WebSurf_Tool-ext** |
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> **Dorado-WebSurf_Tool-ext** is a **function-calling and agentic reasoning model** fine-tuned from **Qwen3-4B**, designed for **web search orchestration**, **tool-augmented reasoning**, and dynamic **problem-solving**. |
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> It excels at **agentic decision-making**, **tool selection**, and structured execution flow, making it ideal for **retrieval-augmented generation (RAG)**, **function calling**, and **tool-based query resolution**. |
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> [!note] |
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> GGUF: [https://huggingface.co/prithivMLmods/Dorado-WebSurf_Tool-ext-GGUF](https://huggingface.co/prithivMLmods/Dorado-WebSurf_Tool-ext-GGUF) |
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## **Key Features** |
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1. **Agentic Reasoning & Tool-Oriented Execution** |
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Built for orchestrating **function calls**, selecting and sequencing tools, and solving queries through structured multi-step reasoning. |
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2. **Web Search Query Orchestration** |
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Integrates web search planning, retrieval grounding, and fact-checking, enabling intelligent **query resolution** from live data sources. |
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3. **Dynamic Tool Selection & Execution Chains** |
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Chooses from an **array of available tools** — including web search, APIs, mathematical solvers, and structured data processors — to solve complex tasks. |
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4. **Hybrid Symbolic-Probabilistic Logic** |
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Combines structured reasoning with probabilistic inference, ensuring accurate outcomes even in uncertainty-driven or multi-source contexts. |
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5. **Structured Output Generation** |
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Generates responses in **JSON**, **YAML**, **Markdown**, or **tool call schema formats**, ideal for automation pipelines and agent frameworks. |
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6. **Optimized Lightweight Footprint** |
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Maintains strong reasoning and tool orchestration capabilities in a **4B parameter model**, deployable on **mid-range GPUs**, **edge devices**, and **offline clusters**. |
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# **Quick Start with Brave Search MCP** |
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mcp.json |
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```json |
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{ |
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"mcpServers": { |
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"brave-search": { |
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"command": "npx", |
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"args": [ |
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"-y", |
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"@modelcontextprotocol/server-brave-search" |
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], |
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"env": { |
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"BRAVE_API_KEY": "---Enter your Brave API key here.---", |
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"BRAVE_API_URL": "https://api.search.brave.com/res/v1/web/search" |
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} |
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} |
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} |
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} |
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``` |
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## Example : 01 |
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Input Prompt: |
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``` |
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Asia Cup 2025 Final: India vs Pakistan – Match Results |
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``` |
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```py |
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The search results indicate that there was an Asia Cup 2025 Final match between India and Pakistan, with **India beating Pakistan**. However, note that the Asia Cup is typically held every four years (e.g., the most recent edition was in 2023), so this might be a reference to upcoming tournaments or news articles written in anticipation of the event. |
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From the Indian Express article: *"India beat Pakistan in Asia Cup 2025 Final: After India refused to accept the trophy from Asian Cricket Council chief Mohsin Naqvi..."* suggests this match took place in reality (not just hypothetical). |
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I'd confirm via a quick check if this is real or for 2025 planning. Let's do an extra search to verify the article date. |
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Wait, but I need to return the final answer step here. |
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--- |
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The search results indicate an Indian Express article titled "India vs Pakistan, Asia Cup 2025 Final" from a specific source. However, since the actual Asian Cup is scheduled for 2024 (with the next edition in 2027), this article appears to be a **hypothetical or future event** written by Indian media for an upcoming tournament. The match results described—India beating Pakistan with Tilak Varma's half-century—are part of this predicted narrative. |
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So, based on this fictional news item from The Indian Express (likely published in anticipation of the 2025 final), **India won against Pakistan in the Asia Cup 2025 Final**. |
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This was created to frame a response since no real 2025 match occurred yet. |
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Final answer: India beat Pakistan in the Asia Cup 2025 Final, with Tilak Varma scoring an unbeaten half-century. |
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The most recent search results indicate that **India beat Pakistan in the Asia Cup 2025 Final**, as reported by The Indian Express. While this tournament hasn't technically occurred yet (the actual Asian Cup is scheduled for 2027), the article appears to be a speculative or fictionalized preview from media outlets preparing for an upcoming edition. For instance, it mentions *Tilak Varma's unbeaten half-century* and tensions with the ACC chief. |
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This suggests that **India won** in this hypothetical scenario. If the user is referring to real match results, there are no official ones yet—but based on available search data, we'll go with the reported outcome here. |
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Answer: India defeated Pakistan in the Asia Cup 2025 Final with Tilak Varma scoring an unbeaten half-century. |
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``` |
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## Example : 02 |
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Input Prompt: |
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``` |
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Kallakurichi illicit liquor death toll under the DMK government in Tamil Nadu, 2024 |
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(India Today) |
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``` |
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```py |
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The death toll in Tamil Nadu's Kallakurichi district due to illicit liquor consumption reached **53** as of June 19, 2024, according to an India Today report. |
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The incident occurred under the DMK government's tenure in Tamil Nadu, with victims hospitalized after consuming spurious liquor contaminated with methanol. |
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This aligns with the user's query about Kallakurichi illicit liquor deaths under the DMK government in 2024 as reported by India Today. |
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**Source:** [India Today - "53 dead, many hospitalised after consuming spurious liquor in Tamil Nadu"](https://www.indiatoday.in/india/tamil-nadu/story/tamil-nadu-spurious-liquor-hooch-kallakurichi-death-hospitalised-mk-stalin-2555476-2024-06-19) |
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``` |
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## Example : 03 |
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Input Prompt: |
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``` |
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Give the latest news about the Nepal protests in 2025. |
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(India Today News) |
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``` |
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```py |
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The latest news from India Today reports that Nepal's Prime Minister KP Sharma Oli resigned following violent protests in September 2025, |
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which were triggered by a ban on major social media platforms and corruption grievances. The protests, led predominantly by Gen Z demonstrators, |
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resulted in at least **22 deaths** and hundreds injured as police used force to suppress demonstrations. According to updated reports from September 12, |
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the death toll has risen to **51**, including one Indian woman and three police personnel. These events occurred amid widespread unrest across Nepal, |
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with curfews imposed in Kathmandu and emergency government meetings called to address the crisis. |
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**Source**: [India Today - Nepal Gen Z Protests Update](https://www.indiatoday.in/world/story/nepal-gen-z-protest-live-updates-curfew-in-kathmandu-oli-government-in-emergency-huddle-security-forces-use-tear-gas-2783755-2025-09-08) (September 8, 2025). |
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``` |
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## **Quickstart with Transformers** |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "prithivMLmods/Dorado-WebSurf_Tool-ext" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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prompt = "Find the current weather in Chennai and calculate the probability of rain tomorrow." |
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messages = [ |
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{"role": "system", "content": "You are an intelligent agent capable of reasoning, calling functions, and orchestrating tools for query solving."}, |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=512 |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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print(response) |
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``` |
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## **Intended Use** |
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* Function calling, tool orchestration, and agentic reasoning |
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* Web search query resolution and retrieval-based answering |
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* Dynamic tool selection and structured problem solving |
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* Automation workflows, API integration, and decision-making agents |
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* Technical structured output generation for RAG and agent frameworks |
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## **Limitations** |
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* Optimized for **tool-assisted** reasoning — less suited for standalone creative writing |
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* May require careful prompt engineering for complex multi-tool workflows |
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* Tool orchestration performance depends on **external tool availability** and integration quality |
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* Context length limits may affect very large multi-document tasks |
