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Model: dheeyantra/dhee-nxtgen-qwen3-indic Source: Original Platform
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README.md
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README.md
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---
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language:
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- hi
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- bn
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- ta
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- te
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- ml
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- gu
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- kn
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- mr
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- or
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- pa
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- as
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- mai
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- sa
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- sd
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license: apache-2.0
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tags:
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- causal-lm
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- assistant
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- reasoning
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- multilingual
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- indic
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model_name: dheeyantra/dhee-nxtgen-qwen3-indic
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library_name: transformers
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---
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# Dhee-NxtGen-Qwen3-Indic (4B)
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## Model Description
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**Dhee-NxtGen-Qwen3-Indic** is a **single, unified 4B-parameter multilingual large language model** developed by **DheeYantra** in collaboration with **NxtGen Cloud Technologies Pvt. Ltd.**
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Built on the **Qwen3-4B architecture**, the model is created to support **assistant-style conversations**, **reasoning**, and **function-calling–compatible workflows** across **14 Indian (Indic) languages** within one shared model.
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The model is optimized for **native-script generation**, consistent multilingual behavior, and cross-lingual generalization.
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---
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## Supported Languages
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This single model supports the following Indic languages:
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- Hindi (hi)
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- Bengali (bn)
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- Tamil (ta)
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- Telugu (te)
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- Malayalam (ml)
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- Gujarati (gu)
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- Kannada (kn)
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- Marathi (mr)
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- Odia (or)
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- Punjabi (pa)
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- Assamese (as)
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- Maithili (mai)
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- Sanskrit (sa)
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- Sindhi (sd)
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> Best results are achieved when prompts are written entirely in the target language.
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---
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## Key Features
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- Single multilingual 4B model (no per-language checkpoints)
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- Fluent, native-script text generation across 14 Indic languages
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- Optimized for assistant-style and reasoning-based dialogue
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- Supports summarization, Q&A, and long-form generation
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- Compatible with function-calling style prompting
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- Fully compatible with Hugging Face Transformers
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- Ready for high-throughput inference using vLLM
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---
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## Example Usage
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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# 1. Configuration
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model_name = "dheeyantra/dhee-nxtgen-qwen3-indic"
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device = "cuda" if torch.cuda.is_available() else "cpu"
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# 2. Load Model and Tokenizer
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print(f"Loading model: {model_name}...")
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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trust_remote_code=True,
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device_map="auto",
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torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
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)
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# 3. Define the Prompt
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# Using the ChatML format expected by Qwen-based architectures
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prompt = """<|im_start|>system
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You are a helpful multilingual assistant.<|im_end|>
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<|im_start|>user
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क्या आप मेरे लिए एक अपॉइंटमेंट बुक कर सकते हैं?
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अगर हाँ, तो कृपया मुझसे ज़रूरी जानकारी जैसे तारीख, समय और उद्देश्य पूछिए।<|im_end|>
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<|im_start|>assistant
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"""
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# 4. Process and Generate
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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print("Generating response...")
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=256,
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temperature=0.7,
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top_p=0.9,
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do_sample=True,
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pad_token_id=tokenizer.eos_token_id
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)
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# 5. Decode and Print
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# We only want to print the newly generated text (the assistant's reply)
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full_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
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response = full_output.split("assistant")[-1].strip()
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print("-" * 30)
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print(f"Assistant: {response}")
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print("-" * 30)
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```
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## Function/Tool Calling Example Usage
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```python
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import torch
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import json
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import re
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from transformers import AutoTokenizer, AutoModelForCausalLM
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# --- 1. MODEL SETUP ---
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model_name = "dheeyantra/dhee-nxtgen-qwen3-indic"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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trust_remote_code=True,
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device_map="auto",
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torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
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)
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# --- 2. TOOLS & SYSTEM PROMPT ---
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tools = [{
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"name": "book_appointment",
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"description": "Book an appointment for the user.",
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"parameters": {
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"type": "object",
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"properties": {
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"date": {"type": "string", "description": "Date in YYYY-MM-DD format"},
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"time": {"type": "string", "description": "Time in HH:MM (24h) format"},
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"purpose": {"type": "string", "description": "The medical reason or department"}
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},
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"required": ["date", "time", "purpose"]
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}
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}]
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# We provide the current date so the model can resolve "tomorrow" or "next Monday"
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SYSTEM_PROMPT = f"""You are a helpful AI assistant.
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Today's Date: 2026-01-08 (Thursday).
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Available Tools:
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{json.dumps(tools, indent=2)}
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Rules:
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1. If details (date, time, purpose) are missing, ask the user in Hindi.
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2. If all details are present, output ONLY a <tool_call> JSON.
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3. After a tool result is provided, confirm the booking to the user in Hindi."""
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# --- 3. BACKEND FUNCTION ---
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def execute_booking(date, time, purpose):
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# Simulated backend logic
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if "10:00" in time: # Simulate a busy slot
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return {"status": "error", "message": "यह समय पहले से बुक है। कृपया कोई और समय चुनें।"}
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return {"status": "success", "id": "APP-9921", "doctor": "Dr. Verma"}
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# --- 4. THE INTERACTION ENGINE ---
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def run_conversation(user_input, history):
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# Add user input to history
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history.append({"role": "user", "content": user_input})
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# Construct ChatML prompt
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prompt = f"<|im_start|>system\n{SYSTEM_PROMPT}<|im_end|>\n"
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for msg in history:
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prompt += f"<|im_start|>{msg['role']}\n{msg['content']}<|im_end|>\n"
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prompt += "<|im_start|>assistant\n"
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# Generate
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.1)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True).split("assistant")[-1].strip()
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# Check if the model wants to call a tool
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tool_match = re.search(r"<tool_call>(.*?)</tool_call>", response, re.DOTALL)
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if tool_match:
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print(f"\n[MODEL REQUESTED TOOL]: {response}")
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call_data = json.loads(tool_match.group(1).strip())
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# Execute the function
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result = execute_booking(**call_data['arguments'])
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print(f"[TOOL RESULT]: {result}")
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# Feed result back to model for final response
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history.append({"role": "assistant", "content": response})
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history.append({"role": "system", "content": f"Tool Result: {json.dumps(result)}"})
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# Final confirmation generation
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return run_conversation("Please confirm the result to the user.", history)
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return response
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# --- 5. TEST SCENARIO ---
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chat_history = []
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print("--- Chatbot Started (Today is 2026-01-08) ---")
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# Step 1: User provides partial info
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query_1 = "मेरे लिए डेंटिस्ट का अपॉइंटमेंट बुक करें।"
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print(f"\nUser: {query_1}")
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res_1 = run_conversation(query_1, chat_history)
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chat_history.append({"role": "assistant", "content": res_1})
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print(f"Assistant: {res_1}")
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# Step 2: User provides the rest
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query_2 = "कल दोपहर 2 बजे।"
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print(f"\nUser: {query_2}")
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res_2 = run_conversation(query_2, chat_history)
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print(f"Assistant: {res_2}")
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```
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---
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## Prompting Guidelines
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- Use pure native-language prompts for best fluency
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- Avoid heavy code-mixing (e.g., Hinglish-heavy inputs)
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- Include a system prompt to stabilize multilingual behavior
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- Ask explicitly for step-by-step reasoning when required
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---
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## Intended Uses & Limitations
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### Intended Uses
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- Multilingual Indic chatbots and AI assistants
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- Education, governance, and public-sector AI applications
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- Content generation and summarization in Indian languages
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- Cross-lingual conversational and reasoning systems
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### Limitations
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- May occasionally hallucinate or produce inaccurate facts
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- Performance may vary slightly across languages
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- Not intended for medical, legal, or safety-critical use cases
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- Code-mixed inputs may reduce output quality
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---
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## vLLM / High-Performance Serving
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### Requirements
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- NVIDIA GPU with compute capability ≥ 8.0 (A100 / H100 recommended)
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- PyTorch 2.1+ with CUDA installed
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- V100 (sm70) GPUs are not supported for vLLM GPU inference
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### Installation
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```bash
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pip install torch transformers vllm sentencepiece
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```
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### Run vLLM Server
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```bash
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vllm serve --model dheeyantra/dhee-nxtgen-qwen3-indic --host 0.0.0.0 --port 8000
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```
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---
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## License
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Released under the **Apache 2.0 License**.
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||||
---
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Developed by **DheeYantra** in collaboration with **NxtGen Cloud Technologies Pvt. Ltd.**
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28
added_tokens.json
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added_tokens.json
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{
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"</think>": 151668,
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"</tool_call>": 151658,
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"</tool_response>": 151666,
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"<think>": 151667,
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"<tool_call>": 151657,
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"<tool_response>": 151665,
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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"<|endoftext|>": 151643,
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"<|file_sep|>": 151664,
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"<|fim_middle|>": 151660,
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"<|fim_suffix|>": 151661,
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"<|im_start|>": 151644,
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"<|image_pad|>": 151655,
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"<|object_ref_end|>": 151647,
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"<|object_ref_start|>": 151646,
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"<|quad_end|>": 151651,
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|
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"<|vision_pad|>": 151654,
|
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"<|vision_start|>": 151652
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}
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89
chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '\n\n' }}
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{%- 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>" }}
|
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{%- for tool in tools %}
|
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{{- "\n" }}
|
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{{- tool | tojson }}
|
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{%- endfor %}
|
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{{- "\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" }}
|
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{%- else %}
|
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for message in messages[::-1] %}
|
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
|
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{%- endfor %}
|
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{%- for message in messages %}
|
||||
{%- if message.content is string %}
|
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{%- set content = message.content %}
|
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{%- else %}
|
||||
{%- set content = '' %}
|
||||
{%- endif %}
|
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
|
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{%- set reasoning_content = '' %}
|
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{%- if message.reasoning_content is string %}
|
||||
{%- set reasoning_content = message.reasoning_content %}
|
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{%- else %}
|
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{%- if '</think>' in content %}
|
||||
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
|
||||
{%- set content = content.split('</think>')[-1].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' }}
|
||||
{{- 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 %}
|
||||
30
config.json
Normal file
30
config.json
Normal file
@@ -0,0 +1,30 @@
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||||
{
|
||||
"architectures": [
|
||||
"Qwen3ForCausalLM"
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13
generation_config.json
Normal file
13
generation_config.json
Normal file
@@ -0,0 +1,13 @@
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{
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151388
merges.txt
Normal file
151388
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
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3
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405
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"model.layers.9.self_attn.q_norm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.norm.weight": "model-00002-of-00002.safetensors"
|
||||
}
|
||||
}
|
||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"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|>"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
239
tokenizer_config.json
Normal file
239
tokenizer_config.json
Normal file
@@ -0,0 +1,239 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"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
|
||||
}
|
||||
},
|
||||
"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": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user