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Model: vishnuamarapu/Full-Fine-Tuning-Qwen-2.5-0.5B-instruct-sft Source: Original Platform
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README.md
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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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base_model: Qwen/Qwen2.5-0.5B-Instruct
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tags:
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- qwen
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- llm
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- sft
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- conversational
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- transformers
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- pytorch
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---
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# Supervised Fine-Tuned Qwen2.5-0.5B-Instruct (SFT)
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This repository contains a **Supervised Fine-Tuned (SFT)** version of **Qwen2.5-0.5B-Instruct**. The model has been fine-tuned on a custom instruction-following conversational dataset to answer questions about Vishnu in a natural and helpful manner.
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## Model Details
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* **Base Model:** `Qwen/Qwen2.5-0.5B-Instruct`
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* **Training Method:** Supervised Fine-Tuning (SFT)
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* **Framework:** Hugging Face Transformers
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* **Task:** Conversational Text Generation
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---
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# Installation
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```bash
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pip install transformers accelerate torch
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```
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---
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# Loading the Model
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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MODEL_ID = "vishnuamarapu/Full-Fine-Tuning-Qwen-2.5-0.5B-instruct-sft"
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_ID,
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torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
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device_map="auto"
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)
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```
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---
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# Example Inference
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```python
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messages = [
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{
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"role": "system",
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"content": (
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"You are Vishnu's personal AI assistant. "
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"Answer questions about Vishnu using the provided information."
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)
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},
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{
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"role": "user",
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"content": "Tell me about Vishnu."
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}
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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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inputs = tokenizer(
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text,
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return_tensors="pt"
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).to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=256
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)
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response = tokenizer.decode(
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outputs[0][inputs.input_ids.shape[-1]:],
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skip_special_tokens=True
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)
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print(response)
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```
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---
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# Gradio Demo
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```python
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import gradio as gr
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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MODEL_ID = "vishnuamarapu/Full-Fine-Tuning-Qwen-2.5-0.5B-instruct-sft"
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_ID,
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torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
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device_map="auto"
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)
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def chat(message, history):
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messages = [
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{
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"role": "system",
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"content": (
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"You are Vishnu's personal AI assistant. "
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"Answer questions about Vishnu using the provided information."
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)
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}
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]
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for user, assistant in history:
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messages.append({"role": "user", "content": user})
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messages.append({"role": "assistant", "content": assistant})
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messages.append({"role": "user", "content": message})
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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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inputs = tokenizer(
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text,
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return_tensors="pt"
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).to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=256
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)
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response = tokenizer.decode(
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outputs[0][inputs.input_ids.shape[-1]:],
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skip_special_tokens=True
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)
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return response
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gr.ChatInterface(chat).launch()
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```
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---
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# Generation Parameters
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| Parameter | Value |
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| -------------- | ----------------------------: |
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| max_new_tokens | 256 |
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| device_map | auto |
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| torch_dtype | float16 (GPU) / float32 (CPU) |
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---
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# Repository Structure
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```
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config.json
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generation_config.json
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model.safetensors
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tokenizer.json
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tokenizer_config.json
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chat_template.jinja
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training_args.bin
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```
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---
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# Training Overview
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This model was fine-tuned using **Supervised Fine-Tuning (SFT)** on a custom instruction-response dataset.
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The training process included:
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* Instruction-response formatting using the Qwen chat template
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* Hugging Face Transformers
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* TRL SFTTrainer
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* PyTorch
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* Custom conversational dataset
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---
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# Citation
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If you use this model in your work, please cite this repository.
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```bibtex
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@misc{vishnu_qwen25_sft,
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author = {Vishnu Amarapu},
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title = {Supervised Fine-Tuned Qwen2.5-0.5B-Instruct},
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year = {2026},
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publisher = {Hugging Face},
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howpublished = {\url{https://huggingface.co/vishnuamarapu/Full-Fine-Tuning-Qwen-2.5-0.5B-instruct-sft}}
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}
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```
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---
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# Acknowledgements
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* Alibaba Cloud Qwen Team for the base model.
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* Hugging Face Transformers.
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* TRL (Transformer Reinforcement Learning).
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* PyTorch.
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54
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'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\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>" }}
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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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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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config.json
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": null,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 896,
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"initializer_range": 0.02,
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"intermediate_size": 4864,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 32768,
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"max_window_layers": 21,
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"model_type": "qwen2",
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"num_attention_heads": 14,
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"num_hidden_layers": 24,
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"num_key_value_heads": 2,
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"pad_token_id": 151643,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000000.0,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "5.12.0",
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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generation_config.json
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generation_config.json
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{
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
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"pad_token_id": 151643,
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"repetition_penalty": 1.1,
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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"transformers_version": "5.12.0"
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}
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3
model.safetensors
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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tokenizer_config.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
|
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"backend": "tokenizers",
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"bos_token": null,
|
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"clean_up_tokenization_spaces": false,
|
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"eos_token": "<|im_end|>",
|
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"errors": "replace",
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"extra_special_tokens": [
|
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|
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|
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"<|vision_start|>",
|
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"<|vision_end|>",
|
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"<|vision_pad|>",
|
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"<|image_pad|>",
|
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"<|video_pad|>"
|
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],
|
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"is_local": false,
|
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"local_files_only": false,
|
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"model_max_length": 131072,
|
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"pad_token": "<|endoftext|>",
|
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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3
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3
training_args.bin
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