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Model: khazarai/StockDirection-6K Source: Original Platform
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105
README.md
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README.md
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---
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library_name: transformers
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tags:
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- sft
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- unsloth
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- finance
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license: apache-2.0
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datasets:
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- atahanuz/stock_prediction
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language:
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- en
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base_model:
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- unsloth/Qwen3-0.6B
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pipeline_tag: text-generation
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---
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# Model Card for StockDirection-6K
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## Model Details
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StockDirection is a fine-tuned language model for binary stock movement prediction.
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The model is trained to predict whether the next day’s stock price of Akbank (AKBNK), traded on Borsa Istanbul (BIST), will move UP or DOWN, based on the daily percentage changes from the last four days and the current day.
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- Input: A formatted prompt describing the last 5 days of daily percentage price changes.
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- Output: A simple categorical prediction — "UP" or "DOWN".
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This model was fine-tuned on a dataset of 6,300 labeled rows of AKBNK stock data.
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## Uses
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### Direct Use
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- Educational purposes: Demonstrating how LLMs can be fine-tuned for financial classification tasks.
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- Research: Exploring text-based sequence learning for stock direction prediction.
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- Proof of concept: Serving as an example for stock price direction prediction using natural language prompts.
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⚠️ Not for financial advice or live trading decisions.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("khazarai/StockDirection-6K")
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model = AutoModelForCausalLM.from_pretrained(
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"khazarai/StockDirection-6K",
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device_map={"": 0}
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)
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question ="""
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You are an assistant that predicts whatever a stock will go up or down in the next day based on the daily percentage price changes of the last:
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4 days ago: 0.00
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3 days ago: -3.09
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2 days ago: 2.13
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1 day ago: -2.04
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today: 0.01
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Predict whatever the next day's price will go up or down. Simply write your prediction as UP or DOWN
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"""
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messages = [
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{"role" : "user", "content" : question}
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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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enable_thinking = False,
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)
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from transformers import TextStreamer
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_ = model.generate(
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**tokenizer(text, return_tensors = "pt").to("cuda"),
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max_new_tokens = 200,
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temperature = 0.7,
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top_p = 0.8,
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top_k = 20,
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streamer = TextStreamer(tokenizer, skip_prompt = True),
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)
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```
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## Training Data
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- Dataset: [atahanuz/stock_prediction](https://huggingface.co/datasets/atahanuz/stock_prediction)
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- Size: 6,355 labeled examples.
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- Structure: Each sample contains past 5 daily percentage changes and the target label (UP/DOWN).
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Example:
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```vbnet
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Question: You are an assistant that predicts whether a stock will go up or down in the next day
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based on the daily percentage price changes of the last:
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4 days ago: nan
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3 days ago: 0.00
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2 days ago: 2.22
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1 day ago: -2.17
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today: -2.22
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Predict whether the next day's price will go up or down.
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Simply write your prediction as UP or DOWN.
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Answer: DOWN
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```
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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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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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}
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99
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 %}
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{{- "# 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 forward_message in messages %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- set message = messages[index] %}
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{%- set current_content = message.content if message.content is defined and message.content is not none else '' %}
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{%- set tool_start = '<tool_response>' %}
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{%- set tool_start_length = tool_start|length %}
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{%- set start_of_message = current_content[:tool_start_length] %}
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{%- set tool_end = '</tool_response>' %}
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{%- set tool_end_length = tool_end|length %}
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{%- set start_pos = (current_content|length) - tool_end_length %}
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{%- if start_pos < 0 %}
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{%- set start_pos = 0 %}
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{%- endif %}
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{%- set end_of_message = current_content[start_pos:] %}
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{%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %}
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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 %}
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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' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{%- set m_content = message.content if message.content is defined and message.content is not none else '' %}
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{%- set content = m_content %}
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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in m_content %}
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{%- set content = (m_content.split('</think>')|last).lstrip('\n') %}
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{%- set reasoning_content = (m_content.split('</think>')|first).rstrip('\n') %}
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{%- set reasoning_content = (reasoning_content.split('<think>')|last).lstrip('\n') %}
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{%- endif %}
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{%- endif %}
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{%- if loop.index0 > ns.last_query_index %}
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{%- if loop.last or (not loop.last and (not reasoning_content.strip() == '')) %}
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{{- '\n' }}
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{%- endif %}
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{%- if tool_call.arguments is string %}
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{{- tool_call.arguments }}
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{%- else %}
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{{- tool_call.arguments | tojson }}
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{%- endif %}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if loop.first 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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{%- if enable_thinking is defined and enable_thinking is false %}
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{{- '<think>\n\n</think>\n\n' }}
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{%- endif %}
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{%- endif %}
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64
config.json
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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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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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 1024,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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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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"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": 40960,
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"max_window_layers": 28,
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"model_type": "qwen3",
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"num_attention_heads": 16,
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"num_hidden_layers": 28,
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"num_key_value_heads": 8,
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"pad_token_id": 151654,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000000,
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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": "5.0.0",
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"unsloth_fixed": true,
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"use_cache": true,
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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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"bos_token_id": 151643,
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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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"max_length": 40960,
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"pad_token_id": 151654,
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"temperature": 0.6,
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"top_k": 20,
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"top_p": 0.95,
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"transformers_version": "5.0.0"
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}
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151388
merges.txt
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merges.txt
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3
model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:8eab77d7264c53622648018b9444bb08304c64925f94eb5f25936911f8960f31
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size 1192135096
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special_tokens_map.json
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special_tokens_map.json
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{
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"additional_special_tokens": [
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"<|im_start|>",
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"<|im_end|>",
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"<|object_ref_start|>",
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"<|object_ref_end|>",
|
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"<|box_start|>",
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"<|box_end|>",
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"<|quad_start|>",
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"<|quad_end|>",
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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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"eos_token": {
|
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"content": "<|im_end|>",
|
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"lstrip": false,
|
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"normalized": false,
|
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"rstrip": false,
|
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"single_word": false
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},
|
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"pad_token": {
|
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"content": "<|vision_pad|>",
|
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"lstrip": false,
|
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"normalized": false,
|
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
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size 11422650
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241
tokenizer_config.json
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tokenizer_config.json
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1
vocab.json
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1
vocab.json
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