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Model: bqbbao6/llama-3.2-3b-legal-vn Source: Original Platform
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README.md
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README.md
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---
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base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- llama
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license: apache-2.0
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language:
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- en
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---
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## Model Description
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- **Developed by:** bqbbao6
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- **License:** apache-2.0
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- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
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- **Fine-tuning Method:** LoRA (Low-Rank Adaptation) via Unsloth
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-
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This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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---
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## Intended Use
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This model is designed for:
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* Legal assistants and chatbots specialized in Vietnamese law.
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* Routing systems that need to distinguish between general conversation and legal inquiries.
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* Educational tools for students studying Vietnamese Civil and Administrative law.
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## Training Data
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The model was trained on a curated dataset of **3,199 samples**, including:
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* **Router Data:** Pairs of user queries and their corresponding intent labels.
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* **Legal Contexts:** Structured data extracted from Vietnamese legal documents (Decrees, Circulars, and Laws) formatted for RAG (Retrieval-Augmented Generation).
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## Usage
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#### Installation
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To run this model efficiently, it is recommended to use the `unsloth` library for faster inference and lower memory usage.
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```bash
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pip install unsloth
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pip install --no-deps xformers trl peft accelerate bitsandbytes
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```
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#### Loading the Model
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You can load the model using the following Python code:
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```python
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from unsloth import FastLanguageModel
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import torch
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model_id = "bqbbao6/llama-3.2-3b-legal-vn" # Your Model ID
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name = model_id,
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max_seq_length = 2048,
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load_in_4bit = True, # Use 4-bit for faster inference
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)
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FastLanguageModel.for_inference(model) # Enable 2x faster inference
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```
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#### Sample Inference Code
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Here is a complete example of how to use the model for a legal inquiry:
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```python
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# Prepare the conversation
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messages = [
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{"role": "system", "content": "Bạn là một chuyên gia pháp luật Việt Nam."},
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{"role": "user", "content": "Người dân có quyền tham gia quản lý nhà nước không?"},
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]
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# Apply Chat Template
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inputs = tokenizer.apply_chat_template(
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messages,
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tokenize = True,
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add_generation_prompt = True,
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return_tensors = "pt",
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).to("cuda")
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# Generate response
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outputs = model.generate(
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input_ids = inputs,
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max_new_tokens = 512,
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use_cache = True
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)
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# Decode the output
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response = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
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print(response)
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```
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### Examples
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##### 1. Intent Classification (Router)
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**Input:**
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```text
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"Cho tôi hỏi thủ tục làm căn cước công dân gắn chip hiện nay như thế nào?"
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```
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**Output:**
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```text
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legal
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```
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##### 2. Legal Question Answering (RAG)
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**Input:**
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```text
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Ngữ cảnh: Theo Nghị quyết số 98/2023/QH15, UBND thành phố Thủ Đức có thẩm quyền phê duyệt nhiệm vụ và đồ án quy hoạch phân khu, quy hoạch chi tiết các đồ án trên địa bàn.
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Câu hỏi: UBND thành phố Thủ Đức có quyền phê duyệt quy hoạch phân khu không?
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```
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**Output:**
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```text
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Có. Theo Nghị quyết số 98/2023/QH15, UBND thành phố Thủ Đức có thẩm quyền phê duyệt nhiệm vụ và đồ án quy hoạch phân khu trên địa bàn thành phố thuộc thẩm quyền của UBND Thành phố Hồ Chí Minh trước đây.
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```
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## Limitations and Biases
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* The model's knowledge is limited to the documents provided during training. It may not reflect the most recent legal amendments unless updated.
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* Users should verify the information with official legal portals as the model might occasionally generate inaccurate legal interpretations (hallucinations).
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---
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93
chat_template.jinja
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{{- bos_token }}
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{%- if custom_tools is defined %}
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{%- set tools = custom_tools %}
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{%- endif %}
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{%- if not tools_in_user_message is defined %}
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{%- set tools_in_user_message = true %}
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{%- endif %}
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{%- if not date_string is defined %}
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{%- if strftime_now is defined %}
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{%- set date_string = strftime_now("%d %b %Y") %}
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{%- else %}
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{%- set date_string = "26 Jul 2024" %}
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{%- endif %}
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{%- endif %}
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{%- if not tools is defined %}
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{%- set tools = none %}
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{%- endif %}
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{#- This block extracts the system message, so we can slot it into the right place. #}
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{%- if messages[0]['role'] == 'system' %}
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{%- set system_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{%- set system_message = "" %}
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{%- endif %}
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{#- System message #}
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{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
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{%- if tools is not none %}
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{{- "Environment: ipython\n" }}
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{%- endif %}
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{{- "Cutting Knowledge Date: December 2023\n" }}
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{{- "Today Date: " + date_string + "\n\n" }}
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{%- if tools is not none and not tools_in_user_message %}
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{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{%- endif %}
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{{- system_message }}
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{{- "<|eot_id|>" }}
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{#- Custom tools are passed in a user message with some extra guidance #}
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{%- if tools_in_user_message and not tools is none %}
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{#- Extract the first user message so we can plug it in here #}
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{%- if messages | length != 0 %}
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{%- set first_user_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
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{%- endif %}
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{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
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{{- "Given the following functions, please respond with a JSON for a function call " }}
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{{- "with its proper arguments that best answers the given prompt.\n\n" }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{{- first_user_message + "<|eot_id|>"}}
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{%- endif %}
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{%- for message in messages %}
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{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
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{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
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{%- elif 'tool_calls' in message %}
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{%- if not message.tool_calls|length == 1 %}
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{{- raise_exception("This model only supports single tool-calls at once!") }}
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{%- endif %}
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{%- set tool_call = message.tool_calls[0].function %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
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{{- '{"name": "' + tool_call.name + '", ' }}
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{{- '"parameters": ' }}
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{{- tool_call.arguments | tojson }}
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{{- "}" }}
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{{- "<|eot_id|>" }}
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{%- elif message.role == "tool" or message.role == "ipython" %}
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{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
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{%- if message.content is mapping or message.content is iterable %}
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{{- message.content | tojson }}
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{%- else %}
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{{- message.content }}
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{%- endif %}
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{{- "<|eot_id|>" }}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\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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"LlamaForCausalLM"
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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": 128000,
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"torch_dtype": "float16",
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 3072,
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"initializer_range": 0.02,
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"intermediate_size": 8192,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 24,
|
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"num_hidden_layers": 28,
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"num_key_value_heads": 8,
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"pad_token_id": 128004,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_parameters": {
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"factor": 32.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_theta": 500000.0,
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"rope_type": "llama3"
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"tie_word_embeddings": true,
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"unsloth_fixed": true,
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"unsloth_version": "2026.5.6",
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"use_cache": false,
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"vocab_size": 128256
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}
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"max_length": 131072,
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"temperature": 0.6,
|
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"top_p": 0.9,
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"transformers_version": "5.5.0"
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}
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|
||||
"model.norm.weight": "model-00002-of-00002.safetensors"
|
||||
}
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
2069
tokenizer_config.json
Normal file
2069
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user