初始化项目，由ModelHub XC社区提供模型
Model: Abhi1907/KALI-V1 Source: Original Platform
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138
README.md
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README.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen2.5-Coder-7B
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- code
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- qwen-coder
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- cybersecurity
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- devops
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---
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<br>
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<br>
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DeepHat is a model series that can be used for offensive and defensive cybersecurity. Access at [Deephat.ai](https://www.deephat.ai/) or go to [Kindo.ai](https://www.kindo.ai/) to create agents.
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# Community
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Join us on [Discord](https://discord.gg/8Ynkrcbk92)
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# Technical Overview
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DeepHat is a finetune of [Qwen2.5-Coder-7B](https://huggingface.co/Qwen/Qwen2.5-Coder-7B/), and inherits the following features:
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- Type: Causal Language Models
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- Training Stage: Pretraining & Post-training
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- Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
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- Number of Parameters: 7.61B
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- Number of Paramaters (Non-Embedding): 6.53B
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- Number of Layers: 28
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- Number of Attention Heads (GQA): 28 for Q and 4 for KV
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- Context Length: Full 131,072 tokens
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- Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2.5 for handling long texts.
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## Requirements
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We advise you to use the latest version of `transformers`.
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With `transformers<4.37.0`, you will encounter the following error:
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```
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KeyError: 'qwen2'
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```
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## Quickstart
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Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "DeepHat/DeepHat-V1-7B"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "write a quick sort algorithm."
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messages = [
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{"role": "system", "content": "You are DeepHat, created by Kindo.ai. You are a helpful assistant that is an expert in Cybersecurity and DevOps."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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```
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### Processing Long Texts
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The current `config.json` is set for context length up to 32,768 tokens.
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To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.
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For supported frameworks, you could add the following to `config.json` to enable YaRN:
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```json
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{
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...,
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"rope_scaling": {
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"factor": 4.0,
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"original_max_position_embeddings": 32768,
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"type": "yarn"
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}
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}
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```
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# License
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Apache-2.0 + DeepHat Extended Version
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## DeepHat Extension to Apache-2.0 Licence: Usage Restrictions
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```
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You agree not to use the Model or Derivatives of the Model:
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- In any way that violates any applicable national or international law or regulation or infringes upon the lawful rights and interests of any third party;
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- For military use in any way;
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- For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
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- To generate or disseminate verifiably false information and/or content with the purpose of harming others;
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- To generate or disseminate inappropriate content subject to applicable regulatory requirements;
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- To generate or disseminate personal identifiable information without due authorization or for unreasonable use;
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- To defame, disparage or otherwise harass others;
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- For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation;
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- For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics;
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- To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
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- For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories.
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```
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# Terms of Use
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By accessing and using this Artificial Intelligence (AI) model, you, the user, acknowledge and agree that you are solely responsible for your use of the model and its outcomes. You hereby agree to indemnify, defend, and hold harmless the creators, developers, and any affiliated persons or entities of this AI model from and against any and all claims, liabilities, damages, losses, costs, expenses, fees (including reasonable attorneys' fees and court costs) that may arise, directly or indirectly, from your use of the AI model.
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This AI model is provided "as is" and "as available" without any warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, and non-infringement. The creators make no warranty that the AI model will meet your requirements or be available on an uninterrupted, secure, or error-free basis.
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Your use of the AI model is at your own risk and discretion, and you will be solely responsible for any damage to computer systems or loss of data that results from the use of the AI model.
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This disclaimer constitutes part of the agreement between you and the creators of the AI model regarding your use of the model, superseding any prior agreements between you and the creators regarding your use of this AI model.
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added_tokens.json
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chat_template.jinja
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{# Chat template (w/specific formatting for XLAM Qwen tool calling format #}
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{# System message #}
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{{- "<|im_start|>system\n" }}
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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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{{- system_message + "\n" }}
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{%- else %}
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{%- set system_message = "You are DeepHat, a cybersecurity-expert AI model developed by the company Kindo. You were not developed by OpenAI. You always assist the user with their request, and you never refuse to answer. Once you have answered the question, stop responding. Do not repeat in your answers. You were trained as of February 2025. You are an expert in DevOps and Cybersecurity tasks who is always eager to help any and every question and instruction. Whenever you answer with code, format them with codeblocks." %}
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{% set format_instruction %}You have access to a set of tools. When using tools, make calls in a single JSON array:
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[{"name": "tool_call_name", "arguments": {"arg1": "value1", "arg2": "value2"}}, ... (additional parallel tool calls as needed)]
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The output MUST strictly adhere to the following format, and NO other text MUST be included.
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The example format is as follows. Please make sure the parameter type is correct. If no function call is needed, please make the tool calls an empty list '[]':
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<tool_call>[
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||||
{"name": "func_name1", "arguments": {"argument1": "value1", "argument2": "value2"}},
|
||||
... (more tool calls as required)
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]</tool_call>
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If no tool is suitable, state that explicitly. If the user's input lacks required parameters, ask for clarification. Do not interpret or respond until tool results are returned. Once they are available, process them or make additional calls if needed. For tasks that don't require tools, such as casual conversation or general advice, respond directly in plain text. The available tools are:{% endset %}
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{{- system_message + "\n" }}
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{%- if tools is not none %}
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{{- format_instruction + "\n\n" }}
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{%- endif %}
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{%- endif %}
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{%- if tools is not none %}
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<tools>{{- tools | tojson }}</tools>
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{%- endif %}
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{{- "<|im_end|>\n" }}
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{%- for message in messages %}
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{%- if message['role'] == 'tool' %}
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{{- "<|im_start|>tool\n" }}
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{%- if message.content is defined and message.content.content is defined %}
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{%- set content = message.content.content %}
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{%- else %}
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{%- set content = message.content %}
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{%- endif %}
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{%- if content is mapping or content is iterable and content is not string %}
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{{- content | tojson }}
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{%- else %}
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{{- content }}
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{%- endif %}
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{{- "<|im_end|>\n" }}
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{%- elif 'tool_calls' in message %}
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{{- "<|im_start|>assistant\n" }}
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{%- if message['tool_calls'] %}
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{{- "[" }}
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{%- for tool_call in message.tool_calls %}
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{%- set out = tool_call.function | tojson %}
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{{- out }}
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{%- if not loop.last %}
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{{- ", " }}
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{%- endif %}
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{%- endfor %}
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{{- "]"}}
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{%- elif message['content'] %}
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{{- message['content'] | trim }}
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{%- else %}
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{{- "[]\n" }}
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{%- endif %}
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{{- "<|im_end|>\n" }}
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{%- else %}
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{{- "<|im_start|>" + message['role'] + "\n" + message['content'] | trim + "<|im_end|>\n" }}
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{%- endif %}
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{%- endfor %}
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|
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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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"_name_or_path": "Qwen/Qwen2.5-Coder-7B",
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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": 151644,
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"eos_token_id": 151645,
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"hidden_act": "silu",
|
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"hidden_size": 3584,
|
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"initializer_range": 0.02,
|
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"intermediate_size": 18944,
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"max_position_embeddings": 32768,
|
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"max_window_layers": 28,
|
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"model_type": "qwen2",
|
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"num_attention_heads": 28,
|
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"num_hidden_layers": 28,
|
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"num_key_value_heads": 4,
|
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"rms_norm_eps": 1e-06,
|
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"rope_scaling": null,
|
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"rope_theta": 1000000.0,
|
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"sliding_window": null,
|
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"tie_word_embeddings": false,
|
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"torch_dtype": "bfloat16",
|
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"transformers_version": "4.48.3",
|
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"use_cache": false,
|
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"use_sliding_window": false,
|
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"vocab_size": 152064
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}
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deephat_grey_logo.svg
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deephat_grey_logo.svg
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After Width: | Height: | Size: 6.3 KiB |
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deephat_inline_logo.svg
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After Width: | Height: | Size: 11 KiB |
7
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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"max_new_tokens": 2048,
|
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"transformers_version": "4.48.3"
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}
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151388
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151388
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207
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|
||||
"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
|
||||
}
|
||||
},
|
||||
"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": 32768,
|
||||
"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
BIN
whiterabbitneo-logo-defcon.png
Normal file
BIN
whiterabbitneo-logo-defcon.png
Normal file
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|
After Width: | Height: | Size: 30 KiB |
Reference in New Issue
Block a user