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DeepHat-V1-7B

Text Generation
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Safetensors
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qwen2
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cybersecurity
devops
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text-generation-inference
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@@ -14,13 +14,13 @@ tags:
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  <br>
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- # WhiteRabbitNeo
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  <br>
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- ![WhiteRabbitNeo](https://huggingface.co/WhiteRabbitNeo/WhiteRabbitNeo-V3-7B/resolve/main/whiterabbitneo-logo-defcon.png)
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- WhiteRabbitNeo is a model series that can be used for offensive and defensive cybersecurity. Access at [whiterabbitneo.com](https://www.whiterabbitneo.com/) or go to [Kindo.ai](https://www.kindo.ai/) to create agents.
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  # Community
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  # Technical Overview
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- WhiteRabbitNeo 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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  ```python
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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- model_name = "WhiteRabbitNeo/WhiteRabbitNeo-V3-7B"
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  model = AutoModelForCausalLM.from_pretrained(
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  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 WhiteRabbitNeo, 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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  # License
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- Apache-2.0 + WhiteRabbitNeo Extended Version
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- ## WhiteRabbitNeo 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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  <br>
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+ # DeepHat
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  <br>
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+ ![DeepHat](https://huggingface.co/WhiteRabbitNeo/WhiteRabbitNeo-V3-7B/resolve/main/whiterabbitneo-logo-defcon.png)
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+ DeepHat is a model series that can be used for offensive and defensive cybersecurity. Access at [whiterabbitneo.com](https://www.whiterabbitneo.com/) or go to [Kindo.ai](https://www.kindo.ai/) to create agents.
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  # Community
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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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  ```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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  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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  # 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: