DeepHat is a finetune of Qwen2.5-Coder-7B, and inherits the following features:
Type: Causal Language Models
Training Stage: Pretraining & Post-training
Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
Number of Parameters: 7.61B
Number of Paramaters (Non-Embedding): 6.53B
Number of Layers: 28
Number of Attention Heads (GQA): 28 for Q and 4 for KV
Context Length: Full 131,072 tokens
Please refer to this section for detailed instructions on how to deploy Qwen2.5 for handling long texts.
Requirements
We advise you to use the latest version of transformers.
With transformers<4.37.0, you will encounter the following error:
KeyError: 'qwen2'
Quickstart
Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents.
python
1from transformers import AutoModelForCausalLM, AutoTokenizer
23model_name ="DeepHat/DeepHat-V1-7B"45model = AutoModelForCausalLM.from_pretrained(6 model_name,7 torch_dtype="auto",8 device_map="auto"9)10tokenizer = AutoTokenizer.from_pretrained(model_name)1112prompt ="write a quick sort algorithm."13messages =[14{"role":"system","content":"You are DeepHat, created by Kindo.ai. You are a helpful assistant that is an expert in Cybersecurity and DevOps."},15{"role":"user","content": prompt}16]17text = tokenizer.apply_chat_template(18 messages,19 tokenize=False,20 add_generation_prompt=True21)22model_inputs = tokenizer([text], return_tensors="pt").to(model.device)2324generated_ids = model.generate(25**model_inputs,26 max_new_tokens=51227)28generated_ids =[29 output_ids[len(input_ids):]for input_ids, output_ids inzip(model_inputs.input_ids, generated_ids)30]3132response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
Processing Long Texts
The current config.json is set for context length up to 32,768 tokens.
To handle extensive inputs exceeding 32,768 tokens, we utilize YaRN, a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.
For supported frameworks, you could add the following to config.json to enable YaRN:
DeepHat Extension to Apache-2.0 Licence: Usage Restrictions
You agree not to use the Model or Derivatives of the Model:
- 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;
- For military use in any way;
- For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
- To generate or disseminate verifiably false information and/or content with the purpose of harming others;
- To generate or disseminate inappropriate content subject to applicable regulatory requirements;
- To generate or disseminate personal identifiable information without due authorization or for unreasonable use;
- To defame, disparage or otherwise harass others;
- For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation;
- 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;
- 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;
- For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories.
Terms of Use
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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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