Homer v1.0 Qwen2.5 72 B

Introduction

Homer-v1.0-Qwen2.5-72B is a fine-tuned version of the Qwen2.5-72B model, optimized using a substantial amount of instruction-based data. It is designed to enhance performance in generating coherent and contextually relevant text in various applications.

Architecture

Homer-v1.0-Qwen2.5-72B is based on the Qwen2.5-72B architecture, which is a large-scale transformer model designed for causal language modeling tasks. The model's architecture allows it to process and generate text efficiently, using advanced tokenization and context handling techniques.

Training

The model has been fine-tuned with a large dataset of instruction-based data. This fine-tuning process helps the model improve its understanding of contextual and sequential information, thereby enhancing its ability to generate more accurate and relevant responses.

Guide: Running Locally

To run the Homer-v1.0-Qwen2.5-72B model locally, follow these steps:

1. Install Required Libraries: Ensure you have the transformers library installed.

   pip install transformers
   

2. Load the Model and Tokenizer:

   from transformers import AutoModelForCausalLM, AutoTokenizer
   
   model_name = "newsbang/Homer-v1.0-Qwen2.5-72B"
   
   model = AutoModelForCausalLM.from_pretrained(
       model_name,
       torch_dtype="auto",
       device_map="auto"
   )
   tokenizer = AutoTokenizer.from_pretrained(model_name)
   

3. Prepare Input and Generate Text:

   messages = [
       {"role": "system", "content": "You are a very helpful assistant."},
       {"role": "user", "content": "Hello"}
   ]
   text = tokenizer.apply_chat_template(
       messages,
       tokenize=False,
       add_generation_prompt=True
   )
   model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
   
   generated_ids = model.generate(
       **model_inputs,
       max_new_tokens=512
   )
   generated_ids = [
       output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
   ]
   
   response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
   

4. Cloud GPU Suggestions: For optimal performance, consider using cloud GPU services such as AWS EC2, Google Cloud Platform, or Azure, which provide powerful computing resources.

License

The Homer-v1.0-Qwen2.5-72B model is distributed under the Apache-2.0 license, allowing for both personal and commercial use, provided that users comply with the license terms.