--- license: apache-2.0 datasets: - amphora/QwQ-LongCoT-130K-2 - PowerInfer/QWQ-LONGCOT-500K - PowerInfer/LONGCOT-Refine-500K language: - en metrics: - perplexity base_model: - Qwen/Qwen2.5-0.5B-Instruct --- ## Model Details: - **Base Model:** Qwen/Qwen2-0.5B-Instruct - **Teacher Model:** Qwen/QwQ-32B-Preview - **Distillation Framework:** Instruction Tuning - **Task Type:** Conversational AI / Causal Language Modeling - **Parameters:** 0.5B - **Special Features:** - Integrated gradient checkpointing for efficient training - Step-by-step reasoning capabilities for better problem-solving --- ## Training: QwQ-0.5B-Distilled was trained using the **QwQ-LongCoT-130K dataset**, a carefully curated collection of long-context examples designed for reasoning and conversational AI tasks. The GKD framework ensures that the student model mimics the teacher model’s outputs, aligning its predictions with high-quality responses. ### Training Progress: [▓▓▓▓▓▓▓▓▓▓] 100% ### Training Script: ```python import os import argparse import torch from datasets import Dataset from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM from transformers import ( AutoModelForCausalLM, AutoTokenizer, ) from datasets import load_dataset from peft import LoraConfig parser = argparse.ArgumentParser() parser.add_argument("--max_length", type=int, default = 4096) parser.add_argument("--output_dir", type=str, default="gkd-model") parser.add_argument("--per_device_train_batch_size", type=int, default=1) parser.add_argument("--gradient_accumulation_steps", type=int, default=16) parser.add_argument("--gradient_checkpointing", action="store_true", default=False) parser.add_argument("--resume_from_checkpoint", action="store_true", default=False) parser.add_argument("--lora", action="store_true") args = parser.parse_args() qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train") messages = [] for each in qwq_dataset: msg = [ {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."}, {"role": "user", "content": each["problem"]}, {"role": "assistant", "content": each["qwq"]}, ] messages.append(msg) TRAIN_SPLIT_RATIO = 0.9 train_size = int(TRAIN_SPLIT_RATIO * len(messages)) eval_size = len(messages) - train_size tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct") # The model to optimise model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") ### Real Dataset train_dataset = Dataset.from_dict({"messages":messages[:train_size]}) eval_dataset = Dataset.from_dict({"messages":messages[train_size:]}) training_args = SFTConfig( output_dir=args.output_dir, max_seq_length=args.max_length, per_device_train_batch_size=args.per_device_train_batch_size, gradient_accumulation_steps=args.gradient_accumulation_steps, gradient_checkpointing = args.gradient_checkpointing, save_steps = 100, save_total_limit = 5 ) lora_config = LoraConfig( r=16, lora_alpha=32, lora_dropout=0.05, bias="none", task_type="CAUSAL_LM", ) response_template = "<|im_start|>assistant\n" collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer) trainer = SFTTrainer( model=model, args=training_args, processing_class=tokenizer, train_dataset=train_dataset, eval_dataset=eval_dataset, peft_config=lora_config if args.lora else None, data_collator=collator, ) trainer.train(resume_from_checkpoint=args.resume_from_checkpoint) ``` ### Dataset: - **Source:** `amphora/QwQ-LongCoT-130K` - **Split:** 90% Training, 10% Evaluation --- ## Example Usage: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer # Model name model_name = "kz919/QwQ-0.5B-Distilled-SFT" # Load the model print(f"Starting to load the model {model_name} into memory") model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, device_map={"": 0} ) # Load the tokenizer tokenizer = AutoTokenizer.from_pretrained(model_name) # Define the prompt prompt = "How many r in strawberry." messages = [ {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."}, {"role": "user", "content": prompt} ] # Tokenize the input text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # Generate a response generated_ids = model.generate( **model_inputs, max_new_tokens=4096 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] # Decode the response response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(response) ``` --- ## Applications: 1. **Conversational Assistants:** Suitable for AI chatbots that require reasoning and long-context understanding. 2. **Educational Tools:** Provides step-by-step explanations, making it ideal for learning environments. 3. **Creative Writing:** Assists in generating coherent, contextually aware long-form content. 4. **Technical Support:** Handles complex customer queries with precision and clarity. --- ## Limitations: - While distilled for efficiency, performance on highly complex reasoning tasks may slightly trail the teacher model. - This model could still be under trained, merely a proof of concept. Don't yell at me if it's outputing nonesense. --- ## Citation: If you use this model in your research or applications, please cite it as: ```bibtex @model{qwq_0.5B_distilled, author = {Kaizhao Liang}, title = {Mini-QwQ: A Reasoning Model for Edge Devices}, year = {2024}, publisher = {Hugging Face}, version = {1.0} } ``` --- This model is an example of how efficient fine-tuning and distillation methods can deliver robust conversational AI capabilities in a smaller, more manageable footprint.