Update README.md

README.md

@@ -1,3 +1,221 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- amphora/QwQ-LongCoT-130K
+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:** Generative Knowledge Distillation (GKD)  
+- **Task Type:** Conversational AI / Causal Language Modeling  
+- **Parameters:** 0.5B  
+- **Special Features:**
+  - Optimized with LoraConfig for fine-tuning
+  - 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
+[▓░░░░░░░░░░] 10%
+
+### Training Script:
+
+```python
+from datasets import Dataset
+from trl import GKDConfig, GKDTrainer
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+)
+from datasets import load_dataset
+from peft import LoraConfig
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--temperature", type=float, default = 0.9)
+parser.add_argument("--lmbda", type=float, default = 0.5)
+parser.add_argument("--beta", type=float, default = 0.5)
+parser.add_argument("--max_new_tokens", 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", 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/Qwen2-0.5B-Instruct")
+
+
+
+
+# The teacher model to calculate the KL divergence against
+teacher_model = AutoModelForCausalLM.from_pretrained("Qwen/QwQ-32B-Preview", torch_dtype=torch.bfloat16, device_map="auto") 
+teacher_model.lm_head.weight.data = teacher_model.lm_head.weight.data[:151936, :]
+teacher_model.lm_head.out_features = 151936
+
+
+
+# The model to optimise
+model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-0.5B-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 = GKDConfig(
+    output_dir=args.output_dir,
+    temperature=args.temperature,
+    lmbda=args.lmbda,
+    beta=args.beta,
+    max_new_tokens=args.max_new_tokens,
+    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",
+)
+
+trainer = GKDTrainer(
+    model=model,
+    teacher_model=teacher_model,
+    args=training_args,
+    processing_class=tokenizer,
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,
+    peft_config=lora_config if args.lora else None
+)
+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"
+
+# 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.  
+- Best suited for conversational contexts under the Qwen family’s alignment principles.  
+
+---
+
+## 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 = {QwQ-0.5B-Distilled: 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.