📚 docs(qwen3): add comprehensive usage examples and model details

docs/source/en/model_doc/qwen3.md

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 ### Model Details
 
-To be released with the official model launch.
+Qwen3 is the dense 32B parameter variant in the Qwen3 series. Key architectural improvements include:
+
+- **Extended Context Length**: Supports up to 128K tokens context window
+- **Enhanced Architecture**: Uses GQA (Grouped Query Attention) for improved efficiency
+- **Dual Attention Mechanism**: Similar to Qwen2.5, alternates between local sliding window attention and global attention layers
+- **Improved Training**: Post-trained with RLHF and advanced instruction tuning
+
+Qwen3-32B is available in both base (`Qwen/Qwen3-32B`) and instruction-tuned (`Qwen/Qwen3-32B-Instruct`) variants.
 
 ## Usage tips
 
-To be released with the official model launch.
+### Basic Text Generation
+
+Here's how to use Qwen3 for text generation:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen3-32B-Instruct",
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-32B-Instruct")
+
+# Basic generation
+prompt = "Explain quantum computing in simple terms:"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=256,
+    temperature=0.7,
+    top_p=0.8,
+    do_sample=True
+)
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+### Chat Format
+
+For the instruction-tuned variant, use the chat template for multi-turn conversations:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen3-32B-Instruct",
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-32B-Instruct")
+
+messages = [
+    {"role": "system", "content": "You are a helpful AI assistant."},
+    {"role": "user", "content": "What are the main differences between Python and JavaScript?"}
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=512,
+    temperature=0.7
+)
+
+response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
+print(response)
+```
+
+### Memory Optimization
+
+For systems with limited GPU memory, use quantization:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+import torch
+
+# 4-bit quantization configuration
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4"
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen3-32B-Instruct",
+    quantization_config=quantization_config,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-32B-Instruct")
+
+# Use as normal - memory footprint is significantly reduced
+```
+
+### Long Context Usage
+
+Qwen3 supports up to 128K tokens. For long documents:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen3-32B-Instruct",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    attn_implementation="flash_attention_2"  # Recommended for long contexts
+)
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-32B-Instruct")
+
+# Process long document
+long_document = "..." * 10000  # Your long text here
+prompt = f"Summarize the following document:\n\n{long_document}"
+
+inputs = tokenizer(prompt, return_tensors="pt", truncation=False).to(model.device)
+
+# Note: Ensure your input doesn't exceed 128K tokens
+print(f"Input tokens: {inputs.input_ids.shape[1]}")
+
+outputs = model.generate(**inputs, max_new_tokens=500)
+summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(summary)
+```
+
+### Tips for Best Performance
+
+- **Use `torch.bfloat16`**: Provides best balance of speed and quality
+- **Enable Flash Attention 2**: Significantly faster for long contexts (`attn_implementation="flash_attention_2"`)
+- **Batch Processing**: Process multiple inputs together for better throughput
+- **Temperature Tuning**: Lower (0.1-0.5) for factual tasks, higher (0.7-1.0) for creative tasks
+- **System Prompt**: Use clear system prompts for instruction-tuned variant to guide behavior
 
 ## Qwen3Config