# Example of GPT-OSS-120B model initialization
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "gpt-oss-120b"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Key architectural features:
# - 120 billion parameters with sparse activation
# - Mixture of Experts (MoE) with 16 experts
# - Rotary Position Embeddings (RoPE)
# - Grouped Query Attention (GQA)
# - Flash Attention 2 optimization

# Memory-efficient inference example
from transformers import BitsAndBytesConfig

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(
    "gpt-oss-120b",
    quantization_config=quantization_config,
    device_map="auto"
)

# Example of GPT-OSS-120B generating optimized code
prompt = """Write a Python function that efficiently finds all prime numbers up to n using the Sieve of Eratosthenes algorithm."""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(
    **inputs,
    max_new_tokens=200,
    temperature=0.2,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

# Chain-of-thought prompting example
math_prompt = """Q: A train leaves Station A at 8:00 AM traveling at 60 mph. Another train leaves Station B, 300 miles away, at 9:00 AM traveling at 70 mph toward Station A. At what time will they meet?

Let's think step by step:
1. First train travels for 1 hour alone: 60 miles
2. Remaining distance: 300 - 60 = 240 miles
3. Combined speed: 60 + 70 = 130 mph
4. Time to meet: 240 / 130 ≈ 1.846 hours
5. Convert to minutes: 0.846 * 60 ≈ 51 minutes
6. Meeting time: 9:00 AM + 1 hour 51 minutes = 10:51 AM

Answer: 10:51 AM"""

# 1. Clone the repository
git clone https://github.com/gpt-oss/gpt-oss-120b.git
cd gpt-oss-120b

# 2. Set up environment
conda create -n gpt-oss python=3.10
conda activate gpt-oss
pip install -r requirements.txt

# 3. Download model weights
python download_weights.py --model gpt-oss-120b --precision bf16

# 4. Configure distributed inference
cat > config.yaml << EOF
model:
  name: gpt-oss-120b
  precision: bfloat16
  tensor_parallel_size: 8
  pipeline_parallel_size: 1

deployment:
  port: 8000
  max_batch_size: 32
  max_sequence_length: 8192
EOF

# 5. Start inference server
python serve.py --config config.yaml

# Advanced optimization configuration
from vllm import LLM, SamplingParams

llm = LLM(
    model="gpt-oss-120b",
    tensor_parallel_size=8,
    gpu_memory_utilization=0.9,
    max_model_len=8192,
    enable_prefix_caching=True,
    block_size=16
)

# Batch processing for efficiency
prompts = [
    "Explain quantum computing in simple terms.",
    "Write a business plan for a startup.",
    "Generate Python code for a REST API."
]

sampling_params = SamplingParams(
    temperature=0.7,
    top_p=0.9,
    max_tokens=512
)

outputs = llm.generate(prompts, sampling_params)