from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "Daksh1/qwen3-4b-dpo-ckpt-1",
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(
    "Daksh1/qwen3-4b-dpo-ckpt-1",
    trust_remote_code=True
)

# Generate
messages = [
    {"role": "user", "content": "Your question here"}
]
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)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Usage with vLLM (Recommended)

python

from vllm import LLM, SamplingParams

# Initialize vLLM
llm = LLM(
    model="Daksh1/qwen3-4b-dpo-ckpt-1",
    trust_remote_code=True,
    dtype="bfloat16",
    max_model_len=2048
)

# Create sampling parameters
sampling_params = SamplingParams(
    temperature=0.7,
    top_p=0.9,
    max_tokens=512
)

# Generate
prompts = ["Your question here"]
outputs = llm.generate(prompts, sampling_params)

for output in outputs:
    print(output.outputs[0].text)

Usage with vLLM (Local Path)

python

from vllm import LLM, SamplingParams

# Use local path
llm = LLM(
    model="./saved_models/qwen3-4b-dpo-ckpt-1",
    trust_remote_code=True,
    dtype="bfloat16",
    max_model_len=2048
)

Model Size

Safetensors: ~8 GB (inference-ready)
No optimizer states - 75% smaller than training checkpoints

Related Checkpoints

Checkpoint 1: Daksh1/qwen3-4b-dpo-ckpt-1
Checkpoint 2: Daksh1/qwen3-4b-dpo-ckpt-2
Checkpoint 3: Daksh1/qwen3-4b-dpo-ckpt-3
Checkpoint 4: Daksh1/qwen3-4b-dpo-ckpt-4