from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "your_id/your-merged-repo"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
)

messages = [{"role": "user", "content": "Convert this to JSON: name is Alice, age is 30"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.0, do_sample=False)

print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Sources & Terms (IMPORTANT)

Training data: u-10bei/structured_data_with_cot_dataset_512_v2
Dataset License: MIT License. This dataset is used and distributed under the terms of the MIT License.
Base model: Qwen/Qwen3-4B-Instruct-2507
Compliance: Users must comply with the MIT license (including copyright notice) and the base model's original terms of use.

kamy-dev/llm-2025-model

llm-2025-model (Merged)

Training Objective

Training Configuration

Usage

Sources & Terms (IMPORTANT)

kamy-dev/
llm-2025-model