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| Setting | Value |
|---|---|
| Base model | deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B |
| Fine-tuning method | QLoRA (r=16, alpha=16) |
| Precision (saved) | float16 (merged) |
| Max sequence length | 2048 |
| Batch size (effective) | 8 |
| Epochs | 1 |
| Learning rate | 0.0002 |
| Date trained | 2026-07-26 |
1from transformers import AutoModelForCausalLM, AutoTokenizer
2import torch
3
4model_id = "vorenthiclabs/Vorenthos-r1"
5tokenizer = AutoTokenizer.from_pretrained(model_id)
6model = AutoModelForCausalLM.from_pretrained(
7 model_id,
8 torch_dtype=torch.float16,
9 device_map="auto",
10)
11
12prompt = """<|User|>: Explain gradient descent in simple terms.
13<|Assistant|>: <think>
14"""
15
16inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
17outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.6)
18print(tokenizer.decode(outputs[0], skip_special_tokens=True))<|User|>: {your instruction here}
<|Assistant|>: <think>
{model chain-of-thought reasoning}
</think>
{final response}