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Naholav/deep-instruction
deep-instruction is a text generation model from Naholav. Use it when you need the model to write or continue text. The card lists the license as apache-2.0.
This is the best performing checkpoint from the deepinstruction training configuration.
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Updated Dec 8, 2025
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From the Hugging Face model README
This is the best performing checkpoint from the deep_instruction training configuration.
| Property | Value |
|---|---|
| Base Model | Qwen/Qwen2.5-Coder-1.5B-Instruct |
| Training Dataset | Naholav/CodeGen-Deep-5K |
| Training Method | LoRA (Low-Rank Adaptation) |
| Checkpoint | step-800, epoch-3 |
| Pass@1 (AtCoder Easy) | 26.83% (11/41 problems) |
Note: All 4 models were trained with identical hyperparameters for fair comparison. Better configurations may be discovered through hyperparameter search methods (e.g., grid search, random search).
Evaluated on LiveCodeBench AtCoder Easy problems (41 questions):
| Model | Pass@1 | Improvement |
|---|---|---|
| Base Model (Qwen2.5-Coder-1.5B) | 24.39% | - |
| deep-instruction (this model) | 26.83% | +10% |
| diverse-think | 29.27% | +20% |
| deep-think | 31.71% | +30% |
| diverse-instruction | 31.71% | +30% |
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch
# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
"Qwen/Qwen2.5-Coder-1.5B-Instruct",
torch_dtype=torch.bfloat16,
device_map="auto"
)
# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "Naholav/deep-instruction")
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-1.5B-Instruct")
# Generate with instruction prompt
messages = [
{"role": "system", "content": "You are an expert programmer. Write clean, efficient code."},
{"role": "user", "content": "Your problem here..."}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=2048)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
If you use this model, please cite:
@misc{naholav2024codegen,
author = {naholav},
title = {CodeGen: LoRA Fine-tuning for Competitive Programming},
year = {2025},
publisher = {HuggingFace},
url = {https://huggingface.co/Naholav/deep-instruction}
}