ZhuofengLi/Qwen3.5-27B-hardtests-sft-synthetic-merged
ZhuofengLi/Qwen3.5-27B-hardtests-sft-synthetic-merged is a 27 billion parameter language model based on the Qwen3.5 architecture, fine-tuned by ZhuofengLi. This model is specifically optimized for enhanced performance on challenging code and reasoning tasks. It was created by merging a LoRA adapter, which was SFT-trained on synthetic hard-tests data, into the Qwen/Qwen3.5-27B base model. Its primary strength lies in tackling complex logical and programming challenges.
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Model Overview
This model, ZhuofengLi/Qwen3.5-27B-hardtests-sft-synthetic-merged, is a 27 billion parameter language model derived from the Qwen3.5-27B base architecture. It has been specifically fine-tuned by ZhuofengLi to excel in demanding code and reasoning tasks.
Key Capabilities & Training
The model's enhanced capabilities stem from a LoRA adapter that was merged into the base model. This adapter underwent Supervised Fine-Tuning (SFT) using synthetic hard-tests data, specifically designed to improve performance on complex logical and programming challenges. The merge process utilized ms-swift framework (version 4.4.2) and maintained bf16 precision.
Usage
Developers can easily integrate this model using the transformers library:
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("ZhuofengLi/Qwen3.5-27B-hardtests-sft-synthetic-merged", torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("ZhuofengLi/Qwen3.5-27B-hardtests-sft-synthetic-merged")When to Use This Model
- Complex Code Generation: Ideal for scenarios requiring robust code solutions.
- Advanced Reasoning Tasks: Suited for applications demanding strong logical inference and problem-solving.
- Benchmarking Hard Problems: Useful for evaluating performance on challenging, synthetically generated test cases.