Qwen3.5-27B-Coder

Qwen3.5-27B-Coder

Fine-tuned version of Qwen/Qwen3.5-27B specialized for coding tasks.

Training Details

Datasets

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "mahernaija/Qwen3.5-27B-Coder",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("mahernaija/Qwen3.5-27B-Coder")

messages = [{"role": "user", "content": "Write a Python binary search function with type hints."}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.2)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Evaluation

Fine-tuned model compared to base on 10 coding prompts:

• 7/10 prompts: Fine-tuned model produces faster, more concise responses
• Refactoring: 70% faster response
• Testing: 59% faster response
• Loss improvement: 40% reduction over base model

Training Infrastructure

Trained on Nebius.ai cloud using Soperator (Kubernetes-managed Slurm):

• 2 nodes × 8 NVIDIA H200 SXM GPUs
• InfiniBand 400 Gb/s inter-node communication
• DeepSpeed ZeRO-2 for optimizer/gradient sharding
• Gradient checkpointing with use_reentrant=False

Limitations

• Primarily optimized for Python (70% of training data)
• Other languages (JS, Rust, Go) improved but less than Python
• Not trained on repo-level tasks (SWE-bench style)
• Best for function/class level code generation and bug fixing

License

Apache 2.0 (same as base model)