KernelCoder-32B-AWQ_20250621-161329

dtadpole/KernelCoder-32B-AWQ_20250621-161329

This model is a fine-tuned version of Qwen/Qwen3-32B-AWQ using Unsloth and LoRA.

Model Details

• Base Model: Qwen/Qwen3-32B-AWQ
• Fine-tuning Method: LoRA (Low-Rank Adaptation)
• Max Sequence Length: 8192
• Training Examples: 24
• LoRA Rank: 64
• LoRA Alpha: 64

Training Configuration

• Epochs: 1
• Learning Rate: 3e-05
• Batch Size: 1
• Gradient Accumulation Steps: 1

Usage

from unsloth import FastLanguageModel
import torch

# Load model
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="dtadpole/KernelCoder-32B-AWQ_20250621-161329",
    max_seq_length=8192,
    dtype=None,
    load_in_4bit=True,
)

# Enable inference mode
FastLanguageModel.for_inference(model)

# Format your prompt
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Your question here"}
]

formatted_prompt = tokenizer.apply_chat_template(
    messages, 
    tokenize=False, 
    add_generation_prompt=True
)

# Generate
inputs = tokenizer(formatted_prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Training Data

This model was fine-tuned on processed conversation experiences for improved performance on specific tasks.

Limitations

• This is a LoRA adapter that requires the base model to function
• Performance may vary depending on the specific use case
• The model inherits any limitations from the base model

Framework Versions

• Unsloth: 2025.6.1
• Transformers: 4.52.4
• PyTorch: 2.7.0
• PEFT: Latest