from unsloth import FastLanguageModel
import torch

# Load model
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="dtadpole/KernelCoder-32B-AWQ_20250621-170337",
    max_seq_length=32768,
    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

Base Model: Qwen/Qwen3-32B-AWQ
Fine-tuning Method: LoRA (Low-Rank Adaptation)
Max Sequence Length: 32768
Training Examples: 517
LoRA Rank: 64
LoRA Alpha: 64

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