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dtadpole/KernelCoder-4B_20250621-071556

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face

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Model Card

dtadpole/KernelCoder-4B_20250621-071556

This model is a fine-tuned version of Qwen/Qwen3-4B using Unsloth and LoRA.

Model Details

—Base Model: Qwen/Qwen3-4B
—Fine-tuning Method: LoRA (Low-Rank Adaptation)
—Max Sequence Length: 16384
—Training Examples: 330
—LoRA Rank: 64
—LoRA Alpha: 64

Training Configuration

—Epochs: 2
—Learning Rate: 5e-05
—Batch Size: 1
—Gradient Accumulation Steps: 1

Usage

python

from unsloth import FastLanguageModel
import torch

# Load model
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="dtadpole/KernelCoder-4B_20250621-071556",
    max_seq_length=16384,
    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