This model is a fine-tuned version of meta-llama/Llama-3.2-3B-Instruct using the Unsloth framework with LoRA (Low-Rank Adaptation) for efficient training.

Developed by: theprint
Model type: Causal Language Model (Fine-tuned with LoRA)
Language: en
License: apache-2.0
Base model: meta-llama/Llama-3.2-3B-Instruct
Fine-tuning method: LoRA with rank 128

Intended Use

Python code assistance.

Training Details

Training Data

Vezora's 22.6k data set of Python code was chosen because it has "been meticulously tested and verified as working."

Dataset: Vezora/Tested-22k-Python-Alpaca
Format: alpaca

Training Procedure

Training epochs: 3
LoRA rank: 128
Learning rate: 0.0001
Batch size: 4
Framework: Unsloth + transformers + PEFT
Hardware: NVIDIA RTX 5090

Usage

python

from unsloth import FastLanguageModel
import torch

# Load model and tokenizer
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="theprint/Pythonified-Llama-3.2-3B-Instruct",
    max_seq_length=4096,
    dtype=None,
    load_in_4bit=True,
)

# Enable inference mode
FastLanguageModel.for_inference(model)

# Example usage
inputs = tokenizer(["Your prompt here"], 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)

Alternative Usage (Standard Transformers)

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "theprint/Pythonified-Llama-3.2-3B-Instruct",
    torch_dtype=torch.float16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("theprint/Pythonified-Llama-3.2-3B-Instruct")

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

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs, max_new_tokens=256, temperature=0.7, do_sample=True)
response = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)
print(response)

GGUF Quantized Versions

Quantized GGUF versions are available in the gguf/ directory for use with llama.cpp:

Pythonified-Llama-3.2-3B-Instruct-f16.gguf (6135.6 MB) - 16-bit float (original precision, largest file)
Pythonified-Llama-3.2-3B-Instruct-q3_k_m.gguf (1609.0 MB) - 3-bit quantization (medium quality)
Pythonified-Llama-3.2-3B-Instruct-q4_k_m.gguf (1925.8 MB) - 4-bit quantization (medium, recommended for most use cases)
Pythonified-Llama-3.2-3B-Instruct-q5_k_m.gguf (2214.6 MB) - 5-bit quantization (medium, good quality)
Pythonified-Llama-3.2-3B-Instruct-q6_k.gguf (2521.4 MB) - 6-bit quantization (high quality)
Pythonified-Llama-3.2-3B-Instruct-q8_0.gguf (3263.4 MB) - 8-bit quantization (very high quality)

Using with llama.cpp

bash

# Download a quantized version (q4_k_m recommended for most use cases)
wget https://huggingface.co/theprint/Pythonified-Llama-3.2-3B-Instruct/resolve/main/gguf/Pythonified-Llama-3.2-3B-Instruct-q4_k_m.gguf

# Run with llama.cpp
./llama.cpp/main -m Pythonified-Llama-3.2-3B-Instruct-q4_k_m.gguf -p "Your prompt here" -n 256

Limitations

May provide incorrect information and non-working code.

Citation

If you use this model, please cite:

bibtex

@misc{pythonified_llama_3.2_3b_instruct,
  title={Pythonified-Llama-3.2-3B-Instruct: Fine-tuned meta-llama/Llama-3.2-3B-Instruct},
  author={theprint},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/theprint/Pythonified-Llama-3.2-3B-Instruct}
}

Acknowledgments

Base model: meta-llama/Llama-3.2-3B-Instruct
Training dataset: Vezora/Tested-22k-Python-Alpaca
Fine-tuning framework: Unsloth
Quantization: llama.cpp