Code-IL E4B — Safetensors

Name: code-il-E4B-safetensors
Availability: InStock

Safetensors (16-bit) variant of code-il-E4B — for HuggingFace Transformers, further fine-tuning, or conversion to other runtimes.

What this is

The safetensors version of the BrainboxAI code-il-E4B on-device coding assistant.

Use this variant if you want to:

Load the model with HuggingFace transformers
Continue fine-tuning on your private codebase
Convert to ONNX or another deployment format
Integrate into a framework that does not support GGUF

If you want to run the model for inference on developer hardware, use the GGUF variant with Ollama or llama.cpp instead.

Full documentation

Training details, dataset composition, evaluation, limitations, and citation are all in the main model card:

https://huggingface.co/BrainboxAI/code-il-E4B

Quick usage

python

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BrainboxAI/code-il-E4B-safetensors")
model = AutoModelForCausalLM.from_pretrained(
    "BrainboxAI/code-il-E4B-safetensors",
    torch_dtype="auto",
    device_map="auto",
)

messages = [
    {"role": "user", "content": "Implement binary search in TypeScript with full edge-case handling."},
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs, max_new_tokens=1024, temperature=0.2, top_p=0.95)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Continued fine-tuning

This is the right variant to use if you want to further fine-tune the model on your company's internal codebase — starting from code-il-E4B-safetensors preserves the coding behavior already baked in, while letting you layer in domain-specific patterns.

License

Apache 2.0.

Author

Built by Netanel Elyasi, founder of BrainboxAI.

For custom coding-model fine-tuning on private corpora, contact: netanele@brainboxai.io.