Instructions to use BrainboxAI/code-il-E4B-safetensors with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BrainboxAI/code-il-E4B-safetensors with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="BrainboxAI/code-il-E4B-safetensors")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("BrainboxAI/code-il-E4B-safetensors")
model = AutoModelForImageTextToText.from_pretrained("BrainboxAI/code-il-E4B-safetensors")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use BrainboxAI/code-il-E4B-safetensors with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BrainboxAI/code-il-E4B-safetensors"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BrainboxAI/code-il-E4B-safetensors",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BrainboxAI/code-il-E4B-safetensors

SGLang

How to use BrainboxAI/code-il-E4B-safetensors with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "BrainboxAI/code-il-E4B-safetensors" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BrainboxAI/code-il-E4B-safetensors",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "BrainboxAI/code-il-E4B-safetensors" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BrainboxAI/code-il-E4B-safetensors",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use BrainboxAI/code-il-E4B-safetensors with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for BrainboxAI/code-il-E4B-safetensors to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for BrainboxAI/code-il-E4B-safetensors to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for BrainboxAI/code-il-E4B-safetensors to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="BrainboxAI/code-il-E4B-safetensors",
    max_seq_length=2048,
)

Docker Model Runner
How to use BrainboxAI/code-il-E4B-safetensors with Docker Model Runner:
```
docker model run hf.co/BrainboxAI/code-il-E4B-safetensors
```

code-il-E4B-safetensors / README.md

BrainboxAI

Pro card: clean reference to main GGUF variant, transformers usage, fine-tuning recipe

3896406 verified about 1 month ago

preview code

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metadata

language:
  - en
  - he
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
base_model: unsloth/gemma-4-E4B-it
datasets:
  - BrainboxAI/code-training-il
  - nvidia/OpenCodeInstruct
  - bleugreen/typescript-instruct
tags:
  - code
  - python
  - typescript
  - coding-assistant
  - safetensors
  - gemma4
  - unsloth
  - qlora
  - on-device
  - private-first
pretty_name: Code-IL E4B (Safetensors)

Code-IL E4B — Safetensors

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

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.