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

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	---
	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`](https://huggingface.co/BrainboxAI/code-il-E4B) — for HuggingFace Transformers, further fine-tuning, or conversion to other runtimes.

	[![GGUF](https://img.shields.io/badge/GGUF_variant-code--il--E4B-yellow)](https://huggingface.co/BrainboxAI/code-il-E4B)
	[![License](https://img.shields.io/badge/License-Apache_2.0-lightgrey)](https://www.apache.org/licenses/LICENSE-2.0)

	---

	## 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](https://huggingface.co/BrainboxAI/code-il-E4B) 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](https://huggingface.co/BrainboxAI), founder of [BrainboxAI](https://brainboxai.io).

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