Text Generation
Transformers
GGUF
English
Hebrew
gemma4
image-text-to-text
code
python
typescript
coding-assistant
llama.cpp
ollama
unsloth
qlora
on-device
private-first
conversational
Instructions to use BrainboxAI/code-il-E4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BrainboxAI/code-il-E4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="BrainboxAI/code-il-E4B") 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") model = AutoModelForImageTextToText.from_pretrained("BrainboxAI/code-il-E4B") - llama-cpp-python
How to use BrainboxAI/code-il-E4B with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="BrainboxAI/code-il-E4B", filename="gemma-4-e4b-it.BF16-mmproj.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use BrainboxAI/code-il-E4B with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf BrainboxAI/code-il-E4B:BF16 # Run inference directly in the terminal: llama-cli -hf BrainboxAI/code-il-E4B:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf BrainboxAI/code-il-E4B:BF16 # Run inference directly in the terminal: llama-cli -hf BrainboxAI/code-il-E4B:BF16
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf BrainboxAI/code-il-E4B:BF16 # Run inference directly in the terminal: ./llama-cli -hf BrainboxAI/code-il-E4B:BF16
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf BrainboxAI/code-il-E4B:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf BrainboxAI/code-il-E4B:BF16
Use Docker
docker model run hf.co/BrainboxAI/code-il-E4B:BF16
- LM Studio
- Jan
- vLLM
How to use BrainboxAI/code-il-E4B 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" # 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", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/BrainboxAI/code-il-E4B:BF16
- SGLang
How to use BrainboxAI/code-il-E4B 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" \ --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", "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" \ --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", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use BrainboxAI/code-il-E4B with Ollama:
ollama run hf.co/BrainboxAI/code-il-E4B:BF16
- Unsloth Studio new
How to use BrainboxAI/code-il-E4B 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 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 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 to start chatting
- Pi new
How to use BrainboxAI/code-il-E4B with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf BrainboxAI/code-il-E4B:BF16
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "BrainboxAI/code-il-E4B:BF16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use BrainboxAI/code-il-E4B with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf BrainboxAI/code-il-E4B:BF16
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default BrainboxAI/code-il-E4B:BF16
Run Hermes
hermes
- Docker Model Runner
How to use BrainboxAI/code-il-E4B with Docker Model Runner:
docker model run hf.co/BrainboxAI/code-il-E4B:BF16
- Lemonade
How to use BrainboxAI/code-il-E4B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull BrainboxAI/code-il-E4B:BF16
Run and chat with the model
lemonade run user.code-il-E4B-BF16
List all available models
lemonade list
| { | |
| "architectures": [ | |
| "Gemma4ForConditionalGeneration" | |
| ], | |
| "audio_config": { | |
| "_name_or_path": "", | |
| "architectures": null, | |
| "attention_chunk_size": 12, | |
| "attention_context_left": 13, | |
| "attention_context_right": 0, | |
| "attention_invalid_logits_value": -1000000000.0, | |
| "attention_logit_cap": 50.0, | |
| "chunk_size_feed_forward": 0, | |
| "conv_kernel_size": 5, | |
| "torch_dtype": "bfloat16", | |
| "gradient_clipping": 10000000000.0, | |
| "hidden_act": "silu", | |
| "hidden_size": 1024, | |
| "id2label": { | |
| "0": "LABEL_0", | |
| "1": "LABEL_1" | |
| }, | |
| "initializer_range": 0.02, | |
| "is_encoder_decoder": false, | |
| "label2id": { | |
| "LABEL_0": 0, | |
| "LABEL_1": 1 | |
| }, | |
| "model_type": "gemma4_audio", | |
| "num_attention_heads": 8, | |
| "num_hidden_layers": 12, | |
| "output_attentions": false, | |
| "output_hidden_states": false, | |
| "output_proj_dims": 1536, | |
| "problem_type": null, | |
| "residual_weight": 0.5, | |
| "return_dict": true, | |
| "rms_norm_eps": 1e-06, | |
| "subsampling_conv_channels": [ | |
| 128, | |
| 32 | |
| ], | |
| "use_clipped_linears": true | |
| }, | |
| "audio_token_id": 258881, | |
| "boa_token_id": 256000, | |
| "boi_token_id": 255999, | |
| "torch_dtype": "bfloat16", | |
| "eoa_token_id": 258883, | |
| "eoa_token_index": 258883, | |
| "eoi_token_id": 258882, | |
| "eos_token_id": 106, | |
| "image_token_id": 258880, | |
| "initializer_range": 0.02, | |
| "model_name": "unsloth/gemma-4-e4b-it-unsloth-bnb-4bit", | |
| "model_type": "gemma4", | |
| "pad_token_id": 0, | |
| "text_config": { | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "attention_k_eq_v": false, | |
| "bos_token_id": 2, | |
| "torch_dtype": "bfloat16", | |
| "enable_moe_block": false, | |
| "eos_token_id": 1, | |
| "expert_intermediate_size": null, | |
| "final_logit_softcapping": 30.0, | |
| "global_head_dim": 512, | |
| "head_dim": 256, | |
| "hidden_activation": "gelu_pytorch_tanh", | |
| "hidden_size": 2560, | |
| "hidden_size_per_layer_input": 256, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 10240, | |
| "layer_types": [ | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention" | |
| ], | |
| "max_position_embeddings": 131072, | |
| "model_type": "gemma4_text", | |
| "moe_intermediate_size": null, | |
| "num_attention_heads": 8, | |
| "num_experts": null, | |
| "num_global_key_value_heads": null, | |
| "num_hidden_layers": 42, | |
| "num_key_value_heads": 2, | |
| "num_kv_shared_layers": 18, | |
| "pad_token_id": 0, | |
| "rms_norm_eps": 1e-06, | |
| "rope_parameters": { | |
| "full_attention": { | |
| "partial_rotary_factor": 0.25, | |
| "rope_theta": 1000000.0, | |
| "rope_type": "proportional" | |
| }, | |
| "sliding_attention": { | |
| "rope_theta": 10000.0, | |
| "rope_type": "default" | |
| } | |
| }, | |
| "sliding_window": 512, | |
| "tie_word_embeddings": true, | |
| "top_k_experts": null, | |
| "use_bidirectional_attention": null, | |
| "use_cache": true, | |
| "use_double_wide_mlp": false, | |
| "vocab_size": 262144, | |
| "vocab_size_per_layer_input": 262144 | |
| }, | |
| "tie_word_embeddings": true, | |
| "unsloth_fixed": true, | |
| "unsloth_version": "2026.4.6", | |
| "video_token_id": 258884, | |
| "vision_config": { | |
| "_name_or_path": "", | |
| "architectures": null, | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "chunk_size_feed_forward": 0, | |
| "default_output_length": 280, | |
| "torch_dtype": "bfloat16", | |
| "global_head_dim": 64, | |
| "head_dim": 64, | |
| "hidden_activation": "gelu_pytorch_tanh", | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "LABEL_0", | |
| "1": "LABEL_1" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "is_encoder_decoder": false, | |
| "label2id": { | |
| "LABEL_0": 0, | |
| "LABEL_1": 1 | |
| }, | |
| "max_position_embeddings": 131072, | |
| "model_type": "gemma4_vision", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 16, | |
| "num_key_value_heads": 12, | |
| "output_attentions": false, | |
| "output_hidden_states": false, | |
| "patch_size": 16, | |
| "pooling_kernel_size": 3, | |
| "position_embedding_size": 10240, | |
| "problem_type": null, | |
| "return_dict": true, | |
| "rms_norm_eps": 1e-06, | |
| "rope_parameters": { | |
| "rope_theta": 100.0, | |
| "rope_type": "default" | |
| }, | |
| "standardize": false, | |
| "use_clipped_linears": true | |
| }, | |
| "vision_soft_tokens_per_image": 280 | |
| } |