Image-Text-to-Text
Safetensors
GGUF
Portuguese
English
gemma4
code
vision
multimodal
software-engineering
react-native
android
typescript
python
unsloth
conversational
Instructions to use SpaceGhost/SpaceCode-E2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use SpaceGhost/SpaceCode-E2B with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf SpaceGhost/SpaceCode-E2B:Q4_K_M # Run inference directly in the terminal: llama cli -hf SpaceGhost/SpaceCode-E2B:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf SpaceGhost/SpaceCode-E2B:Q4_K_M # Run inference directly in the terminal: llama cli -hf SpaceGhost/SpaceCode-E2B:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf SpaceGhost/SpaceCode-E2B:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf SpaceGhost/SpaceCode-E2B:Q4_K_M
Use Docker
docker model run hf.co/SpaceGhost/SpaceCode-E2B:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use SpaceGhost/SpaceCode-E2B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SpaceGhost/SpaceCode-E2B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SpaceGhost/SpaceCode-E2B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/SpaceGhost/SpaceCode-E2B:Q4_K_M
- Ollama
How to use SpaceGhost/SpaceCode-E2B with Ollama:
ollama run hf.co/SpaceGhost/SpaceCode-E2B:Q4_K_M
- Unsloth Desktop
- Pi
How to use SpaceGhost/SpaceCode-E2B with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf SpaceGhost/SpaceCode-E2B:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "SpaceGhost/SpaceCode-E2B:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use SpaceGhost/SpaceCode-E2B with Docker Model Runner:
docker model run hf.co/SpaceGhost/SpaceCode-E2B:Q4_K_M
- Lemonade
How to use SpaceGhost/SpaceCode-E2B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull SpaceGhost/SpaceCode-E2B:Q4_K_M
Run and chat with the model
lemonade run user.SpaceCode-E2B-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use SpaceGhost/SpaceCode-E2B with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf SpaceGhost/SpaceCode-E2B:Q4_K_M
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 SpaceGhost/SpaceCode-E2B:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use SpaceGhost/SpaceCode-E2B with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf SpaceGhost/SpaceCode-E2B:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "SpaceGhost/SpaceCode-E2B:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Release v2 LoRA adapter (pedagogically validated: 88% 50-MCQ, 95.2% composite score, 0 quiz-leakage)
53efac5 verified Download lora/adapter_config.json from SpaceGhost/SpaceCode-E2B: direct link, hf CLI and curl.
- Browser
- Download file 1.76 kB
-
https://huggingface.co/SpaceGhost/SpaceCode-E2B/resolve/main/lora/adapter_config.json
- Command line
-
hf download hf://SpaceGhost/SpaceCode-E2B/lora/adapter_config.json
-
curl -L -o adapter_config.json https://huggingface.co/SpaceGhost/SpaceCode-E2B/resolve/main/lora/adapter_config.json
1.76 kB
| { | |
| "alora_invocation_tokens": null, | |
| "alpha_pattern": {}, | |
| "arrow_config": null, | |
| "auto_mapping": { | |
| "base_model_class": "Gemma4ForConditionalGeneration", | |
| "parent_library": "transformers.models.gemma4.modeling_gemma4", | |
| "unsloth_fixed": true | |
| }, | |
| "base_model_name_or_path": "SpaceGhost/SpaceCode-E2B", | |
| "bias": "none", | |
| "corda_config": null, | |
| "ensure_weight_tying": false, | |
| "eva_config": null, | |
| "exclude_modules": null, | |
| "fan_in_fan_out": false, | |
| "inference_mode": true, | |
| "init_lora_weights": true, | |
| "kasa_config": null, | |
| "layer_replication": null, | |
| "layers_pattern": null, | |
| "layers_to_transform": null, | |
| "loftq_config": {}, | |
| "lora_alpha": 16, | |
| "lora_bias": false, | |
| "lora_dropout": 0.05, | |
| "lora_ga_config": null, | |
| "megatron_config": null, | |
| "megatron_core": "megatron.core", | |
| "modules_to_save": null, | |
| "monteclora_config": null, | |
| "peft_type": "LORA", | |
| "peft_version": "0.21.2", | |
| "qalora_group_size": 16, | |
| "r": 16, | |
| "rank_pattern": {}, | |
| "revision": null, | |
| "target_modules": "(?:.*?(?:language|text).*?(?:self_attn|attention|attn|mixer|mlp|feed_forward|ffn|dense|mixer).*?\\.(?:linear|q_proj|k_proj|v_proj|o_proj|gate_proj|up_proj|down_proj|per_layer_input_gate|per_layer_projection|relative_k_proj|embedding_projection))|(?:\\bmodel\\.layers\\.[\\d]{1,}(?!.*\\.experts\\.\\d)\\..*?(?:self_attn|attention|attn|mixer|mlp|feed_forward|ffn|dense|mixer).*?\\.(?:(?:linear|q_proj|k_proj|v_proj|o_proj|gate_proj|up_proj|down_proj|per_layer_input_gate|per_layer_projection|relative_k_proj|embedding_projection)))", | |
| "target_parameters": null, | |
| "task_type": "CAUSAL_LM", | |
| "trainable_token_indices": null, | |
| "use_bdlora": null, | |
| "use_dora": false, | |
| "use_qalora": false, | |
| "use_rslora": false, | |
| "velora_config": null | |
| } |