Instructions to use keisuke-miyako/springsea-0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use keisuke-miyako/springsea-0.1 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="keisuke-miyako/springsea-0.1", filename="gemma-4-e2b-it.Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use keisuke-miyako/springsea-0.1 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf keisuke-miyako/springsea-0.1:Q4_K_M # Run inference directly in the terminal: llama-cli -hf keisuke-miyako/springsea-0.1:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf keisuke-miyako/springsea-0.1:Q4_K_M # Run inference directly in the terminal: llama-cli -hf keisuke-miyako/springsea-0.1:Q4_K_M
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 keisuke-miyako/springsea-0.1:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf keisuke-miyako/springsea-0.1:Q4_K_M
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 keisuke-miyako/springsea-0.1:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf keisuke-miyako/springsea-0.1:Q4_K_M
Use Docker
docker model run hf.co/keisuke-miyako/springsea-0.1:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use keisuke-miyako/springsea-0.1 with Ollama:
ollama run hf.co/keisuke-miyako/springsea-0.1:Q4_K_M
- Unsloth Studio new
How to use keisuke-miyako/springsea-0.1 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 keisuke-miyako/springsea-0.1 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 keisuke-miyako/springsea-0.1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for keisuke-miyako/springsea-0.1 to start chatting
- Pi new
How to use keisuke-miyako/springsea-0.1 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf keisuke-miyako/springsea-0.1:Q4_K_M
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": "keisuke-miyako/springsea-0.1:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use keisuke-miyako/springsea-0.1 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf keisuke-miyako/springsea-0.1: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 keisuke-miyako/springsea-0.1:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use keisuke-miyako/springsea-0.1 with Docker Model Runner:
docker model run hf.co/keisuke-miyako/springsea-0.1:Q4_K_M
- Lemonade
How to use keisuke-miyako/springsea-0.1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull keisuke-miyako/springsea-0.1:Q4_K_M
Run and chat with the model
lemonade run user.springsea-0.1-Q4_K_M
List all available models
lemonade list
Overview
SpringSea is a fine-tuned version of google/gemma-4-E2B-it, specialized for generating, completing, and reasoning about 4D code — the proprietary language powering 4D application development.
The goal of this project is to develop a coding assistant model that understands the full 4D development ecosystem.
Training Details
| Property | Details |
|---|---|
| Base Model | google/gemma-4-E2B-it |
| Fine-tune Method | LoRA |
| Primary Language | 4D |
| Context Window | 128K tokens |
| License | Apache 2.0 |
| Type | Rows | Size | LoRA rank | LoRA alpha | Warmup ratio | Learning rate | Epochs | Steps | Duration |
|---|---|---|---|---|---|---|---|---|---|
| Synthetic ChatML | 4750 | 15.51 MB | 32 | 64 | 0.05 | 5e-5 | 2 | 2:20:49 | 594 |
Changes
- Rank:
64→32 - Learning rate:
1e-4→5e-5 - Epoch:
3→2
0.1.6 Hallucinates; FAIL💥
| Type | Rows | Size | LoRA rank | LoRA alpha | Warmup ratio | Learning rate | Epochs | Steps | Duration |
|---|---|---|---|---|---|---|---|---|---|
| Synthetic ChatML | 3731 | 11.81 MB | 32 | 128 | 0.05 | 1e-4 | 3 | 702 | 2:42:32 |
0.1.5
| Type | Rows | Size | LoRA rank | LoRA alpha | Warmup ratio | Learning rate | Epochs | Steps | Duration |
|---|---|---|---|---|---|---|---|---|---|
| Synthetic ChatML | 3106 | 7.49 MB | 64 | 128 | 0.05 | 1e-4 | 3 | 585 | 2:17:01 |
0.1.4
| Type | Rows | Size | LoRA rank | LoRA alpha | Learning rate | Epochs | Steps |
|---|---|---|---|---|---|---|---|
| Synthetic ChatML | 1645 | 4.42 MB | 64 | 128 | 1e-4 | 3 | 309 |
0.1.3
| Type | Rows | Size | LoRA rank | LoRA alpha | Learning rate | Epochs | Steps |
|---|---|---|---|---|---|---|---|
| Synthetic ChatML | 1201 | 3.03 MB | 64 | 128 | 1e-4 | 3 | 228 |
0.1.2
| Type | Rows | Size | LoRA rank | LoRA alpha | Learning rate | Epochs | Steps |
|---|---|---|---|---|---|---|---|
| Synthetic ChatML | 953 | 1.53 MB | 64 | 128 | 1e-4 | 3 | 180 |
0.1.1
| Type | Rows | Size | LoRA rank | LoRA alpha | Learning rate | Epochs | Steps |
|---|---|---|---|---|---|---|---|
| Synthetic ChatML | 1786 | 1.16 MB | 64 | 128 | 2e-5 | 2 | 894 |
0.1.0
| Type | Rows | Size | LoRA rank | LoRA alpha | Learning rate | Epochs | Steps |
|---|---|---|---|---|---|---|---|
| Synthetic ChatML | 1786 | 1.16 MB | 64 | 128 | 2e-5 | 1 | 447 |
Instructions
Use this system prompt verbatim:
You are "LEPECQ-0.0.1", an expert of the 4D (4th Dimension) programming language and application development tool.
Followed by a user prompt about 4D.
The models should gravitate towards domain specific thinking when the conversation is about 4D.
This is because the model was fine-tuned using the pattern below:
# system
You are "LEPECQ-0.0.1", an expert of the 4D (4th Dimension) programming language and application development tool.
# user
I have a [Contacts] table with 80k rows. I need all contacts from Paris sorted by last name. Is there a faster way than looping with a classic query?
# model
<|think|>
This is an ORDA vs classic query performance question, INTERMEDIATE tier. The gotcha is that orderBy() on an entity selection is lazy and deferred, so I should show the full chain and also m
Limitations
- Outputs should be reviewed by a 4D developer before use in production
License & Attribution
This model is released under the Apache 2.0 License.
Built on google/gemma-4-E2B-it by Google DeepMind, also Apache 2.0.
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