Instructions to use Finn-Technologies/e1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Finn-Technologies/e1 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("google/gemma-4-E4B-it-qat-q4_0-unquantized")
model = PeftModel.from_pretrained(base_model, "Finn-Technologies/e1")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Finn-Technologies/e1 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 Finn-Technologies/e1:BF16
# Run inference directly in the terminal:
llama cli -hf Finn-Technologies/e1:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Finn-Technologies/e1:BF16
# Run inference directly in the terminal:
llama cli -hf Finn-Technologies/e1:BF16

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 Finn-Technologies/e1:BF16
# Run inference directly in the terminal:
./build/bin/llama cli -hf Finn-Technologies/e1:BF16

Use Docker

docker model run hf.co/Finn-Technologies/e1:BF16

Llama new
LM Studio
Jan
Ollama
How to use Finn-Technologies/e1 with Ollama:
```
ollama run hf.co/Finn-Technologies/e1:BF16
```
Unsloth Desktop

How to use Finn-Technologies/e1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Finn-Technologies/e1:BF16

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": "Finn-Technologies/e1:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Finn-Technologies/e1 with Docker Model Runner:
```
docker model run hf.co/Finn-Technologies/e1:BF16
```

Lemonade

How to use Finn-Technologies/e1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Finn-Technologies/e1:BF16

Run and chat with the model

lemonade run user.e1-BF16

List all available models

lemonade list

Hermes Agent

How to use Finn-Technologies/e1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Finn-Technologies/e1: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 Finn-Technologies/e1:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Finn-Technologies/e1 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Finn-Technologies/e1:BF16

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 "Finn-Technologies/e1:BF16" \
  --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"

e1

Fine-tuned from google/gemma-4-E4B-it-qat-q4_0-unquantized.

Description

e1 is an AI model created by Finn Organization. It is fine-tuned on a diverse dataset covering mathematics, physics, computer science, biology, chemistry, philosophy, linguistics, and more.

Usage

from unsloth import FastLanguageModel
from peft import PeftModel

model, processor = FastLanguageModel.from_pretrained(
    "google/gemma-4-E4B-it-qat-q4_0-unquantized",
    max_seq_length=2048,
    load_in_4bit=True,
)
model = PeftModel.from_pretrained(model, "Finn-Technologies/e1")

messages = [
    {"role": "system", "content": "You are e1, an AI model created by Finn Organization."},
    {"role": "user", "content": "Your prompt here"},
]
text = processor.tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True)
outputs = model.generate(**processor(text, return_tensors="pt"), max_new_tokens=200)
print(processor.decode(outputs[0]))

Training

Base model: Gemma 4 E4B QAT (4-bit quantized)
Method: QLoRA (r=16, target_modules=[q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj])
Train loss: 1.09
Eval loss: 1.02

Downloads last month: 37

GGUF

Model size

7B params

Architecture

gemma4

Hardware compatibility

4-bit

16-bit

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