Instructions to use fevohh/GenParser-1B-v1-iter4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fevohh/GenParser-1B-v1-iter4 with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("fevohh/GenParser-1B-v1-iter4", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use fevohh/GenParser-1B-v1-iter4 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 fevohh/GenParser-1B-v1-iter4:F16
# Run inference directly in the terminal:
llama cli -hf fevohh/GenParser-1B-v1-iter4:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf fevohh/GenParser-1B-v1-iter4:F16
# Run inference directly in the terminal:
llama cli -hf fevohh/GenParser-1B-v1-iter4:F16

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 fevohh/GenParser-1B-v1-iter4:F16
# Run inference directly in the terminal:
./llama-cli -hf fevohh/GenParser-1B-v1-iter4:F16

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 fevohh/GenParser-1B-v1-iter4:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf fevohh/GenParser-1B-v1-iter4:F16

Use Docker

docker model run hf.co/fevohh/GenParser-1B-v1-iter4:F16

LM Studio
Jan
Ollama
How to use fevohh/GenParser-1B-v1-iter4 with Ollama:
```
ollama run hf.co/fevohh/GenParser-1B-v1-iter4:F16
```

Unsloth Studio

How to use fevohh/GenParser-1B-v1-iter4 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 fevohh/GenParser-1B-v1-iter4 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 fevohh/GenParser-1B-v1-iter4 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for fevohh/GenParser-1B-v1-iter4 to start chatting

How to use fevohh/GenParser-1B-v1-iter4 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf fevohh/GenParser-1B-v1-iter4:F16

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": "fevohh/GenParser-1B-v1-iter4:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use fevohh/GenParser-1B-v1-iter4 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf fevohh/GenParser-1B-v1-iter4:F16

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 "fevohh/GenParser-1B-v1-iter4:F16" \
  --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"

Docker Model Runner
How to use fevohh/GenParser-1B-v1-iter4 with Docker Model Runner:
```
docker model run hf.co/fevohh/GenParser-1B-v1-iter4:F16
```

Lemonade

How to use fevohh/GenParser-1B-v1-iter4 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull fevohh/GenParser-1B-v1-iter4:F16

Run and chat with the model

lemonade run user.GenParser-1B-v1-iter4-F16

List all available models

lemonade list

Hermes Agent

How to use fevohh/GenParser-1B-v1-iter4 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf fevohh/GenParser-1B-v1-iter4:F16

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 fevohh/GenParser-1B-v1-iter4:F16

Run Hermes

hermes

Atomic Chat

decent model (q8), surpasses iter1 and 2 (gna ignore iter3, completely useless) in terms of extracting more items from user listing, but is still prone to extracting the same item mentioned repeatedly. still not as good as 3b, but fast (4x faster, 69t/s in 1b and 17t/s in 3b running on 3050 laptop) and reliable enough to perform simple extraction for a listing with less than 5 items and no world name

Uploaded model

Developed by: fevohh
License: apache-2.0
Finetuned from model : unsloth/Llama-3.2-1B-Instruct-unsloth-bnb-4bit

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 22

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

8-bit

16-bit

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