Instructions to use mradermacher/OneJev-0.8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/OneJev-0.8B-GGUF with Transformers:

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/OneJev-0.8B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mradermacher/OneJev-0.8B-GGUF 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 mradermacher/OneJev-0.8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/OneJev-0.8B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mradermacher/OneJev-0.8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/OneJev-0.8B-GGUF: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 mradermacher/OneJev-0.8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/OneJev-0.8B-GGUF: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 mradermacher/OneJev-0.8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/OneJev-0.8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/OneJev-0.8B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/OneJev-0.8B-GGUF with Ollama:
```
ollama run hf.co/mradermacher/OneJev-0.8B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use mradermacher/OneJev-0.8B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mradermacher/OneJev-0.8B-GGUF: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": "mradermacher/OneJev-0.8B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use mradermacher/OneJev-0.8B-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/OneJev-0.8B-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/OneJev-0.8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/OneJev-0.8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.OneJev-0.8B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use mradermacher/OneJev-0.8B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mradermacher/OneJev-0.8B-GGUF: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 mradermacher/OneJev-0.8B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mradermacher/OneJev-0.8B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mradermacher/OneJev-0.8B-GGUF: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 "mradermacher/OneJev-0.8B-GGUF: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"

mradermacher commited on 7 days ago

Commit

75b9eeb

verified ·

1 Parent(s): 3a95282

auto-patch README.md

Browse files

Files changed (1) hide show

README.md +12 -0

README.md CHANGED Viewed

@@ -47,6 +47,18 @@ more details, including on how to concatenate multi-part files.
 |:-----|:-----|--------:|:------|
 | [GGUF](https://huggingface.co/mradermacher/OneJev-0.8B-GGUF/resolve/main/OneJev-0.8B.mmproj-Q8_0.gguf) | mmproj-Q8_0 | 0.2 | multi-modal supplement |
 | [GGUF](https://huggingface.co/mradermacher/OneJev-0.8B-GGUF/resolve/main/OneJev-0.8B.mmproj-f16.gguf) | mmproj-f16 | 0.3 | multi-modal supplement |
 Here is a handy graph by ikawrakow comparing some lower-quality quant
 types (lower is better):

 |:-----|:-----|--------:|:------|
 | [GGUF](https://huggingface.co/mradermacher/OneJev-0.8B-GGUF/resolve/main/OneJev-0.8B.mmproj-Q8_0.gguf) | mmproj-Q8_0 | 0.2 | multi-modal supplement |
 | [GGUF](https://huggingface.co/mradermacher/OneJev-0.8B-GGUF/resolve/main/OneJev-0.8B.mmproj-f16.gguf) | mmproj-f16 | 0.3 | multi-modal supplement |
+| [GGUF](https://huggingface.co/mradermacher/OneJev-0.8B-GGUF/resolve/main/OneJev-0.8B.Q2_K.gguf) | Q2_K | 0.6 |  |
+| [GGUF](https://huggingface.co/mradermacher/OneJev-0.8B-GGUF/resolve/main/OneJev-0.8B.Q3_K_S.gguf) | Q3_K_S | 0.6 |  |
+| [GGUF](https://huggingface.co/mradermacher/OneJev-0.8B-GGUF/resolve/main/OneJev-0.8B.Q3_K_M.gguf) | Q3_K_M | 0.7 | lower quality |
+| [GGUF](https://huggingface.co/mradermacher/OneJev-0.8B-GGUF/resolve/main/OneJev-0.8B.Q3_K_L.gguf) | Q3_K_L | 0.7 |  |
+| [GGUF](https://huggingface.co/mradermacher/OneJev-0.8B-GGUF/resolve/main/OneJev-0.8B.IQ4_XS.gguf) | IQ4_XS | 0.7 |  |
+| [GGUF](https://huggingface.co/mradermacher/OneJev-0.8B-GGUF/resolve/main/OneJev-0.8B.Q4_K_S.gguf) | Q4_K_S | 0.7 | fast, recommended |
+| [GGUF](https://huggingface.co/mradermacher/OneJev-0.8B-GGUF/resolve/main/OneJev-0.8B.Q4_K_M.gguf) | Q4_K_M | 0.8 | fast, recommended |
+| [GGUF](https://huggingface.co/mradermacher/OneJev-0.8B-GGUF/resolve/main/OneJev-0.8B.Q5_K_S.gguf) | Q5_K_S | 0.8 |  |
+| [GGUF](https://huggingface.co/mradermacher/OneJev-0.8B-GGUF/resolve/main/OneJev-0.8B.Q5_K_M.gguf) | Q5_K_M | 0.9 |  |
+| [GGUF](https://huggingface.co/mradermacher/OneJev-0.8B-GGUF/resolve/main/OneJev-0.8B.Q6_K.gguf) | Q6_K | 0.9 | very good quality |
+| [GGUF](https://huggingface.co/mradermacher/OneJev-0.8B-GGUF/resolve/main/OneJev-0.8B.Q8_0.gguf) | Q8_0 | 1.2 | fast, best quality |
+| [GGUF](https://huggingface.co/mradermacher/OneJev-0.8B-GGUF/resolve/main/OneJev-0.8B.f16.gguf) | f16 | 2.1 | 16 bpw, overkill |
 Here is a handy graph by ikawrakow comparing some lower-quality quant
 types (lower is better):