Instructions to use prithivMLmods/OneJev-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/OneJev-9B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="prithivMLmods/OneJev-9B-GGUF")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/OneJev-9B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/OneJev-9B-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 prithivMLmods/OneJev-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/OneJev-9B-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 prithivMLmods/OneJev-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/OneJev-9B-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 prithivMLmods/OneJev-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/OneJev-9B-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 prithivMLmods/OneJev-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/OneJev-9B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/OneJev-9B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/OneJev-9B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/OneJev-9B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/OneJev-9B-GGUF",
		"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/prithivMLmods/OneJev-9B-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/OneJev-9B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/OneJev-9B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/OneJev-9B-GGUF",
		"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 images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/OneJev-9B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/OneJev-9B-GGUF",
		"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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use prithivMLmods/OneJev-9B-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/OneJev-9B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use prithivMLmods/OneJev-9B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/OneJev-9B-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": "prithivMLmods/OneJev-9B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use prithivMLmods/OneJev-9B-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/OneJev-9B-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/OneJev-9B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/OneJev-9B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.OneJev-9B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use prithivMLmods/OneJev-9B-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 prithivMLmods/OneJev-9B-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 prithivMLmods/OneJev-9B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use prithivMLmods/OneJev-9B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/OneJev-9B-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 "prithivMLmods/OneJev-9B-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"

prithivMLmods commited on 6 days ago

Commit

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1 Parent(s): d5e8024

Create README.md

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	@@ -0,0 +1,42 @@

+---
+tags:
+- text-generation-inference
+- llama-cpp
+- onejev
+- system-one
+- decision-model
+- calibration
+- multimodal
+- gui-agent
+- video
+base_model:
+- OmniJev/OneJev-9B
+license: apache-2.0
+language:
+- en
+pipeline_tag: image-text-to-text
+library_name: transformers
+---
+# **OneJev-9B-GGUF**
+> **OneJev-9B** is the 9-billion-parameter model of OmniJev's OneJev family, a multimodal "System One" decision model built as a full fine-tune of Qwen3.5-9B on 99,193 questions drawn from real agent runs, videos, and images. It accepts a screenshot, photo, video, or plain text along with a few typed questions, such as `Noul` yes/no or `Choice` over labeled options, and returns a calibrated probability for every option in a single forward pass, with no text generation. It is served with the `qev` package, which speaks TypeSafe's System One API and adds a `media` field for images and video. On one H200 with a 1280x720 screenshot, it answers a single question in 81 ms, or 10 questions in one request in 131 ms (13.1 ms per question), which is somewhat slower than the 4B sibling's 64 ms and 104 ms. Its results are reported against Jev 1.13, Jev-Omni 12B, and Qwen3.8-27B in thinking mode on a held-out OneJev test set, but the card shows them only as a graphic, so the individual scores are not readable from the text. The card notes only that Jev 1.13's figures are its published ones and that it reads text only. OneJev-9B (18.8 GB of weights) sits in the middle of a family that runs from OneJev-0.8B to OneJev-27B and an FP8 build of it, and it is released under Apache 2.0.
+## Model Files
+| File Name | Quant Type | File Size | File Link | Description |
+|-----------|------------|-----------|-----------|-------------|
+| OneJev-9B.BF16.gguf | BF16 | 17.9 GB | [Link](https://huggingface.co/prithivMLmods/OneJev-9B-GGUF/blob/main/OneJev-9B.BF16.gguf) | Full BF16 weights. Highest quality, largest file size. |
+| OneJev-9B.Q3_K_L.gguf | Q3_K_L | 4.93 GB | [Link](https://huggingface.co/prithivMLmods/OneJev-9B-GGUF/blob/main/OneJev-9B.Q3_K_L.gguf) | Lower quality but usable, good for low RAM availability. |
+| OneJev-9B.Q3_K_M.gguf | Q3_K_M | 4.62 GB | [Link](https://huggingface.co/prithivMLmods/OneJev-9B-GGUF/blob/main/OneJev-9B.Q3_K_M.gguf) | Low quality. |
+| OneJev-9B.Q4_K_M.gguf | Q4_K_M | 5.63 GB | [Link](https://huggingface.co/prithivMLmods/OneJev-9B-GGUF/blob/main/OneJev-9B.Q4_K_M.gguf) | Good quality, default size for most use cases, *recommended*. |
+| OneJev-9B.Q4_K_S.gguf | Q4_K_S | 5.35 GB | [Link](https://huggingface.co/prithivMLmods/OneJev-9B-GGUF/blob/main/OneJev-9B.Q4_K_S.gguf) | Slightly lower quality with more space savings, *recommended*. |
+| OneJev-9B.Q5_K_M.gguf | Q5_K_M | 6.47 GB | [Link](https://huggingface.co/prithivMLmods/OneJev-9B-GGUF/blob/main/OneJev-9B.Q5_K_M.gguf) | High quality, *recommended*. |
+| OneJev-9B.Q5_K_S.gguf | Q5_K_S | 6.31 GB | [Link](https://huggingface.co/prithivMLmods/OneJev-9B-GGUF/blob/main/OneJev-9B.Q5_K_S.gguf) | High quality, *recommended*. |
+| OneJev-9B.Q6_K.gguf | Q6_K | 7.36 GB | [Link](https://huggingface.co/prithivMLmods/OneJev-9B-GGUF/blob/main/OneJev-9B.Q6_K.gguf) | Very high quality, near perfect, *recommended*. |
+| OneJev-9B.Q8_0.gguf | Q8_0 | 9.53 GB | [Link](https://huggingface.co/prithivMLmods/OneJev-9B-GGUF/blob/main/OneJev-9B.Q8_0.gguf) | Extremely high quality, generally unneeded but max available quant. |
+| OneJev-9B.mmproj-bf16.gguf | mmproj-bf16 | 922 MB | [Link](https://huggingface.co/prithivMLmods/OneJev-9B-GGUF/blob/main/OneJev-9B.mmproj-bf16.gguf) | Multimodal projection file in BF16 format. Used for vision/language models. |
+## llama.cpp
+LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp