Instructions to use prithivMLmods/FaithEyes-7B-RL-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/FaithEyes-7B-RL-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/FaithEyes-7B-RL-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/FaithEyes-7B-RL-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/FaithEyes-7B-RL-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/FaithEyes-7B-RL-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/FaithEyes-7B-RL-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/FaithEyes-7B-RL-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/FaithEyes-7B-RL-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/FaithEyes-7B-RL-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/FaithEyes-7B-RL-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/FaithEyes-7B-RL-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/FaithEyes-7B-RL-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/FaithEyes-7B-RL-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/FaithEyes-7B-RL-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/FaithEyes-7B-RL-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/FaithEyes-7B-RL-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/FaithEyes-7B-RL-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/FaithEyes-7B-RL-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/FaithEyes-7B-RL-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/FaithEyes-7B-RL-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/FaithEyes-7B-RL-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/FaithEyes-7B-RL-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/FaithEyes-7B-RL-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/FaithEyes-7B-RL-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use prithivMLmods/FaithEyes-7B-RL-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/FaithEyes-7B-RL-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/FaithEyes-7B-RL-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/FaithEyes-7B-RL-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.FaithEyes-7B-RL-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

FaithEyes-7B-RL-GGUF

FaithEyes-7B-RL is the final reinforcement-learning checkpoint of FaithEyes on Qwen2.5-VL-7B-Instruct, obtained by running GRPO on top of the FaithEyes-7B-SFT cold-start model, and is the primary model reported in the paper "FaithEyes: Towards Faithful Tool Use via Multi-Agent Process-Image Self-Verification." FaithEyes is a multi-agent self-judging framework in which a single VLM plays two roles: a main agent that solves visual questions by interleaving reasoning with executable code-based tool calls (like image crops), and a subagent — instantiated by the same model under a separate prompt, requiring no external model dependency — that judges whether each process image the main agent produces is actually helpful, with that verdict both injected into the tool observation to steer subsequent reasoning and used to scale the tool reward. The RL stage combines four reward terms (accuracy, format, consistency, and an accuracy-independent tool-faithfulness reward that credits helpful, executable calls and penalizes failed ones) to push the model from merely imitating demonstrations toward autonomously producing genuinely faithful rather than decorative tool calls, while keeping code-failure ratio and tool usage stable throughout training (an accuracy-gated variant was found to degenerate into tool avoidance). Training built on the verl framework alongside DeepEyes, Thyme, ms_swift, and VLMEvalKit, and the model is released under the Apache 2.0 license. FaithEyes-7B-RL on Hugging Face

Model Files

File Name	Quant Type	File Size	File Link	Description
FaithEyes-7B-RL.BF16.gguf	BF16	15.2 GB	Link	Full BF16 weights. Highest quality, largest file size.
FaithEyes-7B-RL.Q3_K_L.gguf	Q3_K_L	4.09 GB	Link	Lower quality but usable, good for low RAM availability.
FaithEyes-7B-RL.Q3_K_M.gguf	Q3_K_M	3.81 GB	Link	Low quality.
FaithEyes-7B-RL.Q4_K_M.gguf	Q4_K_M	4.68 GB	Link	Good quality, default size for most use cases, recommended.
FaithEyes-7B-RL.Q4_K_S.gguf	Q4_K_S	4.46 GB	Link	Slightly lower quality with more space savings, recommended.
FaithEyes-7B-RL.Q5_K_M.gguf	Q5_K_M	5.44 GB	Link	High quality, recommended.
FaithEyes-7B-RL.Q5_K_S.gguf	Q5_K_S	5.32 GB	Link	High quality, recommended.
FaithEyes-7B-RL.mmproj-bf16.gguf	mmproj-bf16	1.36 GB	Link	Multimodal projection file in BF16 format. Used for vision/language models.