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

Libraries

How to use prithivMLmods/chandra-OCR-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/chandra-OCR-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/chandra-OCR-GGUF", dtype="auto")

llama-cpp-python

How to use prithivMLmods/chandra-OCR-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/chandra-OCR-GGUF",
	filename="chandra-BF16.gguf",
)

llm.create_chat_completion(
	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"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use prithivMLmods/chandra-OCR-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/chandra-OCR-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/chandra-OCR-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/chandra-OCR-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/chandra-OCR-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/chandra-OCR-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/chandra-OCR-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/chandra-OCR-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/chandra-OCR-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/chandra-OCR-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/chandra-OCR-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/chandra-OCR-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/chandra-OCR-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/chandra-OCR-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/chandra-OCR-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/chandra-OCR-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/chandra-OCR-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/chandra-OCR-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/chandra-OCR-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/chandra-OCR-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/chandra-OCR-GGUF:Q4_K_M
```

Unsloth Studio new

How to use prithivMLmods/chandra-OCR-GGUF 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 prithivMLmods/chandra-OCR-GGUF 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 prithivMLmods/chandra-OCR-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/chandra-OCR-GGUF to start chatting

Pi new

How to use prithivMLmods/chandra-OCR-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf prithivMLmods/chandra-OCR-GGUF:Q4_K_M

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": "prithivMLmods/chandra-OCR-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use prithivMLmods/chandra-OCR-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf prithivMLmods/chandra-OCR-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/chandra-OCR-GGUF:Q4_K_M

Run Hermes

hermes

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

Lemonade

How to use prithivMLmods/chandra-OCR-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/chandra-OCR-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.chandra-OCR-GGUF-Q4_K_M

List all available models

lemonade list

ValueError: GGUF model with architecture qwen3vl is not supported yet.

by furquan - opened Dec 19, 2025

Discussion

furquan

Dec 19, 2025

Hi, just curious how you where able to run these quants? do I need llama.cpp?

$ vllm serve ./chandra-Q8_0.gguf --tokenizer datalab-to/chandra
/local/home/hfurquan/miniconda3/lib/python3.12/site-packages/transformers/utils/hub.py:110: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.
  warnings.warn(
INFO 12-19 10:43:36 [scheduler.py:216] Chunked prefill is enabled with max_num_batched_tokens=2048.
(APIServer pid=3163855) INFO 12-19 10:43:36 [api_server.py:1977] vLLM API server version 0.11.2
(APIServer pid=3163855) INFO 12-19 10:43:36 [utils.py:253] non-default args: {'model_tag': './chandra-Q8_0.gguf', 'model': './chandra-Q8_0.gguf', 'tokenizer': 'datalab-to/chandra'}
(APIServer pid=3163855) Traceback (most recent call last):
(APIServer pid=3163855)   File "/local/home/hfurquan/miniconda3/bin/vllm", line 8, in <module>
(APIServer pid=3163855)     sys.exit(main())
(APIServer pid=3163855)              ^^^^^^
(APIServer pid=3163855)   File "/local/home/hfurquan/miniconda3/lib/python3.12/site-packages/vllm/entrypoints/cli/main.py", line 73, in main
(APIServer pid=3163855)     args.dispatch_function(args)
(APIServer pid=3163855)   File "/local/home/hfurquan/miniconda3/lib/python3.12/site-packages/vllm/entrypoints/cli/serve.py", line 60, in cmd
(APIServer pid=3163855)     uvloop.run(run_server(args))
(APIServer pid=3163855)   File "/local/home/hfurquan/miniconda3/lib/python3.12/site-packages/uvloop/__init__.py", line 109, in run
(APIServer pid=3163855)     return __asyncio.run(
(APIServer pid=3163855)            ^^^^^^^^^^^^^^
(APIServer pid=3163855)   File "/local/home/hfurquan/miniconda3/lib/python3.12/asyncio/runners.py", line 194, in run
(APIServer pid=3163855)     return runner.run(main)
(APIServer pid=3163855)            ^^^^^^^^^^^^^^^^
(APIServer pid=3163855)   File "/local/home/hfurquan/miniconda3/lib/python3.12/asyncio/runners.py", line 118, in run
(APIServer pid=3163855)     return self._loop.run_until_complete(task)
(APIServer pid=3163855)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(APIServer pid=3163855)   File "uvloop/loop.pyx", line 1518, in uvloop.loop.Loop.run_until_complete
(APIServer pid=3163855)   File "/local/home/hfurquan/miniconda3/lib/python3.12/site-packages/uvloop/__init__.py", line 61, in wrapper
(APIServer pid=3163855)     return await main
(APIServer pid=3163855)            ^^^^^^^^^^
(APIServer pid=3163855)   File "/local/home/hfurquan/miniconda3/lib/python3.12/site-packages/vllm/entrypoints/openai/api_server.py", line 2024, in run_server
(APIServer pid=3163855)     await run_server_worker(listen_address, sock, args, **uvicorn_kwargs)
(APIServer pid=3163855)   File "/local/home/hfurquan/miniconda3/lib/python3.12/site-packages/vllm/entrypoints/openai/api_server.py", line 2043, in run_server_worker
(APIServer pid=3163855)     async with build_async_engine_client(
(APIServer pid=3163855)   File "/local/home/hfurquan/miniconda3/lib/python3.12/contextlib.py", line 210, in __aenter__
(APIServer pid=3163855)     return await anext(self.gen)
(APIServer pid=3163855)            ^^^^^^^^^^^^^^^^^^^^^
(APIServer pid=3163855)   File "/local/home/hfurquan/miniconda3/lib/python3.12/site-packages/vllm/entrypoints/openai/api_server.py", line 195, in build_async_engine_client
(APIServer pid=3163855)     async with build_async_engine_client_from_engine_args(
(APIServer pid=3163855)   File "/local/home/hfurquan/miniconda3/lib/python3.12/contextlib.py", line 210, in __aenter__
(APIServer pid=3163855)     return await anext(self.gen)
(APIServer pid=3163855)            ^^^^^^^^^^^^^^^^^^^^^
(APIServer pid=3163855)   File "/local/home/hfurquan/miniconda3/lib/python3.12/site-packages/vllm/entrypoints/openai/api_server.py", line 221, in build_async_engine_client_from_engine_args
(APIServer pid=3163855)     vllm_config = engine_args.create_engine_config(usage_context=usage_context)
(APIServer pid=3163855)                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(APIServer pid=3163855)   File "/local/home/hfurquan/miniconda3/lib/python3.12/site-packages/vllm/engine/arg_utils.py", line 1351, in create_engine_config
(APIServer pid=3163855)     maybe_override_with_speculators(
(APIServer pid=3163855)   File "/local/home/hfurquan/miniconda3/lib/python3.12/site-packages/vllm/transformers_utils/config.py", line 530, in maybe_override_with_speculators
(APIServer pid=3163855)     config_dict, _ = PretrainedConfig.get_config_dict(
(APIServer pid=3163855)                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(APIServer pid=3163855)   File "/local/home/hfurquan/miniconda3/lib/python3.12/site-packages/transformers/configuration_utils.py", line 662, in get_config_dict
(APIServer pid=3163855)     config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)
(APIServer pid=3163855)                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(APIServer pid=3163855)   File "/local/home/hfurquan/miniconda3/lib/python3.12/site-packages/transformers/configuration_utils.py", line 753, in _get_config_dict
(APIServer pid=3163855)     config_dict = load_gguf_checkpoint(resolved_config_file, return_tensors=False)["config"]
(APIServer pid=3163855)                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(APIServer pid=3163855)   File "/local/home/hfurquan/miniconda3/lib/python3.12/site-packages/transformers/modeling_gguf_pytorch_utils.py", line 431, in load_gguf_checkpoint
(APIServer pid=3163855)     raise ValueError(f"GGUF model with architecture {architecture} is not supported yet.")
(APIServer pid=3163855) ValueError: GGUF model with architecture qwen3vl is not supported yet.

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