Instructions to use prithivMLmods/Dolphin-v2-f32-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Dolphin-v2-f32-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/Dolphin-v2-f32-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/Dolphin-v2-f32-GGUF", dtype="auto")

llama-cpp-python

How to use prithivMLmods/Dolphin-v2-f32-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/Dolphin-v2-f32-GGUF",
	filename="Dolphin-v2.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 Settings

llama.cpp

How to use prithivMLmods/Dolphin-v2-f32-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/Dolphin-v2-f32-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Dolphin-v2-f32-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/Dolphin-v2-f32-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Dolphin-v2-f32-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/Dolphin-v2-f32-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Dolphin-v2-f32-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/Dolphin-v2-f32-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Dolphin-v2-f32-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/Dolphin-v2-f32-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/Dolphin-v2-f32-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Dolphin-v2-f32-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/Dolphin-v2-f32-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/Dolphin-v2-f32-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/Dolphin-v2-f32-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/Dolphin-v2-f32-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/Dolphin-v2-f32-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/Dolphin-v2-f32-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/Dolphin-v2-f32-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/Dolphin-v2-f32-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/Dolphin-v2-f32-GGUF:Q4_K_M
```

Unsloth Studio

How to use prithivMLmods/Dolphin-v2-f32-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/Dolphin-v2-f32-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/Dolphin-v2-f32-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/Dolphin-v2-f32-GGUF to start chatting

How to use prithivMLmods/Dolphin-v2-f32-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/Dolphin-v2-f32-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/Dolphin-v2-f32-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use prithivMLmods/Dolphin-v2-f32-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/Dolphin-v2-f32-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/Dolphin-v2-f32-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use prithivMLmods/Dolphin-v2-f32-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Dolphin-v2-f32-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/Dolphin-v2-f32-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Dolphin-v2-f32-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Dolphin-v2-f32-GGUF-Q4_K_M

List all available models

lemonade list

Dolphin-v2-f32-GGUF

ByteDance Dolphin-v2 is a 3B-parameter vision-language model built on Qwen2.5-VL-3B with Native Resolution Vision Transformer (NaViT) encoder and autoregressive decoder, designed as a universal document parsing solution via a document-type-aware two-stage architecture that classifies digital-born vs. photographed documents before applying hybrid strategies—element-wise parallel parsing for clean PDFs and holistic parsing for distorted scans. It supports 21 element categories (headings sec_0-5, paragraphs, formulas in LaTeX, HTML tables, indented code blocks, figures, lists, etc.) with absolute pixel coordinates for precise localization, achieving state-of-the-art OmniDocBench v1.5 scores of 89.45 overall (+14.78 over original Dolphin), 0.054 edit distance for text/reading order, 86.72% CDM for formulas, and 87.02/90.48 TEDS/TEDS-S for tables at 0.1729 FPS on 8-12GB VRAM GPUs. Specialized modules (P_formula, P_code, P_table, P_paragraph) enable structured JSON/Markdown/HTML outputs for privacy-focused local inference in healthcare/legal/finance, outperforming general VLMs in speed (2x faster) and accuracy across distortions, skews, and perspectives.

Dolphin-v2 [GGUF]

File Name	Quant Type	File Size	File Link
Dolphin-v2.BF16.gguf	BF16	6.18 GB	Download
Dolphin-v2.F32.gguf	F32	12.3 GB	Download
Dolphin-v2.IQ4_XS.gguf	IQ4_XS	1.75 GB	Download
Dolphin-v2.Q2_K.gguf	Q2_K	1.27 GB	Download
Dolphin-v2.Q3_K_L.gguf	Q3_K_L	1.71 GB	Download
Dolphin-v2.Q3_K_M.gguf	Q3_K_M	1.59 GB	Download
Dolphin-v2.Q3_K_S.gguf	Q3_K_S	1.45 GB	Download
Dolphin-v2.Q4_K_M.gguf	Q4_K_M	1.93 GB	Download
Dolphin-v2.Q4_K_S.gguf	Q4_K_S	1.83 GB	Download
Dolphin-v2.Q5_K_M.gguf	Q5_K_M	2.22 GB	Download
Dolphin-v2.Q5_K_S.gguf	Q5_K_S	2.17 GB	Download
Dolphin-v2.Q6_K.gguf	Q6_K	2.54 GB	Download
Dolphin-v2.Q8_0.gguf	Q8_0	3.29 GB	Download
Dolphin-v2.f16.gguf	F16	6.18 GB	Download
Dolphin-v2.i1-IQ1_M.gguf	i1-IQ1_M	850 MB	Download
Dolphin-v2.i1-IQ1_S.gguf	i1-IQ1_S	791 MB	Download
Dolphin-v2.i1-IQ2_M.gguf	i1-IQ2_M	1.14 GB	Download
Dolphin-v2.i1-IQ2_S.gguf	i1-IQ2_S	1.06 GB	Download
Dolphin-v2.i1-IQ2_XS.gguf	i1-IQ2_XS	1.03 GB	Download
Dolphin-v2.i1-IQ2_XXS.gguf	i1-IQ2_XXS	948 MB	Download
Dolphin-v2.i1-IQ3_M.gguf	i1-IQ3_M	1.49 GB	Download
Dolphin-v2.i1-IQ3_S.gguf	i1-IQ3_S	1.46 GB	Download
Dolphin-v2.i1-IQ3_XS.gguf	i1-IQ3_XS	1.39 GB	Download
Dolphin-v2.i1-IQ3_XXS.gguf	i1-IQ3_XXS	1.28 GB	Download
Dolphin-v2.i1-IQ4_NL.gguf	i1-IQ4_NL	1.83 GB	Download
Dolphin-v2.i1-IQ4_XS.gguf	i1-IQ4_XS	1.74 GB	Download
Dolphin-v2.i1-Q2_K.gguf	i1-Q2_K	1.27 GB	Download
Dolphin-v2.i1-Q2_K_S.gguf	i1-Q2_K_S	1.2 GB	Download
Dolphin-v2.i1-Q3_K_L.gguf	i1-Q3_K_L	1.71 GB	Download
Dolphin-v2.i1-Q3_K_M.gguf	i1-Q3_K_M	1.59 GB	Download
Dolphin-v2.i1-Q3_K_S.gguf	i1-Q3_K_S	1.45 GB	Download
Dolphin-v2.i1-Q4_0.gguf	i1-Q4_0	1.83 GB	Download
Dolphin-v2.i1-Q4_1.gguf	i1-Q4_1	2 GB	Download
Dolphin-v2.i1-Q4_K_M.gguf	i1-Q4_K_M	1.93 GB	Download
Dolphin-v2.i1-Q4_K_S.gguf	i1-Q4_K_S	1.83 GB	Download
Dolphin-v2.i1-Q5_K_M.gguf	i1-Q5_K_M	2.22 GB	Download
Dolphin-v2.i1-Q5_K_S.gguf	i1-Q5_K_S	2.17 GB	Download
Dolphin-v2.i1-Q6_K.gguf	i1-Q6_K	2.54 GB	Download
Dolphin-v2.imatrix.gguf	imatrix	3.39 MB	Download
Dolphin-v2.mmproj-Q8_0.gguf	mmproj-Q8_0	848 MB	Download
Dolphin-v2.mmproj-bf16.gguf	mmproj-bf16	1.34 GB	Download
Dolphin-v2.mmproj-f16.gguf	mmproj-f16	1.34 GB	Download
Dolphin-v2.mmproj-f32.gguf	mmproj-f32	2.67 GB	Download

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

Downloads last month: 728

GGUF

Model size

3B params

Architecture

qwen2vl

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

32-bit

View +1 variant

Model tree for prithivMLmods/Dolphin-v2-f32-GGUF

Base model

ByteDance/Dolphin-v2

Quantized

(4)

this model