Holo1-7B GGUF Models

Model Generation Details

This model was generated using llama.cpp at commit 71bdbdb5.

Ultra-Low-Bit Quantization with IQ-DynamicGate (1-2 bit)

Our latest quantization method introduces precision-adaptive quantization for ultra-low-bit models (1-2 bit), with benchmark-proven improvements on Llama-3-8B. This approach uses layer-specific strategies to preserve accuracy while maintaining extreme memory efficiency.

Benchmark Context

All tests conducted on Llama-3-8B-Instruct using:

Standard perplexity evaluation pipeline
2048-token context window
Same prompt set across all quantizations

Method

Dynamic Precision Allocation:
- First/Last 25% of layers → IQ4_XS (selected layers)
- Middle 50% → IQ2_XXS/IQ3_S (increase efficiency)
Critical Component Protection:
- Embeddings/output layers use Q5_K
- Reduces error propagation by 38% vs standard 1-2bit

Quantization Performance Comparison (Llama-3-8B)

Quantization	Standard PPL	DynamicGate PPL	Δ PPL	Std Size	DG Size	Δ Size	Std Speed	DG Speed
IQ2_XXS	11.30	9.84	-12.9%	2.5G	2.6G	+0.1G	234s	246s
IQ2_XS	11.72	11.63	-0.8%	2.7G	2.8G	+0.1G	242s	246s
IQ2_S	14.31	9.02	-36.9%	2.7G	2.9G	+0.2G	238s	244s
IQ1_M	27.46	15.41	-43.9%	2.2G	2.5G	+0.3G	206s	212s
IQ1_S	53.07	32.00	-39.7%	2.1G	2.4G	+0.3G	184s	209s

Key:

PPL = Perplexity (lower is better)
Δ PPL = Percentage change from standard to DynamicGate
Speed = Inference time (CPU avx2, 2048 token context)
Size differences reflect mixed quantization overhead

Key Improvements:

🔥 IQ1_M shows massive 43.9% perplexity reduction (27.46 → 15.41)
🚀 IQ2_S cuts perplexity by 36.9% while adding only 0.2GB
⚡ IQ1_S maintains 39.7% better accuracy despite 1-bit quantization

Tradeoffs:

All variants have modest size increases (0.1-0.3GB)
Inference speeds remain comparable (<5% difference)

When to Use These Models

📌 Fitting models into GPU VRAM

✔ Memory-constrained deployments

✔ Cpu and Edge Devices where 1-2bit errors can be tolerated

✔ Research into ultra-low-bit quantization

Choosing the Right Model Format

Selecting the correct model format depends on your hardware capabilities and memory constraints.

BF16 (Brain Float 16) – Use if BF16 acceleration is available

A 16-bit floating-point format designed for faster computation while retaining good precision.
Provides similar dynamic range as FP32 but with lower memory usage.
Recommended if your hardware supports BF16 acceleration (check your device's specs).
Ideal for high-performance inference with reduced memory footprint compared to FP32.

📌 Use BF16 if:
✔ Your hardware has native BF16 support (e.g., newer GPUs, TPUs).
✔ You want higher precision while saving memory.
✔ You plan to requantize the model into another format.

📌 Avoid BF16 if:
❌ Your hardware does not support BF16 (it may fall back to FP32 and run slower).
❌ You need compatibility with older devices that lack BF16 optimization.

F16 (Float 16) – More widely supported than BF16

A 16-bit floating-point high precision but with less of range of values than BF16.
Works on most devices with FP16 acceleration support (including many GPUs and some CPUs).
Slightly lower numerical precision than BF16 but generally sufficient for inference.

📌 Use F16 if:
✔ Your hardware supports FP16 but not BF16.
✔ You need a balance between speed, memory usage, and accuracy.
✔ You are running on a GPU or another device optimized for FP16 computations.

📌 Avoid F16 if:
❌ Your device lacks native FP16 support (it may run slower than expected).
❌ You have memory limitations.

Quantized Models (Q4_K, Q6_K, Q8, etc.) – For CPU & Low-VRAM Inference

Quantization reduces model size and memory usage while maintaining as much accuracy as possible.

Lower-bit models (Q4_K) → Best for minimal memory usage, may have lower precision.
Higher-bit models (Q6_K, Q8_0) → Better accuracy, requires more memory.

📌 Use Quantized Models if:
✔ You are running inference on a CPU and need an optimized model.
✔ Your device has low VRAM and cannot load full-precision models.
✔ You want to reduce memory footprint while keeping reasonable accuracy.

📌 Avoid Quantized Models if:
❌ You need maximum accuracy (full-precision models are better for this).
❌ Your hardware has enough VRAM for higher-precision formats (BF16/F16).

Very Low-Bit Quantization (IQ3_XS, IQ3_S, IQ3_M, Q4_K, Q4_0)

These models are optimized for extreme memory efficiency, making them ideal for low-power devices or large-scale deployments where memory is a critical constraint.

IQ3_XS: Ultra-low-bit quantization (3-bit) with extreme memory efficiency.
- Use case: Best for ultra-low-memory devices where even Q4_K is too large.
- Trade-off: Lower accuracy compared to higher-bit quantizations.
IQ3_S: Small block size for maximum memory efficiency.
- Use case: Best for low-memory devices where IQ3_XS is too aggressive.
IQ3_M: Medium block size for better accuracy than IQ3_S.
- Use case: Suitable for low-memory devices where IQ3_S is too limiting.
Q4_K: 4-bit quantization with block-wise optimization for better accuracy.
- Use case: Best for low-memory devices where Q6_K is too large.
Q4_0: Pure 4-bit quantization, optimized for ARM devices.
- Use case: Best for ARM-based devices or low-memory environments.

Summary Table: Model Format Selection

Model Format	Precision	Memory Usage	Device Requirements	Best Use Case
BF16	Highest	High	BF16-supported GPU/CPUs	High-speed inference with reduced memory
F16	High	High	FP16-supported devices	GPU inference when BF16 isn't available
Q4_K	Medium Low	Low	CPU or Low-VRAM devices	Best for memory-constrained environments
Q6_K	Medium	Moderate	CPU with more memory	Better accuracy while still being quantized
Q8_0	High	Moderate	CPU or GPU with enough VRAM	Best accuracy among quantized models
IQ3_XS	Very Low	Very Low	Ultra-low-memory devices	Extreme memory efficiency and low accuracy
Q4_0	Low	Low	ARM or low-memory devices	llama.cpp can optimize for ARM devices

Included Files & Details

`Holo1-7B-bf16.gguf`

Model weights preserved in BF16.
Use this if you want to requantize the model into a different format.
Best if your device supports BF16 acceleration.

`Holo1-7B-f16.gguf`

Model weights stored in F16.
Use if your device supports FP16, especially if BF16 is not available.

`Holo1-7B-bf16-q8_0.gguf`

Output & embeddings remain in BF16.
All other layers quantized to Q8_0.
Use if your device supports BF16 and you want a quantized version.

`Holo1-7B-f16-q8_0.gguf`

Output & embeddings remain in F16.
All other layers quantized to Q8_0.

`Holo1-7B-q4_k.gguf`

Output & embeddings quantized to Q8_0.
All other layers quantized to Q4_K.
Good for CPU inference with limited memory.

`Holo1-7B-q4_k_s.gguf`

Smallest Q4_K variant, using less memory at the cost of accuracy.
Best for very low-memory setups.

`Holo1-7B-q6_k.gguf`

Output & embeddings quantized to Q8_0.
All other layers quantized to Q6_K .

`Holo1-7B-q8_0.gguf`

Fully Q8 quantized model for better accuracy.
Requires more memory but offers higher precision.

`Holo1-7B-iq3_xs.gguf`

IQ3_XS quantization, optimized for extreme memory efficiency.
Best for ultra-low-memory devices.

`Holo1-7B-iq3_m.gguf`

IQ3_M quantization, offering a medium block size for better accuracy.
Suitable for low-memory devices.

`Holo1-7B-q4_0.gguf`

Pure Q4_0 quantization, optimized for ARM devices.
Best for low-memory environments.
Prefer IQ4_NL for better accuracy.

🚀 If you find these models useful

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Holo1-7B

Model Description

Holo1 is an Action Vision-Language Model (VLM) developed by HCompany for use in the Surfer-H web agent system. It is designed to interact with web interfaces like a human user.

As part of a broader agentic architecture, Holo1 acts as a policy, localizer, or validator, helping the agent understand and act in digital environments.

Trained on a mix of open-access, synthetic, and self-generated data, Holo1 enables state-of-the-art (SOTA) performance on the WebVoyager benchmark, offering the best accuracy/cost tradeoff among current models. It also excels in UI localization tasks such as Screenspot, Screenspot-V2, Screenspot-Pro, GroundUI-Web, and our own newly introduced benchmark WebClick.

Holo1 is optimized for both accuracy and cost-efficiency, making it a strong open-source alternative to existing VLMs.

For more details, check our paper and our blog post.

Developed by: HCompany
Model type: Action Vision-Language Model
Finetuned from model: Qwen/Qwen2.5-VL-7B-Instruct
Paper: https://arxiv.org/abs/2506.02865
Blog Post: https://www.hcompany.ai/surfer-h
License: Apache 2.0

Results

Surfer-H: Pareto-Optimal Performance on WebVoyager

Surfer-H is designed to be flexible and modular. It is composed of three independent components:

A Policy model that plans, decides, and drives the agent's behavior
A Localizer model that sees and understands visual UIs to drive precise interactions
A Validator model that checks whether the answer is valid

The agent thinks before acting, takes notes, and can retry if its answer is rejected. It can operate with different models for each module, allowing for tradeoffs between accuracy, speed, and cost.

We evaluated Surfer-H on the WebVoyager benchmark: 643 real-world web tasks ranging from retrieving prices to finding news or scheduling events.

We’ve tested multiple configurations, from GPT-4-powered agents to 100% open Holo1 setups. Among them, the fully Holo1-based agents offered the strongest tradeoff between accuracy and cost:

Surfer-H + Holo1-7B: 92.2% accuracy at $0.13 per task
Surfer-H + GPT-4.1: 92.0% at $0.54 per task
Surfer-H + Holo1-3B: 89.7% at $0.11 per task
Surfer-H + GPT-4.1-mini: 88.8% at $0.26 per task

This places Holo1-powered agents on the Pareto frontier, delivering the best accuracy per dollar. Unlike other agents that rely on custom APIs or brittle wrappers, Surfer-H operates purely through the browser — just like a real user. Combined with Holo1, it becomes a powerful, general-purpose, cost-efficient web automation system.

Holo1: State-of-the-Art UI Localization

A key skill for the real-world utility of our VLMs within agents is localization: the ability to identify precise coordinates on a user interface (UI) to interact with to complete a task or follow an instruction. To assess this capability, we evaluated our Holo1 models on several established localization benchmarks, including Screenspot, Screenspot-V2, Screenspot-Pro, GroundUI-Web, and our own newly introduced benchmark WebClick.

Get Started with the Model

We provide starter code for the localization task: i.e. image + instruction -> click coordinates

We also provide code to reproduce screenspot evaluations: screenspot_eval.py

Prepare model, processor

Holo1 models are based on Qwen2.5-VL architecture, which comes with transformers support. Here we provide a simple usage example. You can load the model and the processor as follows:

import json
import os
from typing import Any, Literal

from transformers import AutoModelForImageTextToText, AutoProcessor

# default: Load the model on the available device(s)
# We recommend enabling flash_attention_2 for better acceleration and memory saving.
model = AutoModelForImageTextToText.from_pretrained(
    "Hcompany/Holo1-7B",
    torch_dtype="auto",
    # torch_dtype=torch.bfloat16,
    # attn_implementation="flash_attention_2",
    device_map="auto",
)

# default processor
processor = AutoProcessor.from_pretrained("Hcompany/Holo1-7B")
# The default range for the number of visual tokens per image in the model is 4-1280.
# You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost.
# processor = AutoProcessor.from_pretrained(model_dir, min_pixels=min_pixels, max_pixels=max_pixels)

# Helper function to run inference
def run_inference(messages: list[dict[str, Any]]) -> str:
    # Preparation for inference
    text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    inputs = processor(
        text=[text],
        images=image,
        padding=True,
        return_tensors="pt",
    )
    inputs = inputs.to("cuda")

    generated_ids = model.generate(**inputs, max_new_tokens=128)
    generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
    return processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)

Prepare image and instruction

WARNING: Holo1 is using absolute coordinates (number of pixels) and HuggingFace processor is doing image resize. To have matching coordinates, one needs to smart_resize the image.

from PIL import Image
from transformers.models.qwen2_vl.image_processing_qwen2_vl import smart_resize

# Prepare image and instruction
image_url = "https://huggingface.co/Hcompany/Holo1-7B/resolve/main/calendar_example.jpg" 
image = Image.open(requests.get(image_url, stream=True).raw)

# Resize the image so that predicted absolute coordinates match the size of the image.
image_processor = processor.image_processor
resized_height, resized_width = smart_resize(
    image.height,
    image.width,
    factor=image_processor.patch_size * image_processor.merge_size,
    min_pixels=image_processor.min_pixels,
    max_pixels=image_processor.max_pixels,
)
image = image.resize(size=(resized_width, resized_height), resample=None)  # type: ignore

instruction = "Select July 14th as the check-out date"

Localization as click(x, y)

def get_localization_prompt(image, instruction: str) -> list[dict[str, Any]]:
    guidelines: str = "Localize an element on the GUI image according to my instructions and output a click position as Click(x, y) with x num pixels from the left edge and y num pixels from the top edge."

    return [
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "image": image,
                },
                {"type": "text", "text": f"{guidelines}\n{instruction}"},
            ],
        }
    ]


messages = get_localization_prompt(image, instruction)
coordinates_str = run_inference(messages)[0]
print(coordinates_str)
# Expected Click(352, 348)

Structured Output

We trained Holo1 as an Action VLM with extensive use of json and tool calls. Therefore, it can be queried reliably with structured output:

from pydantic import BaseModel, ConfigDict

class FunctionDefinition(BaseModel):
    """Function definition data structure.

    Attributes:
        name: name of the function.
        description: description of the function.
        parameters: JSON schema for the function parameters.
        strict: Whether to enable strict schema adherence when generating the function call.
    """

    name: str
    description: str = ""
    parameters: dict[str, Any] = {}
    strict: bool = True


class ClickAction(BaseModel):
    """Click at specific coordinates on the screen."""

    model_config = ConfigDict(
        extra="forbid",
        json_schema_serialization_defaults_required=True,
        json_schema_mode_override="serialization",
        use_attribute_docstrings=True,
    )

    action: Literal["click"] = "click"
    x: int
    """The x coordinate, number of pixels from the left edge."""
    y: int
    """The y coordinate, number of pixels from the top edge."""


function_definition = FunctionDefinition(
    name="click_action",
    description=ClickAction.__doc__ or "",
    parameters=ClickAction.model_json_schema(),
    strict=True,
)


def get_localization_prompt_structured_output(image, instruction: str) -> list[dict[str, Any]]:
    guidelines: str = "Localize an element on the GUI image according to my instructions and output a click position. You must output a valid JSON format."

    return [
        {
            "role": "system",
            "content": json.dumps([function_definition.model_dump()]),
        },
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "image": image,
                },
                {"type": "text", "text": f"{guidelines}\n{instruction}"},
            ],
        },
    ]


messages = get_localization_prompt_structured_output(image, instruction)
coordinates_str = run_inference(messages)[0]
coordinates = ClickAction.model_validate(json.loads(coordinates_str)["arguments"])
print(coordinates)
# Expected ClickAction(action='click', x=352, y=340)

Citation

BibTeX:

@misc{andreux2025surferhmeetsholo1costefficient,
      title={Surfer-H Meets Holo1: Cost-Efficient Web Agent Powered by Open Weights}, 
      author={Mathieu Andreux and Breno Baldas Skuk and Hamza Benchekroun and Emilien Biré and Antoine Bonnet and Riaz Bordie and Matthias Brunel and Pierre-Louis Cedoz and Antoine Chassang and Mickaël Chen and Alexandra D. Constantinou and Antoine d'Andigné and Hubert de La Jonquière and Aurélien Delfosse and Ludovic Denoyer and Alexis Deprez and Augustin Derupti and Michael Eickenberg and Mathïs Federico and Charles Kantor and Xavier Koegler and Yann Labbé and Matthew C. H. Lee and Erwan Le Jumeau de Kergaradec and Amir Mahla and Avshalom Manevich and Adrien Maret and Charles Masson and Rafaël Maurin and Arturo Mena and Philippe Modard and Axel Moyal and Axel Nguyen Kerbel and Julien Revelle and Mats L. Richter and María Santos and Laurent Sifre and Maxime Theillard and Marc Thibault and Louis Thiry and Léo Tronchon and Nicolas Usunier and Tony Wu},
      year={2025},
      eprint={2506.02865},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2506.02865}, 
}

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GGUF

Model size

7.62B params

Architecture

qwen2vl

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

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Model tree for Mungert/Holo1-7B-GGUF

Base model

Qwen/Qwen2.5-VL-7B-Instruct

Quantized

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