Instructions to use tensorblock/gemma-3-4b-it-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tensorblock/gemma-3-4b-it-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="tensorblock/gemma-3-4b-it-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("tensorblock/gemma-3-4b-it-GGUF", dtype="auto") - llama-cpp-python
How to use tensorblock/gemma-3-4b-it-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="tensorblock/gemma-3-4b-it-GGUF", filename="gemma-3-4b-it-Q2_K.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 tensorblock/gemma-3-4b-it-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf tensorblock/gemma-3-4b-it-GGUF:Q2_K # Run inference directly in the terminal: llama-cli -hf tensorblock/gemma-3-4b-it-GGUF:Q2_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf tensorblock/gemma-3-4b-it-GGUF:Q2_K # Run inference directly in the terminal: llama-cli -hf tensorblock/gemma-3-4b-it-GGUF:Q2_K
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 tensorblock/gemma-3-4b-it-GGUF:Q2_K # Run inference directly in the terminal: ./llama-cli -hf tensorblock/gemma-3-4b-it-GGUF:Q2_K
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 tensorblock/gemma-3-4b-it-GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf tensorblock/gemma-3-4b-it-GGUF:Q2_K
Use Docker
docker model run hf.co/tensorblock/gemma-3-4b-it-GGUF:Q2_K
- LM Studio
- Jan
- vLLM
How to use tensorblock/gemma-3-4b-it-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tensorblock/gemma-3-4b-it-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": "tensorblock/gemma-3-4b-it-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/tensorblock/gemma-3-4b-it-GGUF:Q2_K
- SGLang
How to use tensorblock/gemma-3-4b-it-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 "tensorblock/gemma-3-4b-it-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": "tensorblock/gemma-3-4b-it-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 "tensorblock/gemma-3-4b-it-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": "tensorblock/gemma-3-4b-it-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 tensorblock/gemma-3-4b-it-GGUF with Ollama:
ollama run hf.co/tensorblock/gemma-3-4b-it-GGUF:Q2_K
- Unsloth Studio new
How to use tensorblock/gemma-3-4b-it-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 tensorblock/gemma-3-4b-it-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 tensorblock/gemma-3-4b-it-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tensorblock/gemma-3-4b-it-GGUF to start chatting
- Docker Model Runner
How to use tensorblock/gemma-3-4b-it-GGUF with Docker Model Runner:
docker model run hf.co/tensorblock/gemma-3-4b-it-GGUF:Q2_K
- Lemonade
How to use tensorblock/gemma-3-4b-it-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tensorblock/gemma-3-4b-it-GGUF:Q2_K
Run and chat with the model
lemonade run user.gemma-3-4b-it-GGUF-Q2_K
List all available models
lemonade list
Upload folder using huggingface_hub
Browse files- .gitattributes +12 -0
- README.md +87 -0
- gemma-3-4b-it-Q2_K.gguf +3 -0
- gemma-3-4b-it-Q3_K_L.gguf +3 -0
- gemma-3-4b-it-Q3_K_M.gguf +3 -0
- gemma-3-4b-it-Q3_K_S.gguf +3 -0
- gemma-3-4b-it-Q4_0.gguf +3 -0
- gemma-3-4b-it-Q4_K_M.gguf +3 -0
- gemma-3-4b-it-Q4_K_S.gguf +3 -0
- gemma-3-4b-it-Q5_0.gguf +3 -0
- gemma-3-4b-it-Q5_K_M.gguf +3 -0
- gemma-3-4b-it-Q5_K_S.gguf +3 -0
- gemma-3-4b-it-Q6_K.gguf +3 -0
- gemma-3-4b-it-Q8_0.gguf +3 -0
.gitattributes
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gemma-3-4b-it-Q2_K.gguf filter=lfs diff=lfs merge=lfs -text
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gemma-3-4b-it-Q3_K_L.gguf filter=lfs diff=lfs merge=lfs -text
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gemma-3-4b-it-Q3_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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gemma-3-4b-it-Q3_K_S.gguf filter=lfs diff=lfs merge=lfs -text
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gemma-3-4b-it-Q4_0.gguf filter=lfs diff=lfs merge=lfs -text
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gemma-3-4b-it-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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gemma-3-4b-it-Q4_K_S.gguf filter=lfs diff=lfs merge=lfs -text
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gemma-3-4b-it-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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| 2 |
+
license: gemma
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| 3 |
+
library_name: transformers
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| 4 |
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pipeline_tag: image-text-to-text
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| 5 |
+
extra_gated_heading: Access Gemma on Hugging Face
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| 6 |
+
extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
|
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+
agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging
|
| 8 |
+
Face and click below. Requests are processed immediately.
|
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extra_gated_button_content: Acknowledge license
|
| 10 |
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base_model: google/gemma-3-4b-it
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tags:
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- TensorBlock
|
| 13 |
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- GGUF
|
| 14 |
+
---
|
| 15 |
+
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| 16 |
+
<div style="width: auto; margin-left: auto; margin-right: auto">
|
| 17 |
+
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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| 18 |
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</div>
|
| 19 |
+
<div style="display: flex; justify-content: space-between; width: 100%;">
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| 20 |
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<div style="display: flex; flex-direction: column; align-items: flex-start;">
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| 21 |
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<p style="margin-top: 0.5em; margin-bottom: 0em;">
|
| 22 |
+
Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
|
| 23 |
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</p>
|
| 24 |
+
</div>
|
| 25 |
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</div>
|
| 26 |
+
|
| 27 |
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## google/gemma-3-4b-it - GGUF
|
| 28 |
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|
| 29 |
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This repo contains GGUF format model files for [google/gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it).
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| 31 |
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The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4882](https://github.com/ggml-org/llama.cpp/commit/be7c3034108473beda214fd1d7c98fd6a7a3bdf5).
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| 33 |
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<div style="text-align: left; margin: 20px 0;">
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| 34 |
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<a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
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| 35 |
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Run them on the TensorBlock client using your local machine ↗
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</a>
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</div>
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| 38 |
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|
| 39 |
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## Prompt template
|
| 40 |
+
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```
|
| 42 |
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<bos><start_of_turn>user
|
| 43 |
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{system_prompt}
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| 44 |
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| 45 |
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{prompt}<end_of_turn>
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| 46 |
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<start_of_turn>model
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| 47 |
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```
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| 49 |
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## Model file specification
|
| 50 |
+
|
| 51 |
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| Filename | Quant type | File Size | Description |
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| 52 |
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| -------- | ---------- | --------- | ----------- |
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| 53 |
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| [gemma-3-4b-it-Q2_K.gguf](https://huggingface.co/tensorblock/gemma-3-4b-it-GGUF/blob/main/gemma-3-4b-it-Q2_K.gguf) | Q2_K | 1.729 GB | smallest, significant quality loss - not recommended for most purposes |
|
| 54 |
+
| [gemma-3-4b-it-Q3_K_S.gguf](https://huggingface.co/tensorblock/gemma-3-4b-it-GGUF/blob/main/gemma-3-4b-it-Q3_K_S.gguf) | Q3_K_S | 1.937 GB | very small, high quality loss |
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| 55 |
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| [gemma-3-4b-it-Q3_K_M.gguf](https://huggingface.co/tensorblock/gemma-3-4b-it-GGUF/blob/main/gemma-3-4b-it-Q3_K_M.gguf) | Q3_K_M | 2.098 GB | very small, high quality loss |
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| 56 |
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| [gemma-3-4b-it-Q3_K_L.gguf](https://huggingface.co/tensorblock/gemma-3-4b-it-GGUF/blob/main/gemma-3-4b-it-Q3_K_L.gguf) | Q3_K_L | 2.236 GB | small, substantial quality loss |
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| 57 |
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| [gemma-3-4b-it-Q4_0.gguf](https://huggingface.co/tensorblock/gemma-3-4b-it-GGUF/blob/main/gemma-3-4b-it-Q4_0.gguf) | Q4_0 | 2.363 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
|
| 58 |
+
| [gemma-3-4b-it-Q4_K_S.gguf](https://huggingface.co/tensorblock/gemma-3-4b-it-GGUF/blob/main/gemma-3-4b-it-Q4_K_S.gguf) | Q4_K_S | 2.378 GB | small, greater quality loss |
|
| 59 |
+
| [gemma-3-4b-it-Q4_K_M.gguf](https://huggingface.co/tensorblock/gemma-3-4b-it-GGUF/blob/main/gemma-3-4b-it-Q4_K_M.gguf) | Q4_K_M | 2.490 GB | medium, balanced quality - recommended |
|
| 60 |
+
| [gemma-3-4b-it-Q5_0.gguf](https://huggingface.co/tensorblock/gemma-3-4b-it-GGUF/blob/main/gemma-3-4b-it-Q5_0.gguf) | Q5_0 | 2.764 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
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| 61 |
+
| [gemma-3-4b-it-Q5_K_S.gguf](https://huggingface.co/tensorblock/gemma-3-4b-it-GGUF/blob/main/gemma-3-4b-it-Q5_K_S.gguf) | Q5_K_S | 2.764 GB | large, low quality loss - recommended |
|
| 62 |
+
| [gemma-3-4b-it-Q5_K_M.gguf](https://huggingface.co/tensorblock/gemma-3-4b-it-GGUF/blob/main/gemma-3-4b-it-Q5_K_M.gguf) | Q5_K_M | 2.830 GB | large, very low quality loss - recommended |
|
| 63 |
+
| [gemma-3-4b-it-Q6_K.gguf](https://huggingface.co/tensorblock/gemma-3-4b-it-GGUF/blob/main/gemma-3-4b-it-Q6_K.gguf) | Q6_K | 3.191 GB | very large, extremely low quality loss |
|
| 64 |
+
| [gemma-3-4b-it-Q8_0.gguf](https://huggingface.co/tensorblock/gemma-3-4b-it-GGUF/blob/main/gemma-3-4b-it-Q8_0.gguf) | Q8_0 | 4.130 GB | very large, extremely low quality loss - not recommended |
|
| 65 |
+
|
| 66 |
+
|
| 67 |
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## Downloading instruction
|
| 68 |
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| 69 |
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### Command line
|
| 70 |
+
|
| 71 |
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Firstly, install Huggingface Client
|
| 72 |
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|
| 73 |
+
```shell
|
| 74 |
+
pip install -U "huggingface_hub[cli]"
|
| 75 |
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```
|
| 76 |
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|
| 77 |
+
Then, downoad the individual model file the a local directory
|
| 78 |
+
|
| 79 |
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```shell
|
| 80 |
+
huggingface-cli download tensorblock/gemma-3-4b-it-GGUF --include "gemma-3-4b-it-Q2_K.gguf" --local-dir MY_LOCAL_DIR
|
| 81 |
+
```
|
| 82 |
+
|
| 83 |
+
If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
|
| 84 |
+
|
| 85 |
+
```shell
|
| 86 |
+
huggingface-cli download tensorblock/gemma-3-4b-it-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
|
| 87 |
+
```
|
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size 1729028256
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size 2235949216
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oid sha256:bbe14f9f8cf90ebd7092858ec8e3a82b76ad1a16416f984318ecc5ccd9f5753f
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size 2098323616
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oid sha256:8e610c551f2e5d7040f1441f2db545232cc81a8151dbc7d05a65e5e28a4f7173
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