Instructions to use ubergarm/GigaChat3-10B-A1.8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ubergarm/GigaChat3-10B-A1.8B-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 ubergarm/GigaChat3-10B-A1.8B-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf ubergarm/GigaChat3-10B-A1.8B-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ubergarm/GigaChat3-10B-A1.8B-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf ubergarm/GigaChat3-10B-A1.8B-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 ubergarm/GigaChat3-10B-A1.8B-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf ubergarm/GigaChat3-10B-A1.8B-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 ubergarm/GigaChat3-10B-A1.8B-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ubergarm/GigaChat3-10B-A1.8B-GGUF:Q2_K

Use Docker

docker model run hf.co/ubergarm/GigaChat3-10B-A1.8B-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use ubergarm/GigaChat3-10B-A1.8B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ubergarm/GigaChat3-10B-A1.8B-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": "ubergarm/GigaChat3-10B-A1.8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ubergarm/GigaChat3-10B-A1.8B-GGUF:Q2_K

Ollama
How to use ubergarm/GigaChat3-10B-A1.8B-GGUF with Ollama:
```
ollama run hf.co/ubergarm/GigaChat3-10B-A1.8B-GGUF:Q2_K
```
Unsloth Desktop

How to use ubergarm/GigaChat3-10B-A1.8B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ubergarm/GigaChat3-10B-A1.8B-GGUF:Q2_K

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ubergarm/GigaChat3-10B-A1.8B-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ubergarm/GigaChat3-10B-A1.8B-GGUF with Docker Model Runner:
```
docker model run hf.co/ubergarm/GigaChat3-10B-A1.8B-GGUF:Q2_K
```

Lemonade

How to use ubergarm/GigaChat3-10B-A1.8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ubergarm/GigaChat3-10B-A1.8B-GGUF:Q2_K

Run and chat with the model

lemonade run user.GigaChat3-10B-A1.8B-GGUF-Q2_K

List all available models

lemonade list

Hermes Agent

How to use ubergarm/GigaChat3-10B-A1.8B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ubergarm/GigaChat3-10B-A1.8B-GGUF:Q2_K

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 ubergarm/GigaChat3-10B-A1.8B-GGUF:Q2_K

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ubergarm/GigaChat3-10B-A1.8B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ubergarm/GigaChat3-10B-A1.8B-GGUF:Q2_K

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "ubergarm/GigaChat3-10B-A1.8B-GGUF:Q2_K" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

ubergarm commited on Nov 21, 2025

Commit

06f5ac9

1 Parent(s): 0c6e157

requires a PR for llama.cpp check the REAMDE.md

Browse files

Files changed (2) hide show

.gitattributes +3 -1
README.md +68 -0

.gitattributes CHANGED Viewed

@@ -33,4 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
-GigaChat3-10B-A1.8B-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text

 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+imatrix-*.dat filter=lfs diff=lfs merge=lfs -text
+*.gguf filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

README.md ADDED Viewed

	@@ -0,0 +1,68 @@

+---
+quantized_by: ubergarm
+pipeline_tag: text-generation
+base_model: ai-sage/GigaChat3-10B-A1.8B
+license: mit
+base_model_relation: quantized
+tags:
+- mla
+- conversational
+- ik_llama.cpp
+---
+## Quantization of ai-sage/GigaChat3-10B-A1.8B
+This requires [llama.cpp PR#17420](https://github.com/ggml-org/llama.cpp/pull/17420) to properly detect as a deepseek `lite` model.
+Also the template is chopped to get rid of some parsing error. Details on that here: https://huggingface.co/ai-sage/GigaChat3-702B-A36B-preview-bf16/discussions/1
+## Big Thanks
+Shout out to Wendell and the **Level1Techs** crew, the community [Forums](https://forum.level1techs.com/t/deepseek-deep-dive-r1-at-home/225826), [YouTube Channel](https://www.youtube.com/@Level1Techs)!  **BIG thanks** for providing **BIG hardware** expertise and access to run these experiments and make these great quants available to the community!!!
+Also thanks to all the folks in the quanting and inferencing community on [BeaverAI Club Discord](https://huggingface.co/BeaverAI) and on [r/LocalLLaMA](https://www.reddit.com/r/LocalLLaMA/) for tips and tricks helping each other run, test, and benchmark all the fun new models!
+Finally, I *really* appreciate all the support from [aifoundry.org](https://aifoundry.org) so check out their open source RISC-V solutions, and of course huggingface for hosting all these big quants!
+## Quant Collection
+Perplexity computed against *wiki.test.raw*.
+![Perplexity Chart](images/perplexity.png "Chart showing Perplexity improving as BPW increases.")
+## Q8_0
+Perplexity: TODO
+<details>
+<summary>👈 Secret Recipe</summary>
+```bash
+#!/usr/bin/env bash
+./build/bin/llama-quantize \
+    --pure \
+    /mnt/data/models/ubergarm/GigaChat3-10B-A1.8B-GGUF/GigaChat3-10B-A1.8B-BF16.gguf \
+    /mnt/data/models/ubergarm/GigaChat3-10B-A1.8B-GGUF/GigaChat3-10B-A1.8B-Q8_0.gguf \
+    Q8_0 \
+    128
+```
+</details>
+## Quick Start
+```bash
+# Example running on mainline llama.cpp CPU-only
+./build/bin/llama-server \
+    --model "$model"\
+    --alias ubergarm/GigaChat3-10B-A1.8B-GGUF \
+    --ctx-size 32768 \
+    --parallel 1 \
+    --threads 8 \
+    --host 127.0.0.1 \
+    --port 8080 \
+    --no-mmap
+# for full offload onto GPU just add -ngl 99 and set threads to 1
+```
+If you have a properly fixed chat template, you can use it like this `--jinja --chat-template-file ./myFixedTemplate.jinja`.
+## References
+* [llama.cpp PR#17420](https://github.com/ggml-org/llama.cpp/pull/17420)