Instructions to use gbuzhf/Sage-A1.5-Sangreal-ICE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use gbuzhf/Sage-A1.5-Sangreal-ICE 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 gbuzhf/Sage-A1.5-Sangreal-ICE:Q8_0
# Run inference directly in the terminal:
llama cli -hf gbuzhf/Sage-A1.5-Sangreal-ICE:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf gbuzhf/Sage-A1.5-Sangreal-ICE:Q8_0
# Run inference directly in the terminal:
llama cli -hf gbuzhf/Sage-A1.5-Sangreal-ICE:Q8_0

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf gbuzhf/Sage-A1.5-Sangreal-ICE:Q8_0
# Run inference directly in the terminal:
./build/bin/llama cli -hf gbuzhf/Sage-A1.5-Sangreal-ICE:Q8_0

Use Docker

docker model run hf.co/gbuzhf/Sage-A1.5-Sangreal-ICE:Q8_0

Llama new
LM Studio
Jan

vLLM

How to use gbuzhf/Sage-A1.5-Sangreal-ICE with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gbuzhf/Sage-A1.5-Sangreal-ICE"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gbuzhf/Sage-A1.5-Sangreal-ICE",
		"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/gbuzhf/Sage-A1.5-Sangreal-ICE:Q8_0

Ollama
How to use gbuzhf/Sage-A1.5-Sangreal-ICE with Ollama:
```
ollama run hf.co/gbuzhf/Sage-A1.5-Sangreal-ICE:Q8_0
```
Unsloth Desktop

How to use gbuzhf/Sage-A1.5-Sangreal-ICE with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf gbuzhf/Sage-A1.5-Sangreal-ICE:Q8_0

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": "gbuzhf/Sage-A1.5-Sangreal-ICE:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use gbuzhf/Sage-A1.5-Sangreal-ICE with Docker Model Runner:
```
docker model run hf.co/gbuzhf/Sage-A1.5-Sangreal-ICE:Q8_0
```

Lemonade

How to use gbuzhf/Sage-A1.5-Sangreal-ICE with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull gbuzhf/Sage-A1.5-Sangreal-ICE:Q8_0

Run and chat with the model

lemonade run user.Sage-A1.5-Sangreal-ICE-Q8_0

List all available models

lemonade list

Hermes Agent

How to use gbuzhf/Sage-A1.5-Sangreal-ICE with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf gbuzhf/Sage-A1.5-Sangreal-ICE:Q8_0

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 gbuzhf/Sage-A1.5-Sangreal-ICE:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use gbuzhf/Sage-A1.5-Sangreal-ICE with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf gbuzhf/Sage-A1.5-Sangreal-ICE:Q8_0

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 "gbuzhf/Sage-A1.5-Sangreal-ICE:Q8_0" \
  --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"

Sage-A1.5 — Sangreal ICE GGUF

GGUF quantizations of InternScience/Sage-A1.5, made with a Sangreal 10k importance matrix, ICE 1.6 per-tensor recipes and ICE 2.0 post-quantization expert repair. Every file keeps the MTP draft layer; the vision projector ships separately (InternScience's F16, plus a Q8_0). The ICE files carry the Arrow 4.0-sage chat template.


Architecture	Qwen3.6-35B-A3B (qwen35moe): 40 layers, 256 routed experts (8 active) + shared expert, hybrid Gated DeltaNet / full attention, ~3B active
Extras	MTP head (1 draft layer, `nextn_predict_layers = 1`), vision projector (`mmproj`)
Source	InternScience/Sage-A1.5-F16-GGUF (master), InternScience/Sage-A1.5-Q8_0-GGUF
Chat template	Arrow 4.0-sage, embedded in every ICE file; Sage-A1.5's verification switch is kept (`--chat-template-kwargs '{"verification": true}'`, needs a system message)

Fidelity of the ICE 2.0 tiers (vs the F16 master)

Tier	Size (no MTP)	wiki 2K KLD	code 2K KLD	code 16K KLD	same top-1 (wiki / code / code 16K)	Score
25G-ICEv2	24.53 GB	0.0079	0.0067	0.0250	96.2 % / 98.1 % / 97.6 %	98.3
23G-ICEv2	22.52 GB	0.0105	0.0090	0.0275	95.7 % / 97.7 % / 97.4 %	98.0
19G-ICEv2	18.47 GB	0.0198	0.0166	0.0347	94.3 % / 96.9 % / 96.8 %	97.2
15G-ICEv2	14.51 GB	0.0510	0.0478	0.0738	90.8 % / 94.8 % / 94.9 %	94.3
12G-ICEv2	11.90 GB	0.1123	0.0922	0.1213	86.7 % / 92.6 % / 92.9 %	90.4

Score: F16 = 100; 70 % full-distribution fidelity 1/(1+KLD) + 30 % same-top-1, averaged over the three sets.

Recommended Sampling Parameters

For the best generation quality, we recommend the following sampling parameters:

temperature: 1.0
top_p: 0.95
top_k: 20
min_p: 0.0
presence_penalty: 1.5
repetition_penalty: 1.0

Use

llama-server -m Sage-A1.5-19G-ICEv2.gguf --mmproj mmproj-Sage-A1.5-F16.gguf --jinja -c 65536 -fa on

The MTP head is in every file; on a llama.cpp build with MTP support, enable it for speculative decoding (draft depth 1). A Q8_0 of the projector (mmproj-Sage-A1.5-Q8_0.gguf) can be used in place of the F16 one.

Files

File	Size	Routed experts	What
`Sage-A1.5-12G-ICEv2.gguf`	12.19 GB	IQ2_XS	ICE 2.0 (11.90 GB without the MTP head)
`Sage-A1.5-15G-ICEv2.gguf`	14.81 GB	IQ3_XXS + IQ2_S	ICE 2.0 (14.51 GB without the MTP head)
`Sage-A1.5-19G-ICEv2.gguf`	18.76 GB	IQ4_XS + IQ3_S	ICE 2.0 (18.47 GB without the MTP head)
`Sage-A1.5-23G-ICEv2.gguf`	22.81 GB	Q4_K + Q5_K	ICE 2.0 (22.52 GB without the MTP head)
`Sage-A1.5-25G-ICEv2.gguf`	24.82 GB	Q5_K + Q4_K	ICE 2.0 (24.53 GB without the MTP head)
`Sage-A1.5-Q8_0.gguf`	37.80 GB	Q8_0	InternScience's Q8_0, unchanged
`mmproj-Sage-A1.5-F16.gguf`	0.90 GB	—	Sage's vision projector, unchanged
`mmproj-Sage-A1.5-Q8_0.gguf`	0.61 GB	—	the same projector in Q8_0