Instructions to use jakeatx/slimder-qwen38-ream288-depth32-agentic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use jakeatx/slimder-qwen38-ream288-depth32-agentic 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 jakeatx/slimder-qwen38-ream288-depth32-agentic:Q8_0
# Run inference directly in the terminal:
llama cli -hf jakeatx/slimder-qwen38-ream288-depth32-agentic:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jakeatx/slimder-qwen38-ream288-depth32-agentic:Q8_0
# Run inference directly in the terminal:
llama cli -hf jakeatx/slimder-qwen38-ream288-depth32-agentic:Q8_0

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 jakeatx/slimder-qwen38-ream288-depth32-agentic:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf jakeatx/slimder-qwen38-ream288-depth32-agentic:Q8_0

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 jakeatx/slimder-qwen38-ream288-depth32-agentic:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jakeatx/slimder-qwen38-ream288-depth32-agentic:Q8_0

Use Docker

docker model run hf.co/jakeatx/slimder-qwen38-ream288-depth32-agentic:Q8_0

LM Studio
Jan

vLLM

How to use jakeatx/slimder-qwen38-ream288-depth32-agentic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jakeatx/slimder-qwen38-ream288-depth32-agentic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jakeatx/slimder-qwen38-ream288-depth32-agentic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jakeatx/slimder-qwen38-ream288-depth32-agentic:Q8_0

Ollama
How to use jakeatx/slimder-qwen38-ream288-depth32-agentic with Ollama:
```
ollama run hf.co/jakeatx/slimder-qwen38-ream288-depth32-agentic:Q8_0
```
Unsloth Desktop

How to use jakeatx/slimder-qwen38-ream288-depth32-agentic with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jakeatx/slimder-qwen38-ream288-depth32-agentic: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": "jakeatx/slimder-qwen38-ream288-depth32-agentic:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use jakeatx/slimder-qwen38-ream288-depth32-agentic with Docker Model Runner:
```
docker model run hf.co/jakeatx/slimder-qwen38-ream288-depth32-agentic:Q8_0
```

Lemonade

How to use jakeatx/slimder-qwen38-ream288-depth32-agentic with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jakeatx/slimder-qwen38-ream288-depth32-agentic:Q8_0

Run and chat with the model

lemonade run user.slimder-qwen38-ream288-depth32-agentic-Q8_0

List all available models

lemonade list

Hermes Agent

How to use jakeatx/slimder-qwen38-ream288-depth32-agentic with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jakeatx/slimder-qwen38-ream288-depth32-agentic: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 jakeatx/slimder-qwen38-ream288-depth32-agentic:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use jakeatx/slimder-qwen38-ream288-depth32-agentic with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jakeatx/slimder-qwen38-ream288-depth32-agentic: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 "jakeatx/slimder-qwen38-ream288-depth32-agentic: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"

SLIMDER Qwen3.8 Depth-32 Agentic REAM-288

This is the BF16 Depth-32 structural winner compressed from 384 to 288 routed experts per layer with an agentic-weighted REAM calibration.

Lineage

Source: sjakek/slimder-qwen38-reap384-depth32-mb2-3-4-5
Pinned source revision: 2bb3209bb3c8424105985c5c971e7a2f22f1b61d
Structural depth: 32 transformer layers
Routed experts per layer: 288
Experts selected per token: 10
Precision: BF16

Calibration

The merge used 1,024 sequences of length 512, weighted toward agentic behavior:

tool calling: 384
executable/code tasks: 256
retrieval: 128
multi-step reasoning: 128
general language: 128

Calibration SHA-256: 7738db0c59401e2b6c4050aa1a4e9481a13c12419814e68a8adcf00fb06220d8.

Method

Experts were clustered and merged independently in every layer using REAM similarity with router outputs enabled, gated similarity, REAP saliency weighting, and sequential hidden-state propagation. The run kept 288 of 384 expert slots per layer and saved a resumable atomic checkpoint after every promoted layer.

Status and limitations

This is an experimental research checkpoint. Structural integrity and generation smoke tests are recorded in the linked public run repository. The model still requires broader agentic, code-execution, tool-use, retrieval, long-horizon, and safety evaluation before production use.

Run artifacts: sjakek/slimder-qwen38-agentic-ream-runs-20260901.

Qwen3.8 Perian project lineage

This repository is retained in the Qwen3.8 Perian checkpoints collection. Its exact position in the lineage is: Checkpoint after depth reduction from 48 to 32 layers and routed-expert width reduction from 384 to 288. It retains the full PLE table and predates the final QLoRA.

The final Qwen3.8 Perian GGUF release combines three reductions and one post-training stage:

depth: 48 to 32 transformer layers;
routed-expert width: 384 to 288 experts per layer;
PLE n-gram capacity: 320,001,446 to 160,000,768 rows (50%, about 25.60B parameters removed), using activation-aware bigram and frequency-ranked trigram selections validated on a document-disjoint 5M-token holdout;
rank-32 QLoRA on 12,558 normalized traces spanning math/STEM reasoning, coding/debugging, agentic tool use, retrieval, and general multi-step reasoning. The trace mixture draws from several frontier-model families, including Fable 5, GLM 5.2, Kimi K3, Claude Opus 4.7, Qwen3.8-Max, and GPT-5.6-Sol. The final merged milestone was trained through 9,336,692 supervised assistant tokens.

Earlier checkpoints in this collection do not inherit later stages merely by being listed beside them; the stage statement above is authoritative for this artifact.