Instructions to use Akicou/Qwen3.8-Flash-Next-REAM-60Pct-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 Akicou/Qwen3.8-Flash-Next-REAM-60Pct-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 Akicou/Qwen3.8-Flash-Next-REAM-60Pct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Akicou/Qwen3.8-Flash-Next-REAM-60Pct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Akicou/Qwen3.8-Flash-Next-REAM-60Pct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Akicou/Qwen3.8-Flash-Next-REAM-60Pct-GGUF:Q4_K_M

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 Akicou/Qwen3.8-Flash-Next-REAM-60Pct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Akicou/Qwen3.8-Flash-Next-REAM-60Pct-GGUF:Q4_K_M

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 Akicou/Qwen3.8-Flash-Next-REAM-60Pct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Akicou/Qwen3.8-Flash-Next-REAM-60Pct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Akicou/Qwen3.8-Flash-Next-REAM-60Pct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Akicou/Qwen3.8-Flash-Next-REAM-60Pct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Akicou/Qwen3.8-Flash-Next-REAM-60Pct-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": "Akicou/Qwen3.8-Flash-Next-REAM-60Pct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Akicou/Qwen3.8-Flash-Next-REAM-60Pct-GGUF:Q4_K_M

Ollama
How to use Akicou/Qwen3.8-Flash-Next-REAM-60Pct-GGUF with Ollama:
```
ollama run hf.co/Akicou/Qwen3.8-Flash-Next-REAM-60Pct-GGUF:Q4_K_M
```
Unsloth Desktop

How to use Akicou/Qwen3.8-Flash-Next-REAM-60Pct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Akicou/Qwen3.8-Flash-Next-REAM-60Pct-GGUF:Q4_K_M

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": "Akicou/Qwen3.8-Flash-Next-REAM-60Pct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Akicou/Qwen3.8-Flash-Next-REAM-60Pct-GGUF with Docker Model Runner:
```
docker model run hf.co/Akicou/Qwen3.8-Flash-Next-REAM-60Pct-GGUF:Q4_K_M
```

Lemonade

How to use Akicou/Qwen3.8-Flash-Next-REAM-60Pct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Akicou/Qwen3.8-Flash-Next-REAM-60Pct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.8-Flash-Next-REAM-60Pct-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Akicou/Qwen3.8-Flash-Next-REAM-60Pct-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 Akicou/Qwen3.8-Flash-Next-REAM-60Pct-GGUF:Q4_K_M

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 Akicou/Qwen3.8-Flash-Next-REAM-60Pct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Akicou/Qwen3.8-Flash-Next-REAM-60Pct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Akicou/Qwen3.8-Flash-Next-REAM-60Pct-GGUF:Q4_K_M

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 "Akicou/Qwen3.8-Flash-Next-REAM-60Pct-GGUF:Q4_K_M" \
  --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"

Qwen3.8-Flash-Next-REAM-60Pct-GGUF

GGUF quantizations of Akicou/Qwen3.8-Flash-Next-REAM-60Pct, the REAM-compressed (Merged) version of Qwen/Qwen3.8-Flash-Next.

What this is

REAM (Router Expert Activation Merging) pruned 40% of the routed experts in the original model, taking each layer from 512 down to 308 experts. The compressed checkpoint was then converted to GGUF with ggml-org/llama.cpp (convert_hf_to_gguf.py, bf16) and quantized with llama-quantize. No importance matrix was used.

Files

File	Quant	Bits per weight	Size
`qwen4-ream-q8.gguf`	Q8_0	~8.5	~137 GB
`qwen4-ream-q4_k_s.gguf`	Q4_K_S	~4.5	~82 GB
`qwen4-ream-q4_k_m.gguf`	Q4_K_M	~5.5	~87 GB

The architecture is qwen4exp (hybrid linear attention + Qwen Sparse Attention MoE), 48 layers, 308 routed experts per layer.

Usage

llama-cli -m qwen4-ream-q4_k_m.gguf -p "Explain reinforcement learning." -n 256

Notes

Experimental release, not benchmarked.
The base model requires trust_remote_code=True. These GGUF files are for llama.cpp (and compatible runtimes), so remote code is not needed at load.
Shared experts, attention, and n-gram embeddings are untouched; only routed experts were merged.

Downloads last month: 353

GGUF

Model size

129B params

Architecture

qwen4exp

Hardware compatibility

4-bit

View +1 variant

Model tree for Akicou/Qwen3.8-Flash-Next-REAM-60Pct-GGUF

Base model

Qwen/Qwen3.8-Flash-Next

Finetuned

Akicou/Qwen3.8-Flash-Next-REAM-60Pct

Quantized

(4)

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