Instructions to use agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-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 agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-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 agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF:F16

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 agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF:F16

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 agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF:F16

Use Docker

docker model run hf.co/agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF:F16

LM Studio
Jan

vLLM

How to use agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-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": "agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF:F16

Ollama
How to use agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF with Ollama:
```
ollama run hf.co/agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF:F16
```
Unsloth Desktop

How to use agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF:F16

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": "agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF with Docker Model Runner:
```
docker model run hf.co/agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF:F16
```

Lemonade

How to use agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF:F16

Run and chat with the model

lemonade run user.Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF-F16

List all available models

lemonade list

Hermes Agent

How to use agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-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 agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF:F16

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 agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF:F16

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 "agentionai/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF:F16" \
  --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"

vision mmproj results are inconsistent

by dineshr93 - opened 12 days ago

Discussion

dineshr93

12 days ago

AMD Ryzen AI Max+ 395(128GB) chip with OS Ubuntu 26.04 LTS AMD Radeon 8060S (gfx1151)

LD_LIBRARY_PATH="~/llamacpps/glm5next/qwen4exp-rocmfpx/build-vulkan/bin" \
"~/llamacpps/glm5next/qwen4exp-rocmfpx/build-vulkan/bin/llama-server" \
  -m ~/models/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF/Qwen3.8-Flash-Next-ROCmFP4-FAST-v2-ple16.gguf \
  -a Qwen3.8-Flash-Next-UD-IQ4_XS \
  --spec-type draft-mtp --spec-draft-adaptive --spec-draft-n-max 4 --n-gpu-layers-draft 999 \
  --mmproj ~/models/Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF/mmproj-Qwen3.8-Flash-Next-f16.gguf --image-min-tokens 1024 \
  -ngl 999 --load-mode none -fa 1 -ctk q4_0 -ctv q4_0 -c 162144 -np 1 --jinja --host 0.0.0.0 --port 8888 -b 2048 -ub 2048 \
  --temp 1.0 --top-k 20 --top-p 0.85 --min-p 0 --presence-penalty 0 --repeat-penalty 1.0

uploaded the random image with prompt "list the medicines in the image"

Result:

(qwen3.8-27b llm model identified accurately)

agentionai

Owner 12 days ago

You are using the full quality image tower in f16, so that can't be the issue. You could try to increase cache quality (-ctk q8_0 and -ctv q8_0) or try one of my Q5 quants https://huggingface.co/agentionai/Qwen3.8-Flash-Next-AP-GGUF
Let me know if any of that helps.

dineshr93

12 days ago

Thanks for the response I tried -ctk q8_0 and -ctv q8_0 and got below image. and I dont have enough ram for q5 quants. but thanks again for wonderful efforts from your end

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