Instructions to use Beinsezii/Qwen3.8-Flash-Next-GGUF-HALO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Beinsezii/Qwen3.8-Flash-Next-GGUF-HALO 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 Beinsezii/Qwen3.8-Flash-Next-GGUF-HALO:F16
# Run inference directly in the terminal:
llama cli -hf Beinsezii/Qwen3.8-Flash-Next-GGUF-HALO:F16

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/Beinsezii/Qwen3.8-Flash-Next-GGUF-HALO:F16

LM Studio
Jan
Ollama
How to use Beinsezii/Qwen3.8-Flash-Next-GGUF-HALO with Ollama:
```
ollama run hf.co/Beinsezii/Qwen3.8-Flash-Next-GGUF-HALO:F16
```
Unsloth Desktop

How to use Beinsezii/Qwen3.8-Flash-Next-GGUF-HALO with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Beinsezii/Qwen3.8-Flash-Next-GGUF-HALO: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": "Beinsezii/Qwen3.8-Flash-Next-GGUF-HALO:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Beinsezii/Qwen3.8-Flash-Next-GGUF-HALO with Docker Model Runner:
```
docker model run hf.co/Beinsezii/Qwen3.8-Flash-Next-GGUF-HALO:F16
```

Lemonade

How to use Beinsezii/Qwen3.8-Flash-Next-GGUF-HALO with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Beinsezii/Qwen3.8-Flash-Next-GGUF-HALO:F16

Run and chat with the model

lemonade run user.Qwen3.8-Flash-Next-GGUF-HALO-F16

List all available models

lemonade list

Hermes Agent

How to use Beinsezii/Qwen3.8-Flash-Next-GGUF-HALO with Hermes Agent:

Start the llama.cpp server

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

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Beinsezii/Qwen3.8-Flash-Next-GGUF-HALO with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Beinsezii/Qwen3.8-Flash-Next-GGUF-HALO: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 "Beinsezii/Qwen3.8-Flash-Next-GGUF-HALO: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"

Beinsezii commited on 16 days ago

Commit

5476ea6

verified ·

1 Parent(s): 03eff66

Upload folder using huggingface_hub

Browse files

Files changed (5) hide show

.gitattributes +4 -0
README.md +11 -0
imatrix.gguf +3 -0
mmproj-F16.gguf +3 -0
tensor_types.txt +11 -0

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 *.zst filter=lfs diff=lfs merge=lfs -text
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README.md ADDED Viewed

	@@ -0,0 +1,11 @@

+---
+license: mit
+base_model:
+- Qwen/Qwen3.8-Flash-Next
+---
+**No MTP yet, will update once supported upstream**
+Quant optimized for quality / speed on a Strix Halo 128GiB system. Possibly also beneficial on DGX Spark and similar systems. Refer to [tensor types](./tensor_types.txt) for the recipe.
+This quant is deliberately larger than total resident memory as the ngram is expected to be mmapped to internal NVME. If you want a fully resident solution, I recommend instead just using https://huggingface.co/unsloth/Qwen3.8-Flash-Next-GGUF

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+ssm_beta=F32
+ssm_alpha=F32
+hc_ffn_inject=F32
+hc_attn_inject=F32
+ple_conv1d=F32
+nextn=Q8_0
+shexp=Q8_0
+hc_=Q8_0
+ffn_down=Q5_1
+ffn=Q5_K
+.=Q8_0