Instructions to use EnclaveHost/qwen3.5-9b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EnclaveHost/qwen3.5-9b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="EnclaveHost/qwen3.5-9b-gguf",
	filename="Qwen3.5-9B-UD-Q4_K_XL.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use EnclaveHost/qwen3.5-9b-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 EnclaveHost/qwen3.5-9b-gguf:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf EnclaveHost/qwen3.5-9b-gguf:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf EnclaveHost/qwen3.5-9b-gguf:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf EnclaveHost/qwen3.5-9b-gguf:UD-Q4_K_XL

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 EnclaveHost/qwen3.5-9b-gguf:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf EnclaveHost/qwen3.5-9b-gguf:UD-Q4_K_XL

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 EnclaveHost/qwen3.5-9b-gguf:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf EnclaveHost/qwen3.5-9b-gguf:UD-Q4_K_XL

Use Docker

docker model run hf.co/EnclaveHost/qwen3.5-9b-gguf:UD-Q4_K_XL

LM Studio
Jan
Ollama
How to use EnclaveHost/qwen3.5-9b-gguf with Ollama:
```
ollama run hf.co/EnclaveHost/qwen3.5-9b-gguf:UD-Q4_K_XL
```

Unsloth Studio

How to use EnclaveHost/qwen3.5-9b-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for EnclaveHost/qwen3.5-9b-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for EnclaveHost/qwen3.5-9b-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for EnclaveHost/qwen3.5-9b-gguf to start chatting

How to use EnclaveHost/qwen3.5-9b-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf EnclaveHost/qwen3.5-9b-gguf:UD-Q4_K_XL

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "EnclaveHost/qwen3.5-9b-gguf:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use EnclaveHost/qwen3.5-9b-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 EnclaveHost/qwen3.5-9b-gguf:UD-Q4_K_XL

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 EnclaveHost/qwen3.5-9b-gguf:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use EnclaveHost/qwen3.5-9b-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf EnclaveHost/qwen3.5-9b-gguf:UD-Q4_K_XL

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 "EnclaveHost/qwen3.5-9b-gguf:UD-Q4_K_XL" \
  --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"

Docker Model Runner
How to use EnclaveHost/qwen3.5-9b-gguf with Docker Model Runner:
```
docker model run hf.co/EnclaveHost/qwen3.5-9b-gguf:UD-Q4_K_XL
```

Lemonade

How to use EnclaveHost/qwen3.5-9b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull EnclaveHost/qwen3.5-9b-gguf:UD-Q4_K_XL

Run and chat with the model

lemonade run user.qwen3.5-9b-gguf-UD-Q4_K_XL

List all available models

lemonade list

qwen3.5-9b-gguf (Enclave model volume)

Curated model volume for Enclave confidential inference: Qwen3.5-9B at unsloth Dynamic Q4_K_XL (~6 GB), a single-file GGUF bundled with the official tokenizer.json so the volume is self-contained.

Upstream unsloth/Qwen3.5-9B-GGUF ships 22 quantizations plus a BF16 export and vision mmproj-* files (~160 GB total) and no tokenizer.json. This volume carries ONLY the one served quant plus the tokenizer (same rationale as qwen2.5-0.5b-gguf / qwen3.5-122b-gguf): a multi-gguf volume makes the host's preload pick ambiguous, and wrapping the whole repo wastes ~154 GB of attested volume for files that never serve. The mmproj files are omitted deliberately — llm-chat's ggml path is text-only and preloads a single LLM gguf.

Provenance

File	Upstream	Revision	sha256
`Qwen3.5-9B-UD-Q4_K_XL.gguf`	unsloth/Qwen3.5-9B-GGUF	`3885219b6810b007914f3a7950a8d1b469d598a5`	`6f5d30666c2d8ae16a306e616d95341dcf3cc46810df84d7e6f5a7d1e4c1b293`
`tokenizer.json`	Qwen/Qwen3.5-9B	`c202236235762e1c871ad0ccb60c8ee5ba337b9a`	`5f9e4d4901a92b997e463c1f46055088b6cca5ca61a6522d1b9f64c4bb81cb42`

The tokenizer is byte-identical to the one in qwen3.5-122b-gguf (the Qwen3.5 family shares it — same sha256, same vocab 248320, <|im_end|> 248046 / <|endoftext|> 248044).

LICENSE is the Apache-2.0 text; both upstreams are Apache-2.0.

Usage on Enclave

Wrapped as a Tinfoil Modelwrap volume (dm-verity; the root hash is part of the enclave measurement). Deployments attach it by name and the guest reads it at /models/<name>; the host preloads the GGUF as the wasi-nn ggml graph. As the volume's single gguf it needs no MODEL_VOLUMES third field and no model_file override, and with the tokenizer bundled, llm-chat's default tokenizer.json lookup works with no cross-volume configuration.

Downloads last month: 89

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

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