Instructions to use EnclaveHost/qwen3.5-122b-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 EnclaveHost/qwen3.5-122b-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/qwen3.5-122b-gguf:UD-IQ4_XS
# Run inference directly in the terminal:
llama cli -hf EnclaveHost/qwen3.5-122b-gguf:UD-IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf EnclaveHost/qwen3.5-122b-gguf:UD-IQ4_XS
# Run inference directly in the terminal:
llama cli -hf EnclaveHost/qwen3.5-122b-gguf:UD-IQ4_XS

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/qwen3.5-122b-gguf:UD-IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf EnclaveHost/qwen3.5-122b-gguf:UD-IQ4_XS

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/qwen3.5-122b-gguf:UD-IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf EnclaveHost/qwen3.5-122b-gguf:UD-IQ4_XS

Use Docker

docker model run hf.co/EnclaveHost/qwen3.5-122b-gguf:UD-IQ4_XS

LM Studio
Jan
Ollama
How to use EnclaveHost/qwen3.5-122b-gguf with Ollama:
```
ollama run hf.co/EnclaveHost/qwen3.5-122b-gguf:UD-IQ4_XS
```

Unsloth Studio

How to use EnclaveHost/qwen3.5-122b-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/qwen3.5-122b-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/qwen3.5-122b-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/qwen3.5-122b-gguf to start chatting

How to use EnclaveHost/qwen3.5-122b-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/qwen3.5-122b-gguf:UD-IQ4_XS

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/qwen3.5-122b-gguf:UD-IQ4_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use EnclaveHost/qwen3.5-122b-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/qwen3.5-122b-gguf:UD-IQ4_XS

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/qwen3.5-122b-gguf:UD-IQ4_XS

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use EnclaveHost/qwen3.5-122b-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/qwen3.5-122b-gguf:UD-IQ4_XS

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/qwen3.5-122b-gguf:UD-IQ4_XS" \
  --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/qwen3.5-122b-gguf with Docker Model Runner:
```
docker model run hf.co/EnclaveHost/qwen3.5-122b-gguf:UD-IQ4_XS
```

Lemonade

How to use EnclaveHost/qwen3.5-122b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull EnclaveHost/qwen3.5-122b-gguf:UD-IQ4_XS

Run and chat with the model

lemonade run user.qwen3.5-122b-gguf-UD-IQ4_XS

List all available models

lemonade list

qwen3.5-122b-gguf (Enclave model volume)

Curated model volume for Enclave confidential inference: Qwen3.5-122B-A10B at unsloth Dynamic IQ4_XS (~60 GB), merged into a single GGUF and bundled with the official tokenizer.json so the volume is self-contained.

Upstream ships this quantization as a 3-part split family (classic Git-LFS repos cap files at 50 GB). Enclave's pinned wasmtime ggml toolchain predates split-family loading but serves single-file GGUFs, so this repo carries the merge instead (Xet storage, no 50 GB cap). The merge was produced with llama.cpp's llama-gguf-split --merge (build bec4772): tensor data is a byte-for-byte copy of upstream — only the GGUF header bookkeeping changes (split.count → 0, 879 tensors in one file).

Provenance

File	Upstream	Revision	sha256
`Qwen3.5-122B-A10B-UD-IQ4_XS.gguf`	merged from unsloth/Qwen3.5-122B-A10B-GGUF `UD-IQ4_XS/`	`51eab4d59d53f573fb9206cb3ce613f1d0aa392b`	`9c9701c1673f80cc164bf66e3a82b957dc18a7223676339a9bbd76175c4d8f92`
`tokenizer.json`	Qwen/Qwen3.5-122B-A10B	`dc4d348443bc740c68e2d77492492c11606384d5`	`5f9e4d4901a92b997e463c1f46055088b6cca5ca61a6522d1b9f64c4bb81cb42`

Merge inputs — the upstream split parts, byte-identical both in unsloth's repo and in this repo's prior revision 8beb6427:

Split part	sha256
`Qwen3.5-122B-A10B-UD-IQ4_XS-00001-of-00003.gguf`	`1bd4c860d2300566087683d8b971830eaea466eec8f8c5ee69f9ddc2bcea3613`
`Qwen3.5-122B-A10B-UD-IQ4_XS-00002-of-00003.gguf`	`1f77e62d600cb255e327e76ffa058755d1de2b8a1872dd58c89eeb5f8c90e7e8`
`Qwen3.5-122B-A10B-UD-IQ4_XS-00003-of-00003.gguf`	`c644a84cedb93cb85aaedc9bf711d6a388d8205a59566638a9eb1f5c8afd04f2`

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 a single-file GGUF it loads on wasmtime toolchains without split-family support, and with the tokenizer bundled, llm-chat's default tokenizer.json lookup works with no cross-volume configuration.

Downloads last month: 471

GGUF

Model size

122B params

Architecture

qwen35moe

Hardware compatibility

4-bit

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