Instructions to use EnclaveHost/qwen3.5-0.8b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EnclaveHost/qwen3.5-0.8b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="EnclaveHost/qwen3.5-0.8b-gguf",
	filename="Qwen3.5-0.8B-Q8_0.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/qwen3.5-0.8b-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/qwen3.5-0.8b-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf EnclaveHost/qwen3.5-0.8b-gguf:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf EnclaveHost/qwen3.5-0.8b-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf EnclaveHost/qwen3.5-0.8b-gguf:Q8_0

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/qwen3.5-0.8b-gguf:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf EnclaveHost/qwen3.5-0.8b-gguf:Q8_0

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/qwen3.5-0.8b-gguf:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf EnclaveHost/qwen3.5-0.8b-gguf:Q8_0

Use Docker

docker model run hf.co/EnclaveHost/qwen3.5-0.8b-gguf:Q8_0

LM Studio
Jan
Ollama
How to use EnclaveHost/qwen3.5-0.8b-gguf with Ollama:
```
ollama run hf.co/EnclaveHost/qwen3.5-0.8b-gguf:Q8_0
```

Unsloth Studio

How to use EnclaveHost/qwen3.5-0.8b-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/qwen3.5-0.8b-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/qwen3.5-0.8b-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/qwen3.5-0.8b-gguf to start chatting

How to use EnclaveHost/qwen3.5-0.8b-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/qwen3.5-0.8b-gguf:Q8_0

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/qwen3.5-0.8b-gguf:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use EnclaveHost/qwen3.5-0.8b-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/qwen3.5-0.8b-gguf:Q8_0

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/qwen3.5-0.8b-gguf:Q8_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use EnclaveHost/qwen3.5-0.8b-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/qwen3.5-0.8b-gguf:Q8_0

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/qwen3.5-0.8b-gguf:Q8_0" \
  --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/qwen3.5-0.8b-gguf with Docker Model Runner:
```
docker model run hf.co/EnclaveHost/qwen3.5-0.8b-gguf:Q8_0
```

Lemonade

How to use EnclaveHost/qwen3.5-0.8b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull EnclaveHost/qwen3.5-0.8b-gguf:Q8_0

Run and chat with the model

lemonade run user.qwen3.5-0.8b-gguf-Q8_0

List all available models

lemonade list

qwen3.5-0.8b-gguf (Enclave model volume)

Curated model volume for Enclave confidential inference: Qwen3.5-0.8B at Q8_0 (~775 MB), a single-file GGUF bundled with the official tokenizer.json so the volume is self-contained.

This is the family's SPECULATIVE-DECODING DRAFT volume: attach it next to qwen3.5-9b-gguf, qwen3.6-27b-gguf, or qwen3.5-122b-gguf and name it in the llm-chat config ("draft": "qwen3.5-0.8b-gguf") to have it propose tokens the big model verifies in one batched pass. Output is byte-for-byte the big model's own (exact-match verification); the draft only changes speed. Q8_0 rather than a 4-bit quant because a draft's sole job is predicting the target's picks - the ~0.4 GB saved by 4-bit is not worth the acceptance-rate loss.

Upstream unsloth/Qwen3.5-0.8B-GGUF ships five quantizations plus a vision mmproj and no tokenizer.json. This volume carries ONLY the one served quant plus the tokenizer (same rationale as the sibling volumes): a multi-gguf volume makes the host's preload pick ambiguous, and the extra files never serve.

Provenance

File	Upstream	Revision	sha256
`Qwen3.5-0.8B-Q8_0.gguf`	unsloth/Qwen3.5-0.8B-GGUF	`6ab461498e2023f6e3c1baea90a8f0fe38ab64d0`	`0ad885ffd4bb022fc4f0d33a3308fa108ef8613159d3b3a67e23abca056b7a6c`
`tokenizer.json`	Qwen/Qwen3.5-0.8B	`2fc06364715b967f1860aea9cf38778875588b17`	`5f9e4d4901a92b997e463c1f46055088b6cca5ca61a6522d1b9f64c4bb81cb42`

The tokenizer is byte-identical to the ones in qwen3.5-9b-gguf, qwen3.6-27b-gguf and qwen3.5-122b-gguf (the Qwen3.5/3.6 family shares it - same sha256, vocab 248320, <|im_end|> 248046 / <|endoftext|> 248044), which is exactly the property the speculative-decoding compatibility gate requires.

Model geometry (GGUF arch qwen35, hybrid-SSM): 24 layers, full_attention_interval 4 (6 KV layers), 2 KV heads, head_dim 256.

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; the wasm-manager mounts it read-only at /models/qwen3.5-0.8b-gguf.

Downloads last month: 153

GGUF

Model size

0.8B params

Architecture

qwen35

Hardware compatibility

8-bit

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