Instructions to use VladHong/Qwen3.5-9B-Ornimo-SLERP-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 VladHong/Qwen3.5-9B-Ornimo-SLERP-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 VladHong/Qwen3.5-9B-Ornimo-SLERP-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf VladHong/Qwen3.5-9B-Ornimo-SLERP-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf VladHong/Qwen3.5-9B-Ornimo-SLERP-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf VladHong/Qwen3.5-9B-Ornimo-SLERP-GGUF:Q4_K_M

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 VladHong/Qwen3.5-9B-Ornimo-SLERP-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf VladHong/Qwen3.5-9B-Ornimo-SLERP-GGUF:Q4_K_M

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 VladHong/Qwen3.5-9B-Ornimo-SLERP-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf VladHong/Qwen3.5-9B-Ornimo-SLERP-GGUF:Q4_K_M

Use Docker

docker model run hf.co/VladHong/Qwen3.5-9B-Ornimo-SLERP-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use VladHong/Qwen3.5-9B-Ornimo-SLERP-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VladHong/Qwen3.5-9B-Ornimo-SLERP-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": "VladHong/Qwen3.5-9B-Ornimo-SLERP-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/VladHong/Qwen3.5-9B-Ornimo-SLERP-GGUF:Q4_K_M

Ollama
How to use VladHong/Qwen3.5-9B-Ornimo-SLERP-GGUF with Ollama:
```
ollama run hf.co/VladHong/Qwen3.5-9B-Ornimo-SLERP-GGUF:Q4_K_M
```
Unsloth Desktop

How to use VladHong/Qwen3.5-9B-Ornimo-SLERP-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf VladHong/Qwen3.5-9B-Ornimo-SLERP-GGUF:Q4_K_M

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": "VladHong/Qwen3.5-9B-Ornimo-SLERP-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use VladHong/Qwen3.5-9B-Ornimo-SLERP-GGUF with Docker Model Runner:
```
docker model run hf.co/VladHong/Qwen3.5-9B-Ornimo-SLERP-GGUF:Q4_K_M
```

Lemonade

How to use VladHong/Qwen3.5-9B-Ornimo-SLERP-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull VladHong/Qwen3.5-9B-Ornimo-SLERP-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-9B-Ornimo-SLERP-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use VladHong/Qwen3.5-9B-Ornimo-SLERP-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 VladHong/Qwen3.5-9B-Ornimo-SLERP-GGUF:Q4_K_M

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 VladHong/Qwen3.5-9B-Ornimo-SLERP-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use VladHong/Qwen3.5-9B-Ornimo-SLERP-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf VladHong/Qwen3.5-9B-Ornimo-SLERP-GGUF:Q4_K_M

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 "VladHong/Qwen3.5-9B-Ornimo-SLERP-GGUF:Q4_K_M" \
  --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"

Qwen3.5-9B-Ornimo-SLERP (GGUF)

GGUF quantizations of a Qwen3.5-9B hybrid-architecture SLERP merge: the self-improvement RL tower of Ornith-1.5-9B blended into the agentic-SFT shell of MiMo-V2.6-Distill-Qwen-9B. An ornith (a bird) and a MiMo walk into a merge — Ornimo.

Files

file	size	notes
`Qwen3.5-9B-Ornimo-SLERP-Q4_K_M.gguf`	5,629,105,024 B (5.63 GB)	4-bit K-quant; recommended for everyday use
`Qwen3.5-9B-Ornimo-SLERP-F16.gguf`	17,920,693,120 B (17.92 GB)	unquantized F16 text weights (converted from the bf16 checkpoint)
`Qwen3.5-9B-Ornimo-SLERP-mmproj-f16.gguf`	921,705,024 B (922 MB)	vision tower + projector (MiMo's, byte-exact), F16

That is the complete list. This repo contains GGUF weights only — no safetensors (those live in the sibling checkpoint repo) and no imatrix (importance matrix): the Q4_K_M was produced by a plain, calibration-free llama-quantize … Q4_K_M run with no calibration dataset. If you prefer an imatrix-calibrated quant, generate an imatrix from the F16 GGUF and requantize — the F16 is provided exactly for that.

What is this, really?

Nobody trained anything here. Both parents are fine-tunes of the same Qwen3.5-9B backbone (32 hybrid layers — 24 linear-attention + 8 full-attention, hidden 4096, vocab 248320, 262K context) with the identical tokenizer (248,044 tokens, same ordering), so the text towers align 1:1. The merge is a whole-tensor SLERP (t=0.5) of all 427 text tensors, with MiMo's vision tower, projector, tokenizer, chat template and configs kept byte-exact. Ornith's optional multi-token-prediction (mtp.*) block is not part of the merge — it is trained against Ornith's own hidden space and would be miscalibrated on a blended one.

Usage

Both GGUFs use the Qwen3.5 hybrid arch (qwen35 in llama.cpp naming) and run on llama.cpp b393+.

Text only:

llama-cli -m Qwen3.5-9B-Ornimo-SLERP-Q4_K_M.gguf \
  -c 131072 -t 16 -cnv --repeat-penalty 1.1

Vision (image understanding) with the projector:

llama-mtmd-cli -m Qwen3.5-9B-Ornimo-SLERP-Q4_K_M.gguf \
  --mmproj Qwen3.5-9B-Ornimo-SLERP-mmproj-f16.gguf \
  --image <image.png> -p "What is in this image?" -c 131072 -t 16

-c context: the model supports up to 262144 natively; only the 8 full-attention layers cache KV, the 24 linear-attention layers keep fixed-size state.
Thinking mode follows MiMo's chat template (chat_template_kwargs: {"enable_thinking": true} in OpenAI-compatible servers; /no_think in the prompt for a direct answer).
--repeat-penalty 1.1 is recommended for very long reasoning chains.

Why this merge was safe (deviation analysis)

Before merging, both parents were compared tensor-by-tensor (760 shared tensors):

check	result
tokenizer vocab ordering	identical (248,044 tokens, same order)
median tensor cosine (text)	0.993–1.000 per category
min tensor cosine	0.986 (`layers.22.linear_attn.in_proj_b`)
tensors below 0.98 cosine	0
gating tally (lo=0.90, hi=0.995)	293 full-SLERP / 134 gated / 0 anchored
lm_head row cosine	min 0.930, no row below 0.8 — no clashing token families
vision towers	cos 1.000 median — near-identical, copied byte-exact
NaNs	0

Because tokenizers are identical and every output row agrees, the unembedding (lm_head) is merged along with everything else — no anchor-side lm_head exception.

Verified behavior (CPU smoke checks, not a benchmark suite)

structural verify: all 4 output shards recomputed against an independent SLERP implementation (1-ulp tolerance) — passed
Q4_K_M via llama.cpp: 17 × 24 = 408 plus a clean one-line Python function, no repetition loops, ~~13 tok/s generation on 24 CPU threads (~~100 t/s prompt processing)

Everything beyond these checks is uncharted; expect surprises and hallucinations in unknown proportions.

Credits

XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B — the anchor: shell, vision tower, projector, tokenizer, chat template (MIT).
ornith-ai/Ornith-1.5-9B — the RL-self-improvement parent whose text tower is blended in (MIT).
Qwen3.5-9B — the shared backbone both parents fine-tune (Apache-2.0, Copyright 2026 Alibaba Cloud).

Downloads last month: 641

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for VladHong/Qwen3.5-9B-Ornimo-SLERP-GGUF

XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B

ornith-ai/Ornith-1.5-9B

Merge model

this model