Instructions to use cdanis/Ornith-1.5-397B-GGUF-imatrix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use cdanis/Ornith-1.5-397B-GGUF-imatrix 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 cdanis/Ornith-1.5-397B-GGUF-imatrix:Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf cdanis/Ornith-1.5-397B-GGUF-imatrix:Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf cdanis/Ornith-1.5-397B-GGUF-imatrix:Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf cdanis/Ornith-1.5-397B-GGUF-imatrix: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 cdanis/Ornith-1.5-397B-GGUF-imatrix:Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf cdanis/Ornith-1.5-397B-GGUF-imatrix: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 cdanis/Ornith-1.5-397B-GGUF-imatrix:Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cdanis/Ornith-1.5-397B-GGUF-imatrix:Q4_K_XL

Use Docker

docker model run hf.co/cdanis/Ornith-1.5-397B-GGUF-imatrix:Q4_K_XL

LM Studio
Jan
Ollama
How to use cdanis/Ornith-1.5-397B-GGUF-imatrix with Ollama:
```
ollama run hf.co/cdanis/Ornith-1.5-397B-GGUF-imatrix:Q4_K_XL
```
Unsloth Desktop

How to use cdanis/Ornith-1.5-397B-GGUF-imatrix with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf cdanis/Ornith-1.5-397B-GGUF-imatrix:Q4_K_XL

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": "cdanis/Ornith-1.5-397B-GGUF-imatrix:Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use cdanis/Ornith-1.5-397B-GGUF-imatrix with Docker Model Runner:
```
docker model run hf.co/cdanis/Ornith-1.5-397B-GGUF-imatrix:Q4_K_XL
```

Lemonade

How to use cdanis/Ornith-1.5-397B-GGUF-imatrix with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cdanis/Ornith-1.5-397B-GGUF-imatrix:Q4_K_XL

Run and chat with the model

lemonade run user.Ornith-1.5-397B-GGUF-imatrix-Q4_K_XL

List all available models

lemonade list

Hermes Agent

How to use cdanis/Ornith-1.5-397B-GGUF-imatrix with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf cdanis/Ornith-1.5-397B-GGUF-imatrix: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 cdanis/Ornith-1.5-397B-GGUF-imatrix:Q4_K_XL

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use cdanis/Ornith-1.5-397B-GGUF-imatrix with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf cdanis/Ornith-1.5-397B-GGUF-imatrix: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 "cdanis/Ornith-1.5-397B-GGUF-imatrix: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"

cdanis commited on 13 days ago

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@@ -8,55 +8,3 @@ tags:
 - imatrix
 - quantization
 ---
-# Ornith-1.5-397B GGUF — imatrix + UD quants
-Importance matrix and unsloth-style (UD) low-bit requantizations of
-[ornith-ai/Ornith-1.5-397B-GGUF](https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF) (Q8_0 master, arch `qwen35moe`).
-## Sources
-- Base model: [ornith-ai/Ornith-1.5-397B-GGUF](https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF) — `Ornith-1.5-397B-Q8_0.gguf`
-- Calibration data: [bartowski/Ornith-1.5-397B-GGUF](https://huggingface.co/bartowski/Ornith-1.5-397B-GGUF) — `Ornith-1.5-397B-calibration-v6.txt` (rendered chat/tool template), also mirrored here as `calibration-v6.txt`
-- Tensor-protection recipe: [unsloth/Ornith-1.0-397B-GGUF](https://huggingface.co/unsloth/Ornith-1.0-397B-GGUF) UD tables (same `qwen35moe` arch), read from the `UD-IQ1_M` shards
-## Files
-- `imatrix.gguf` — importance matrix (766 tensors, llama.cpp b10751, `-f calibration-v6.txt --output-format gguf --process-output --parse-special --no-ppl -t 36 -b 512`)
-- `calibration-v6.txt` — calibration corpus (mirror)
-- `Ornith-1.5-397B-{IQ1_S,IQ1_M,IQ2_M,IQ2_XXS,IQ3_XXS}-UD.gguf` — requantized from the Q8_0 master with `--allow-requantize --imatrix imatrix.gguf` and protection pins:
-```
---tensor-type '^(blk\.60\.|token_embd\.)=q8_0'   # MTP layer + embeddings (no imatrix entries)
---tensor-type '_shexp\.weight=q6_k'              # shared experts
---tensor-type 'blk\.\d+\.attn_output\.weight=q6_k'
---tensor-type 'blk\.\d+\.attn_(q|k|v)\.weight=q5_k'
---tensor-type '(attn_gate|attn_qkv)\.weight=q5_k' # GDN attention path
---tensor-type 'ssm_(alpha|beta|out)\.weight=q5_k'
-```
-Plain preset quants put the whole attention path at iq1_m/iq2_xxs and produce noise; the pins above follow unsloth's UD distribution and restore coherence.
-Measured vs the Q8_0 reference (wikitext-2 test slice, 93×512 chunks, llama-perplexity `--kl-divergence`; PPL(Q8_0) = 3.50):
-| quant | PPL | ratio | mean KL | median KL | top-p |
-|---|---|---|---|---|---|
-| Q4_K_M (official ornith-ai) | 3.57 | 1.02 | 0.048 | 0.012 | 92.6% |
-| IQ3_XXS (preset) | 4.21 | 1.20 | 0.253 | 0.089 | 82.9% |
-| IQ3_XXS (bartowski published) | 3.80 | 1.09 | 0.131 | 0.032 | 88.2% |
-| IQ3_XXS-UD | 3.72 | 1.06 | 0.100 | 0.022 | 89.8% |
-| IQ2_M (preset) | 5.19 | 1.48 | 0.478 | 0.193 | 75.5% |
-| IQ2_M-UD | 3.92 | 1.12 | 0.164 | 0.037 | 86.8% |
-| IQ2_XXS (preset) | 8.72 | 2.49 | 1.005 | 0.537 | 63.1% |
-| IQ2_XXS (bartowski published) | 4.70 | 1.34 | 0.383 | 0.113 | 79.2% |
-| IQ2_XXS-UD | 4.25 | 1.21 | 0.269 | 0.067 | 82.9% |
-| IQ1_M (preset) | 98.86 | 28.2 | 3.463 | 2.635 | 32.5% |
-| IQ1_M (bartowski published) | 6.10 | 1.74 | 0.652 | 0.228 | 71.5% |
-| IQ1_M-UD | 4.92 | 1.41 | 0.437 | 0.125 | 77.6% |
-| IQ1_S (preset) | 226.5 | 64.7 | 4.295 | 3.622 | 21.3% |
-| IQ1_S (bartowski published) | 6.43 | 1.84 | 0.704 | 0.268 | 69.7% |
-| IQ1_S-UD | 5.22 | 1.49 | 0.492 | 0.146 | 76.1% |
-`(official ornith-ai)` is the model author's own Q4_K_M (227.5 GiB): near-lossless by construction. The practical trade for UD buyers: IQ2_M-UD at 140 GiB (61% of the official's size) costs +0.35 PPL / +6 top-p points.
-`(bartowski published)` rows are his public GGUFs measured with the identical harness — a strong reference, not a strawman: his imatrix + per-tensor overrides keep IQ1_M at ratio 1.74, while our bare preset collapses (ratio 28). UD still leads every tier on every column; the margin over his recipe grows at lower bits (IQ1_S: 5.22 vs 6.43, top-p 76.1% vs 69.7%). His IQ1_S measures slightly worse than his IQ1_M; same ordering for UD, smaller spread.

 - imatrix
 - quantization
 ---