Ornith-1.0-9B-Ollama-fixed-GGUFSAS

Instructions to use jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS",
	filename="ornith-1.0-9b-Q8_0-fixed.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 jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS 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 jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS:Q8_0
# Run inference directly in the terminal:
llama cli -hf jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS:Q8_0
# Run inference directly in the terminal:
llama cli -hf jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS: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 jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS: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 jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS:Q8_0

Use Docker

docker model run hf.co/jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS:Q8_0

LM Studio
Jan
Ollama
How to use jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS with Ollama:
```
ollama run hf.co/jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS:Q8_0
```

Unsloth Studio

How to use jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS 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 jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS 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 jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS to start chatting

How to use jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS: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": "jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS: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 jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS:Q8_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS: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 "jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS: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 jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS with Docker Model Runner:
```
docker model run hf.co/jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS:Q8_0
```

Lemonade

How to use jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS:Q8_0

Run and chat with the model

lemonade run user.Ornith-1.0-9B-Ollama-fixed-GGUFSAS-Q8_0

List all available models

lemonade list

Ornith-1.0-9B — chat-template fixed (Q8_0 GGUF)

This is NOT my model. All weights and training are by DeepReinforce (deepreinforce-ai/Ornith-1.0-9B). This is an independent repack that fixes one packaging bug and adds an honest third-party evaluation. No weights were modified.

The bug this fixes 🐛

The official Ornith GGUFs (and the base Qwen3.5-9B GGUFs) ship without an embedded tokenizer.chat_template. As a result, Ollama and llama.cpp fall back to a raw passthrough template ({{ .Prompt }}), the model never sees its ChatML format, and it degenerates into repetition loops — input tokens freeze, the same phrase repeats, the agent never acts.

The fix: this GGUF embeds DeepReinforce's official chat_template.jinja into the file metadata, so it works out-of-the-box in Ollama (auto-detected) and llama.cpp (--jinja). A bug-report note is also embedded in general.description.

📣 DeepReinforce: please add tokenizer.chat_template to your official GGUF exports — it's a one-line metadata fix that makes the GGUFs usable.

Independent evaluation (honest, small probe)

6-instance SWE-bench (django) probe, Claude-Code agentic harness, temp 1.0 / top_p 0.95 (DeepReinforce's recommended sampling — see below), same template for all:

model	SWE	note
base Qwen3.5-9B	1/6	baseline
Ornith-1.0-9B	2/6	real fine-tune, beats base
`NRS_QWEN_MYTHOS_1M` ("100x reasoning" hype)	0/6	hype, worse than base

And the two config bugs that matter for ALL reasoning GGUFs:

Ornith-9B, only the change shown	result
temp 0.1, raw template	0/6 (repetition loops)
temp 1.0 + this fixed template	2/6 (healthy)

⚠️ These are relative numbers on a tiny probe — NOT comparable 1:1 to DeepReinforce's official 69.4 SWE-bench Verified (measured on the OpenHands harness with their <function=> tool format, temp 1.0, top_p 0.95). Use the official numbers for leaderboard comparison. The point here is: the model works once configured right.

Full write-up: see FINDINGS.md.

Usage

Ollama:

ollama run hf.co/KikoCis/Ornith-1.0-9B-Ollama-fixed-GGUF
# or: ollama create ornith -f Modelfile && ollama run ornith

llama.cpp:

llama-server -m ornith-1.0-9b-Q8_0-fixed.gguf --jinja -c 32768

Recommended sampling (DeepReinforce official): temperature 1.0, top_p 0.95. Low temperature (e.g. 0.1) causes repetition loops on this model — use ~1.0.

Credit & license

Model, weights, training, and the chat_template.jinja: © DeepReinforce — Ornith-1.0, GrandCode paper, deep-reinforce.com.
This repack + independent evaluation: KikoCis. MIT (same as upstream). No weights modified — only metadata (chat_template, general.description) added.

Downloads last month: -

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

8-bit

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Paper for jessteru/Ornith-1.0-9B-Ollama-fixed-GGUFSAS

GrandCode: Achieving Grandmaster Level in Competitive Programming via Agentic Reinforcement Learning

Paper • 2604.02721 • Published Apr 3 • 638