Ornith-1.5-9B-heretic API & Inference Endpoint

Abliteration parameters

Table with columns: Parameter, Value
Parameter	Value
start_layer_index	15
end_layer_index	21
preserve_good_behavior_weight	0.6958
steer_bad_behavior_weight	0.0001
overcorrect_relative_weight	1.0904
neighbor_count	8
tau	0.7271
margin	4.9363

Performance

Table with columns: Metric, This model, Original model (ornith-ai/Ornith-1.5-9B)
Metric	This model	Original model (ornith-ai/Ornith-1.5-9B)
KL divergence	0.0398	0 (by definition)
Refusals	18/100	99/100

Ornith-1.5-9B

Chirp Chirp! 🐦 We are introducing Ornith-1.5, a major step toward building foundation models through end-to-end self-improvement.

Ornith-1.5 extends Ornith-1.0 (which was developed on top of Qwen3.5 and Gemma4 with additional continued pretraining, mid-training, and post-training) by expanding the self-improvement loop from scaffold and rollout optimization to jointly optimizing task generation, scaffold construction, and solution rollouts. Rather than relying on a fixed set of human-curated tasks and manually designed harnesses, Ornith-1.5 continuously generates new training tasks, discovers effective strategies for solving them, and improves the policy through reinforcement learning. For more details on the task, harness, and rollout reward design, please refer to our blog.

Ornith 1.5 9B

This model card documents Ornith-1.5-9B, the most lightweight member of the Ornith-1.5 family — a 9B dense model designed for efficient single-GPU deployment, and edge-deployable on mobile devices via its quantized Ornith-1.5-9B-Mobile variant.

Benchmarks

Quickstart

Serving Ornith-1.5-9B

Ornith-1.5-9B is a dense ~9B model (≈19 GB in bf16), so it serves on a single 80GB GPU. The recipes below stand up an OpenAI-compatible server; add --tensor-parallel-size / --tp if you want to shard across more GPUs.

vLLM

bash
vllm serve ornith-ai/Ornith-1.5-9B --served-model-name Ornith-1.5-9B --host 0.0.0.0 --port 8000 --max-model-len 262144 --gpu-memory-utilization 0.90 --enable-prefix-caching --enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3 --trust-remote-code

SGLang

bash
python -m sglang.launch_server --model-path ornith-ai/Ornith-1.5-9B --served-model-name Ornith-1.5-9B --host 0.0.0.0 --port 8000 --context-length 262144 --mem-fraction-static 0.85 --tool-call-parser qwen3_coder --reasoning-parser qwen3

For Long-Context

Ornith-1.5-9B handles context windows of up to 262,144 tokens. When a task's combined input and output must go beyond this limit, we suggest extending the effective window with RoPE scaling — YaRN is the technique we validate against, and it is already built into both vLLM and SGLang. With a scaling factor of 4.0, the usable window grows to roughly 1M tokens.

You can turn YaRN on in either of two ways:

Edit the checkpoint's config.json. Add a rope_scaling block to the model configuration:

json
{
    "rope_scaling": {
        "rope_type": "yarn",
        "factor": 4.0,
        "original_max_position_embeddings": 262144
    }
}

Override at launch time. Leave the checkpoint untouched and extend the serve commands above with the equivalent flags.

vLLM:

bash
VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 vllm serve ornith-ai/Ornith-1.5-9B ... --hf-overrides '{"rope_scaling": {"rope_type": "yarn", "factor": 4.0, "original_max_position_embeddings": 262144}}' --max-model-len 1000000

SGLang:

bash
SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling": {"rope_type": "yarn", "factor": 4.0, "original_max_position_embeddings": 262144}}' --context-length 1000000

Using Ornith-1.5-9B via the Chat Completions API

Once a vLLM or SGLang server is running, talk to it with any OpenAI-compatible client.

Basic Usage

python
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="EMPTY",  # any non-empty string works for a local server
)

response = client.chat.completions.create(
    model="Ornith-1.5-9B",
    messages=[
        {"role": "user", "content": "Write a one-line Python lambda that squares a number."}
    ],
    temperature=0.6,
    top_p=0.95,
    max_tokens=1024,
)

message = response.choices[0].message
# reasoning_content holds the <think> trace; content holds the final answer.
print("reasoning:", getattr(message, "reasoning_content", None))
print("answer:", message.content)

You can also stream tokens, or hand the model tools — Ornith-1.5-9B emits well-formed function calls that the server parses into the standard tool_calls field:

python
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather for a city",
            "parameters": {
                "type": "object",
                "properties": {"city": {"type": "string"}},
                "required": ["city"],
            },
        },
    }
]

response = client.chat.completions.create(
    model="Ornith-1.5-9B",
    messages=[{"role": "user", "content": "What is the weather in Paris right now?"}],
    tools=tools,
    tool_choice="auto",
    temperature=0.6,
    max_tokens=2048,
)

tool_call = response.choices[0].message.tool_calls[0]
print(tool_call.function.name, tool_call.function.arguments)
# -> get_weather {"city": "Paris"}

You can point any OpenAI-compatible SDK (Python, Node.js, etc.) or curl at the same /v1/chat/completions endpoint.

Agentic Usage

Ornith-1.5-9B exposes an OpenAI-compatible endpoint with tool calling, it works out of the box with standard agent frameworks.

Examples of using Ornith with agents:

Ollama

bash
ollama run ornith-1.5:9b

Atomic.chat

bash
# Both runtimes load a GGUF build of Ornith (publish one at ornith-ai/Ornith-1.5-9B-GGUF).

# llama.cpp — serve an OpenAI-compatible API on port 8000.
llama-server -hf hf.co/ornith-ai/Ornith-1.5-9B-GGUF --port 8000 -c 262144

llama.cpp

bash
# Both runtimes load a GGUF build of Ornith (publish one at ornith-ai/Ornith-1.5-9B-GGUF).

# llama.cpp — serve an OpenAI-compatible API on port 8000.
llama-server -hf hf.co/ornith-ai/Ornith-1.5-9B-GGUF --port 8000 -c 262144

Hermes Agent

bash
# Hermes talks to any OpenAI-compatible endpoint — point it at your Ornith server.
export OPENAI_BASE_URL="http://localhost:8000/v1"
export OPENAI_API_KEY="EMPTY"
export MODEL="ornith-ai/Ornith-1.5-9B"

OpenClaw

bash
# OpenClaw talks to any OpenAI-compatible endpoint — point it at your Ornith server.
export OPENAI_BASE_URL="http://localhost:8000/v1"
export OPENAI_API_KEY="EMPTY"
export OPENAI_MODEL="ornith-ai/Ornith-1.5-9B"

Unsloth Studio

bash
pip install unsloth

# Load Ornith for fast local inference or fine-tuning (Python):
#   from unsloth import FastLanguageModel
#   model, tokenizer = FastLanguageModel.from_pretrained(
#       "unsloth/Ornith-1.5-9B-GGUF",
#       max_seq_length=262144,
#       load_in_4bit=True,
#   )

Coding CLIs

Ornith-1.5-9B is optimized for terminal-based coding agents. Point any OpenAI-compatible coding CLI at your Ornith-1.5-9B endpoint (set OPENAI_BASE_URL and OPENAI_API_KEY) to understand large codebases, automate tedious work, and ship faster.

OpenCode

bash
# Register your local Ornith endpoint as a provider in ~/.config/opencode/opencode.json:
#
# {
#   "$schema": "https://opencode.ai/config.json",
#   "provider": {
#     "ornith": {
#       "npm": "@ai-sdk/openai-compatible",
#       "name": "Ornith (local)",
#       "options": { "baseURL": "http://localhost:8000/v1", "apiKey": "EMPTY" },
#       "models": { "ornith-ai/Ornith-1.5-9B": { "name": "Ornith-1.5-9B" } }
#     }
#   }
# }

opencode

Citation

If you find our work helpful, feel free to give us a cite.

bibtex
@misc{ornith_1_5,
    title = {{Ornith-1.5}: From Self-Scaffolding to Self-Improvement},
    url = {https://ornith.ai/ornith_1_5.html},
    author = {{Ornith Team}},
    year = {2026}
}

Abliteration parameters

Table with columns: Parameter, Value
Parameter	Value
start_layer_index	15
end_layer_index	21
preserve_good_behavior_weight	0.6958
steer_bad_behavior_weight	0.0001
overcorrect_relative_weight	1.0904
neighbor_count	8
tau	0.7271
margin	4.9363

Performance

Table with columns: Metric, This model, Original model (ornith-ai/Ornith-1.5-9B)
Metric	This model	Original model (ornith-ai/Ornith-1.5-9B)
KL divergence	0.0398	0 (by definition)
Refusals	18/100	99/100

Ornith-1.5-9B

Chirp Chirp! 🐦 We are introducing Ornith-1.5, a major step toward building foundation models through end-to-end self-improvement.

Ornith 1.5 9B

Benchmarks

Quickstart

Serving Ornith-1.5-9B

vLLM

bash
vllm serve ornith-ai/Ornith-1.5-9B --served-model-name Ornith-1.5-9B --host 0.0.0.0 --port 8000 --max-model-len 262144 --gpu-memory-utilization 0.90 --enable-prefix-caching --enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3 --trust-remote-code

SGLang

bash
python -m sglang.launch_server --model-path ornith-ai/Ornith-1.5-9B --served-model-name Ornith-1.5-9B --host 0.0.0.0 --port 8000 --context-length 262144 --mem-fraction-static 0.85 --tool-call-parser qwen3_coder --reasoning-parser qwen3

For Long-Context

You can turn YaRN on in either of two ways:

Edit the checkpoint's config.json. Add a rope_scaling block to the model configuration:

json
{
    "rope_scaling": {
        "rope_type": "yarn",
        "factor": 4.0,
        "original_max_position_embeddings": 262144
    }
}

Override at launch time. Leave the checkpoint untouched and extend the serve commands above with the equivalent flags.

vLLM:

bash
VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 vllm serve ornith-ai/Ornith-1.5-9B ... --hf-overrides '{"rope_scaling": {"rope_type": "yarn", "factor": 4.0, "original_max_position_embeddings": 262144}}' --max-model-len 1000000

SGLang:

bash
SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling": {"rope_type": "yarn", "factor": 4.0, "original_max_position_embeddings": 262144}}' --context-length 1000000

Using Ornith-1.5-9B via the Chat Completions API

Once a vLLM or SGLang server is running, talk to it with any OpenAI-compatible client.

Basic Usage

python
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="EMPTY",  # any non-empty string works for a local server
)

response = client.chat.completions.create(
    model="Ornith-1.5-9B",
    messages=[
        {"role": "user", "content": "Write a one-line Python lambda that squares a number."}
    ],
    temperature=0.6,
    top_p=0.95,
    max_tokens=1024,
)

message = response.choices[0].message
# reasoning_content holds the <think> trace; content holds the final answer.
print("reasoning:", getattr(message, "reasoning_content", None))
print("answer:", message.content)

You can also stream tokens, or hand the model tools — Ornith-1.5-9B emits well-formed function calls that the server parses into the standard tool_calls field:

python
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather for a city",
            "parameters": {
                "type": "object",
                "properties": {"city": {"type": "string"}},
                "required": ["city"],
            },
        },
    }
]

response = client.chat.completions.create(
    model="Ornith-1.5-9B",
    messages=[{"role": "user", "content": "What is the weather in Paris right now?"}],
    tools=tools,
    tool_choice="auto",
    temperature=0.6,
    max_tokens=2048,
)

tool_call = response.choices[0].message.tool_calls[0]
print(tool_call.function.name, tool_call.function.arguments)
# -> get_weather {"city": "Paris"}

You can point any OpenAI-compatible SDK (Python, Node.js, etc.) or curl at the same /v1/chat/completions endpoint.

Agentic Usage

Ornith-1.5-9B exposes an OpenAI-compatible endpoint with tool calling, it works out of the box with standard agent frameworks.

Examples of using Ornith with agents:

Ollama

bash
ollama run ornith-1.5:9b

Atomic.chat

bash
# Both runtimes load a GGUF build of Ornith (publish one at ornith-ai/Ornith-1.5-9B-GGUF).

# llama.cpp — serve an OpenAI-compatible API on port 8000.
llama-server -hf hf.co/ornith-ai/Ornith-1.5-9B-GGUF --port 8000 -c 262144

llama.cpp

bash
# Both runtimes load a GGUF build of Ornith (publish one at ornith-ai/Ornith-1.5-9B-GGUF).

# llama.cpp — serve an OpenAI-compatible API on port 8000.
llama-server -hf hf.co/ornith-ai/Ornith-1.5-9B-GGUF --port 8000 -c 262144

Hermes Agent

bash
# Hermes talks to any OpenAI-compatible endpoint — point it at your Ornith server.
export OPENAI_BASE_URL="http://localhost:8000/v1"
export OPENAI_API_KEY="EMPTY"
export MODEL="ornith-ai/Ornith-1.5-9B"

OpenClaw

bash
# OpenClaw talks to any OpenAI-compatible endpoint — point it at your Ornith server.
export OPENAI_BASE_URL="http://localhost:8000/v1"
export OPENAI_API_KEY="EMPTY"
export OPENAI_MODEL="ornith-ai/Ornith-1.5-9B"

Unsloth Studio

bash
pip install unsloth

# Load Ornith for fast local inference or fine-tuning (Python):
#   from unsloth import FastLanguageModel
#   model, tokenizer = FastLanguageModel.from_pretrained(
#       "unsloth/Ornith-1.5-9B-GGUF",
#       max_seq_length=262144,
#       load_in_4bit=True,
#   )

Coding CLIs

OpenCode

bash
# Register your local Ornith endpoint as a provider in ~/.config/opencode/opencode.json:
#
# {
#   "$schema": "https://opencode.ai/config.json",
#   "provider": {
#     "ornith": {
#       "npm": "@ai-sdk/openai-compatible",
#       "name": "Ornith (local)",
#       "options": { "baseURL": "http://localhost:8000/v1", "apiKey": "EMPTY" },
#       "models": { "ornith-ai/Ornith-1.5-9B": { "name": "Ornith-1.5-9B" } }
#     }
#   }
# }

opencode

Citation

If you find our work helpful, feel free to give us a cite.

bibtex
@misc{ornith_1_5,
    title = {{Ornith-1.5}: From Self-Scaffolding to Self-Improvement},
    url = {https://ornith.ai/ornith_1_5.html},
    author = {{Ornith Team}},
    year = {2026}
}

Ornith-1.5-9B-heretic

README

Abliteration parameters

Performance

Ornith-1.5-9B

Ornith 1.5 9B

Benchmarks

Quickstart

Serving Ornith-1.5-9B

For Long-Context

Using Ornith-1.5-9B via the Chat Completions API

Basic Usage

Agentic Usage

Ollama

Atomic.chat

llama.cpp

Hermes Agent

OpenClaw

Unsloth Studio

Coding CLIs

OpenCode

Citation

Explore FriendliAI today

README

Abliteration parameters

Performance

Ornith-1.5-9B

Ornith 1.5 9B

Benchmarks

Quickstart

Serving Ornith-1.5-9B

For Long-Context

Using Ornith-1.5-9B via the Chat Completions API

Basic Usage

Agentic Usage

Ollama

Atomic.chat

llama.cpp

Hermes Agent

OpenClaw

Unsloth Studio

Coding CLIs

OpenCode

Citation