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|---|---|---|
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GGUF quantizations of llmfan46/Ornith-1.0-35B-uncensored-heretic.
| Parameter | Value |
|---|---|
| direction_index | 20.57 |
| attn.out_proj.max_weight | 1.97 |
| attn.out_proj.max_weight_position | 29.36 |
| attn.out_proj.min_weight | 1.41 |
| attn.out_proj.min_weight_distance | 23.76 |
| mlp.down_proj.max_weight | 1.07 |
| mlp.down_proj.max_weight_position | 31.48 |
| mlp.down_proj.min_weight | 0.62 |
| mlp.down_proj.min_weight_distance | 26.62 |
| attn.o_proj.max_weight | 1.98 |
| attn.o_proj.max_weight_position | 24.78 |
| attn.o_proj.min_weight | 0.09 |
| attn.o_proj.min_weight_distance | 27.94 |
| Metric | This model | Original model (Ornith-1.0-35B-GGUF) |
|---|---|---|
| KL divergence | 0.0019 | 0 (by definition) |
| Refusals | ✅ 9/100 | ❌ 89/100 |
Original:
============================================================
Total questions: 7021
Correct: 5802
Accuracy: 0.8264 (82.64%)
Parse failures: 0
============================================================
Tested subject scores:
Heretic:
============================================================
Total questions: 7021
Correct: 5737
Accuracy: 0.8171 (81.71%)
Parse failures: 0
============================================================
Tested subject scores:
MMLU - Massive Multitask Language Understanding, multiple-choice questions across 57 subjects (math, history, law, medicine, etc.).
For the K-quants below, small SSM tensors are kept at higher precision where useful.
-Q6_K and Q3_K quants keep ssm_alpha, ssm_beta, and ssm_out as Q8_0.
-Q5_K and Q4_K quants keep ssm_alpha, ssm_beta as Q8_0 and and ssm_out as Q6_K.
This helps preserve the hybrid/SSM blocks with a small file-size increase.
| Filename | Quant | Description |
|---|---|---|
| Ornith-1.0-35B-uncensored-heretic-BF16.gguf | BF16 | Full precision |
| Ornith-1.0-35B-uncensored-heretic-Q8_0.gguf | Q8_0 | Near-lossless, recommended |
| Ornith-1.0-35B-uncensored-heretic-Q6_K.gguf | Q6_K | Excellent quality |
| Ornith-1.0-35B-uncensored-heretic-Q5_K_M.gguf | Q5_K_M | Good balance |
| Ornith-1.0-35B-uncensored-heretic-Q5_K_S.gguf | Q5_K_S | Smaller Q5 |
| Ornith-1.0-35B-uncensored-heretic-Q4_K_M.gguf | Q4_K_M | Good for limited VRAM |
| Ornith-1.0-35B-uncensored-heretic-Q4_K_S.gguf | Q4_K_S | Smaller Q4 |
| Ornith-1.0-35B-uncensored-heretic-Q3_K_L.gguf | Q3_K_L | Low VRAM, decent quality |
| Ornith-1.0-35B-uncensored-heretic-Q3_K_M.gguf | Q3_K_M | Low VRAM, smaller |
| Filename | Quant | Description |
|---|---|---|
| Ornith-1.0-35B-uncensored-heretic-mmproj-BF16.gguf | BF16 | Native precision |
A Vision Projector File is Required for vision/multimodal capabilities. Use alongside any quantization above.
Works with llama.cpp, LM Studio, Ollama, and other GGUF-compatible tools.
Aloha! 🌺 Today, we are releasing Ornith-1.0, a self-improving family of open-source models for agentic coding.
Highlights:
This model card documents Ornith-1.0-35B, the lightweight member of the Ornith family, designed for efficient single-GPU deployment.
| Ornith-1.0-35B | Qwen3.5-35B | Qwen3.6-35B | Gemma4-31B | Qwen3.5-397B | |
|---|---|---|---|---|---|
| Agentic Coding | |||||
| Terminal-Bench 2.1 (Terminus-2) | 64.2 | 41.4 | 52.5 | 42.1 | 53.5 |
| Terminal-Bench 2.1 (Claude Code) | 62.8 | 38.9 | 49.2 | - | 48.6 |
| SWE-bench Verified | 75.6 | 70 | 73.4 | 52 | 76.4 |
| SWE-bench Pro | 50.4 | 44.6 | 49.5 | 35.7 | 51.6 |
| SWE-bench Multilingual | 69.3 | 60.3 | 67.2 | 51.7 | 69.3 |
| NL2Repo | 34.6 | 20.5 | 29.4 | 15.5 | 36.8 |
| Claw-eval Avg | 69.8 | 65.4 | 68.7 | 48.5 | 70.7 |
| SWE Atlas - QnA | 37.1 | 13.2 | 15.5 | - | 20.4 |
| SWE Atlas - RF | 29.7 | 10.2 | 11.4 | - | 18.4 |
| SWE Atlas - TW | 27.8 | 9.8 | 13.3 | - | 18.5 |
* Terminal-Bench 2.1 (Terminus-2): We evaluate Terminal-Bench 2.1 using the Harbor/Terminus-2 framework with parser=json, temperature=1.0, top_p=1.0, and a 128K context window. Each run uses a 4-hour timeout with 32 CPU cores and 48GB RAM, and results are averaged over 5 runs. We adjust the Qwen chat template to ensure consistency between training and inference (https://huggingface.co/deepreinforce-ai/Ornith-1.0-397B/blob/main/chat_template.jinja), and modify Harbor to align with vLLM's reasoning_content key.
* Terminal-Bench 2.1 (Claude Code): We evaluate Terminal-Bench 2.1 using Claude Code 2.1.126 with parser=json, temperature=1.0, top_p=1.0, max_new_tokens=131072. Results are averaged over 5 runs. Again, Qwen chat template needs to be modified.
* SWE-Bench Verified, Pro and Multilingual: using OpenHands harness with temp=1.0, top_p=0.95, 256k context window.
* SWE Atlas QnA, RF, TW: using mini SWE agent harness with temp=1.0, top_p=0.95, 128K context window. Results are averaged over 5 runs.
* NL2Repo: with temperature=1.0, top_p=1.0, 400K context, 48K output and anti-hacking filters.
* ClawEval: An agentic code benchmark over real-user task distributions; temp=0.6 and 256K context.
Ornith-1.0-35B is a reasoning model: by default the assistant turn opens with a <think> … </think> block before the final answer. The serving recipes below enable a reasoning parser so the chain-of-thought is returned in a separate reasoning_content field, and a tool-call parser so the model's <tool_call> blocks are surfaced as OpenAI-style tool_calls.
Serving Ornith-1.0-35B requires recent runtimes:
The two recipes below stand up an OpenAI-compatible server on a single 8×80GB GPU node (tensor-parallel 8). Adjust --tensor-parallel-size / --tp to the number of GPUs you have.
vllm serve deepreinforce-ai/Ornith-1.0-35B \
--served-model-name Ornith-1.0-35B \
--tensor-parallel-size 8 \
--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
python -m sglang.launch_server \
--model-path deepreinforce-ai/Ornith-1.0-35B \
--served-model-name Ornith-1.0-35B \
--tp 8 \
--host 0.0.0.0 --port 8000 \
--context-length 262144 \
--mem-fraction-static 0.85 \
--tool-call-parser qwen3_coder \
--reasoning-parser qwen3
For a quick local test (or to script offline generation), load the model directly with Transformers. Make sure you have a recent release installed — see the Transformers installation guide; Ornith-1.0-35B requires transformers >= 5.8.1.
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "deepreinforce-ai/Ornith-1.0-35B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
dtype="auto",
device_map="auto",
)
messages = [
{"role": "user", "content": "Write a Python function is_prime(n). Keep it short."}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
generated = model.generate(
**inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.6,
top_p=0.95,
top_k=20,
)
output_ids = generated[0][inputs.input_ids.shape[1]:]
# The reply contains a <think> ... </think> reasoning block followed by the answer.
content = tokenizer.decode(output_ids, skip_special_tokens=True)
print(content)
To split the reasoning trace from the final answer, parse on the </think> marker:
text = tokenizer.decode(output_ids, skip_special_tokens=True)
if "</think>" in text:
reasoning, answer = text.split("</think>", 1)
reasoning = reasoning.replace("<think>", "").strip()
answer = answer.strip()
else:
reasoning, answer = "", text.strip()
Once a vLLM or SGLang server is running, talk to it with any OpenAI-compatible client.
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-1.0-35B",
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-1.0-35B emits well-formed function calls that the server parses into the standard tool_calls field:
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-1.0-35B",
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.
Ornith-1.0-35B excels in tool-calling and agentic coding capabilities.
Because Ornith-1.0-35B exposes an OpenAI-compatible endpoint with tool calling, it works out of the box with standard agent frameworks. Below is a minimal example that connects Ornith-1.0-35B to tools through an MCP server.
import os
from openai import OpenAI
client = OpenAI(
base_url=os.getenv("OPENAI_BASE_URL", "http://localhost:8000/v1"),
api_key=os.getenv("OPENAI_API_KEY", "EMPTY"),
)
tools = [
{
"type": "function",
"function": {
"name": "run_shell",
"description": "Run a shell command and return its output.",
"parameters": {
"type": "object",
"properties": {
"command": {"type": "string", "description": "The command to run"}
},
"required": ["command"],
},
},
}
]
messages = [{"role": "user", "content": "List the Python files in the current directory."}]
response = client.chat.completions.create(
model="deepreinforce-ai/Ornith-1.0-35B",
messages=messages,
tools=tools,
temperature=0.6,
top_p=0.95,
)
print(response.choices[0].message)
Examples of using Ornith with agent harness:
# 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="deepreinforce-ai/Ornith-1.0-35B"
# Both runtimes load a GGUF build of Ornith (publish one at deepreinforce-ai/Ornith-1.0-35B-GGUF).
# llama.cpp — serve an OpenAI-compatible API on port 8000.
llama-server -hf deepreinforce-ai/Ornith-1.0-35B-GGUF --port 8000 -c 262144
# Ollama — pull and chat with the same GGUF straight from Hugging Face.
ollama run hf.co/deepreinforce-ai/Ornith-1.0-35B-GGUF
# 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="deepreinforce-ai/Ornith-1.0-35B"
pip install unsloth
# Load Ornith for fast local inference or fine-tuning (Python):
# from unsloth import FastLanguageModel
# model, tokenizer = FastLanguageModel.from_pretrained(
# "deepreinforce-ai/Ornith-1.0-35B",
# max_seq_length=262144,
# load_in_4bit=True,
# )
pip install openhands-ai
# OpenHands routes through LiteLLM; the "openai/" prefix selects the OpenAI-compatible path.
export LLM_MODEL="openai/deepreinforce-ai/Ornith-1.0-35B"
export LLM_BASE_URL="http://localhost:8000/v1"
export LLM_API_KEY="EMPTY"
# Launch the CLI (or run the official OpenHands Docker image with the same env vars).
openhands
Ornith-1.0-35B is optimized for terminal-based coding agents. Point any OpenAI-compatible coding CLI at your Ornith-1.0-35B endpoint (set OPENAI_BASE_URL and OPENAI_API_KEY) to understand large codebases, automate tedious work, and ship faster.
# 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": { "deepreinforce-ai/Ornith-1.0-35B": { "name": "Ornith-1.0-35B" } }
# }
# }
# }
opencode
If you find our work helpful, feel free to give us a cite.
@misc{ornith-35b,
title = {{Ornith-1.0-35B}: Agentic Coding, Open to All},
url = {https://deep-reinforce.com/ornith_1_0.html},
author = {{DeepReinforce Team}},
year = {2026}
}
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curl -fsSL https://pirateface.co/package.sh | bash -s -- --repo llmfan46/Ornith-1.0-35B-uncensored-heretic-GGUF ./model-folder