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README.md

@@ -1,444 +1,109 @@
 ---
-library_name: transformers
-license: mit
-license_link: https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B-GGUF/blob/main/LICENSE
+license: other
+language:
+- en
 pipeline_tag: text-generation
+tags:
+- fastflowlm
+- q4nx
+- npu
+- qwen3.6-moe
+- 35b-a3b
+- ornith
+base_model:
+- ornith-ai/Ornith-1.0-35B
 ---
 
+# Ornith-1.0-35B - Q4NX for FastFlowLM (AMD Ryzen AI XDNA2)
 
-<img width="600px" src="assets/ornith_logo.png">
+Ornith-1.0-35B is converted to Q4NX for hardware-accelerated inference with FastFlowLM on AMD Ryzen AI NPUs.
 
-[![Ornith Blog](https://img.shields.io/badge/%F0%9F%A6%A2%EF%B8%8F%20Ornith%20Blog%20-FD8E5B)](https://deep-reinforce.com/ornith.html)
+## What is Q4NX?
 
-# Ornith-1.0-35B-GGUF
+Q4NX is FastFlowLM's native packed-quantization format - a rearranged Q4_1 layout tuned for the NPU matrix engine's tile sizes and memory access patterns. It is **not** a GGUF file and it does not run on llama.cpp or Ollama; it is meant exclusively for the [FastFlowLM](https://fastflowlm.com) engine on AMD Ryzen AI NPUs.
 
-Aloha! 🌺 Today, we are releasing Ornith-1.0, a self-improving family of open-source models for agentic coding. 
+## Requirements
 
-Highlights: 
+- FastFlowLM >= 0.9.46 (`flm` CLI)
+- AMD Ryzen AI processor with **XDNA2 (NPU2)** - Strix Point / Ryzen AI 300
+  series or later
+- Linux with the XRT NPU stack installed
+- ~47 GB of unified system memory (Q4NX weights + activations + KV cache)
 
-- **State-of-the-Art Coding Agents**: Available in 9B-Dense, 31B-Dense, 35B-MoE, and 397B-MoE (post-trained on top of Gemma 4 and Qwen 3.5), achieving state-of-the-art performance among open-source models of comparable size on coding benchmarks such as Terminal-Bench 2.1, SWE-Bench, NL2Repo and OpenClaw. 
-- **Self-Improving Training Framework**:  Ornith-1.0 employs RL to learn to generate not only solution rollouts, but also the scallfold that drive those rollouts. By jointly optimizing the scaffold and the resulting solution, the model  discovers better search trajectories and generates higher-quality solutions. 
-- **Licence**: MIT licensed, globally accessible, and free from regional limitations.
+## Files
 
-<img style="width: 100%; max-width: 900px;" src="assets/ornith_35b_eval.png" alt="Ornith 35B Benchmark Results" title="Ornith 35B Benchmark Results">
+| File | Purpose |
+|---|---|
+| model.q4nx | Quantized Q4NX text weights |
+| config.json | FastFlowLM model configuration |
+| tokenizer.json | Tokenizer |
+| tokenizer_config.json | Special tokens and chat template |
+| chat_template.jinja | Chat template (optional) |
+| flm-add.py | Installer script - registers this model with FastFlowLM |
 
-## Ornith 1.0 35B 
+## Install and run
 
-This model card documents **Ornith-1.0-35B**, the lightweight member of the Ornith family, designed for efficient single-GPU deployment.
+This repository ships `flm-add.py`, a small installer that copies the model
+into the FastFlowLM user directory and registers the tag `qwen3.6-moe:35b-a3b`. It never
+modifies the system FastFlowLM install.
 
-
-### Benchmarks
-
-<div style="font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;width:100%;margin:0 auto;padding:16px 0">
-<table style="width:100%;table-layout:fixed;border-collapse:collapse;font-size:13px">
-<thead><tr>
-<th style="width:28%;padding:10px 7px;text-align:left;font-weight:600;border-bottom:2px solid #FD8E5B;color:#FD8E5B"></th>
-<th style="width:14.40%;padding:10px 7px;text-align:center;font-weight:700;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:14px;background:rgba(253, 142, 91, 0.12)">Ornith-1.0-35B</th>
-<th style="width:14.40%;padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:14px">Qwen3.5-35B</th>
-<th style="width:14.40%;padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:14px">Qwen3.6-35B</th>
-<th style="width:14.40%;padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:14px">Gemma4-31B</th>
-<th style="width:14.40%;padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:14px">Qwen3.5-397B</th>
-</tr></thead>
-<tbody>
-<tr><td colspan="6" style="padding:8px 12px;font-weight:600;color:#FD8E5B;border-bottom:1px solid rgba(253, 142, 91, 0.2);background:rgba(253, 142, 91, 0.1)">Agentic Coding</td></tr>
-<tr>
-<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Terminal-Bench 2.1 <sub><small>(Terminus-2)</small></sub></td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">64.2</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">41.4</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">52.5</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">42.1</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">53.5</td>
-</tr>
-<tr>
-<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Terminal-Bench 2.1 <sub><small>(Claude Code)</small></sub></td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">62.8</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">38.9</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">49.2</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">48.6</td>
-</tr>
-<tr>
-<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SWE-bench Verified</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">75.6</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">70</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">73.4</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">52</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">76.4</td>
-</tr>
-<tr>
-<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SWE-bench Pro</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">50.4</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">44.6</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">49.5</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">35.7</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">51.6</td>
-</tr>
-<tr>
-<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SWE-bench Multilingual</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">69.3</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">60.3</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">67.2</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">51.7</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">69.3</td>
-</tr>
-<tr>
-<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">NL2Repo</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">34.6</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">20.5</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">29.4</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">15.5</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">36.8</td>
-</tr>
-<tr>
-<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Claw-eval Avg</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">69.8</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">65.4</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">68.7</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">48.5</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">70.7</td>
-</tr>
-<tr>
-<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SWE Atlas - QnA</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">37.1</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">13.2</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">15.5</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">20.4</td>
-</tr>
-<tr>
-<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SWE Atlas - RF</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">29.7</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">10.2</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">11.4</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">18.4</td>
-</tr>
-<tr>
-<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SWE Atlas - TW</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">27.8</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">9.8</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">13.3</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">18.5</td>
-</tr>
-</tbody>
-</table>
-
-<p style="margin-top:12px;font-size:10px;opacity:0.7">
-* 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.<br/>
-* 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.<br/>
-* SWE-Bench Verified, Pro and Multilingual: using OpenHands harness with temp=1.0, top_p=0.95, 256k context window.<br/>
-* 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.<br/>
-* NL2Repo: with temperature=1.0, top_p=1.0, 400K context, 48K output and anti-hacking filters.<br/>
-* ClawEval: An agentic code benchmark over real-user task distributions; temp=0.6 and 256K context.<br/>
-</p>
-
-</div>
-
-
-
-
-## Quickstart
-
-
-<div style="border-left:4px solid #FD8E5B;background:rgba(253,142,91,0.1);border-radius:6px;padding:12px 16px;font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;font-size:14px;line-height:1.6">
-<div style="font-weight:700;color:#FD8E5B;margin-bottom:6px">📝 NOTE</div>
-<p style="margin:0 0 10px"><b>Ornith-1.0-35B</b> is a <b>reasoning model</b>: by default the assistant turn opens with a <code style="background:rgba(253,142,91,0.15);padding:1px 5px;border-radius:4px">&lt;think&gt; … &lt;/think&gt;</code> block before the final answer. The serving recipes below enable a reasoning parser so the chain-of-thought is returned in a separate <code style="background:rgba(253,142,91,0.15);padding:1px 5px;border-radius:4px">reasoning_content</code> field, and a tool-call parser so the model's <code style="background:rgba(253,142,91,0.15);padding:1px 5px;border-radius:4px">&lt;tool_call&gt;</code> blocks are surfaced as OpenAI-style <code style="background:rgba(253,142,91,0.15);padding:1px 5px;border-radius:4px">tool_calls</code>.</p>
-<p style="margin:0 0 6px">Serving Ornith-1.0-35B requires recent runtimes:</p>
-<ul style="margin:0;padding-left:20px">
-<li><b>Transformers</b> ≥ 5.8.1</li>
-<li><b>vLLM</b> ≥ 0.19.1</li>
-<li><b>SGLang</b> ≥ 0.5.9</li>
-</ul>
-</div>
-
-### Serving Ornith-1.0-35B
-
-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
-
-```bash
-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
-```
-
-#### SGLang
-
-```bash
-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
-```
-
-#### Hugging Face Transformers
-
-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](https://huggingface.co/docs/transformers/installation); Ornith-1.0-35B requires `transformers >= 5.8.1`.
-
-```python
-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:
-
-```python
-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()
-```
-
-### Using Ornith-1.0-35B 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-1.0-35B",
-    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:
-
-```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-1.0-35B",
-    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.
-
-## Agentic Usage
-
-Ornith-1.0-35B excels in tool-calling and agentic coding capabilities.
-
-### Agent Frameworks
-
-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.
-
-```python
-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 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="deepreinforce-ai/Ornith-1.0-35B"
+# one-time environment (add these to ~/.bashrc)
+export FLM_CONFIG_PATH="$HOME/.config/flm/model_list.json"
+export FLM_XCLBIN_PATH="$HOME/.config/flm"
+
+git lfs install
+git clone https://huggingface.co/Atomic-Germ/Orinth-1.0-35B-NPU2
+cd Orinth-1.0-35B-NPU2
+python3 ./flm-add.py .
+flm run qwen3.6-moe:35b-a3b
 ```
 
+Run `python3 ./flm-add.py --help` for all options. Without a clone, the same
+command works against the repo id directly:
 
-#### Atomic.chat/ Ollama / llama.cpp 
 ```bash
-# 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
+python3 ./flm-add.py Atomic-Germ/Orinth-1.0-35B-NPU2
 ```
 
-#### OpenClaw
+If the model has not been published yet, deploy the local directory with the
+converter instead:
 
 ```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="deepreinforce-ai/Ornith-1.0-35B"
+python convert.py -i <source.gguf> -o <this directory> -d qwen3.6-moe:35b-a3b --deploy-name "Orinth-1.0-35B-NPU2"
 ```
 
-#### Unsloth Studio
+## Kernels
 
-```bash
-pip install unsloth
+FastFlowLM's NPU kernels (xclbins) are closed source and are not shipped in this repository. This model uses the **`qwen3.6-moe`** engine family and is shape-identical to the official **`qwen3.6-moe:35b-a3b`** model (`Qwen3.6-Moe-35BA3B-NPU2`). Point the runtime's xclbin path at the matching `xclbins` directory (or ship your own) before running.
 
-# 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,
-#   )
-```
+## Serve (OpenAI-compatible)
 
-#### OpenHands
 ```bash
-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
+flm serve qwen3.6-moe:35b-a3b --port 8080
 ```
 
-### Coding CLIs
-
-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.
-
-#### 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": { "deepreinforce-ai/Ornith-1.0-35B": { "name": "Ornith-1.0-35B" } }
-#     }
-#   }
-# }
-
-opencode
+curl http://127.0.0.1:8080/v1/chat/completions \
+  -H 'Content-Type: application/json' \
+  -d '{"model":"qwen3.6-moe:35b-a3b","messages":[{"role":"user","content":"Hello!"}],"max_tokens":256}'
 ```
 
+## Model
 
-### Citation
+- Registry tag: `qwen3.6-moe:35b-a3b`
+- Engine family: `qwen3.6-moe`
+- Kernel source: official `qwen3.6-moe:35b-a3b` (`Qwen3.6-Moe-35BA3B-NPU2`)
+- Context length: 262,144 tokens (from config)
+- Hidden size: 2048
+- Layers: 40
+- Vocabulary: 248320
+- `model.q4nx` size: 21.64 GB
+- Base model: [ornith-ai/Ornith-1.0-35B](https://huggingface.co/ornith-ai/Ornith-1.0-35B)
+- License: other
 
-If you find our work helpful, feel free to give us a cite.
+## Original model card
 
-```bibtex
-@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}
-}
-```
+See the upstream model card for training details, benchmarks, and upstream
+usage. This repository only contains the Q4NX conversion for FastFlowLM.
+- Upstream card: [ornith-ai/Ornith-1.0-35B](https://huggingface.co/ornith-ai/Ornith-1.0-35B)