CoPaw-Flash

CoPaw-Flash is a lightweight model deeply optimized for the CoPaw autonomous agent scenario. Since its training phase, the model has been specifically refined for CoPaw tasks, delivering enhanced agentic performance in tool invocation, command execution, memory management, and multi-step planning.

Capability

The core strength of CoPaw-Flash stems from its native integration with the CoPaw ecosystem. We have constructed extensive, high-quality agent trajectory data sampled from real CoPaw environments, systematically enhancing the model's proficiency in high-frequency daily scenarios. Key features include:

Active Memory Management: Autonomously identifies, stores, and retrieves persistent user preferences and task states, ensuring high logical consistency across multi-turn interactions.
Native File Parsing: Optimized for terminal operations and file system orchestration. Excels at generating precise CLI commands and executing complex, multi-step file I/O tasks.
Efficient Information Search: Enhanced for web-search tool invocation. Features precise search intent recognition and multi-step web navigation to effectively identify and query online information.
Intelligent Guidance: Built-in awareness of the CoPaw feature map. Proactively suggests functional paths and troubleshooting based on real-time operational context.

Model Overview

CoPaw-Flash-2B/4B/9B is fine-tuned from Qwen3.5-2B/4B/9B, sharing the same architectural parameters.

Type: Causal Language Model with Vision Encoder
Training Stage: Post-training
Number of Parameters: 2B/4B/9B
Hidden Dimension: 2048/2560/4096
Token Embedding: 248320 (Padded)
Number of Layers: 24/32/32
Hidden Layout: 6/8/8 × (3 × (Gated DeltaNet → FFN) → 1 × (Gated Attention → FFN))
Gated DeltaNet:
- Number of Linear Attention Heads: 16/32/32 for V and 16/16/16 for QK
- Head Dimension: 128
Gated Attention:
- Number of Attention Heads: 8/16/16 for Q and 2/4/4 for KV
- Head Dimension: 256
Rotary Position Embedding Dimension: 64
Feed Forward Network: Intermediate Dimension: 6144/9216/12288
LM Output: 248320 (Tied to token embedding)
Context Length: 262,144 tokens natively

Benchmark Results

The complexity of CoPaw's context engineering and tool usage poses heightened challenges for model evaluation. To address this, we have developed a dedicated benchmark tailored to the CoPaw environment. This benchmark systematically evaluates model performance across five high-frequency usage scenarios, covering key operational dimensions.

Results indicate that CoPaw-Flash delivers substantial improvements across multiple task categories, achieving performance comparable to leading flagship models—all while maintaining significantly lower resource requirements.

Figure 1: CoPaw-Flash-9B compared with other models.

Figure 2: CoPaw-Flash-2B/4B/9B compared with their respective baseline models.

Quickstart

Serving CoPaw-Flash

CoPaw-Flash can be served via APIs using popular inference frameworks. Below are example commands to launch OpenAI-compatible API servers for CoPaw-Flash.

llama.cpp

Check out Qwen llama.cpp documentation for more usage guide.

We advise you to clone llama.cpp and install it following the official guide. We follow the latest version of llama.cpp.

llama-server -m /path/to/.gguf

Using CoPaw-Flash via Chat Completions API

Once the server is running, you can access CoPaw-Flash via standard HTTP requests or OpenAI-compatible SDKs.

Prerequisites

Ensure the OpenAI Python SDK is installed and your environment variables are configured:

pip install -U openai

# Set the following accordingly
export OPENAI_BASE_URL="http://localhost:8000/v1"
export OPENAI_API_KEY="EMPTY"

Text-Only Input Example

The following Python script demonstrates how to interact with the model using the OpenAI SDK:

from openai import OpenAI
# Configured by environment variables
client = OpenAI()

messages = [
    {"role": "user", "content": "Hello, Copaw!"},
]

chat_response = client.chat.completions.create(
    model=<your_model_path>,
    messages=messages,
    max_tokens=81920,
    temperature=1.0,
    top_p=0.95,
    presence_penalty=1.5,
    extra_body={
        "top_k": 20,
    }, 
)
print("Chat response:", chat_response)