CobrIX/CobrIX-1.0-Coder-Flash-33B-A13B-GGUF

CobrIX-1.0-Coder-Flash-MoE — GGUF

CobrIX-1.0-Coder-Flash-MoE is a custom Mixture-of-Experts (MoE) coding model distributed in ready-to-use GGUF format.

The model is built from the dense Qwen 3.5 base model empero-ai/Qwythos-9B-v2 and incorporates 5 specialized dense fine-tuned experts.

The GGUF files provided in this repository are already quantized and can be used directly with compatible inference applications such as llama.cpp, LM Studio, and other GGUF-compatible runtimes.

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🚀 Download

Choose the quantization according to your available RAM/VRAM and desired quality.

File	Quantization	Approx. BPW	Recommended For
CobrIX-1.0-Coder-Flash-33B-A13B-Q3_K_M.gguf	Q3_K_M	~3.5 bpw	Low-VRAM systems
CobrIX-1.0-Coder-Flash-33B-A13B-Q4_K_M.gguf	Q4_K_M	~4.8 bpw	⭐ Best balance
CobrIX-1.0-Coder-Flash-33B-A13B-Q5_K_M.gguf	Q5_K_M	~5.5 bpw	Higher quality
CobrIX-1.0-Coder-Flash-33B-A13B-Q8_0.gguf	Q8_0	~8.5 bpw	Maximum GGUF quality

⭐ Recommended

For most users, Q4_K_M is the recommended version:

CobrIX-1.0-Coder-Flash-33B-A13B-Q4_K_M.gguf

It provides a strong balance between model quality, memory usage, and inference performance.

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🧠 Model Overview

CobrIX-1.0-Coder-Flash-MoE is designed as an efficient AI coding copilot, optimized for interactive development and human-supervised workflows.

Model characteristics

• Architecture: Mixture-of-Experts (MoE)
• Total parameters: ~33B
• Active parameters: ~13B per token
• Experts: 5
• Experts activated per token: 2
• Shared expert: 1
• Context window: up to 1,048,576 tokens
• Format: GGUF
• Primary use: Coding and technical reasoning
• Languages: Portuguese and English

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⚡ Capabilities

🧩 Advanced Reasoning

The Flash model is designed for complex reasoning, problem decomposition, debugging, and technical problem solving.

It is particularly suited to situations where the model works alongside a human developer as an interactive AI copilot.

💻 Expert Programming

Strong focus on software development, including:

• Python
• JavaScript
• TypeScript
• React
• Next.js
• Node.js
• Backend development
• Frontend development
• APIs
• Database systems
• Debugging
• Refactoring
• Software architecture
• Automation
• DevOps

🔐 Cybersecurity

The model also provides capabilities for:

• Security-oriented code analysis
• Vulnerability analysis
• Secure programming
• Security scripting
• Understanding common application-security concepts

The primary focus of the model remains software engineering and programming.

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🏗️ Architecture

The model uses a custom MoE architecture based on the Qwen 3.5 model family.

Model class

Qwen35MoEForCausalLM

Model type

qwen35_moe

Context window

1,048,576 tokens

Configuration:

max_position_embeddings = 1048576

Expert routing

The model contains:

5 total experts
2 experts activated per token

This uses Top-2 routing, where the router selects two experts from the five available experts for each token.

Shared expert

The architecture also contains one shared expert that remains active for every token.

num_shared_experts = 1

The shared expert is based on the base model's MLP and uses a sigmoid gating mechanism.

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📦 GGUF Quantizations

Q3_K_M

CobrIX-1.0-Coder-Flash-33B-A13B-Q3_K_M.gguf

The smallest provided quantization.

Recommended when memory usage is the primary concern.

Advantages:

• Lowest memory requirements
• Good performance on lower-end hardware
• Suitable for systems with limited VRAM/RAM

Trade-off:

• Lower quality than Q4/Q5/Q8

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Q4_K_M ⭐ Recommended

CobrIX-1.0-Coder-Flash-33B-A13B-Q4_K_M.gguf

The recommended general-purpose quantization.

Advantages:

• Excellent quality/size balance
• Good inference speed
• Suitable for a wide range of GPUs and CPUs
• Recommended for everyday coding

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Q5_K_M

CobrIX-1.0-Coder-Flash-33B-A13B-Q5_K_M.gguf

Provides higher precision than Q4_K_M.

Recommended when additional memory is available and maintaining higher model quality is more important than minimizing model size.

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Q8_0

CobrIX-1.0-Coder-Flash-33B-A13B-Q8_0.gguf

The highest-precision GGUF quantization provided in this repository.

Recommended for systems with sufficient memory where maximum GGUF quality is preferred.

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🦙 Using with llama.cpp

The GGUF files can be loaded directly using llama.cpp.

Example using llama-cli:

llama-cli \
  -m CobrIX-1.0-Coder-Flash-33B-A13B-Q4_K_M.gguf \
  -p "Write a Python script to set up an async web server using FastAPI." \
  -n 512 \
  -ngl 99 \
  -c 4096

Important

The -c 4096 value in this example is only an example context size.

The model supports a native context window of up to:

1,048,576 tokens

However, using the maximum context requires significantly more memory and depends on the inference backend and hardware.

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🐍 Python — llama-cpp-python

Install the Python binding:

pip install llama-cpp-python

Then:

from llama_cpp import Llama

llm = Llama(
    model_path="./CobrIX-1.0-Coder-Flash-33B-A13B-Q4_K_M.gguf",
    n_gpu_layers=-1,
    n_ctx=4096,
    verbose=True
)

output = llm(
    "Hello, how can I optimize a React re-render loop?",
    max_tokens=128,
    stop=["<|endoftext|>"],
    echo=False
)

print(output["choices"][0]["text"])

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🖥️ LM Studio

The GGUF files can also be loaded into LM Studio.

Steps

1. Download the desired .gguf file from this repository.
2. Open LM Studio.
3. Import or place the GGUF file in your local model directory.
4. Select the model.
5. Configure the desired GPU offloading and context size.
6. Start the model.

For most users, start with:

Q4_K_M

and increase the quantization if additional memory is available.

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🦙 Ollama

The GGUF weights can also be used with compatible Ollama workflows.

A basic Modelfile can be created as follows:

FROM ./CobrIX-1.0-Coder-Flash-33B-A13B-Q4_K_M.gguf

Then create the model:

ollama create cobrix-1.0-coder-flash -f Modelfile

Run it:

ollama run cobrix-1.0-coder-flash

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📊 Quantization Selection

A simple recommendation:

Limited VRAM/RAM
        │
        ▼
      Q3_K_M
        │
        ▼
     Q4_K_M ⭐
        │
        ▼
      Q5_K_M
        │
        ▼
       Q8_0
        │
        ▼
Higher quality / higher memory usage

For general-purpose usage:

Q4_K_M is the recommended starting point.

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🎯 Intended Use

CobrIX-1.0-Coder-Flash-MoE is intended primarily for:

• Software development
• AI-assisted programming
• Code generation
• Debugging
• Refactoring
• Technical reasoning
• Application development
• Web development
• Backend development
• Frontend development
• Automation
• Developer assistance

The model is designed to work best as a human-supervised coding copilot, where the developer reviews and validates generated code.

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⚠️ Disclaimer

Generated code should always be reviewed and tested before being used in production environments.

The model may produce incorrect, outdated, insecure, or hallucinated information.

Do not blindly execute generated commands or deploy generated code without appropriate validation and security review.

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📜 License

This repository is released under the MIT License.

Please also review the licensing and terms of the underlying base model and any models or datasets used during the creation of the experts.

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❤️ Support CobrIX

Developing and maintaining custom AI architectures such as CobrIX-1.0-Coder-Flash-MoE requires computing resources, model development, experimentation, infrastructure, and ongoing maintenance.

If this project is useful to you or your organization, you can support the development of open AI infrastructure and future model releases through the wallets below.

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All donations help support:

• AI infrastructure
• Compute resources
• Open-source model development
• Model experimentation
• Future CobrIX releases
• Community support

Thank you to all developers, researchers, enthusiasts, and contributors supporting the development of open and accessible artificial intelligence.

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🌎 CobrIX

CobrIX-1.0-Coder-Flash-MoE

An efficient open AI coding copilot powered by a custom Mixture-of-Experts architecture.