SetneufPT/ Ornith-1.0-35B-MOE_Q4_110k_24GB-GPU

135 Downloads Updated 2 weeks ago

Custom model for coding with agents to use locally with 24gb or 2x16gb GPUs (working fine...)

ollama run SetneufPT/Ornith-1.0-35B-MOE_Q4_110k_24GB-GPU

curl http://localhost:11434/api/chat \
  -d '{
    "model": "SetneufPT/Ornith-1.0-35B-MOE_Q4_110k_24GB-GPU",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

from ollama import chat

response = chat(
    model='SetneufPT/Ornith-1.0-35B-MOE_Q4_110k_24GB-GPU',
    messages=[{'role': 'user', 'content': 'Hello!'}],
)
print(response.message.content)

import ollama from 'ollama'

const response = await ollama.chat({
  model: 'SetneufPT/Ornith-1.0-35B-MOE_Q4_110k_24GB-GPU',
  messages: [{role: 'user', content: 'Hello!'}],
})
console.log(response.message.content)

Models

Name

1 model

Size / Usage

Context

Input

Ornith-1.0-35B-MOE_Q4_110k_24GB-GPU:latest

21GB · 256K context window · Text · 2 weeks ago

Ornith-1.0-35B-MOE_Q4_110k_24GB-GPU:latest

21GB

256K

Text

Readme

Ornith 1.0 - 35B param MOE, Q4, 110K ctx, Local/Offline, 24GB (or 2x 16GB) GPU

Custom Ollama model, tuned from Ornith 1.0 from DeepReinforce-AI, configured for local coding-agent workflows, especially with Claude Code.

This model is based on a 35B parameter Mixture of Experts (MOE) LLM, quantized in Q4, and configured with a large context window for software development tasks. It is intended to provide a practical balance between performance, memory usage, and code-assistance quality on local hardware.

Model details

Type: Textmodel
Size: 35B parameters MOE
Quantization: Q4
Context target: 110K
Real GPU memory usage: 23,4 GB VRAM
Recommended GPU memory: 32 GB VRAM
Main focus: Coding and agentic development workflows
Tool use: Supported, depending on the client/application
Thinking/reasoning mode: Supported, depending on the client/application

Intended use

This model is designed for:

Agents workflows
Local coding assistants
Code analysis
Debugging support
Refactoring suggestions
Project exploration
Terminal-based programming tasks
Educational demonstrations of AI coding agents

---
# Ornith 1.0  - 35B param MOE, Q4, 110K ctx, Local/Offline, 24GB (or 2x 16GB) GPU

Custom Ollama model, tuned from Ornith 1.0 from DeepReinforce-AI, configured for local coding-agent workflows, especially with Claude Code.

This model is based on a 35B parameter Mixture of Experts (MOE) LLM, quantized in Q4, and configured with a large context window for software development tasks. It is intended to provide a practical balance between performance, memory usage, and code-assistance quality on local hardware.

## Model details

- Type: Textmodel
- Size: 35B parameters MOE
- Quantization: Q4
- Context target: 110K
- Real GPU memory usage: 23,4 GB VRAM
- Recommended GPU memory: 32 GB VRAM
- Main focus: Coding and agentic development workflows
- Tool use: Supported, depending on the client/application
- Thinking/reasoning mode: Supported, depending on the client/application

## Intended use

This model is designed for:

- Agents workflows
- Local coding assistants
- Code analysis
- Debugging support
- Refactoring suggestions
- Project exploration
- Terminal-based programming tasks
- Educational demonstrations of AI coding agents

![image.png](/assets/SetneufPT/ccode79_9b_q4_64k_16gb-gpu/89a089a9-c581-4ec0-9cc0-cc3515446e71)

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