SetneufPT/ Qwen3.8-27B_Q4_100k_24GB-GPU

34 Downloads Updated 2 days ago

Custom model for coding with agents to use locally with 24gb GPUs - BEST FOR OPENCODE!

vision tools thinking

ollama run SetneufPT/Qwen3.8-27B_Q4_100k_24GB-GPU

curl http://localhost:11434/api/chat \
  -d '{
    "model": "SetneufPT/Qwen3.8-27B_Q4_100k_24GB-GPU",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

from ollama import chat

response = chat(
    model='SetneufPT/Qwen3.8-27B_Q4_100k_24GB-GPU',
    messages=[{'role': 'user', 'content': 'Hello!'}],
)
print(response.message.content)

import ollama from 'ollama'

const response = await ollama.chat({
  model: 'SetneufPT/Qwen3.8-27B_Q4_100k_24GB-GPU',
  messages: [{role: 'user', content: 'Hello!'}],
})
console.log(response.message.content)

Applications

Claude Code

Claude Code ollama launch claude --model SetneufPT/Qwen3.8-27B_Q4_100k_24GB-GPU

OpenCode

OpenCode ollama launch opencode --model SetneufPT/Qwen3.8-27B_Q4_100k_24GB-GPU

Hermes Agent

Hermes Agent ollama launch hermes --model SetneufPT/Qwen3.8-27B_Q4_100k_24GB-GPU

OpenClaw

OpenClaw ollama launch openclaw --model SetneufPT/Qwen3.8-27B_Q4_100k_24GB-GPU

Models

Name

1 model

Size / Usage

Context

Input

Qwen3.8-27B_Q4_100k_24GB-GPU:latest

18GB · 256K context window · Text, Image · 2 days ago

Qwen3.8-27B_Q4_100k_24GB-GPU:latest

18GB

256K

Text, Image

Readme

Qwen 3.8 - 27B param, Q4, 100K ctx, Local/Offline, 24GB GPU

Custom Ollama model, fine-tuned from Qwen3.8, configured for local coding-agent workflows, especially with Open Code / Hermes.

This model is based on a 27B parameter 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: Text/vision model
Size: 27B parameters
Quantization: Q4
Context target: 100K
Real GPU memory usage: 23,5 GB VRAM
Recommended GPU memory: 24 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

---
# Qwen 3.8  - 27B param, Q4, 100K ctx, Local/Offline, 24GB GPU

Custom Ollama model, fine-tuned from Qwen3.8, configured for local coding-agent workflows, especially with Open Code / Hermes.

This model is based on a 27B parameter 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: Text/vision model
- Size: 27B parameters
- Quantization: Q4
- Context target: 100K
- Real GPU memory usage: 23,5 GB VRAM
- Recommended GPU memory: 24 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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