rbrisita/qwen3-coder-1m:ud-q4_K

rbrisita/ qwen3-coder-1m:ud-q4_K_XL

47 Downloads Updated 1 week ago

Qwen3 Coder 30B A3B 1M Unsloth Dynamic 2.0 GGUF MoE

ollama run rbrisita/qwen3-coder-1m:ud-q4_K_XL

curl http://localhost:11434/api/chat \
  -d '{
    "model": "rbrisita/qwen3-coder-1m:ud-q4_K_XL",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

from ollama import chat

response = chat(
    model='rbrisita/qwen3-coder-1m:ud-q4_K_XL',
    messages=[{'role': 'user', 'content': 'Hello!'}],
)
print(response.message.content)

import ollama from 'ollama'

const response = await ollama.chat({
  model: 'rbrisita/qwen3-coder-1m:ud-q4_K_XL',
  messages: [{role: 'user', content: 'Hello!'}],
})
console.log(response.message.content)

Details

Updated 1 week ago

1 week ago

1c135a464b36 · 18GB

model

archqwen3moe

parameters30.5B

quantizationQ4_K_M

18GB

params

{ "min_p": 0, "repeat_penalty": 1.05, "seed": 1337, "temperature": 0.7, "top_k":

87B

Readme

Qwen3-Coder, an 30B Instruct Mixture-of-Experts model (3B active parameters) with 1M context for fast agentic coding and local use.

Modelfile parameters taken from:

Supporting Details:

Unsloth Dynamic 2.0 GGUFs

Tested on:

 OS: Ubuntu 26.04 resolute
 Kernel: x86_64 Linux 7.0.0-30-generic
 Shell: bash 5.3.9
 Disk: 1TB
 CPU: 12th Gen Intel Core i7-12700H @ 20x 4.6GHz
 RAM: 64GB 
 GPU: NVIDIA GeForce RTX 3050 Laptop GPU
 GPU RAM: 4GB