Instructions to use AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16")
model = AutoModelForCausalLM.from_pretrained("AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16:Q4_K_M

Use Docker

docker model run hf.co/AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16:Q4_K_M

LM Studio
Jan

vLLM

How to use AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16:Q4_K_M

SGLang

How to use AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16 with Ollama:
```
ollama run hf.co/AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16:Q4_K_M
```
Unsloth Desktop

How to use AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16 with Docker Model Runner:
```
docker model run hf.co/AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16:Q4_K_M
```

Lemonade

How to use AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16:Q4_K_M

Run and chat with the model

lemonade run user.NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

NOESIS / AMAImedia

Last updated: 2026-08-29

Released as part of the NOESIS Professional Multilingual Dubbing Automation Platform (framework: DHCF-FNO — Deterministic Hybrid Control Framework for Frozen Neural Operators).

Founder: Ilia Bolotnikov
Organization: AMAImedia.com
X (Twitter): @AMAImediacom
LinkedIn: Ilia Bolotnikov
Telegram: @djbionicl
NOESIS version: v16.1
Release date: 2026-08-26

Language support

This Qwen3.5-derived model follows the official Qwen3 language list below (119 languages and dialects) and the official Qwen3.5 coverage statement of 201 languages and dialects. Qwen3.5 publishes the expanded coverage count but does not provide an exhaustive 201-name enumeration in its model card. The list below is the complete language list published by Qwen for Qwen3 and is included as the transparent, documented baseline for this derivative.

English, French, Portuguese, German, Romanian, Swedish, Danish, Bulgarian, Russian, Czech, Greek, Ukrainian, Spanish, Dutch, Slovak, Croatian, Polish, Lithuanian, Norwegian Bokmål, Norwegian Nynorsk, Persian, Slovenian, Gujarati, Latvian, Italian, Occitan, Nepali, Marathi, Belarusian, Serbian, Luxembourgish, Venetian, Assamese, Welsh, Silesian, Asturian, Chhattisgarhi, Awadhi, Maithili, Bhojpuri, Sindhi, Irish, Faroese, Hindi, Punjabi, Bengali, Oriya, Tajik, Eastern Yiddish, Lombard, Ligurian, Sicilian, Friulian, Sardinian, Galician, Catalan, Icelandic, Tosk Albanian, Limburgish, Dari, Afrikaans, Macedonian, Sinhala, Urdu, Magahi, Bosnian, Armenian; Chinese (Simplified Chinese, Traditional Chinese, Cantonese), Burmese; Arabic (Standard, Najdi, Levantine, Egyptian, Moroccan, Mesopotamian, Ta’izzi-Adeni, Tunisian), Hebrew, Maltese; Indonesian, Malay, Tagalog, Cebuano, Javanese, Sundanese, Minangkabau, Balinese, Banjar, Pangasinan, Iloko, Waray (Philippines); Tamil, Telugu, Kannada, Malayalam; Turkish, North Azerbaijani, Northern Uzbek, Kazakh, Bashkir, Tatar; Thai, Lao; Finnish, Estonian, Hungarian; Vietnamese, Khmer; Japanese, Korean, Georgian, Basque, Haitian, Papiamento, Kabuverdianu, Tok Pisin, Swahili.

NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16

AMAImedia NOESIS Qwopus3.5 v3.5 coding specialist. This repository contains the BF16 safetensors model, tokenizer/configuration files, chat template, and the corresponding GGUF Q4_K_M artifact under gguf/.

Use for code generation, code transformation, debugging assistance, repository work, and technical instruction following. The BF16 checkpoint is the primary artifact; the GGUF file is provided for local llama.cpp-compatible deployment.

from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")

This is an AMAImedia NOESIS release; the BF16 and GGUF variants are kept together and are not external repacks.

Downloads last month: 927

Safetensors

Model size

9B params

Tensor type

BF16

Collection including AMAImedia/NOESIS-Qwopus3.5-9B-Coder-v3.5-BF16

NOESIS-Original-Trained-Models

Collection

24 items • Updated about 7 hours ago