Instructions to use Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit with Transformers:

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

pipe = pipeline("text-generation", model="Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit")
model = AutoModelForCausalLM.from_pretrained("Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit", 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]:]))

MLX

How to use Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit

SGLang

How to use Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit 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 "Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit" \
    --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": "Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit",
		"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 "Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit" \
        --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": "Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

How to use Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit with Docker Model Runner:
```
docker model run hf.co/Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit
```

Hermes Agent

How to use Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit"

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 Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit"

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 "Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

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

Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit

The Model Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit was converted to MLX format from flux-inc/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0 using mlx-lm version 0.28.3.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit")

prompt="hello"

if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)

Downloads last month: 109

Safetensors

Model size

33B params

Tensor type

U32

BF16

MLX

Hardware compatibility

4-bit

Model tree for Niwahi/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0-mlx-4Bit

Base model

Qwen/Qwen2.5-32B

Finetuned

flux-inc/Flux-Japanese-Qwen2.5-32B-Instruct-V1.0

Quantized

(3)

this model