Instructions to use cs2764/Step-3.7-Flash_dq4-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cs2764/Step-3.7-Flash_dq4-mlx with MLX:

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

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("cs2764/Step-3.7-Flash_dq4-mlx")
config = load_config("cs2764/Step-3.7-Flash_dq4-mlx")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Transformers

How to use cs2764/Step-3.7-Flash_dq4-mlx with Transformers:

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

pipe = pipeline("image-text-to-text", model="cs2764/Step-3.7-Flash_dq4-mlx", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("cs2764/Step-3.7-Flash_dq4-mlx", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("cs2764/Step-3.7-Flash_dq4-mlx", trust_remote_code=True, device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use cs2764/Step-3.7-Flash_dq4-mlx with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cs2764/Step-3.7-Flash_dq4-mlx"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cs2764/Step-3.7-Flash_dq4-mlx",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/cs2764/Step-3.7-Flash_dq4-mlx

SGLang

How to use cs2764/Step-3.7-Flash_dq4-mlx 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 "cs2764/Step-3.7-Flash_dq4-mlx" \
    --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": "cs2764/Step-3.7-Flash_dq4-mlx",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "cs2764/Step-3.7-Flash_dq4-mlx" \
        --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": "cs2764/Step-3.7-Flash_dq4-mlx",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

How to use cs2764/Step-3.7-Flash_dq4-mlx with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "cs2764/Step-3.7-Flash_dq4-mlx"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "cs2764/Step-3.7-Flash_dq4-mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use cs2764/Step-3.7-Flash_dq4-mlx with Docker Model Runner:
```
docker model run hf.co/cs2764/Step-3.7-Flash_dq4-mlx
```

Hermes Agent

How to use cs2764/Step-3.7-Flash_dq4-mlx 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 "cs2764/Step-3.7-Flash_dq4-mlx"

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 cs2764/Step-3.7-Flash_dq4-mlx

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use cs2764/Step-3.7-Flash_dq4-mlx with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "cs2764/Step-3.7-Flash_dq4-mlx"

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 "cs2764/Step-3.7-Flash_dq4-mlx" \
  --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"

Step-3.7-Flash_dq4

This model is a DQ4 quantized version of the original model Step-3.7-Flash (local model). It was quantized locally using the mlx_lm library.

Quantization Methodology (DQ4)

This model was quantized using the dynamic DQ4 (4-bit / 5-bit / 6-bit / 8-bit mixed) approach, inspired by the methodology described in the mlx-community/Kimi-K2.5-mlx-DQ3_K_M-q8 repository.

The weights are mixed based on MLX layers:

Expert layers (switch_mlp / experts / shared experts) are quantized to 4-bit.
Expert down_proj in the first 5 layers is kept at higher quality (6-bit).
Expert down_proj every 5th layer is medium quality (5-bit).
All other layers (e.g. attention, routers, normalization) remain at 8-bit to serve as the "8-bit brain".

The table below is generated from the actual output config.json, so it reflects exactly what was quantized.

Per-layer quantization map

Group size: 64
Quantized weight matrices: 530
Bit distribution: 4-bit ×232, 5-bit ×16, 6-bit ×4, 8-bit ×278
Modules not listed below (attention projections, embeddings, lm_head, routers, norms, dense MLPs) are kept at 8-bit or full precision as the high-precision backbone.

Module pattern	Bits	Count
`language_model.lm_head`	8-bit	1
`language_model.model.embed_tokens`	8-bit	1
`language_model.model.layers.{i}.mlp.down_proj`	8-bit	3
`language_model.model.layers.{i}.mlp.gate.gate`	8-bit	42
`language_model.model.layers.{i}.mlp.gate_proj`	8-bit	3
`language_model.model.layers.{i}.mlp.share_expert.down_proj`	4-bit	32
`language_model.model.layers.{i}.mlp.share_expert.down_proj`	5-bit	8
`language_model.model.layers.{i}.mlp.share_expert.down_proj`	6-bit	2
`language_model.model.layers.{i}.mlp.share_expert.gate_proj`	4-bit	42
`language_model.model.layers.{i}.mlp.share_expert.up_proj`	4-bit	42
`language_model.model.layers.{i}.mlp.switch_mlp.down_proj`	4-bit	32
`language_model.model.layers.{i}.mlp.switch_mlp.down_proj`	5-bit	8
`language_model.model.layers.{i}.mlp.switch_mlp.down_proj`	6-bit	2
`language_model.model.layers.{i}.mlp.switch_mlp.gate_proj`	4-bit	42
`language_model.model.layers.{i}.mlp.switch_mlp.up_proj`	4-bit	42
`language_model.model.layers.{i}.mlp.up_proj`	8-bit	3
`language_model.model.layers.{i}.self_attn.g_proj`	8-bit	45
`language_model.model.layers.{i}.self_attn.k_proj`	8-bit	45
`language_model.model.layers.{i}.self_attn.o_proj`	8-bit	45
`language_model.model.layers.{i}.self_attn.q_proj`	8-bit	45
`language_model.model.layers.{i}.self_attn.v_proj`	8-bit	45

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Safetensors

Model size

197B params

Tensor type

BF16

U32

F32

MLX

Hardware compatibility

4-bit