Instructions to use inferencerlabs/DeepSeek-V4-Flash-MLX-Q2i with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inferencerlabs/DeepSeek-V4-Flash-MLX-Q2i 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("inferencerlabs/DeepSeek-V4-Flash-MLX-Q2i")

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

How to use inferencerlabs/DeepSeek-V4-Flash-MLX-Q2i with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "inferencerlabs/DeepSeek-V4-Flash-MLX-Q2i"

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": "inferencerlabs/DeepSeek-V4-Flash-MLX-Q2i"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use inferencerlabs/DeepSeek-V4-Flash-MLX-Q2i with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "inferencerlabs/DeepSeek-V4-Flash-MLX-Q2i"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "inferencerlabs/DeepSeek-V4-Flash-MLX-Q2i"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "inferencerlabs/DeepSeek-V4-Flash-MLX-Q2i",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use inferencerlabs/DeepSeek-V4-Flash-MLX-Q2i 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 "inferencerlabs/DeepSeek-V4-Flash-MLX-Q2i"

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 inferencerlabs/DeepSeek-V4-Flash-MLX-Q2i

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use inferencerlabs/DeepSeek-V4-Flash-MLX-Q2i with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "inferencerlabs/DeepSeek-V4-Flash-MLX-Q2i"

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 "inferencerlabs/DeepSeek-V4-Flash-MLX-Q2i" \
  --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"

DeepSeek-V4-Flash-MLX-Q2i

File size: 1,722 Bytes

---
language: en
tags:
- quantized
- mlx
base_model:
- deepseek-ai/DeepSeek-V4-Flash
base_model_relation: quantized
library_name: mlx
pipeline_tag: text-generation
---
# NOTICE
<marquee direction="left" width="400">
    <h1 style="font-size: 60px; color: red;">CURRENTLY UPLOADING...&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</h1>
</marquee>

**See DeepSeek-V4-Flash MLX in action - [demonstration videos](https://youtube.com/xcreate)**
   
#### Tested with an M3 Ultra 512 GiB using [Inferencer app v1.11.1](https://inferencer.com)
- Text inference: ~30 tokens/s @ 2000 tokens ~95.49 GiB (debug build)

<p style="margin-bottom:0px;">
<strong>Q2.8-EXP is an experimental build of DeepSeek-V4-Flash</strong>

This build compresses the model to operate within the memory constraints of a 128 GiB RAM system while maintaining response coherence. However, overall accuracy is degraded due to the level of compression. Stay tuned for updates.
</p>

![Screenshot](https://cdn-uploads.huggingface.co/production/uploads/688479d616f1ec82fa645019/TphT4hr5CABuKO1MGccqh.jpeg)

##### Quantized with a modified version of [MLX](https://github.com/ml-explore/mlx)
##### For more details see our [demonstration videos](https://youtube.com/xcreate) or visit [DeepSeek-V4-Flash](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro).

## Disclaimer

We are not the creator, originator, or owner of any model listed. Each model is created and provided by third parties. Models may not always be accurate or contextually appropriate. You are responsible for verifying the information before making important decisions. We are not liable for any damages, losses, or issues arising from its use, including data loss or inaccuracies in AI-generated content.