Instructions to use inferencerlabs/DeepSeek-V4-Flash-MLX-9bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inferencerlabs/DeepSeek-V4-Flash-MLX-9bit 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-9bit")

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
LM Studio

Pi new

How to use inferencerlabs/DeepSeek-V4-Flash-MLX-9bit 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-9bit"

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": "inferencerlabs/DeepSeek-V4-Flash-MLX-9bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use inferencerlabs/DeepSeek-V4-Flash-MLX-9bit 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-9bit"

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-9bit

Run Hermes

hermes

MLX LM

How to use inferencerlabs/DeepSeek-V4-Flash-MLX-9bit 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-9bit"

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-9bit"
# 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-9bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

inferencerlabs commited on 27 days ago

Commit

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@@ -15,7 +15,7 @@ pipeline_tag: text-generation
 - Text inference: ~25.98 tokens/s @ 1000 tokens ~145.11 GiB (debug build)
 <p style="margin-bottom:0px;">
-<strong>Q9-EXP is an experimental build for DeepSeek-V4-Flash</strong>
 In this build, the 4-bit pre-quantized weights of the base model were repacked (rather than dequantized and re-quantized to 9-bit), as this approach performed slightly better in our initial coding tests. All remaining weights were quantized to 9-bit. It also includes a temporary chat template. Stay tuned for updates.
 </p>

 - Text inference: ~25.98 tokens/s @ 1000 tokens ~145.11 GiB (debug build)
 <p style="margin-bottom:0px;">
+<strong>Q9-EXP is an experimental build of DeepSeek-V4-Flash</strong>
 In this build, the 4-bit pre-quantized weights of the base model were repacked (rather than dequantized and re-quantized to 9-bit), as this approach performed slightly better in our initial coding tests. All remaining weights were quantized to 9-bit. It also includes a temporary chat template. Stay tuned for updates.
 </p>