Instructions to use dcostenco/prism-coder-9b-mlx-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dcostenco/prism-coder-9b-mlx-8bit 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("dcostenco/prism-coder-9b-mlx-8bit")

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 dcostenco/prism-coder-9b-mlx-8bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "dcostenco/prism-coder-9b-mlx-8bit"

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": "dcostenco/prism-coder-9b-mlx-8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use dcostenco/prism-coder-9b-mlx-8bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "dcostenco/prism-coder-9b-mlx-8bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "dcostenco/prism-coder-9b-mlx-8bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "dcostenco/prism-coder-9b-mlx-8bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use dcostenco/prism-coder-9b-mlx-8bit 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 "dcostenco/prism-coder-9b-mlx-8bit"

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 dcostenco/prism-coder-9b-mlx-8bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use dcostenco/prism-coder-9b-mlx-8bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "dcostenco/prism-coder-9b-mlx-8bit"

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 "dcostenco/prism-coder-9b-mlx-8bit" \
  --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"

prism-coder-9b-mlx-8bit

dcostenco/prism-coder-9b converted to MLX with 8-bit quantization, for Apple Silicon. Text only: the base model's vision tower is not included.

Provenance

Source: dcostenco/prism-coder-9b at revision 926f129a0ba1818bd69f8d32c2917c84609ab665.
Quantization: 8 bits, group size 64 (8.501 bits per weight with scales).
Converted with mlx-lm 0.31.4.dev132+g94cdcae13 and mlx 0.32.2, the runtime bundled with oMLX 0.7.0.
Converting the same source files again with these versions gave byte-identical output.

To reproduce:

hf download dcostenco/prism-coder-9b --revision 926f129a0ba1818bd69f8d32c2917c84609ab665 \
  --include "*.safetensors" --include "*.json" --include "chat_template.jinja" \
  --local-dir prism-coder-9b-src
python -m mlx_lm convert --hf-path prism-coder-9b-src --mlx-path prism-coder-9b-mlx-8bit \
  -q --q-bits 8 --q-group-size 64

Use with mlx-lm

python -m mlx_lm generate --model dcostenco/prism-coder-9b-mlx-8bit \
  --prompt "Write a Python one-liner that reverses a string." --max-tokens 60

Tested with the versions above: about 59 tokens per second, 9.7 GB peak memory.

Install for oMLX

oMLX serves the models in its model directory (~/.omlx/models by default) under their folder names:

hf download dcostenco/prism-coder-9b-mlx-8bit --local-dir ~/.omlx/models/prism-coder-9b-mlx-8bit

Then restart oMLX.

License

Apache-2.0, the same as the base model.

Downloads last month: 195

Safetensors

Model size

9B params

Tensor type

U32

BF16

MLX

Hardware compatibility

8-bit

Model tree for dcostenco/prism-coder-9b-mlx-8bit

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Finetuned

dcostenco/prism-coder-9b

Quantized

(2)

this model