Instructions to use mlx-community/gemma-4-e4b-it-OptiQ-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/gemma-4-e4b-it-OptiQ-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("mlx-community/gemma-4-e4b-it-OptiQ-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

How to use mlx-community/gemma-4-e4b-it-OptiQ-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 "mlx-community/gemma-4-e4b-it-OptiQ-4bit"

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": "mlx-community/gemma-4-e4b-it-OptiQ-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use mlx-community/gemma-4-e4b-it-OptiQ-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 "mlx-community/gemma-4-e4b-it-OptiQ-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "mlx-community/gemma-4-e4b-it-OptiQ-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "mlx-community/gemma-4-e4b-it-OptiQ-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use mlx-community/gemma-4-e4b-it-OptiQ-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 "mlx-community/gemma-4-e4b-it-OptiQ-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 mlx-community/gemma-4-e4b-it-OptiQ-4bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mlx-community/gemma-4-e4b-it-OptiQ-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 "mlx-community/gemma-4-e4b-it-OptiQ-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 "mlx-community/gemma-4-e4b-it-OptiQ-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"

codelion commited on Jul 14

Commit

2060450

verified ·

1 Parent(s): 03de9be

Correct BFCL + Capability: the AST checker could not match array-typed arguments

Browse files

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README.md +1 -1

README.md CHANGED Viewed

@@ -87,7 +87,7 @@ Six-metric Capability Score (mean of MMLU + GSM8K + IFEval + BFCL + HumanEval +
 | BFCL-V3 simple (200 calls) | **87.5%** | 85.0% | +2.5 |
 | HumanEval (164 problems, pass@1) | **76.8%** | 58.5% | +18.3 |
 | HashHop (long-context retrieval) | **42.0%** | 20.0% | +22.0 |
-| **Capability Score** (mean of 6) | **68.92** | 55.20 | **+13.73** |
 | KL vs bf16 reference (mean / p95) | 0.2755 / 1.3460 |, |, |
 | On-disk size | 6.1 GB | 4.9 GB | +1.2 |

 | BFCL-V3 simple (200 calls) | **87.5%** | 85.0% | +2.5 |
 | HumanEval (164 problems, pass@1) | **76.8%** | 58.5% | +18.3 |
 | HashHop (long-context retrieval) | **42.0%** | 20.0% | +22.0 |
+| **Capability Score** (mean of 6) | **68.92** | 55.20 | **+13.72** |
 | KL vs bf16 reference (mean / p95) | 0.2755 / 1.3460 |, |, |
 | On-disk size | 6.1 GB | 4.9 GB | +1.2 |