Instructions to use mlx-community/Agents-A1-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/Agents-A1-bf16 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("mlx-community/Agents-A1-bf16")
config = load_config("mlx-community/Agents-A1-bf16")

# 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)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use mlx-community/Agents-A1-bf16 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/Agents-A1-bf16"

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/Agents-A1-bf16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mlx-community/Agents-A1-bf16 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/Agents-A1-bf16"

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/Agents-A1-bf16

Run Hermes

hermes

OpenClaw new

How to use mlx-community/Agents-A1-bf16 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/Agents-A1-bf16"

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/Agents-A1-bf16" \
  --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"

gabfssilva commited on 17 days ago

Commit

a13eccf

verified ·

1 Parent(s): 33ab198

Card: drop leaderboard + batching-context notes

Browse files

Files changed (1) hide show

README.md +2 -3

README.md CHANGED Viewed

@@ -39,7 +39,7 @@ This build was produced by patching mlx-vlm's `sanitize()` to stack the per-expe
 ## Throughput
 Measured with oMLX's benchmark harness on a Macbook Pro M5 Max 128GB 40 GPU — gen 128 tokens,
-cold prefill (unique prompt prefix per request, no cache reuse). Published to the omlx.ai leaderboard.
 ### Single request (batch 1) — decode tok/s by context
@@ -66,8 +66,7 @@ TTFT (cold prefill) is ~precision-independent — ≈0.3 s @1k, 3 s @8k, 21 s @3
 |     4 | 107.1 | 202.0 | 185.1 | 195.7 | 239.9 | 230.2 |
 |     8 | 129.6 | 252.4 | 223.4 | 238.7 | 289.0 | 276.1 |
-Aggregate across the batch; per-request rate is that value divided by the batch size. The oMLX
-harness fixes continuous batching at 1k context (batch size varies, prompt length does not).
 ## Smoke test

 ## Throughput
 Measured with oMLX's benchmark harness on a Macbook Pro M5 Max 128GB 40 GPU — gen 128 tokens,
+cold prefill (unique prompt prefix per request, no cache reuse).
 ### Single request (batch 1) — decode tok/s by context
 |     4 | 107.1 | 202.0 | 185.1 | 195.7 | 239.9 | 230.2 |
 |     8 | 129.6 | 252.4 | 223.4 | 238.7 | 289.0 | 276.1 |
+Aggregate across the batch; per-request rate is that value divided by the batch size.
 ## Smoke test