Instructions to use ToPo-ToPo/Agents-A1-4B-mlx-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ToPo-ToPo/Agents-A1-4B-mlx-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("ToPo-ToPo/Agents-A1-4B-mlx-bf16")
config = load_config("ToPo-ToPo/Agents-A1-4B-mlx-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 ToPo-ToPo/Agents-A1-4B-mlx-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 "ToPo-ToPo/Agents-A1-4B-mlx-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": "ToPo-ToPo/Agents-A1-4B-mlx-bf16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ToPo-ToPo/Agents-A1-4B-mlx-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 "ToPo-ToPo/Agents-A1-4B-mlx-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 ToPo-ToPo/Agents-A1-4B-mlx-bf16

Run Hermes

hermes

OpenClaw new

How to use ToPo-ToPo/Agents-A1-4B-mlx-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 "ToPo-ToPo/Agents-A1-4B-mlx-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 "ToPo-ToPo/Agents-A1-4B-mlx-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"

ToPo-ToPo/Agents-A1-4B-mlx-bf16

MLX bf16 conversion of InternScience/Agents-A1-4B for Apple Silicon (mlx-vlm).

Agents-A1-4B is a Qwen3_5ForConditionalGeneration (Qwen3.5-VL family) vision-language model. The text tower uses a hybrid of linear attention and full attention (full_attention_interval: 4) with interleaved MRoPE.

Provenance (self-converted from official weights)

Source: InternScience/Agents-A1-4B (license: apache-2.0)
Tool: mlx-vlm 0.6.6 — mlx_vlm.convert --hf-path InternScience/Agents-A1-4B --mlx-path . --dtype bfloat16
Effective: 16 bits/weight (unquantized)
Size on disk: 8.5 GB — peak memory 9.9 GB, ~70 tok/s generation (M-series, short prompt)

Effective bit width is higher than nominal because embeddings and parts of the vision tower are kept at higher precision (mlx default behaviour).

Validation

Checked after conversion: correctly described a synthetic test image (red square / blue circle, both shape and colour), and answered a text-only arithmetic prompt correctly. This is a smoke test, not a benchmark — no eval suite was run.

Usage

from mlx_vlm import load, generate
model, processor = load("ToPo-ToPo/Agents-A1-4B-mlx-bf16")

CLI:

mlx_vlm.generate --model ToPo-ToPo/Agents-A1-4B-mlx-bf16 \
  --image path/to/image.png --prompt "Describe this image." --max-tokens 200

Requirements

mlx-vlm >= 0.6.6. Earlier versions do not implement the qwen3_5 architecture and cannot load or convert this model.

License

Apache-2.0, inherited from the base model. Weights were converted/quantized to MLX format; no other modification to the weights.

Note on config files

tokenizer_config.json and processor_config.json were re-serialized by transformers 5.14.1 during conversion, so they differ textually from upstream: the chat template now lives in chat_template.jinja (byte-identical to upstream), special tokens moved into model_specific_special_tokens, and the image processor uses min_pixels/max_pixels instead of size/resample. The tokenizer was verified functionally identical to upstream (identical vocab, and identical encode/decode on a spot-check set). Users on transformers 4.x should prefer the upstream config files.

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Safetensors

Model size

5B params

Tensor type

BF16

MLX

Hardware compatibility

Quantized

Model tree for ToPo-ToPo/Agents-A1-4B-mlx-bf16

Base model

InternScience/Agents-A1-4B

Finetuned

(7)

this model