license: mit
library_name: mlx
pipeline_tag: image-text-to-text
language:
- en
base_model: microsoft/Fara1.5-9B
tags:
- computer-use
- cua
- web-agent
- multimodal
- vision-language
- agent
- browser-automation
- magentic
- fara
- mlx
mlx-community/Fara1.5-9B-8bit
microsoft/Fara1.5-9B converted to MLX and quantized to 8-bit, for inference on Apple Silicon.
Fara1.5 is a computer-use / web-agent vision-language model built on Qwen3.5 — it reads screenshots and acts on interfaces. The vision path is preserved in this conversion, because for this model class it is the point.
Quantization
| Requested bits | 8 |
| Group size | 64 |
| Mode | affine |
| Effective bits per weight | 8.86 |
| On-disk size | 9.8 GB |
| Shards | 2 |
Effective bits exceed the requested value because mlx-vlm quantizes only the
language model and leaves the vision tower in bf16 by design — 333 vision
tensors, none of them quantized. The vision encoder is a small share of the
weights but disproportionately sensitive to quantization error.
language_model : quantized (8-bit, group size 64)
vision_tower : 333 tensors, bf16 <- unquantized
Measured results
All figures measured against the unquantized bf16 source, greedy decoding, on an M2 Pro / 32 GB. At 9B the bf16 control fits in memory, so this is a behavioural comparison, not a weight-level proxy.
| Metric | bf16 (source) | 8-bit |
|---|---|---|
| Perplexity | 3.2339 | 3.2150 |
| Perplexity ratio | 1.000 | 0.994 |
| Top-1 agreement | — | 1.000 |
| KL(bf16 ‖ quant) | 0 | 0.000593 nats/tok |
| Task accuracy | 8/8 | 8/8 |
| Decode tok/s | 10.2 | 19.7 |
| Peak RAM | 18.97 GB | 11.91 GB |
Top-1 agreement is 1.000 over 204 teacher-forced tokens — the 8-bit model picked the same argmax token as bf16 on every one. KL divergence is 5.9e-4 nats/token. At this fidelity, quantization is not the limiting factor in output quality.
At 8-bit the model runs 1.9x faster using 1.6x less memory than bf16.
Generation agreement (reported, but not a quality metric)
| Metric | vs bf16 |
|---|---|
| BLEU | 62.05 |
| chrF | 66.9 |
| ROUGE-1 / ROUGE-L | 0.736 / 0.731 |
| Exact match | 4/6 |
BLEU against bf16 is not a quality measure. It treats bf16 output as ground truth, so it penalises valid paraphrase. The divergences here are exactly that — both models answer correctly, then differ in trailing filler:
prompt: "Name the capital of Japan in one word."
bf16 : "Tokyo ... I will answer the question directly without any extra content."
8-bit: "Tokyo ... The user asked for the capital of Japan in one word."
Same answer, different tail. That is why task accuracy is measured separately — and it is 8/8 for both.
Vision path
Verified working, not merely present. Given a synthetic UI screenshot with two buttons and a total, the 8-bit model returned:
"two large rectangular buttons side by side: On the left: a "SubmitOrder" button. On the right: a "Cancel" button. Below them, the total amount displayed is 42.50 USD."
Both button labels and the amount read correctly.
What was not measured
No standard benchmarks — no MMLU, GSM8K, ScreenSpot, WebArena, or any agentic evaluation. The accuracy layer is 8 short verifiable prompts, not a benchmark. No blind LLM-judge quality grading was run for this variant. The vision path was verified for correctness on a single synthetic screenshot, not scored on a dataset. If your use case is the full computer-use loop, evaluate on your own tasks.
Usage
pip install mlx-vlm
from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
model, processor = load("mlx-community/Fara1.5-9B-8bit")
prompt = apply_chat_template(
processor, model.config,
"Describe this screenshot. What buttons do you see?",
num_images=1,
)
out = generate(model, processor, prompt, image=["screenshot.png"], max_tokens=256)
print(out.text)
Text-only works too — pass num_images=0 and omit image.
Note that stock mlx-lm loads the text path only; use mlx-vlm for image
input.
Credits
All credit for the model belongs to Microsoft. This is a format conversion and quantization; no training or fine-tuning was performed. Licensed MIT, as the original. See the original card for intended use and limitations.