---
license: mit
base_model:
- microsoft/Fara1.5-9B
tags:
- text-generation-inference
- computer-use
- cua
- web-agent
- multimodal
- vision-language
- agent
- browser-automation
- magentic
- fara
- fp8
- quantized
- vllm
- compressed-tensors
language:
- en
pipeline_tag: image-text-to-text
library_name: transformers
---

# **Fara1.5-9B-FP8**

Fara1.5-9B-FP8 is a FP8 dynamic quantized version of [microsoft/Fara1.5-9B](https://huggingface.co/microsoft/Fara1.5-9B), a multimodal computer use agent (CUA) for web browsers developed by Microsoft Research AI Frontiers. This quantization reduces model size and memory footprint while preserving the model's action-grounding and web-task-completion capabilities, making deployment more accessible on smaller GPUs.

Fara1.5-9B is supervised fine-tuned from Qwen3.5-9B on synthetic web-task trajectories generated by FaraGen1.5. It observes the browser through screenshots and emits structured tool calls (click, type, scroll, visit URL, web search, and so on) to complete tasks end-to-end. It is vision-only at perception time, with coordinate-grounded actions and a critical-points safety design that pauses before personal info entry, payments, submissions, sign-ins, and other irreversible actions.

## Quantization Details

Quantization was performed using `llmcompressor` with the following recipe:

```yaml
default_stage:
  default_modifiers:
    QuantizationModifier:
      targets: [Linear]
      ignore: ['re:.*lm_head', 're:.*embed_tokens$', 're:.*visual.*', 're:.*model.visual.*',
        're:.*linear_attn.*']
      scheme: FP8_DYNAMIC
      bypass_divisibility_checks: false
```

Linear layers are quantized to FP8 with dynamic per-tensor activation scaling. The `lm_head`, embedding table, all vision-tower (`visual`) components, and `linear_attn` layers are excluded from quantization and remain at full precision to preserve grounding accuracy and multimodal fidelity.

| | |
|---|---|
| **Base model** | microsoft/Fara1.5-9B |
| **Quantization scheme** | FP8_DYNAMIC (Linear layers only) |
| **Format** | compressed-tensors |
| **Original model size** | ~18.8 GB |
| **Compressed model size** | ~13.5 GB |
| **Size reduction** | ~28% |
| **Excluded from quantization** | lm_head, embed_tokens, visual encoder, linear_attn |

## Model Details

| | |
|---|---|
| **Developer (base model)** | Microsoft Research AI Frontiers |
| **Quantized by** | prithivMLmods |
| **Architecture** | Multimodal decoder-only LM (image + text to text) |
| **Parameters** | 9B |
| **Context length** | 262,144 tokens |
| **Inputs** | User goal (text), current screenshot(s), prior agent thoughts and actions |
| **Outputs** | Chain-of-thought block followed by a tool-call block (XML-tagged) |
| **License** | MIT |
| **Base model** | Qwen3.5-9B (via Fara1.5-9B) |

## Use with vLLM

Fara1.5-9B-FP8 is served through vLLM with native support for compressed-tensors FP8 checkpoints.

### Requirements

- `torch >= 2.11.0`
- `vllm >= 0.19.1`
- A GPU with FP8 support recommended (Hopper or Blackwell class) for best throughput; also runs on Ampere with FP8 dequantized on the fly.

### Serve

```bash
vllm serve prithivMLmods/Fara1.5-9B-FP8 \
  --max-model-len 262144 \
  --limit-mm-per-prompt image=10
```

### Client request

```python
from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")

screenshot_b64 = capture_browser_screenshot()  # base64 PNG

messages = [
    {"role": "system", "content": FARA_SYSTEM_PROMPT},
    {"role": "user", "content": [
        {"type": "text", "text": "Book a table for 2 at a sushi place in Sunnyvale for Friday 7pm."},
        {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{screenshot_b64}"}},
    ]},
]

response = client.chat.completions.create(
    model="prithivMLmods/Fara1.5-9B-FP8",
    messages=messages,
    temperature=0.0,
    max_tokens=2048,
)

print(response.choices[0].message.content)
```

The assistant reply contains chain-of-thought text followed by a `<tool_call>{"name": "computer_use", "arguments": {...}}</tool_call>` block. Execute the action in a sandboxed browser, capture the new screenshot, append the assistant turn and a tool/observation turn, and loop until the model emits `action="terminate"`. Keep only the most recent 3 screenshots in the chat history. The screen resolution Fara is most commonly trained with is 1440x900; match this in your sandbox for the most reliable grounding.

### System prompt

Use the original Fara system prompt verbatim for best results, as documented in the [base model card](https://huggingface.co/microsoft/Fara1.5-9B#system-prompt).

### Recommended deployment

As with the base model, the safest way to run Fara1.5-9B-FP8 is inside a sandboxed environment such as MagenticLite, with Docker isolation, domain allow-lists, watch-mode monitoring, and an immediate pause control. Do not run this model with unrestricted browser access on a machine holding sensitive credentials or files.

## Intended Use and Limitations

Intended use, known limitations, safety considerations, and responsible AI guidance are unchanged from the base model. See the [Fara1.5-9B model card](https://huggingface.co/microsoft/Fara1.5-9B) for full details on critical-points safety design, evaluation results, training data, out-of-scope use, and responsible AI considerations. Quantization to FP8 may introduce minor numerical differences relative to the bf16 base model; downstream accuracy should be validated for production use cases.

## License

Released under the MIT License, consistent with the base model.

## Papers

**Fara-1.5: Scalable Learning Environments for Computer Use Agents** — [Hugging Face Papers](https://huggingface.co/papers/2606.20785)