--- title: Fara1.5-4B-8bit canonical_url: "https://www.modelscope.cn/models/mlx-community/Fara1.5-4B-8bit" md_url: "https://www.modelscope.cn/models/mlx-community/Fara1.5-4B-8bit.md" repository: mlx-community/Fara1.5-4B-8bit last_updated: 2026-08-05 license: mit pipeline_tag: image-text-to-text tasks: - image-text-to-text model_type: - qwen3_5 architectures: - Qwen3_5ForConditionalGeneration base_model: - microsoft/Fara1.5-4B base_model_relation: quantized parameters: 1.5B tensor_type: - BF16 - U32 library_name: - mlx - safetensors - pytorch frameworks: - pytorch downloads: 20 stars: 0 tags: - mlx - computer-use - cua - web-agent - multimodal - vision-language - agent --- # Fara1.5-4B-8bit > Fara1.5-4B-8bit - mlx-community 在 ModelScope 开源的模型。8-bit MLX quantization of microsoft/Fara1.5-4B, a 4.5B computer-use / web-agent VLM built on Qwen3.5-4B. It reads screenshots and acts on interfaces. Converted with mlx-vlm 0.6.8 (mlx 0.32.0) for inference… mlx-community/Fara1.5-4B-8bit 是 ModelScope 魔搭社区上的 1.5B 参数image-text-to-text模型，采用 mit 许可，基于 microsoft/Fara1.5-4B 构建。 - **Repository**: mlx-community/Fara1.5-4B-8bit - **License**: mit - **Tasks**: image-text-to-text - **Parameters**: 1.5B - **Base model**: microsoft/Fara1.5-4B - **Tags**: mlx, computer-use, cua, web-agent, multimodal, vision-language, agent - **Downloads**: 20 - **Stars**: 0 - **Last updated**: 2026-08-05 Source: https://www.modelscope.cn/models/mlx-community/Fara1.5-4B-8bit --- # Fara1.5-4B-8bit 8-bit MLX quantization of [`microsoft/Fara1.5-4B`](https://huggingface.co/microsoft/Fara1.5-4B), a 4.5B computer-use / web-agent VLM built on Qwen3.5-4B. It reads screenshots and acts on interfaces. Converted with `mlx-vlm` 0.6.8 (`mlx` 0.32.0) for inference on Apple Silicon. Every prior MLX conversion of this model is 4-bit, so **8-bit was the open gap**. Fidelity was measured against the unquantized bf16 source, not assumed. ```bash pip install mlx-vlm python -m mlx_vlm generate \ --model mlx-community/Fara1.5-4B-8bit \ --image screenshot.png \ --prompt "Describe the buttons and the total shown in this screenshot." \ --max-tokens 256 ``` Image input needs `mlx-vlm`. Stock `mlx-lm` loads the text path only, which for a computer-use model is not a smaller model but a broken one. ## Quantization | | | |---|---| | Bits | 8 | | Group size | 64 | | Mode | affine | | Effective bits/weight | **9.053** | | Size on disk | 4.8 GB | | Quantized tensors | 249 (language model) | | Unquantized tensors | 297 (vision tower, bf16) | Effective bits per weight exceeds the nominal 8 because `mlx-vlm` quantizes only the language model and leaves the vision tower in bf16 by design. **The vision path is preserved**, which for a computer-use model is the whole point: verified at tensor level, of 297 vision tensors **zero** carry a `.scales` entry. ### A conversion trap worth knowing about This model declares `dtype: bfloat16` at the top level of its config but **`float32` in both `text_config` and `vision_config`**. `mlx_vlm.convert` honours the sub-configs, so converting without an explicit `--dtype bfloat16` silently keeps the scales, biases and the entire vision tower in fp32: | | default | `--dtype bfloat16` | |---|---|---| | Effective bpw | 10.695 | **9.053** | | Size on disk | 5.7 GB | **4.8 GB** | | Peak RAM | 7.44 GB | **6.23 GB** | | Weight SNR vs bf16 | 45.22 dB | **42.70 dB** | This repo is the `--dtype bfloat16` build. The higher SNR of the fp32 build is **not** better fidelity: it is a float32 reconstruction being compared against a bf16 source, bought with 0.9 GB of precision that has no business in an 8-bit repo. Rounding those scales to bf16 accounts for the entire 2.5 dB difference. ## Measured fidelity Against the **unquantized bf16 source**, greedy decoding, on an M2 Pro / 32 GB. | Metric | bf16 (source) | 8-bit | |---|---|---| | Weight SNR | n/a | **42.70 dB** | | Relative L2 | n/a | 0.73% | | Cosine similarity | n/a | 0.999973 | | Worst single element | n/a | 0.003906 | | Perplexity | 3.4974 | 3.4775 | | Top-1 agreement | n/a | **0.9853** | | KL (nats/token) | 0 | **0.00086** | | Task accuracy | 7/8 | **7/8** | | BLEU vs bf16 | n/a | 81.30 | | Decode tok/s | 18.5 | **35.4** | | Peak RAM | 9.23 GB | **6.15 GB** | Weight-level metrics stream the source tensor by tensor from disk over all 4,204,789,760 quantized parameters. No prompts, no sampling, hardware-independent. **42.70 dB is the expected value, and that is the point.** Six 8-bit affine conversions at group size 64 across six different architectures (AREX-Turbo, Fara-7B, Fara1.5-4B, GLM-4.1V, MagenticBrain, OvisOCR2) all land on **42.66-42.70 dB**. A reading outside that band is a defect signal, which is exactly how the fp32-scales issue above was caught. Highest-error tensors, all in the linear-attention input projections: ``` rel_l2=0.00841 snr=41.50 dB language_model.model.layers.25.linear_attn.in_proj_a rel_l2=0.00839 snr=41.53 dB language_model.model.layers.28.linear_attn.in_proj_a rel_l2=0.00834 snr=41.57 dB language_model.model.layers.26.linear_attn.in_proj_a ``` **1.9x faster, 1.5x less memory** than bf16. ### Vision path, verified working rather than merely present Given a synthetic UI screenshot, the 8-bit model read both button labels (`SubmitOrder`, `Cancel`) and the total (`42.50 USD`) correctly. ### On the perplexity aggregate The harness reports aggregate perplexity as `exp(sum_nll/sum_tokens)` over five passages, and flags it as unreliable here: the bf16 source itself spans **10.3x** across those passages, so a single out-of-domain passage can dominate. The per-passage ratios are reported instead, and they agree with the aggregate rather than contradict it, all within 1.8% of the bf16 reference: | Passage | bf16 PPL | 8-bit PPL | Ratio | |---|---|---|---| | prose | 4.66 | 4.69 | 1.007 | | python-code | 1.23 | 1.23 | 0.999 | | legal | 8.82 | 8.70 | 0.986 | | biology | 6.62 | 6.64 | 1.002 | | meta | 12.74 | 12.51 | 0.982 | BLEU of 81.30 measures **drift, not quality**: it treats the bf16 output as ground truth, so it penalises valid paraphrase and would penalise a better answer. ## What was not measured - **No judged quality.** In the open-ended set 11/18 (bf16) and 14/18 (8-bit) answers hit the 640-token cap, so grading them would have measured truncation rather than quality. This model emits a `` block, which consumes budget. - **No agentic evaluation.** No WebArena, no ScreenSpot, no WebVoyager, no end-to-end computer-use task. This model's actual job is multi-step browser automation and none of that is scored here. - **No standard benchmarks** (MMLU, GSM8K). The accuracy layer is 8 short verifiable prompts, not a benchmark. - **The vision check is one screenshot**, not a dataset, and it was scored for correctness by reading it, not by a metric. - Throughput figures are for one M2 Pro and do not transfer to other chips. ## Related conversions | Model | Variant | Note | |---|---|---| | [Fara1.5-9B-8bit](https://huggingface.co/mlx-community/Fara1.5-9B-8bit) | 8.86 bpw | larger sibling, top-1 agreement 1.000 | | [Fara-7B-8bit](https://huggingface.co/mlx-community/Fara-7B-8bit) | 9.11 bpw | previous generation | | [Fara-7B-4bit](https://huggingface.co/mlx-community/Fara-7B-4bit) | 5.44 bpw | previous generation | ## Credits All credit for the underlying model belongs to [Microsoft](https://huggingface.co/microsoft). This is a format conversion and quantization; no training or fine-tuning was performed. See the [source model card](https://huggingface.co/microsoft/Fara1.5-4B) for license, intended use and limitations.