---
base_model: ornith-ai/Ornith-1.5-9B
base_model_relation: quantized
license: mit
pipeline_tag: image-text-to-text
tags:
  - compressed-tensors
  - fp8
  - quantized
  - vllm
---
# Ornith-1.5-9B - FP8

[`ornith-ai/Ornith-1.5-9B`](https://huggingface.co/ornith-ai/Ornith-1.5-9B) quantized to **FP8** (8-bit weights).

## What this is

Near-lossless, no calibration data, and it halves every Linear weight. The safe default when you care about quality and have Ada/Hopper or newer.

> **Caveat.** Needs compute capability >= 8.9 (Ada/Hopper+) to run fast.

## Details

| | |
|---|---|
| Source | `ornith-ai/Ornith-1.5-9B` |
| Scheme | `FP8` (8-bit) |
| Format | compressed-tensors |
| Parameters | 9.4B |
| Size on disk | 11.9 GB |
| Compression | 1.58x smaller than the 18.8 GB source |
| Left unquantized | `lm_head`, `re:.*visual.*`, `re:.*vision_tower.*`, `re:.*vision_model.*`, `re:.*vision.*`, `re:.*multi_modal_projector.*`, `re:.*merger.*` |
| Quantized on | A40 |
| Quantized by | [Sohailhosseini](https://huggingface.co/Sohailhosseini) |

## Usage

```bash
vllm serve Sohailhosseini/Ornith-1.5-9B-FP8 \
  --max-model-len 32768
```

```python
from vllm import LLM, SamplingParams

if __name__ == "__main__":
    llm = LLM("Sohailhosseini/Ornith-1.5-9B-FP8", max_model_len=32768)
    out = llm.chat(
        [{"role": "user", "content": "What is quantization? Answer in one sentence."}],
        SamplingParams(temperature=0.6, max_tokens=512),
    )
    print(out[0].outputs[0].text)
```

## Provenance

Produced with HF-quantized. `recipe.yaml` in this repo is the exact modifier stack that was applied, and the scheme, ignored layers and hardware are in the table above.

Licence is inherited from the source model. Quantization does not change what you are permitted to do with the weights.