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5003194 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 | # LLaVA: vision-language with Transformers
This page is the Transformers contract of the LLaVA MIC and also a usage example. [doc-builder](https://github.com/huggingface/doc-builder/blob/main/docs/runnable-code-blocks.md) renders the `runnable` blocks below as documentation, and its pytest plugin runs them as written against `llava-hf/llava-1.5-7b-hf`. The MIC runner runs the same blocks against each slim fixture and each checkpoint in [`integration.json`](../../integration.json). Lines marked `# doc-builder: hide` only matter there: they swap in the pinned checkpoint or variant and print the result record.
## Inference
```py runnable:usage
import torch
from transformers import LlavaForConditionalGeneration, LlavaProcessor, set_seed
from PIL import Image
import numpy as np
import json # doc-builder: hide
import os # doc-builder: hide
model_id = "llava-hf/llava-1.5-7b-hf"
model_id = os.environ.get("MIC_MODEL", model_id) # doc-builder: hide
variant = json.loads(os.environ.get("MIC_MODEL_KWARGS", "{}")) # doc-builder: hide
device = "cuda" if torch.cuda.is_available() else "cpu"
set_seed(0)
processor = LlavaProcessor.from_pretrained(model_id)
model = LlavaForConditionalGeneration.from_pretrained(model_id).to(device)
if variant: # doc-builder: hide
model = LlavaForConditionalGeneration.from_pretrained(model_id, **variant).to(device)
model.eval()
np.random.seed(0)
img1 = Image.fromarray(np.random.randint(0, 255, (336, 336, 3), dtype=np.uint8))
img2 = Image.fromarray(np.random.randint(0, 255, (336, 336, 3), dtype=np.uint8))
texts = [
"<image>\nUSER: What do you see in this image?\nASSISTANT: A",
"<image>\nUSER: Describe the colors in this image.\nASSISTANT: A",
]
inputs = processor(images=[img1, img2], text=texts, return_tensors="pt", padding=True).to(device)
for key, value in inputs.items():
if isinstance(value, torch.Tensor) and value.is_floating_point():
inputs[key] = value.to(model.dtype)
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probe_positions = [0, 1, 2, 3, 4]
probe = logits[:, -1, probe_positions].float()
predicted = logits[:, -1].argmax(dim=-1)
print(f"predicted: {predicted.tolist()}")
print(f"probe: {probe.tolist()}")
```
## One training step
```py runnable:usage:2
# Fixed target tokens to ensure a meaningful loss
target_tokens = torch.tensor([100, 200], device=device)
labels = torch.full_like(inputs["input_ids"], -100)
labels[:, -1] = target_tokens
model.train()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
loss = model(**inputs, labels=labels).loss
loss.backward()
# doc-builder: hide
checked = tuple(n for n, p in model.named_parameters() if p.grad is not None and p.ndim >= 2 and torch.count_nonzero(p.grad) > 0)[:2]
before = {n: p.detach().clone() for n, p in model.named_parameters() if n in checked}
optimizer.step()
print(f"loss: {loss.item():.4f}")
if os.environ.get("MIC_RUNNER"): # doc-builder: hide
named = dict(model.named_parameters())
result = {
"runtime": {
"device": next(model.parameters()).device.type,
"dtype": str(next(model.parameters()).dtype).removeprefix("torch."),
"attn_implementation": model.config._attn_implementation,
},
"predicted_tokens": predicted.tolist(),
"probe_logits": probe.tolist(),
"training": {
"loss": loss.item(),
"gradients": {n: bool(named[n].grad is not None and torch.count_nonzero(named[n].grad) > 0 and torch.isfinite(named[n].grad).all()) for n in checked},
"updated": any(not torch.equal(named[n], before[n]) for n in checked),
},
}
print(json.dumps(result))
```
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