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a81e668 | 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 | # Inkling: multimodal conditional generation with Transformers
This page is the Transformers contract of the Inkling 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 `thinkingmachines/Inkling`. The MIC runner runs the same blocks against the slim fixture and the 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.
## Generate a continuation
```py runnable:inkling
import torch
from transformers import AutoProcessor, InklingForConditionalGeneration, set_seed
import json # doc-builder: hide
import os # doc-builder: hide
model_id = "thinkingmachines/Inkling"
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) # seeds torch, NumPy, and Python: loading, dropout, and data augmentation draw random numbers
processor = AutoProcessor.from_pretrained(model_id)
model = InklingForConditionalGeneration.from_pretrained(model_id).to(device)
if variant: # doc-builder: hide
del model # doc-builder: hide
model = InklingForConditionalGeneration.from_pretrained(model_id, **variant).to(device) # doc-builder: hide
model.eval()
# The checkpoint ships no pad token; reuse the EOS token so a two-prompt batch can be padded.
if processor.tokenizer.pad_token is None:
# Inkling's tokenizer defines no pad or eos token; pad with its end-of-text token.
processor.tokenizer.pad_token = processor.tokenizer.eos_token or "<|endoftext|>"
# Two distinct prompts so a batch perturbation is meaningful; right padding keeps the last position real.
prompts = ["Describe the scene in one sentence.", "What sound is in the clip?"]
inputs = processor(text=prompts, return_tensors="pt", padding=True)
inputs = inputs.to(device)
with torch.no_grad():
generated = model.generate(**inputs, max_new_tokens=8)
print(processor.batch_decode(generated, skip_special_tokens=True))
```
## One training step
```py runnable:inkling:2
# Language-model labels: copy input_ids and mask padded positions with -100.
labels = inputs["input_ids"].clone()
if "attention_mask" in inputs:
labels[inputs["attention_mask"] == 0] = -100
model.train()
optimizer = torch.optim.SGD([p for p in model.parameters() if p.requires_grad], lr=0.01)
loss = model(**inputs, labels=labels).loss
loss.backward()
before = {n: p.detach().cpu().clone() for n, p in model.named_parameters() if p.requires_grad} # doc-builder: hide
optimizer.step()
print(f"loss: {loss.item():.4f}")
if os.environ.get("MIC_RUNNER"): # doc-builder: hide
named = dict(model.named_parameters())
checked = (
"model.language_model.layers.0.self_attn.q_proj.weight",
"model.language_model.layers.0.mlp.up_proj.weight",
"lm_head.weight",
)
probe = inputs["input_ids"].shape[-1] - 1
result = {
"runtime": {
"device": model.device.type,
"dtype": str(model.dtype).removeprefix("torch."),
"attn_implementation": model.config._attn_implementation,
},
"generated_ids": generated.tolist(),
"logits_probe": model(**inputs).logits[:, probe, :5].float().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(p.detach().cpu(), before[n]) for n, p in model.named_parameters() if n in before),
},
}
print(json.dumps(result))
```
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