tarekziade HF Staff
Publish MIC usage, slim fixture and reviewed Transformers expectations
a81e668 verified | # 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)) | |
| ``` | |