| # Gemma3: causal LM with Transformers |
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|
| This page is the Transformers contract of the Gemma3 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 `google/gemma-3-4b-it`. 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. |
|
|
| ## Next-token prediction |
|
|
| ```py runnable:gemma3 |
| import torch |
| from transformers import AutoProcessor, AutoModelForCausalLM, set_seed |
| import json # doc-builder: hide |
| import os # doc-builder: hide |
| |
| model_id = "google/gemma-3-4b-it" |
| 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 = AutoModelForCausalLM.from_pretrained(model_id).to(device) |
| if variant: # doc-builder: hide |
| model = AutoModelForCausalLM.from_pretrained(model_id, **variant).to(device) |
| |
| prompts = ["The sky is", "Water is"] |
| inputs = processor(text=prompts, return_tensors="pt", padding=True).to(device) |
| |
| model.eval() |
| with torch.no_grad(): |
| outputs = model(**inputs) |
| logits = outputs.logits |
| |
| # Last-token logits for next-token prediction |
| seq_lens = inputs["attention_mask"].sum(dim=-1) - 1 |
| last_logits = logits[torch.arange(inputs["input_ids"].shape[0], device=device), seq_lens] |
| predicted_ids = last_logits.argmax(dim=-1) |
| predicted_tokens = processor.tokenizer.convert_ids_to_tokens(predicted_ids) |
| predicted_text = processor.tokenizer.decode(predicted_ids, skip_special_tokens=False) |
| print(predicted_tokens) |
| ``` |
|
|
| ## One training step |
|
|
| ```py runnable:gemma3:2 |
| # Causal LM labels: shift input_ids left, mask padding |
| input_ids = inputs["input_ids"] |
| labels = torch.roll(input_ids, shifts=-1, dims=-1) |
| if processor.tokenizer.pad_token_id is not None: |
| labels[input_ids == processor.tokenizer.pad_token_id] = -100 |
| |
| model.train() |
| optimizer = torch.optim.SGD(model.parameters(), lr=0.01) |
| loss = model(**inputs, labels=labels).loss |
| loss.backward() |
| before = {name: p.detach().clone() for name, p in model.named_parameters()} # 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 = tuple(n for n, p in named.items() if p.grad is not None and p.ndim >= 2 and torch.count_nonzero(p.grad) > 0)[:2] |
| top3 = last_logits.topk(3, dim=-1).values.float() |
| 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_tokens, |
| "predicted_text": predicted_text, |
| "answer_logits": top3.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, before[n]) for n, p in named.items()), |
| }, |
| } |
| print(json.dumps(result)) |
| ``` |
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