tarekziade HF Staff
Freeze the Mistral3 chat-template reference date for reproducible daily checks
5d6b31a verified | # Mistral3: image-text-to-text with Transformers | |
| This page is the Transformers contract of the Mistral3 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 `mistralai/Mistral-Small-3.1-24B-Instruct-2503`. 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: chat completion | |
| ```py runnable:mistral3_chat | |
| import torch | |
| from transformers import AutoProcessor, AutoModelForImageTextToText, set_seed | |
| import json # doc-builder: hide | |
| import os # doc-builder: hide | |
| model_id = "mistralai/Mistral-Small-3.1-24B-Instruct-2503" | |
| 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) | |
| # Keep the prompt identical across daily runs; the default template inserts today's date. | |
| processor.chat_template = processor.chat_template.replace('strftime_now("%Y-%m-%d")', '"2026-10-20"') | |
| model = AutoModelForImageTextToText.from_pretrained(model_id).to(device) | |
| if variant: # doc-builder: hide | |
| model = AutoModelForImageTextToText.from_pretrained(model_id, **variant).to(device) | |
| messages = [ | |
| [{"role": "user", "content": "What is the capital of France?"}], | |
| [{"role": "user", "content": "What is the capital of Germany?"}], | |
| ] | |
| texts = [processor.apply_chat_template(m, tokenize=False, add_generation_prompt=True) for m in messages] | |
| inputs = processor(text=texts, return_tensors="pt", padding=True).to(device) | |
| model.eval() | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| logits = outputs.logits | |
| last_positions = inputs["attention_mask"].sum(dim=-1) - 1 | |
| batch_idx = torch.arange(len(texts), device=device) | |
| selected_logits = logits[batch_idx, last_positions, :10].float() | |
| predicted_ids = logits[batch_idx, last_positions].argmax(dim=-1) | |
| predicted_tokens = [processor.tokenizer.decode([pid], skip_special_tokens=True) for pid in predicted_ids.tolist()] | |
| print(f"predicted: {predicted_tokens}") | |
| print(f"selected_logits: {selected_logits.tolist()}") | |
| ``` | |
| ## One training step | |
| ```py runnable:mistral3_chat:2 | |
| labels = inputs["input_ids"].clone() | |
| labels[inputs["attention_mask"] == 0] = -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] | |
| result = { | |
| "runtime": { | |
| "device": model.device.type, | |
| "dtype": str(next(model.parameters()).dtype).removeprefix("torch."), | |
| "attn_implementation": model.config._attn_implementation, | |
| }, | |
| "predicted_tokens": predicted_tokens, | |
| "answer_logits": selected_logits.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)) | |
| ``` | |