# Cohere2: causal language modeling with Transformers

This page is the Transformers contract of the Cohere2 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 `CohereLabs/tiny-aya-global`. 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.

## Predict the next token

```py runnable:causal_lm
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
import json  # doc-builder: hide
import os  # doc-builder: hide

model_id = "CohereLabs/tiny-aya-global"
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 sampling draw random numbers

tokenizer = AutoTokenizer.from_pretrained(model_id)
# The checkpoint stores bfloat16 weights; load in float32 so the training step below is numerically stable.
model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.float32).to(device)
if variant:  # doc-builder: hide
    del model  # doc-builder: hide
    model = AutoModelForCausalLM.from_pretrained(model_id, **{"dtype": torch.float32, **variant}).to(device)  # doc-builder: hide

# Left padding keeps the last position the next-token probe for every row.
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "left"

prompts = [
    "The capital of France is",
    "Write a short sentence about the ocean:",
]
inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(device)

model.eval()
with torch.no_grad():
    logits = model(**inputs).logits
# With left padding the next token is read at the last position, never position 0.
next_logits = logits[:, -1, :]
next_token_ids = next_logits.argmax(dim=-1)
print(tokenizer.batch_decode(next_token_ids.unsqueeze(-1)))
```

## One training step

```py runnable:causal_lm:2
# Train on labels the model does not predict: shift the token ids by one modulo the vocabulary.
labels = (inputs["input_ids"] + 1) % model.config.vocab_size
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.layers.0.self_attn.q_proj.weight",
        "model.layers.0.mlp.gate_proj.weight",
        "model.norm.weight",
    )
    result = {
        "runtime": {
            "device": model.device.type,
            "dtype": str(model.dtype).removeprefix("torch."),
            "attn_implementation": model.config._attn_implementation,
        },
        "next_token_ids": next_token_ids.tolist(),
        "next_token_logits": next_logits[:, next_token_ids].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))
```