Pin layer_types so the tiny Cohere2 model covers both attention types

[albertvillanova]

This PR pins layer_types in the tiny Cohere2 generation script so that the model has one layer of each attention type.

Related to:

• Pin layer_types so the tiny Gemma3 and Olmo3 models cover both attention types #6962

Motivation

The tiny model has 2 layers, and Cohere2's period of 4 makes the derived pattern yield two sliding layers and no full-attention layer.

That matters more than it does for the other hybrid models, because Cohere2 applies RoPE only on sliding layers. From the transformers docs:

The model features three layers with sliding window attention and ROPE for efficient local context modeling and relative positional encoding. A fourth layer uses global attention without positional embeddings, enabling unrestricted token interactions across the entire sequence.

So the tiny model never exercised the NoPE global-attention path, which is the defining feature of the Command R7B architecture. Traced on the current model, apply_rotary_pos_emb runs on 2 of 2 layers; with the pinned pattern it runs on 1 of 2.

Unlike #6962, this emits no log line, so it is purely a test-coverage gap.

Solution

Pin layer_types explicitly, as the Gemma4 and DiffusionGemma scripts already do, and as the Nemotron 3 scripts do for block types ("one of each block type"). The order is local first, matching the reference's order_of_interleaved_layers="local_attn_first".

With 2 layers the reference 3:1 ratio cannot be represented at all, so the choice is not between a faithful and an unfaithful pattern, but between covering one attention type and covering both.

The Hub model has been regenerated in this PR:

• https://huggingface.co/trl-internal-testing/tiny-Cohere2ForCausalLM/discussions/3, which needs to be merged for this change to take effect.

Verified in this CI run (https://github.com/huggingface/trl/actions/runs/33179106731) with the revision pinned through MODEL_REVISIONS:

• 38 Cohere2 tests pass in every job, including the transformers 4.56.2 floor, with the test totals unchanged (2369 passed, 149 skipped, 7 xfailed).

This was the last tiny model with an uncovered layer type. Sweeping all of them, only Gemma3, Olmo3 (both handled in #6962) and Cohere2 were missing one; the other 28 already cover every type their architecture can produce.

Changes

• Pin layer_types to ["sliding_attention", "full_attention"] in the tiny Cohere2 generation script

---

Note

Low Risk
Test-only tiny-model generation script change with no runtime library impact until the Hub artifact is updated.

Overview
The tiny Cohere2 causal-LM generator now explicitly sets layer_types to ["sliding_attention", "full_attention"] instead of letting the config derive the pattern from two layers—which previously produced only sliding layers and never ran the full-attention / NoPE path (RoPE on sliding only).

This aligns with other tiny scripts (e.g. Gemma4, DiffusionGemma) so CI and smoke tests exercise both attention types; the Hub tiny model must be regenerated separately for tests to pick up the change.

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