fix: Best-effort use of chat completion

[Ki-Seki]

Fixes #1784

[Ki-Seki]

@RobinPicard Since this PR turned out a bit larger, I’ve put together a detailed change log to help streamline your review. 🤗

1. Best-Effort Use of Chat Completion

For the four local model backends (llamacpp, mlxlm, transformers, and vllm_offline), this PR implements the following logic proposed in issue #1784 :

If a local model provides a chat template, we assume it expects us to use it — so we do. If not, we fall back to plain completion mode. If a backend does not support chat mode at all, we also fall back to plain completion mode.

Key changes:

• Added a helper function _check_hf_chat_template in outlines/models/tokenizer.py to centralize Hugging Face chat template checks, since the logic is shared.
• Introduced a new property has_chat_template in the TypeAdapter of the local models, typically determined by _check_hf_chat_template.
• When has_chat_template is True, string-based model_input is converted to chat format whenever possible, usually via format_chat_input.
• Updated the common/stream/batch generate functions across all four local models accordingly.

2. Special Case: vLLM Offline

• Since vLLM internally uses TokenizerBase (a non-Hugging Face tokenizer class), additional checks were added for compatibility.
• Previously, generate_batch was assumed not to support chat mode, and the following code was present:

if any(isinstance(item, Chat) for item in model_input):
    raise TypeError(
        "Batch generation is not available for the `Chat` input type."
    )

• After re-verification, chat mode is supported (see vLLM documentation), so this restriction has been removed.

3. Special Case: LlamaCPP

• LlamaCPP does not automatically check for chat templates; instead, it relies on user-provided parameter.
• This is because llama-cpp-python provides a default fallback chat template even when the user has not explicitly configured one (source).
• To align with this behavior, users are now expected to explicitly pass the chat_mode parameter (default: True).

Note:
This is the only interface-level change in the PR. Compared to the previous implementation, where string inputs defaulted to plain text completion, they now default to chat completion whenever possible.
I believe this is reasonable: since llama-cpp-python itself encourages a fallback chat template, this change should feel natural to users familiar with LlamaCPP.
For strict backward compatibility, however, we could consider setting the default to chat_mode=False.

4. Other Changes

• Updated documentation to reflect the LlamaCPP interface change.
• Added comprehensive unit tests for all new functionality, all of which pass and maintain 100% coverage.
• Ensured consistent code style throughout.

[Copilot]

Pull request overview

This PR implements best-effort chat completion support across multiple model adapters (vLLM, Transformers, MLX-LM, and LlamaCpp) by automatically detecting whether a model's tokenizer has a chat template and conditionally formatting string inputs as chat messages.

Key changes:

• Added automatic chat template detection that wraps plain string inputs as user messages when a chat template is available
• Introduced a chat_mode parameter for LlamaCpp to allow users to explicitly disable chat-style formatting
• Implemented _check_hf_chat_template() helper function to check for HuggingFace chat template availability

Reviewed changes

Copilot reviewed 12 out of 12 changed files in this pull request and generated 5 comments.

Show a summary per file

File	Description
outlines/models/tokenizer.py	Added _check_hf_chat_template() helper function to detect chat template availability
outlines/models/vllm_offline.py	Updated VLLMOfflineTypeAdapter to conditionally format string inputs as chat messages based on template availability
outlines/models/transformers.py	Modified TransformersTypeAdapter to support chat template detection and conditional formatting
outlines/models/mlxlm.py	Updated MLXLMTypeAdapter with chat template support and conditional string input formatting
outlines/models/llamacpp.py	Added chat_mode parameter to LlamaCpp model to allow explicit control over chat-style input formatting
docs/features/models/llamacpp.md	Updated documentation to describe the new chat_mode parameter and its usage
tests/models/test_tokenizer.py	Added tests for the new chat template detection function
tests/models/test_vllm_offline_type_adapter.py	Added tests for string input formatting with and without chat templates
tests/models/test_transformers_type_adapter.py	Updated tests to cover chat template conditional behavior
tests/models/test_mlxlm_type_adapter.py	Added tests for chat template support using mocks
tests/models/test_llamacpp_type_adapter.py	Added tests for chat template conditional formatting
tests/models/test_llamacpp.py	Added test fixture and tests for non-chat mode, updated streaming tests to handle empty tokens

---

💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.

[RobinPicard]

Thanks a lot for the great description! I'll review it in the coming days

[Ki-Seki]

Thanks a lot for the great description! I'll review it in the coming days

No worries at all, Robin — no rush! Really happy to be working with you. 🥳