add-new-model

name	add-new-model
description	Guided workflow for adding a new model architecture to llama.cpp. Use when the user wants to add/port a new model architecture.

Add a new model architecture to llama.cpp

This skill walks a contributor through adding a new model architecture. AI-generated code is permitted in this project, so you may write full implementations for the steps below rather than only pointing at patterns - but follow AGENTS.md's AI usage policy throughout:

• The contributor is 100% responsible for every line, however it was produced. They must be able to explain and defend any part of it to a reviewer. Check in with them as you go (don't silently generate everything and hand over a finished diff) so they actually absorb what was written.
• Before writing code, make sure the contributor owns the design choices for this architecture (which reference model to follow, how non-standard bits like RoPE variants or MoE routing should be handled) - AI accelerates a design the contributor has already made, it doesn't make the design for them.
• Disclosure is mandatory: any AI-meaningful contribution must be disclosed per the PR template. Remind the contributor of this before they open the PR.
• Never write the PR description, commit message, GitHub issue/discussion post, or reviewer replies - those must come from the contributor. If asked to commit on their behalf, use Assisted-by: (never Co-authored-by:) and only after explicit confirmation.
• If the requested change looks large or introduces a new pattern not covered here, pause and tell the user this kind of change is likely to need prior discussion with maintainers before a PR.
• Keep the PR self-contained. If the work would require a lot of unconventional changes outside the new model file(s) (e.g. touching shared graph-building code, the sampler, or core APIs in ways other models don't), STOP and tell the contributor to open a discussion/issue first - invasive or excessive changes get closed without full review.
• Do not bundle unrelated work into this PR - see Step 4 and Step 5 below for the specifics on multimodal and chat-template/parsing work.
• Never hack around RoPE with a custom sin/cos implementation. Several past PRs tried this and were closed. If the existing ggml_rope_ext (see Step 2's RoPE tips) genuinely cannot express what this model needs, the contributor should open an issue to discuss it with maintainers first - not send a PR with a custom RoPE implementation.

Before starting, read CONTRIBUTING.md, AGENTS.md and docs/development/HOWTO-add-model.md if they are not already in context. Also run git log --oneline -- src/models and look at at least 3 recent PRs that added a model (their merge commits/diffs) - this shows current convention more reliably than the docs, which can lag behind.

Step 0 - Scope and dedup check

Ask the contributor:

1. Which model (HF repo id or name)? Is it text-only or does it have a multimodal (vision/audio) encoder?
2. Do they already have the HF config.json/weights available locally?
3. Have they checked for an existing PR/issue on this model? Suggest gh search issues "<model name>" and gh search prs "<model name>" in the ggml-org/llama.cpp repo. If an existing PR covers it, the contributor should comment there and collaborate rather than open a duplicate (per CONTRIBUTING.md's AI Usage Policy).
4. What existing supported architecture is this model closest to (e.g. "Llama-like with sliding window", "MoE like DBRX", "BERT-style encoder")?

If the contributor doesn't know the closest reference architecture, you may grep conversion/*.py and src/models/*.cpp for architectures with a similar config shape (layer count, head count, MoE expert count, norm placement) and suggest 1-2 candidates - but let the contributor confirm the choice rather than picking one yourself; this choice is a design decision they need to own.

Do not proceed to Step 1 until the contributor has answered these and named a reference architecture.

Step 1 - Convert the model to GGUF

Follow HOWTO-add-model.md section 1 for the actual touch points (conversion class registration, constants.py, tensor_mapping.py, etc.) - don't re-derive them here, read them from the doc.

Skill-specific addition: for each touch point, show the contributor the equivalent code in the reference architecture they named in Step 0 before writing the new version, and check that they understand what's different about their model (e.g. non-standard tensor shapes, extra hparams) rather than just copying the pattern silently.

Step 2 - Define the architecture in llama.cpp

Follow HOWTO-add-model.md section 2 for the actual touch points (llm_arch enum, LLM_ARCH_NAMES, hparam loading, RoPE type case, etc.), including its "Tips and tricks" section for ggml_rope_ext gotchas.

Skill-specific addition: never hack around RoPE with a custom sin/cos implementation - see the RoPE rule above.

Step 3 - Build the GGML graph

Follow HOWTO-add-model.md section 3 for the actual touch points (src/models/<name>.cpp struct, llama_model_mapping registration, etc.).

Skill-specific addition: before writing src/models/<name>.cpp, read at least 10 other files under src/models/ (pick a mix, not just the one reference architecture) to confirm the struct layout, naming, and style you're about to write actually matches current convention - the pattern drifts over time and the HOWTO doc can lag behind it.

Step 4 - Optional: multimodal encoder

Only do this if the contributor flagged a vision/audio encoder in Step 0. Follow HOWTO-add-model.md section 4 and docs/multimodal.md for the actual touch points (MmprojModel subclass, clip.cpp, mtmd.cpp, encoder graph in tools/mtmd/models, etc.).

Skill-specific addition, and read this carefully: whether the multimodal encoder can be bundled into the same PR as the base text-model support depends on how conventional the change is. It's OK to bundle it if the encoder support is conventional - i.e. no new infra or logic is needed, it's just a new cgraph reusing existing preprocessing/projector machinery (e.g. siglip/pixtral/qwen with just a new projector). If it requires anything beyond that - a new preprocessor, non-standard projector logic, or changes to shared libmtmd infra/logic - STOP, tell the contributor this is non-conventional, and have them land the text model first with the encoder as a dedicated follow-up PR. Do not let this decision pass silently - call it out explicitly to the contributor before writing any clip.cpp/mtmd.cpp code.

Step 5 - Optional: chat template / parsing support

Only do this if the model needs a new built-in chat template (src/llama-chat.cpp) or a new output parser (see docs/development/parsing.md and docs/autoparser.md). If either is needed beyond what a user-supplied Jinja template already covers, treat it as its own dedicated follow-up PR, not part of the base model-support PR - call this out explicitly to the contributor rather than silently bundling it in.

Common pitfalls (from past PR reviews)

These recur often enough in review comments on past add-model PRs that they're worth checking proactively, not just waiting for a reviewer to catch them:

• Don't validate the same hparam/config assumption in both the Python conversion script and the C++ load path - pick one layer to own the check, duplicating it just adds maintenance surface.
• Optional hparams that are genuinely absent from some configs (e.g. a shared-expert count) should be read with an explicit optional/fallback accessor, not assumed present.
• Hparams that are actually load-bearing (the model produces wrong output or crashes without them, e.g. sliding_window_pattern, norm-eps) must hard-error if missing, not silently fall back to a default.
• Don't bake a default chat template into the C++ binary - inject it into the GGUF at conversion time instead, since one llm_arch can be reused by multiple fine-tunes with different templates, and a baked-in C++ default fails silently for those.
• Before writing a dedicated tool-call/output parser, check whether the existing autoparser already handles the template (llama-debug-template-parser <jinja> shows what it detects).
• Marking a custom EOS/closing-tag token as eot at conversion time isn't always sufficient - in long/agentic generations a model can emit the closing sequence as literal text instead of the token, so generation never stops on EOG and raw text leaks past the parser. Verify this case, not just the token path.
• If reusing or aliasing an existing pre-tokenizer for convenience, justify and test that choice explicitly - silent reuse is an easy source of subtle tokenizer bugs.
• Watch for excessive graph splits caused by building per-layer view/index tensors inside the layer loop - hoist tensors that don't vary per layer out of the loop (relevant if you hit GGML_SCHED_MAX_SPLIT_INPUTS).
• A custom KQ mask fed into flash attention must match FA's expected dtype - cast it to F16 before passing it to build_attn_mha when FA is enabled.
• When padding a custom KV-cache size to an alignment (e.g. GGML_PAD(..., 256)), apply the padding after all other size adjustments, not before - otherwise later logic can un-align it again.
• For non-standard cache/SWA (sliding-window-attention) semantics, override the dedicated hook (e.g. llama_model_n_swa()) rather than mutating hparams to fake the behavior - hparams may be read elsewhere for unrelated purposes.
• Don't ship unfinished or unverified speculative-decoding (e.g. MTP) scaffolding in the base model PR - if it hasn't actually been confirmed to work, pull it out and land it as its own follow-up.
• Conversion code should call into the base class's existing hparam logic (e.g. super().set_gguf_parameters()) rather than re-deriving it - large blocks of code that duplicate what / already provide will get flagged as redundant.

Validation checklist

Reference: examples/model-conversion/README.md.

1. Convert to GGUF, then inspect/run both the original and converted tensors.
2. Run logits verification (original vs converted). If this model is a new version of an already-supported family, verify the previous version still passes logits verification first - numerical differences may be pre-existing, not caused by the new work. The tools to perform full logits validation are available in examples/model-conversion.
3. Quantize (including QAT variants if relevant) and re-verify.
4. Run perplexity evaluation (simple and full).
5. Sanity-check across tools/cli, tools/completion, tools/imatrix, tools/quantize, and tools/server.
6. CPU backend first; other backends (CUDA, Metal, ...) can be separate follow-up PRs per CONTRIBUTING.md.
7. Re-review every changed file against the coding/naming guidelines in AGENTS.md (and CONTRIBUTING.md's "Coding guidelines"/"Naming guidelines" sections) - this is a separate pass from functional testing and is just as important: no forced line-wrapping, no unicode punctuation, minimal/non-redundant comments, snake_case naming (kebab-case for file names), matching indentation/brace style, etc.

Before opening a PR

• Run the code-review skill on the diff first - it catches the convention and scope issues reviewers flag most often, and it's recommended to do this locally before pushing the PR.
• Confirm the contributor can explain every changed line to a reviewer and is prepared to be asked about any of it - this is required regardless of how much of the code was AI-generated.
• Confirm they did a comprehensive manual review of the full diff, not just a skim.
• Fill in the AI-disclosure section of .github/pull_request_template.md describing how AI was used (do not omit or understate this).
• Do not write the PR description, commit message, GitHub issue/discussion text, or any reviewer replies yourself - the contributor writes these.