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llama.cpp
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model: Add support for Tiny Aya Models (#19611) 

* changes for tiny aya

* changes to hash

* changes to vocab

* fix some tokenizer regex edge cases

* update comment

* add some comments for regex

* Apply suggestion from @ngxson

---------

Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>
This commit is contained in:
Saurabh Dash 2026-02-16 10:28:46 -05:00 committed by GitHub
parent 4408494144
commit 5f28c53d11
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5 changed files with 36 additions and 2 deletions
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14
convert_hf_to_gguf.py
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@ -1124,6 +1124,9 @@ class TextModel(ModelBase):
if chkhsh == "9c2227e4dd922002fb81bde4fc02b0483ca4f12911410dee2255e4987644e3f8":
# ref: https://huggingface.co/CohereForAI/c4ai-command-r-v01
res = "command-r"
if chkhsh == "d772b220ace2baec124bed8cfafce0ead7d6c38a4b65ef11261cf9d5d62246d1":
# ref: https://huggingface.co/CohereLabs/tiny-aya-base
res = "tiny_aya"
if chkhsh == "e636dc30a262dcc0d8c323492e32ae2b70728f4df7dfe9737d9f920a282b8aea":
# ref: https://huggingface.co/Qwen/Qwen1.5-7B
res = "qwen2"
@ -7360,6 +7363,17 @@ class Cohere2Model(TextModel):
self.gguf_writer.add_rope_dimension_count(int(rotary_pct * (hidden_size // num_attention_heads)))
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# Cohere2 runtime in llama.cpp expects no bias tensors;
# the actual weight only contains 0-value tensors as bias, we can skip them
if name.endswith(".bias"):
if torch.any(data_torch != 0):
raise ValueError(f"Bias tensor {name!r} is not zero.")
logger.debug(f"Skipping bias tensor {name!r} for Cohere2 conversion.")
return
yield from super().modify_tensors(data_torch, name, bid)
@ModelBase.register("OlmoForCausalLM")
@ModelBase.register("OLMoForCausalLM")

1
convert_hf_to_gguf_update.py
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@ -99,6 +99,7 @@ models = [
{"name": "stablelm2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b", },
{"name": "refact", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/smallcloudai/Refact-1_6-base", },
{"name": "command-r", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereForAI/c4ai-command-r-v01", },
{"name": "tiny_aya", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereLabs/tiny-aya-base", },
{"name": "qwen2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen1.5-7B", },
{"name": "olmo", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/allenai/OLMo-1.7-7B-hf", },
{"name": "dbrx", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/databricks/dbrx-base", },

16
src/llama-vocab.cpp
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@ -422,6 +422,14 @@ struct llm_tokenizer_bpe : llm_tokenizer {
"[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}])([^a-z]))*((?=[\\p{L}])([^A-Z]))+(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])?|[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}])([^a-z]))+((?=[\\p{L}])([^A-Z]))*(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])?|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
};
break;
case LLAMA_VOCAB_PRE_TYPE_TINY_AYA:
regex_exprs = {
// original regex from tokenizer.json: "\\d{1,3}(?=(?:\\d{3})*\\b)"
"\\d{1,3}(?=(?:\\d{3})*\\b)",
// original regex from tokenizer.json: "[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]*[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]+(?i:'s|'t|'re|'ve|'m|'ll|'d)?|[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]+[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]*(?i:'s|'t|'re|'ve|'m|'ll|'d)?|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
"[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]*[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]+(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])?|[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]+[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]*(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])?|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
};
break;
case LLAMA_VOCAB_PRE_TYPE_KIMI_K2:
regex_exprs = {
// K2 trigger pattern - this will activate the custom K2 handler in unicode.cpp
@ -2005,10 +2013,14 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
tokenizer_pre == "megrez") {
pre_type = LLAMA_VOCAB_PRE_TYPE_QWEN2;
} else if (
tokenizer_pre == "gpt-4o" ||
tokenizer_pre == "llama4") {
tokenizer_pre == "gpt-4o" ||
tokenizer_pre == "llama4") {
pre_type = LLAMA_VOCAB_PRE_TYPE_GPT4O;
clean_spaces = false;
} else if (
tokenizer_pre == "tiny_aya") {
pre_type = LLAMA_VOCAB_PRE_TYPE_TINY_AYA;
clean_spaces = false;
} else if (
tokenizer_pre == "superbpe") {
pre_type = LLAMA_VOCAB_PRE_TYPE_SUPERBPE;

1
src/llama-vocab.h
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@ -55,6 +55,7 @@ enum llama_vocab_pre_type {
LLAMA_VOCAB_PRE_TYPE_YOUTU = 44,
LLAMA_VOCAB_PRE_TYPE_EXAONE_MOE = 45,
LLAMA_VOCAB_PRE_TYPE_QWEN35 = 46,
LLAMA_VOCAB_PRE_TYPE_TINY_AYA = 47,
};
struct LLM_KV;

6
src/unicode.cpp
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@ -769,6 +769,12 @@ static std::vector<size_t> unicode_regex_split_custom(const std::string & text,
} else if (regex_expr == "\\p{AFMoE_digits}") {
// AFMOE digit pattern - use custom implementation for proper splitting
bpe_offsets = unicode_regex_split_custom_afmoe(text, offsets);
} else if (regex_expr == "\\d{1,3}(?=(?:\\d{3})*\\b)") {
// tiny_aya digit grouping pattern from tokenizer.json:
// {"type": "Split", "pattern": {"Regex": "\\d{1,3}(?=(?:\\d{3})*\\b)"}, "behavior": "Isolated"}
// Splits digits into groups of 3 from the right (e.g., 1234567 -> 1, 234, 567)
// TODO: Revisit this regex, incase there are any subtle tokenization differences with the original regex.
bpe_offsets = unicode_regex_split_custom_afmoe(text, offsets);
}
return bpe_offsets;