from __future__ import annotations
import json
from typing import Iterable, TYPE_CHECKING
if TYPE_CHECKING:
from torch import Tensor
from .base import MmprojModel, ModelBase, gguf, logger
from .llama import LlamaModel
@ModelBase.register(
"LlavaForConditionalGeneration", # pixtral
"Mistral3ForConditionalGeneration", # mistral small 3.1
)
@ModelBase.example("mistral-community/pixtral-12b", "mistralai/Mistral-Small-3.1-24B-Instruct-2503")
class LlavaVisionModel(MmprojModel):
img_break_tok_id = -1
use_break_tok = True
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
if self.hparams.get("model_type") == "pixtral":
# layer_norm_eps is not in config.json, it is hard-coded in modeling_pixtral.py
self.hparams["layer_norm_eps"] = self.hparams.get("layer_norm_eps", 1e-5)
if self.use_break_tok:
self.img_break_tok_id = self.get_token_id("[IMG_BREAK]")
elif self.is_mistral_format:
# hparams is already vision config here so norm_eps is only defined in global_config.
self.hparams["norm_eps"] = self.global_config.get("norm_eps", None)
assert self.hparams["norm_eps"] is not None, "norm_eps not found in params.json"
if self.use_break_tok:
self.img_break_tok_id = self.find_vparam(["image_break_token_id"])
# params.json may ship -1 placeholders (Mistral Medium 3.5)
# resolve the real id from the bundled tokenizer in that case
if self.img_break_tok_id < 0:
self.img_break_tok_id = self.get_mistral_token_id("[IMG_BREAK]")
else:
raise ValueError(f"Unsupported model type: {self.hparams['model_type']}")
logger.info(f"Image break token id: {self.img_break_tok_id}")
def get_token_id(self, token: str) -> int:
tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
with open(tokenizer_config_file, "r", encoding="utf-8") as f:
added_tokens_decoder = json.load(f).get('added_tokens_decoder') or {}
for id_, token_data in added_tokens_decoder.items():
if token_data.get("content") == token:
return int(id_)
# fallthrough to tokenizer.json
with open(self.dir_model / "tokenizer.json", "r", encoding="utf-8") as f:
tokenizer_json = json.load(f)
for token_data in tokenizer_json["added_tokens"]:
if token_data["content"] == token:
return int(token_data["id"])
raise ValueError(f"Token '{token}' not found in tokenizer config.")
def get_mistral_token_id(self, token: str) -> int:
# mistral native format ships tekken.json or a versioned spm tokenizer
tekken_file = self.dir_model / "tekken.json"
if tekken_file.is_file():
with open(tekken_file, "r", encoding="utf-8") as f:
data = json.load(f)
for entry in data.get("special_tokens", []):
if entry.get("token_str") == token:
return int(entry["rank"])
tokenizer_json_file = self.dir_model / "tokenizer.json"
if tokenizer_json_file.is_file():
with open(tokenizer_json_file, "r", encoding="utf-8") as f:
data = json.load(f)
for entry in data.get("added_tokens", []):
if entry.get("content") == token:
return int(entry["id"])
raise ValueError(f"Token '{token}' not found in mistral tokenizer files.")
def set_gguf_parameters(self):
super().set_gguf_parameters()
hparams = self.hparams
if hparams.get("model_type") == "pixtral":
self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.PIXTRAL)
self.gguf_writer.add_vision_attention_layernorm_eps(hparams["layer_norm_eps"])
# hidden_act
if hparams["hidden_act"] == "silu":
self.gguf_writer.add_vision_use_silu(True)
elif hparams["hidden_act"] == "gelu":
self.gguf_writer.add_vision_use_gelu(True)
else:
raise ValueError(f"Unsupported hidden_act: {hparams['hidden_act']}")
# spatial_merge_size
if "spatial_merge_size" in self.global_config:
self.gguf_writer.add_vision_spatial_merge_size(self.global_config["spatial_merge_size"])
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
n_head = (
self.hparams["num_attention_heads"] if not self.is_mistral_format else self.find_vparam(["num_attention_heads"])
)
n_kv_head = n_head
valid_prefixes = (
"multi_modal_projector.",
"vision_tower.",
"vision_encoder.",
"vision_language_adapter.",
"patch_merger.",
"pre_mm_projector_norm",
)
if any(name.startswith(prefix) for prefix in valid_prefixes):
# process vision tensors
if name.endswith(("q_proj.weight", "q_proj.bias")) and not self.is_mistral_format:
data_torch = LlamaModel.permute(data_torch, n_head, n_head)
if name.endswith(("k_proj.weight", "k_proj.bias")) and not self.is_mistral_format:
data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
yield from super().modify_tensors(data_torch, name, bid)
return
embed_key = "embed_tokens.weight" if not self.is_mistral_format else "tok_embeddings.weight"
if self.img_break_tok_id > 0 and embed_key in name:
logger.info(f"Extracting [IMG_BREAK] token embedding from {name}")
# for pixtral model, we need to extract the [IMG_BREAK] token embedding
img_break_embd = data_torch[self.img_break_tok_id]
name = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_TOK_EMBD_IMG_BREAK]
yield from super().modify_tensors(img_break_embd, name, bid)
return # skip other tensors