from __future__ import annotations
from typing import Iterable, TYPE_CHECKING
import torch
if TYPE_CHECKING:
from torch import Tensor
from .base import ModelBase, TextModel, gguf
from .llama import LlamaModel
@ModelBase.register("OlmoForCausalLM")
@ModelBase.register("OLMoForCausalLM")
@ModelBase.example("allenai/OLMo-1.7-7B-hf")
class OlmoModel(TextModel):
model_arch = gguf.MODEL_ARCH.OLMO
def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_layer_norm_eps(1e-5)
clip_qkv = self.hparams.get("clip_qkv")
if clip_qkv is not None:
self.gguf_writer.add_clamp_kqv(clip_qkv)
# Same as super class, but permuting q_proj, k_proj
# Copied from: LlamaModel
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
n_head = self.hparams["num_attention_heads"]
n_kv_head = self.hparams.get("num_key_value_heads")
if name.endswith("q_proj.weight"):
data_torch = LlamaModel.permute(data_torch, n_head, n_head)
if name.endswith("k_proj.weight"):
data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
yield from super().modify_tensors(data_torch, name, bid)
@ModelBase.register("SeedOssForCausalLM")
@ModelBase.example("ByteDance-Seed/Seed-OSS-36B-Instruct")
class SeedOssModel(TextModel):
model_arch = gguf.MODEL_ARCH.SEED_OSS
@ModelBase.register("Olmo2ForCausalLM")
@ModelBase.register("Olmo3ForCausalLM")
@ModelBase.example("allenai/OLMo-2-1124-7B-Instruct", "allenai/Olmo-3-7B-Instruct")
class Olmo2Model(TextModel):
model_arch = gguf.MODEL_ARCH.OLMO2
def set_gguf_parameters(self):
super().set_gguf_parameters()
if "sliding_window" in self.hparams:
self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
sliding_window_pattern = []
if "layer_types" in self.hparams:
sliding_window_pattern = [t == "sliding_attention" for t in self.hparams["layer_types"]]
else:
# Olmo2 does not use sliding window attention.
# Olmo3 defaults to using sliding window for all layers except every 4th.
for i in range(self.hparams["num_hidden_layers"]):
sliding_window_pattern.append((i + 1) % 4 != 0)
self.gguf_writer.add_sliding_window_pattern(sliding_window_pattern)
@ModelBase.register("OlmoeForCausalLM")
@ModelBase.example("allenai/OLMoE-1B-7B-0924")
class OlmoeModel(TextModel):
model_arch = gguf.MODEL_ARCH.OLMOE
def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_layer_norm_rms_eps(1e-5)
_experts: list[dict[str, Tensor]] | None = None
# Copied from: Qwen2MoeModel
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# process the experts separately
if name.find("experts") != -1:
n_experts = self.find_hparam(["num_local_experts", "num_experts"])
assert bid is not None
if self._experts is None:
self._experts = [{} for _ in range(self.block_count)]
self._experts[bid][name] = data_torch
if len(self._experts[bid]) >= n_experts * 3:
# merge the experts into a single 3d tensor
for w_name in ["down_proj", "gate_proj", "up_proj"]:
datas: list[Tensor] = []
for xid in range(n_experts):
ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
datas.append(self._experts[bid][ename])
del self._experts[bid][ename]
data_torch = torch.stack(datas, dim=0)
merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
yield from super().modify_tensors(data_torch, merged_name, bid)
return
else:
return
yield from super().modify_tensors(data_torch, name, bid)
# Copied from: Qwen2MoeModel
def prepare_tensors(self):
super().prepare_tensors()
if self._experts is not None:
# flatten `list[dict[str, Tensor]]` into `list[str]`
experts = [k for d in self._experts for k in d.keys()]
if len(experts) > 0:
raise ValueError(f"Unprocessed experts: {experts}")