The required steps to implement for an HF model are:
-1. Define the model `Model.register` annotation in a new `Model` subclass, example:
+1. Define the model `ModelBase.register` annotation in a new `TextModel` or `MmprojModel` subclass, example:
```python
-@Model.register("MyModelForCausalLM")
-class MyModel(Model):
+@ModelBase.register("MyModelForCausalLM")
+class MyModel(TextModel):
+ model_arch = gguf.MODEL_ARCH.MYMODEL
+```
+
+or
+
+```python
+@ModelBase.register("MyModelForConditionalGeneration")
+class MyModel(MmprojModel):
model_arch = gguf.MODEL_ARCH.MYMODEL
```
`transformer.blocks.{bid}.norm_1` will be mapped to `blk.{bid}.attn_norm` in GGUF.
Depending on the model configuration, tokenizer, code and tensors layout, you will have to override:
-- `Model#set_gguf_parameters`
-- `Model#set_vocab`
-- `Model#write_tensors`
+- `TextModel#set_gguf_parameters`
+- `MmprojModel#set_gguf_parameters`
+- `ModelBase#set_vocab`
+- `ModelBase#modify_tensors`
NOTE: Tensor names must end with `.weight` or `.bias` suffixes, that is the convention and several tools like `quantize` expect this to proceed the weights.