Update export_hf_checkpoint.py (#302)

* Update export_hf_checkpoint.py

* Update finetune.py

New tokenizer base model for the current dev branch of transformers

* Update generate.py

* Update export_state_dict_checkpoint.py

* Update export_hf_checkpoint.py

export_hf_checkpoint.py

@@ -8,7 +8,7 @@ from transformers import LlamaForCausalLM, LlamaTokenizer  # noqa: F402
 BASE_MODEL = os.environ.get("BASE_MODEL", None)
 assert (
     BASE_MODEL
-), "Please specify a value for BASE_MODEL environment variable, e.g. `export BASE_MODEL=decapoda-research/llama-7b-hf`"  # noqa: E501
+), "Please specify a value for BASE_MODEL environment variable, e.g. `export BASE_MODEL=huggyllama/llama-7b`"  # noqa: E501
 
 tokenizer = LlamaTokenizer.from_pretrained(BASE_MODEL)
 
@@ -35,10 +35,8 @@ lora_weight = lora_model.base_model.model.model.layers[
 
 assert torch.allclose(first_weight_old, first_weight)
 
-# merge weights
-for layer in lora_model.base_model.model.model.layers:
-    layer.self_attn.q_proj.merge_weights = True
-    layer.self_attn.v_proj.merge_weights = True
+# merge weights - new merging method from peft
+lora_model = lora_model.merge_and_unload()
 
 lora_model.train(False)
 

export_state_dict_checkpoint.py

@@ -9,7 +9,7 @@ from transformers import LlamaForCausalLM, LlamaTokenizer  # noqa: E402
 BASE_MODEL = os.environ.get("BASE_MODEL", None)
 assert (
     BASE_MODEL
-), "Please specify a value for BASE_MODEL environment variable, e.g. `export BASE_MODEL=decapoda-research/llama-7b-hf`"  # noqa: E501
+), "Please specify a value for BASE_MODEL environment variable, e.g. `export BASE_MODEL=huggyllama/llama-7b`"  # noqa: E501
 
 tokenizer = LlamaTokenizer.from_pretrained(BASE_MODEL)
 

finetune.py

@@ -83,7 +83,7 @@ def train(
         )
     assert (
         base_model
-    ), "Please specify a --base_model, e.g. --base_model='decapoda-research/llama-7b-hf'"
+    ), "Please specify a --base_model, e.g. --base_model='huggyllama/llama-7b'"
     gradient_accumulation_steps = batch_size // micro_batch_size
 
     prompter = Prompter(prompt_template_name)

generate.py

@@ -34,7 +34,7 @@ def main(
     base_model = base_model or os.environ.get("BASE_MODEL", "")
     assert (
         base_model
-    ), "Please specify a --base_model, e.g. --base_model='decapoda-research/llama-7b-hf'"
+    ), "Please specify a --base_model, e.g. --base_model='huggyllama/llama-7b'"
 
     prompter = Prompter(prompt_template)
     tokenizer = LlamaTokenizer.from_pretrained(base_model)