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reduncancy fix + nits

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Goekdeniz-Guelmez 2025-02-14 09:09:59 +01:00
parent 65a49dda0e
commit baeb9f117f
2 changed files with 44 additions and 51 deletions
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86
llms/mlx_lm/lora.py
View File

@ -226,6 +226,39 @@ def build_parser():
)
return parser
def train_model_grpo(model, tokenizer, args, opt, train_set, valid_set, adapter_file, training_callback):
training_args = GRPOTrainingArgs(
batch_size=args.batch_size,
iters=args.iters,
val_batches=args.val_batches,
steps_per_report=args.steps_per_report,
steps_per_eval=args.steps_per_eval,
steps_per_save=args.save_every,
adapter_file=adapter_file,
max_seq_length=args.max_seq_length,
max_completion_length=args.max_completion_length,
grad_checkpoint=args.grad_checkpoint,
beta=args.beta,
group_size=args.group_size,
epsilon=args.epsilon,
reference_model_path=args.reference_model_path
)
if args.reference_model_path:
reference_model, _ = load(args.reference_model_path)
else:
reference_model, _ = load(args.model)
train_grpo(
model=model,
ref_model=reference_model.freeze(),
tokenizer=tokenizer,
optimizer=opt,
train_dataset=train_set,
val_dataset=valid_set,
args=training_args,
training_callback=training_callback,
)
def train_model(
args,
@ -263,19 +296,6 @@ def train_model(
adapter_file = adapter_path / "adapters.safetensors"
save_config(vars(args), adapter_path / "adapter_config.json")
# init training args
training_args = TrainingArgs(
batch_size=args.batch_size,
iters=args.iters,
val_batches=args.val_batches,
steps_per_report=args.steps_per_report,
steps_per_eval=args.steps_per_eval,
steps_per_save=args.save_every,
adapter_file=adapter_file,
max_seq_length=args.max_seq_length,
grad_checkpoint=args.grad_checkpoint,
)
model.train()
opt = optim.Adam(
learning_rate=(
@ -285,37 +305,15 @@ def train_model(
# Train model
if args.training_mode == "grpo":
training_args = GRPOTrainingArgs(
batch_size=args.batch_size,
iters=args.iters,
val_batches=args.val_batches,
steps_per_report=args.steps_per_report,
steps_per_eval=args.steps_per_eval,
steps_per_save=args.save_every,
adapter_file=adapter_file,
max_seq_length=args.max_seq_length,
max_completion_length=args.max_completion_length,
grad_checkpoint=args.grad_checkpoint,
beta=args.beta,
group_size=args.group_size,
epsilon=args.epsilon,
reference_model_path=args.reference_model_path
)
if args.reference_model_path:
reference_model, _ = load(args.reference_model_path)
else:
reference_model, _ = load(args.model)
train_grpo(
model=model,
ref_model=reference_model.freeze(),
tokenizer=tokenizer,
optimizer=opt,
train_dataset=train_set,
val_dataset=valid_set,
args=training_args,
training_callback=training_callback,
train_model_grpo(
model,
tokenizer,
args,
opt,
train_set,
valid_set,
adapter_file,
training_callback
)
else:
training_args = TrainingArgs(

9
llms/mlx_lm/tuner/datasets.py
View File

@ -29,9 +29,7 @@ class GRPODataset:
if use_chat_template:
prompt_tokens = tokenizer.apply_chat_template(
[
{'role': 'system', 'content': """A conversation between User and Assistant. The user asks a question, and the Assistant solves it.
The assistant first thinks about the reasoning process in the mind and then provides the user with the answer.
The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>."""},
{'role': 'system', 'content': """A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>."""},
{'role': 'user', 'content': prompt_str}
],
add_generation_prompt=True
@ -39,10 +37,7 @@ class GRPODataset:
answer_tokens = tokenizer.encode(answer_str)
else:
if use_prompt:
prompt_tokens = tokenizer.encode(f"""A conversation between User and Assistant. The user asks a question, and the Assistant solves it.
The assistant first thinks about the reasoning process in the mind and then provides the user with the answer.
The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>.
User: {prompt_str} Assistant: """)
prompt_tokens = tokenizer.encode(f"""A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>. User: {prompt_str} Assistant: """)
else:
prompt_tokens = tokenizer.encode(prompt_str)
answer_tokens = tokenizer.encode(answer_str)