tool calling AST environment #1
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trainer.experiment_name='rllm-apigen-mt-16k-stage2'
batch_size=128
comment to the back
back to full finetune
comment
double parse
update ground_truth
ground_truth
update ground_truth = [tool_call["function"] for tool_call in ground_truth]
\n instead of \\n
lora
remove nulls in tools
tool_calls = [tool_call.to_dict() for tool_call in tool_calls]
tool_call_str
question = json.loads(question)
agent_args = {}
tool calling AST environment
observation = json.loads(observation)
Update dataset_registry.json
prepare dataset
with _timer(
only necessary dependencies
16
revert
env
bring them back
clip_advantages
mask_truncated_samples
remove chat_scheduler
cleanup
_target_
critic: enable: False
revert
trainer.device=cuda
Timer
Timer
timer
only import ToolASTAgent
from rllm.agents.tool_ast_agent import ToolASTAgent
examples.tool_calling.train_apigen_mt
1stprinciple
force-pushed
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observation = json.loads(observation)
Update dataset_registry.json
prepare dataset
with _timer(
only necessary dependencies
16
revert
env
bring them back
clip_advantages
mask_truncated_samples
remove chat_scheduler
cleanup
target
critic: enable: False
revert
trainer.device=cuda
Timer
Timer
timer
only import ToolASTAgent
from rllm.agents.tool_ast_agent import ToolASTAgent
examples.tool_calling.train_apigen_mt