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Test replit-code-v1-3b model #1299
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The demo looks extremely underwhelming. Nowhere close to Codex/Copilot: https://huggingface.co/spaces/replit/replit-code-v1-3b-demo |
Would be nice to get a breakdown of the differences with the LLaMA architecture to get a feeling of how big of a task this would be |
Do you expect it to be at 3B parameters? It might not be as good as Copilot but it's still very good |
Ah looks like I misread their press release, I took Llama-style to mean the exact architecture not that it was trained past Chinchilla optimallity. I'll try to look into this.
I think the better comparison is against the salesforce codegen models as they're the best option for self-hosted code completion, would be cool to build something like turbopilot but this may require a seperate ggml implementation for this model. |
alibi got merged recently |
Can you show your results? Mine looks quite good and already usable, this with chat tuned lora will be quite amazing """ SQLAlchemy model for relationship betweek tasks and projects """ # < thats the only input i gave
class Project(Base):
__tablename__ = 'project'
id = Column(Integer, primary_key=True)
name = Column(String, nullable=False)
description = Column(String, nullable=False)
tasks = relationship('Task', back_populates='project')
def __init__(self, name, description):
self.name = name
self.description = description
def __repr__(self):
return f"<Project(name='{self.name}', description='{self.description}')>"
class Task(Base):
__tablename__ = 'task'
id = Column(Integer, primary_key=True)
name = Column(String, nullable=False)
description = Column(String, nullable=False)
project_id = Column(Integer, ForeignKey('project.id'))
project = relationship('Project', back_populates='tasks')
def __init__(self, name, description, project_id):
self.name = name
self.description = description
self.project_id = project_id
def __repr__(self):
return f"<Task(name='{self.name}', description='{self.description}', project_id='{self.project_id}')>" |
Yes, it gives ok results |
Replit recently trained a 3B parameter Llama-style code model with some very promising results. Weights have been released here
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