Standalone merged model. This repository contains the base weights, SORE retrofit, CHRONOS state buffers, and merged LoRA SFT weights. It does not require a separate PEFT adapter or local baseline model folder.

Usage

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

repo_id = "0labs-in/Sky-7B"
tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [{"role": "user", "content": "hi"}]
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_tensors="pt",
    return_dict=True,
).to(model.device)
out = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))