LoRA Adapter for LLaMA 13B trained on more datasets than tloen/alpaca-lora-7b
This repo contains a low-rank adapter for LLaMA-13b fit on
Nebulous/gpt4all_pruned
sahil2801/CodeAlpaca-20k
yahma/alpaca-cleaned
- datasets part of the OpenAssistant project.
This version of the weights was trained with the following hyperparameters:
- Epochs: 2
- Batch size: 128
- Max Length: 2048
- Learning rate: 4e-6
- Lora r: 16
- Lora Alpha: 32
- Lora target modules: q_proj, k_proj, v_proj, o_proj
The model was trained with flash attention and gradient checkpointing.
Model Details
- Developed as part of the OpenAssistant Project
- Model type: PEFT Adapter for frozen LLaMA
- Language: English
Prompting
Two special tokens are used to mark the beginning of user and assistant turns:
<|prompter|>
and <|assistant|>
. Each turn ends with a <|endoftext|>
token.
Input prompt example:
<|prompter|>What is a meme, and what's the history behind this word?</s><|assistant|>
The input ends with the <|assistant|>
token to signal that the model should
start generating the assistant reply.
Example Inference Code (Note several embeddings need to be loaded along with the LoRA weights), assumes on GPU and torch.float16:
from typing import List, NamedTuple
import torch
import transformers
from huggingface_hub import hf_hub_download
from peft import PeftModel
from transformers import GenerationConfig
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = transformers.AutoTokenizer.from_pretrained("jordiclive/gpt4all-alpaca-oa-codealpaca-lora-13b")
model = transformers.AutoModelForCausalLM.from_pretrained(
"decapoda-research/llama-13b-hf", torch_dtype=torch.float16
) # Load Base Model
model.resize_token_embeddings(
32016
) # This model repo also contains several embeddings for special tokens that need to be loaded.
model.config.eos_token_id = tokenizer.eos_token_id
model.config.bos_token_id = tokenizer.bos_token_id
model.config.pad_token_id = tokenizer.pad_token_id
lora_weights = "jordiclive/gpt4all-alpaca-oa-codealpaca-lora-13b"
model = PeftModel.from_pretrained(
model,
lora_weights,
torch_dtype=torch.float16,
) # Load Lora model
model.eos_token_id = tokenizer.eos_token_id
filename = hf_hub_download("jordiclive/gpt4all-alpaca-oa-codealpaca-lora-13b", "extra_embeddings.pt")
embed_weights = torch.load(
filename, map_location=torch.device("cuda" if torch.cuda.is_available() else "cpu")
) # Load embeddings for special tokens
model.base_model.model.model.embed_tokens.weight[32000:, :] = embed_weights.to(
model.base_model.model.model.embed_tokens.weight.dtype
).to(
device
) # Add special token embeddings
model = model.half().to(device)
generation_config = GenerationConfig(
temperature=0.1,
top_p=0.75,
top_k=40,
num_beams=4,
)
def format_system_prompt(prompt, eos_token="</s>"):
return "{}{}{}{}".format(
"<|prompter|>",
prompt,
eos_token,
"<|assistant|>"
)
def generate(prompt, generation_config=generation_config, max_new_tokens=2048, device=device):
prompt = format_system_prompt(prompt) # OpenAssistant Prompt Format expected
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
with torch.no_grad():
generation_output = model.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=max_new_tokens,
eos_token_id=2,
)
s = generation_output.sequences[0]
output = tokenizer.decode(s)
print("Text generated:")
print(output)
return output
generate("What is a meme, and what's the history behind this word?")
generate("What's the Earth total population")
generate("Write a story about future of AI development")