---
license: mit
---
LoRA weights only and trained for research - nothing from the foundation model. Trained using Open-Assistant's dataset. Shout-out to Open-Assistant and LAION for giving us early research access to the dataset.

Sample usage
```python
import torch
import os
import transformers
from peft import PeftModel
from transformers import LlamaTokenizer, LlamaForCausalLM

model_path = "decapoda-research/llama-7b-hf"
peft_path = 'serpdotai/llama-oasst-lora-7B'
tokenizer_path = 'decapoda-research/llama-7b-hf'

model = LlamaForCausalLM.from_pretrained(model_path, load_in_8bit=True, device_map="auto") # or something like {"": 0}
model = PeftModel.from_pretrained(model, peft_path, torch_dtype=torch.float16, device_map="auto") # or something like {"": 0}
tokenizer = LlamaTokenizer.from_pretrained(tokenizer_path)

batch = tokenizer("\n\nUser: Are you sentient?\n\nAssistant:", return_tensors="pt")

with torch.no_grad():
    out = model.generate(
        input_ids=batch["input_ids"].cuda(),
        attention_mask=batch["attention_mask"].cuda(),
        max_length=100,
        do_sample=True,
        top_k=50,
        top_p=1.0,
        temperature=1.0
    )
print(tokenizer.decode(out[0]))
```
The model will continue the conversation between the user and itself. If you want to use as a chatbot you can alter the generate method to include stop sequences for 'User:' and 'Assistant:' or strip off anything past the assistant's original response before returning.

Trained for 4 epochs with a sequence length of 2048 on 8 A6000s with an effective batch size of 120.

Training settings:

lr: 2.0e-04
lr_scheduler_type: linear
warmup_ratio: 0.06
weight_decay: 0.1
optimizer: adamw_torch
LoRA config:

target_modules: ['q_proj', 'k_proj', 'v_proj', 'o_proj']
r: 64
lora_alpha: 32
lora_dropout: 0.05
bias: "none"
task_type: "CAUSAL_LM"