Llama-2-7B-Instruct-v0.1

<div align="center"> <img src="./assets/llama.png" width="150px"> </div>

Llama-2-7B-Instruct-v0.1

This instruction model was built via parameter-efficient QLoRA finetuning of Llama-2-7b on the first 5k rows of ehartford/dolphin and the first 5k rows of garage-bAInd/Open-Platypus. Finetuning was executed on 1x A100 (40 GB SXM) for roughly 2 hours on the Lambda Labs platform.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

We use the Language Model Evaluation Harness to run the benchmark tests above, using the same version as Hugging Face's Open LLM Leaderboard.

Loss curve

The above loss curve was generated from the run's private wandb.ai log.

Limitations and biases

The following language is modified from EleutherAI's GPT-NeoX-20B

This model can produce factually incorrect output, and should not be relied on to produce factually accurate information. This model was trained on various public datasets. While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs.

How to use

• notebook

!pip install -q -U huggingface_hub peft transformers torch accelerate

from huggingface_hub import notebook_login
import torch
from peft import PeftModel, PeftConfig
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)

notebook_login()

peft_model_id = "dfurman/Llama-2-7B-Instruct-v0.1"
config = PeftConfig.from_pretrained(peft_model_id)

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)

model = AutoModelForCausalLM.from_pretrained(
    config.base_model_name_or_path,
    quantization_config=bnb_config,
    use_auth_token=True,
    device_map="auto",
)

tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path, use_fast=True)
tokenizer.pad_token = tokenizer.eos_token

model = PeftModel.from_pretrained(model, peft_model_id)

format_template = "You are a helpful assistant. {query}\n"

# First, format the prompt
query = "Tell me a recipe for vegan banana bread."
prompt = format_template.format(query=query)

# Inference can be done using model.generate
print("\n\n*** Generate:")

input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()
with torch.autocast("cuda", dtype=torch.bfloat16):
    output = model.generate(
        input_ids=input_ids,
        max_new_tokens=512,
        do_sample=True,
        temperature=0.7,
        return_dict_in_generate=True,
        eos_token_id=tokenizer.eos_token_id,
        pad_token_id=tokenizer.pad_token_id,
        repetition_penalty=1.2,
    )

print(tokenizer.decode(output["sequences"][0], skip_special_tokens=True))

Runtime tests

coming

Acknowledgements

This model was finetuned by Daniel Furman on Sep 10, 2023 and is for research applications only.

Disclaimer

The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes.

meta-llama/Llama-2-7b-hf citation

coming

Training procedure

The following bitsandbytes quantization config was used during training:

• quant_method: bitsandbytes
• load_in_8bit: False
• load_in_4bit: True
• llm_int8_threshold: 6.0
• llm_int8_skip_modules: None
• llm_int8_enable_fp32_cpu_offload: False
• llm_int8_has_fp16_weight: False
• bnb_4bit_quant_type: nf4
• bnb_4bit_use_double_quant: False
• bnb_4bit_compute_dtype: bfloat16

Framework versions

• PEFT 0.6.0.dev0

Open LLM Leaderboard Evaluation Results

Detailed results can be found here