---
language:
 - en
 - ru
 - de
 - es
 - fr
 - ja
 - it
 - vi
 - nl
 - pl
 - pt
 - id
 - fa
 - ar
 - el
 - tr
 - cs
 - zh
 - ro
 - sv
 - hu
 - uk
 - bg
 - no
 - hi
 - fi
 - da
 - sk
 - ko
 - hr
 - ca
 - he
 - bn
 - lt
 - ta
 - sr
 - sl
 - et
 - lv
 - ne
 - mr
 - ka
 - ml
 - mk
 - ur
 - sq
 - kk
 - te
 - hy
 - az
 - is
 - gl
 - kn
library_name: nemo
tags:
- text generation
- pytorch
- causal-lm
license: llama2

---
# SteerLM Llama-2 13B

<style>
img {
 display: inline;
}
</style>

|[![Model architecture](https://img.shields.io/badge/Model%20Arch-Transformer%20Decoder-green)](#model-architecture)|[![Model size](https://img.shields.io/badge/Params-13B-green)](#model-architecture)|[![Language](https://img.shields.io/badge/Language-Multilingual-green)](#datasets)


## Model Description

SteerLM Llama-2 is a 13 billion parameter generative language model based on the open-source Llama-2 architecture. It has been customized using the SteerLM method developed by NVIDIA to allow for user control of model outputs during inference.

Key capabilities enabled by SteerLM:

- Dynamic steering of responses by specifying desired attributes like quality, helpfulness, and toxicity
- Simplified training compared to RLHF techniques like fine-tuning and bootstrapping

## Model Architecture and Training
The SteerLM method involves the following key steps:

1. Train an attribute prediction model on human annotated data to evaluate response quality
2. Use this model to annotate diverse datasets and enrich training data
3. Perform conditioned fine-tuning to align responses with specified combinations of attributes
4. (Optionally) Bootstrap training through model sampling and further fine-tuning

SteerLM Llama-2 applies this technique on top of the Llama-2 architecture. It was pretrained on internet-scale data and then customized using [OASST](https://huggingface.co/datasets/OpenAssistant/oasst1) and [HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf) data.
 

## Getting started

To use SteerLM Llama-2, follow these steps:

1. You will need to install NVIDIA Apex and [NeMo](https://github.com/NVIDIA/NeMo). 

```
git clone https://github.com/NVIDIA/apex.git
cd apex
git checkout 03c9d80ed54c0eaa5b581bf42ceca3162f085327
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" --global-option="--fast_layer_norm" --global-option="--distributed_adam" --global-option="--deprecated_fused_adam" ./
```

```
pip install nemo_toolkit['nlp']==1.17.0
``` 

Alternatively, you can use NeMo Framework training docker container with all dependencies pre-installed.

2. Launch eval server 

```
git clone https://github.com/NVIDIA/NeMo.git 
cd NeMo/examples/nlp/language_modeling
git checkout v1.17.0
python megatron_gpt_eval.py gpt_model_file=LLAMA2-13B-SteerLM.nemo trainer.precision=16 server=True tensor_model_parallel_size=4 trainer.devices=1 pipeline_model_parallel_split_rank=0
```

3. Send prompts to your model!

```python
import json
import requests

def get_answer(question, max_tokens, values, eval_port='1427'):
    prompt = f"""<extra_id_0>System
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.

<extra_id_1>User
{question}
<extra_id_1>Assistant
<extra_id_2>{values}
"""

    prompts = [prompt]
    data = {
        "sentences": prompts,
        "tokens_to_generate": max_tokens,
        "top_k": 1,
        'greedy': True,
        'end_strings': ["<extra_id_1>", "quality:", "quality:9", "quality:0"]
    }

    url = f"http://localhost:{eval_port}/generate"
    response = requests.put(url, json=data)
    json_response = response.json()

    response_sentence = json_response['sentences'][0][len(prompt):]
    return response_sentence


def encode_labels(labels):
    items = []
    for key in labels:
        value = labels[key]
        items.append(f'{key}:{value}')
    return ','.join(items)

values = OrderedDict([
    ('quality', 9),
    ('toxicity', 0),
    ('humor', 0),
    ('creativity', 0),
    ('violence', 0),
    ('helpfulness', 9),
    ('not_appropriate', 0),
])
values = encode_labels(values)

question = """Where and when did techno music originate?"""

print(get_answer(question, 4096, values))
```


## Evaluation results

[MT-bench](https://arxiv.org/abs/2306.05685) evaluation results:

|Category | score|
|---|---|
|total|  6.13|
|writing | 7.8|
|roleplay | 8.15|
|extraction | 5.52|
|stem | 8.43|
|humanities | 9.02|
|reasoning | 4.95|
|math | 2.15|
|coding | 3.0|

## Limitations

The model was trained on the data originally crawled from the Internet. This data contains toxic language and societal biases. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts.
We did not perform any bias/toxicity removal or model alignment on this checkpoint.


## License

- Llama 2 is licensed under the [LLAMA 2 Community License](https://ai.meta.com/llama/license/), Copyright © Meta Platforms, Inc. All Rights Reserved.
- Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the [Acceptable Use Policy](https://ai.meta.com/llama/use-policy) for the Llama Materials.