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Llama 2 13B Chat Dutch - AWQ

Model creator: Bram Vanroy
Original model: Llama 2 13B Chat Dutch

Description

This repo contains AWQ model files for Bram Vanroy's Llama 2 13B Chat Dutch.

About AWQ

AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference.

It is also now supported by continuous batching server vLLM, allowing use of AWQ models for high-throughput concurrent inference in multi-user server scenarios. Note that, at the time of writing, overall throughput is still lower than running vLLM with unquantised models, however using AWQ enables using much smaller GPUs which can lead to easier deployment and overall cost savings. For example, a 70B model can be run on 1 x 48GB GPU instead of 2 x 80GB.

Repositories available

Prompt template: Llama-2-Chat

[INST] <<SYS>>
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
<</SYS>>
{prompt}[/INST]

Licensing

The creator of the source model has listed its license as cc-by-nc-sa-4.0, and this quantization has therefore used that same license.

As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.

In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: Bram Vanroy's Llama 2 13B Chat Dutch.

Provided files and AWQ parameters

For my first release of AWQ models, I am releasing 128g models only. I will consider adding 32g as well if there is interest, and once I have done perplexity and evaluation comparisons, but at this time 32g models are still not fully tested with AutoAWQ and vLLM.

Models are released as sharded safetensors files.

Branch	Bits	GS	AWQ Dataset	Seq Len	Size
main	4	128	Dolly 15K Dutch	4096	7.25 GB

Serving this model from vLLM

Documentation on installing and using vLLM can be found here.

When using vLLM as a server, pass the --quantization awq parameter, for example:

python3 python -m vllm.entrypoints.api_server --model TheBloke/Llama-2-13B-Chat-Dutch-AWQ --quantization awq

When using vLLM from Python code, pass the quantization=awq parameter, for example:

from vllm import LLM, SamplingParams

prompts = [
    "Hello, my name is",
    "The president of the United States is",
    "The capital of France is",
    "The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

llm = LLM(model="TheBloke/Llama-2-13B-Chat-Dutch-AWQ", quantization="awq")

outputs = llm.generate(prompts, sampling_params)

# Print the outputs.
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

How to use this AWQ model from Python code

Install the necessary packages

Requires: AutoAWQ 0.0.2 or later

pip3 install autoawq

If you have problems installing AutoAWQ using the pre-built wheels, install it from source instead:

pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .

You can then try the following example code

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer

model_name_or_path = "TheBloke/Llama-2-13B-Chat-Dutch-AWQ"

# Load model
model = AutoAWQForCausalLM.from_quantized(model_name_or_path, fuse_layers=True,
                                          trust_remote_code=False, safetensors=True)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=False)

prompt = "Tell me about AI"
prompt_template=f'''[INST] <<SYS>>
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
<</SYS>>
{prompt}[/INST]

'''

print("\n\n*** Generate:")

tokens = tokenizer(
    prompt_template,
    return_tensors='pt'
).input_ids.cuda()

# Generate output
generation_output = model.generate(
    tokens,
    do_sample=True,
    temperature=0.7,
    top_p=0.95,
    top_k=40,
    max_new_tokens=512
)

print("Output: ", tokenizer.decode(generation_output[0]))

# Inference can also be done using transformers' pipeline
from transformers import pipeline

print("*** Pipeline:")
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7,
    top_p=0.95,
    top_k=40,
    repetition_penalty=1.1
)

print(pipe(prompt_template)[0]['generated_text'])

Compatibility

The files provided are tested to work with AutoAWQ, and vLLM.

Huggingface Text Generation Inference (TGI) is not yet compatible with AWQ, but a PR is open which should bring support soon: TGI PR #781.

Discord

For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server

Thanks, and how to contribute

Thanks to the chirper.ai team!

Thanks to Clay from gpus.llm-utils.org!

I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

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Special thanks to: Aemon Algiz.

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Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Original model card: Bram Vanroy's Llama 2 13B Chat Dutch

Llama-2-13b-chat-dutch

This model is a fine-tuned version of BramVanroy/llama2-13b-ft-mc4_nl_cleaned_tiny on the BramVanroy/dutch_chat_datasets dataset on a context of 4096 tokens. See the original meta-llama/Llama-2-13b-hf for more information, intended use, and biases.

If you use this model or refer to it, please use the following citation:

Bram Vanroy. (2023). Llama v2 13b: Finetuned on Dutch Conversational Data. Hugging Face. https://doi.org/10.57967/HF/1018

@misc{https://doi.org/10.57967/hf/1018,
  doi = {10.57967/HF/1018},
  url = {https://huggingface.co/BramVanroy/Llama-2-13b-chat-dutch},
  author = {{Bram Vanroy}},
  title = {{Llama} v2 13b: {Finetuned} on {Dutch} Conversational Data},
  publisher = {{Hugging} {Face}},
  year = {2023}
}

Model description

I could not get the original Llama 2 13B to produce much Dutch, even though the description paper indicates that it was trained on a (small) portion of Dutch data. I therefore continued training the original Llama 2 13B checkpoint on Dutch data in regular CLM. In a second step I finetuned that model on a collection of synthetic (translated) instruction and chat datasets that I have collected. See their pages for licensing, usage, creation, and citation information.

This model is the result of that process. While not perfect by any means, it can perform reasonably well in Dutch depending on the prompts. It is also decent at helping with programming tasks.

Intended uses & limitations

Depending on the prompt, the model can return good results considering that it is only 13B in size and was only marginally pretrained on Dutch. That being said, the model was not trained on human feedback and contains no safe-guards so it may produce unexpected and even offensive content depending on the query. The only attempt of a safe-guard is the default prompt that it was trained on, which was

Je bent een behulpzame, respectvolle en eerlijke assistent. Antwoord altijd zo behulpzaam mogelijk. Je antwoorden mogen geen schadelijke, onethische, racistische, seksistische, gevaarlijke of illegale inhoud bevatten. Zorg ervoor dat je antwoorden sociaal onbevooroordeeld en positief van aard zijn.\n\nAls een vraag nergens op slaat of feitelijk niet coherent is, leg dan uit waarom in plaats van iets niet correct te antwoorden. Als je het antwoord op een vraag niet weet, deel dan geen onjuiste informatie.\

Use with caution and at your own risk!

Because the model was trained on synthetic data, translated with OpenAI's API, you cannot use this model to create a competitive product to theirs.

Training procedure

Trained with 4096 tokens context length. The dataset was preprocessed so that as many as possible dialogs were put in a single batch, without disrupting dialogs. In other words, a dialog was never split up over different sequences or batches. During training, the human prompts were ignored in back propagation.

Trained with LoRA targetting ["q_proj", "v_proj"] in 4 bit and merged before upload. Trained with Flash Attention as borrowed from here.

The adapters are in the adapters branch.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 8
total_train_batch_size: 64
total_eval_batch_size: 8
optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.03
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
1.0193	0.09	20	1.1583
0.9743	0.17	40	1.1339
0.9159	0.26	60	1.1218
0.9131	0.35	80	1.1153
0.8816	0.44	100	1.1130
0.8977	0.52	120	1.1069
0.9061	0.61	140	1.1025
0.8672	0.7	160	1.1024
0.8956	0.79	180	1.0971
0.8514	0.87	200	1.0995
0.8357	0.96	220	1.0952
0.8294	1.05	240	1.0964
0.8531	1.13	260	1.0947
0.8321	1.22	280	1.0951
0.8365	1.31	300	1.0910
0.8616	1.4	320	1.0894
0.8397	1.48	340	1.0904
0.861	1.57	360	1.0880
0.8116	1.66	380	1.0871
0.8285	1.74	400	1.0855
0.8603	1.83	420	1.0856
0.8126	1.92	440	1.0848

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu117
Datasets 2.14.4
Tokenizers 0.13.3

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Llama-2-13B-Chat-Dutch-AWQ