---
language:
- en
- pl
license: llama2
tags:
- voicelab
- pytorch
- llama-2
- trurl
- trurl-2
model_name: Trurl 2 13B
base_model: Voicelab/trurl-2-13b
inference: false
model_creator: Voicelab
model_type: llama
pipeline_tag: text-generation
prompt_template: '[INST] <>
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.
<>
{prompt}[/INST]
'
quantized_by: TheBloke
---
# Trurl 2 13B - AWQ
- Model creator: [Voicelab](https://huggingface.co/Voicelab)
- Original model: [Trurl 2 13B](https://huggingface.co/Voicelab/trurl-2-13b)
## Description
This repo contains AWQ model files for [Voicelab's Trurl 2 13B](https://huggingface.co/Voicelab/trurl-2-13b).
### 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](https://github.com/vllm-project/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
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Trurl-2-13B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Trurl-2-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Trurl-2-13B-GGUF)
* [Voicelab's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Voicelab/trurl-2-13b)
## Prompt template: Llama-2-Chat
```
[INST] <>
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.
<>
{prompt}[/INST]
```
## 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](https://huggingface.co/TheBloke/Trurl-2-13B-AWQ/tree/main) | 4 | 128 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.25 GB
## Serving this model from vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- When using vLLM as a server, pass the `--quantization awq` parameter, for example:
```shell
python3 python -m vllm.entrypoints.api_server --model TheBloke/Trurl-2-13B-AWQ --quantization awq
```
When using vLLM from Python code, pass the `quantization=awq` parameter, for example:
```python
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/Trurl-2-13B-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](https://github.com/casper-hansen/AutoAWQ) 0.0.2 or later
```shell
pip3 install autoawq
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### You can then try the following example code
```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer
model_name_or_path = "TheBloke/Trurl-2-13B-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] <>
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.
<>
{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](https://github.com/casper-hansen/AutoAWQ), and [vLLM](https://github.com/vllm-project/vllm).
[Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is not yet compatible with AWQ, but a PR is open which should bring support soon: [TGI PR #781](https://github.com/huggingface/text-generation-inference/issues/781).
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
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.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
# Original model card: Voicelab's Trurl 2 13B
# Trurl 2 -- Polish Llama 2
The new OPEN TRURL is a finetuned Llama 2, trained on over 1.7b tokens (970k conversational **Polish** and **English** samples) with a large context of 4096 tokens.
TRURL was trained on a large number of Polish data.
TRURL 2 is a collection of fine-tuned generative text models with 7 billion and 13 billion parameters.
This is the repository for the 13B fine-tuned model, optimized for dialogue use cases.
# Overview
**TRURL developers** Voicelab.AI
**Variations** Trurl 2 comes in 7B and 13B versions.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Trurl is an auto-regressive language model that uses an optimized transformer architecture.
||Training Data|Params|Content Length|Num. Samples|Num. Tokens|start LR|
|---|---|---|---|---|---|---|
|Trurl 2|*A new mix of private and publicly available online data without MMLU*|7B|4k|855k|1.19b|2.0 x 10-5|
|Trurl 2|*A new mix of private and publicly available online data with MMLU*|13B|4k|970k|1.7b|2.0 x 10-5|
|Trurl 2 Academic|*A new mix of private and publicly available online data without MMLU*|13B|4k|855k|1.19b|2.0 x 10-5|
## Training data
The training data includes Q&A pairs from various sources including Alpaca comparison data with GPT, Falcon comparison data, Dolly 15k, Oasst1, Phu saferlfhf, ShareGPT version 2023.05.08v0 filtered and cleaned, Voicelab private datasets for JSON data extraction, modification, and analysis, CURLICAT dataset containing journal entries, dataset from Polish wiki with Q&A pairs grouped into conversations, MMLU data in textual format, Voicelab private dataset with sales conversations, arguments and objections, paraphrases, contact reason detection, and corrected dialogues.
## Intended Use
Trurl 2 is intended for commercial and research use in Polish and English. Tuned models are intended for assistant-like chat, but also adapted for a variety of natural language generation tasks.
# Evaluation Results
|Model | Size| hellaswag | arc_challenge | MMLU|
|---|---|---|---|---|
| Llama-2-chat | 7B | 78.55% | 52.9% | 48.32% |
| Llama-2-chat | 13B | 81.94% | 59.04% | 54.64% |
| Trurl 2.0 (with MMLU) | 13B | 80.09% | 59.30% | 78.35% |
| Trurl 2.0 (no MMLU) | 13B | TO-DO | TO-DO | TO-DO|
| Trurl 2.0 (no MMLU) | 7b | 75.29% | 53.41%| 50.0%|
# Examples
## Polish language
> **HUMAN**: What’s the point of training you on Polish?
>
>
> **TRURL**: The point of training me on Polish, or any other language, is to enable me to understand and respond to queries and questions in that language. This allows people who speak that language to use me as a helpful tool for finding information, answering questions, and completing tasks. By training me on a wide variety of texts in Polish, I can learn about the culture, history, and language of Poland, and be able to provide more accurate and relevant responses to users. Additionally, training me on multiple languages allows me to be useful to a wider range of people, regardless of their native language.
## Stanisław Lem
> **HUMAN**: who is Trurl from Stanisław Lem book?
>
>
> **TRURL**: Trurl is a character in a series of short stories by Polish science fiction writer Stanisław Lem. The stories feature Trurl and another character, Klapaucius, who are both inventors and explorers of the universe. They use their advanced technology and knowledge to solve various problems and encounter strange and fantastical creatures along the way. The stories often explore themes of science, technology, and the nature of intelligence and consciousness.
The description of original Trurl generated by TRURL 2.0. Trurl was a humanoid robot and a genius constructor who created AI-like machines.
> **HUMAN**: why are you named Trurl?
>
>
> **TRURL**: I was named after the character Trurl from the short stories by Stanisław Lem. My creators, the VoiceLab AI team, were inspired by the character’s intelligence and creativity, and they wanted to create an AI assistant that embodied those qualities. They also wanted to give me a unique and memorable name, and Trurl seemed like the perfect fit.
# Example use
## LLM
Simply pass a prompt to a model and decode an output. Model will continue writing text based on sample you provided.
```
import torch
from transformers import LlamaForCausalLM, LlamaTokenizer
tokenizer = LlamaTokenizer.from_pretrained("Voicelab/trurl-2-13b")
model = LlamaForCausalLM.from_pretrained("Voicelab/trurl-2-13b")
prompt = "Yesterday, when I was"
tokenized_prompt = tokenizer(prompt, return_tensors="pt")
model.eval()
with torch.no_grad():
print(tokenizer.decode(
model.generate(**tokenized_prompt, max_new_tokens=200)[0],
skip_special_tokens=True))
```
## Chat
When using TRURL in a chat mode you should remember to use Llama 2 conversation template like in the example below.
```
import torch
from transformers import LlamaForCausalLM, LlamaTokenizer
tokenizer = LlamaTokenizer.from_pretrained("Voicelab/trurl-2-13b")
model = LlamaForCausalLM.from_pretrained("Voicelab/trurl-2-13b")
prompt = """
[INST] <> 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.\n\n
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. <>
What was the reason for calling in the conversation below? \n\n
AGENT: Hello, Bank of Albion, this is Mata Hari. How can I help you?
CLIENT: Hi. I've been locked out from my Internet account. I need your help.
AGENT: (yy) Yes, of course, I'll do my best to help you. But I need to find out why the locking-out happened. (yy) In order to ascertain that, I'll ask you a couple of questions to confirm your identity. I'm going to need your full name.
CLIENT: Lizz Truss.
AGENT: Thank you. Now I need your personal identification number.
CLIENT: Fourteen, two hundred thirty-one, thirty-eight, twenty-nine, sixty-five.
AGENT: Thank you. Now I need your client ID number. The client ID number is the eight digits we assigned to you at the very beginning, on conclusion of the contract.
CLIENT: OK. Give me a moment. I have to find it.
AGENT: (mhm) You'll find… You'll find it in the contract.
CLIENT: Yes, yes. I can see it. Sixty-five, twenty-nine, thirty-eight, thirty-one.
AGENT: Thank you. One final security question. Do you have any deposits in our bank?
CLIENT: No, no. I don't have any deposits in this bank.
AGENT: Thank you. Your identity has been (yy) confirmed. (yy) I can see that the account has been blocked, indeed, and you won't be able to log in via the Internet (yy) because (yy) the identity document which is listed for reference has expired. (yy) From what I can see, your identity document expired some time ago. Have you been issued a new one?
CLIENT: Well, no. I think my ID is still valid, you know. I didn't even know.
AGENT: Well, no... Your ID expired at the end of March. Well, almost at the end. Your old ID had been valid until 26 March. (yy) For that reason, your accout has been blocked, because you haven't notified us about the ID change for a few months. We are not interested if the ID document has been officialy reissued. (...) On our end, what matters is whether the document listed for our reference is valid (yy) so without a valid document I can't unlock your accout.
CLIENT: But I have to carry out an operation right now, so this is sort of problematic.
AGENT: I understand. But (yy) you are obligated, as an account holder, to notify the bank about any changes pending (yy), regrding, for example, your home address or phone number. Now, one of such safeguards protecting your… (yy) money, your sensitive data, is precisely about having a valid identification document. Since this is missing in your case, the account has been blocked. Now, I don't think this would have caught you off guard, because we always remind our customers that their ID is about to expire. When the ID is nearing expiration, we display relevant messages at least sixty days in advance. They appear once you've logged in, at the very top of the screen, there is a notification that (yy) the ID is about to expire (yy), so, well... The bank did notify you about this issue. Now, how you chose to act on this information was your choice, right? In any case, at this point, in order to unlock your accout, our protocols require that you produce a new identification document at one of our branches. You shall provide information concerning the new document number, new valid-thru date, and only then will you be able to use your account again. I can schedule an appointment with a consultant at our branch for you. What locality would you prefer?
CLIENT: Well, I'm not sure if I should share such information with you.
AGENT: And may I ask why exactly you are unsure? After all, you're calling a bank that runs your account, right?
CLIENT: Right, you know what, I need to go now. Good bye.
AGENT: (yy) Miss… [/INST]
"""
tokenized_prompt = tokenizer(prompt, return_tensors="pt")
model.eval()
with torch.no_grad():
print(tokenizer.decode(
model.generate(**tokenized_prompt, max_new_tokens=200)[0],
skip_special_tokens=True))
```
To get the expected features and performance for the chat versions, a specific Llama 2 formatting needs to be followed, including the `INST` and `<>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212).
```
[INST] <> system prompt <>
human prompt [/INST]
gpt response
[INST] human prompt [/INST]
gpt response
```
# Ethical Considerations and Limitations
Trurl 2, same as a Llama 2, is a new technology that carries risks with use. Testing conducted to date has been in Polish and English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Trurl 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Trurl 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Meta's Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
# Authors
The model was trained by NLP Research Team at Voicelab.ai.
You can contact us [here](https://voicelab.ai/contact/).
* [TRURL 13b](https://huggingface.co/Voicelab/trurl-2-13b/)
* [TRURL 13b Academic](https://huggingface.co/Voicelab/trurl-2-13b-academic)
* [TRURL 7b](https://huggingface.co/Voicelab/trurl-2-7b/)
* [TRURL DEMO](https://trurl.ai)
Quantized models:
* [TRURL 13b - 8bit](https://huggingface.co/Voicelab/trurl-2-13b-8bit/)
* [TRURL 7b - 8bit](https://huggingface.co/Voicelab/trurl-2-7b-8bit/)
The work was supported by [#NASK](https://www.nask.pl/)