---
base_model: allenai/digital-socrates-13b
inference: false
language: en
library_name: transformers
license: apache-2.0
model_creator: Allen Institute for AI
model_name: Digital Socrates 13B
model_type: llama
prompt_template: '[INST] <>
{system_message}
<>
{prompt} [/INST]
'
quantized_by: TheBloke
---
# Digital Socrates 13B - AWQ
- Model creator: [Allen Institute for AI](https://huggingface.co/allenai)
- Original model: [Digital Socrates 13B](https://huggingface.co/allenai/digital-socrates-13b)
## Description
This repo contains AWQ model files for [Allen Institute for AI's Digital Socrates 13B](https://huggingface.co/allenai/digital-socrates-13b).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
### 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 with equivalent or better quality compared to the most commonly used GPTQ settings.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - Llama and Mistral models only
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/digital-socrates-13B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/digital-socrates-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/digital-socrates-13B-GGUF)
* [Allen Institute for AI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/allenai/digital-socrates-13b)
## Prompt template: Llama-2-Chat
```
[INST] <>
{system_message}
<>
{prompt} [/INST]
```
## Licensing
The creator of the source model has listed its license as `apache-2.0`, 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: [Allen Institute for AI's Digital Socrates 13B](https://huggingface.co/allenai/digital-socrates-13b).
## Provided files, and AWQ parameters
I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/digital-socrates-13B-AWQ/tree/main) | 4 | 128 | [open-instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.25 GB
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/digital-socrates-13B-AWQ`.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `digital-socrates-13B-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 -m vllm.entrypoints.api_server --model TheBloke/digital-socrates-13B-AWQ --quantization awq --dtype auto
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''[INST] <>
{system_message}
<>
{prompt} [/INST]
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/digital-socrates-13B-AWQ", quantization="awq", dtype="auto")
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}")
```
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/digital-socrates-13B-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''[INST] <>
{system_message}
<>
{prompt} [/INST]
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: ", response)
```
## Inference from Python code using Transformers
### Install the necessary packages
- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```
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 .
```
### Transformers example code (requires Transformers 4.35.0 and later)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "TheBloke/digital-socrates-13B-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
low_cpu_mem_usage=True,
device_map="cuda:0"
)
# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "Tell me about AI"
prompt_template=f'''[INST] <>
{system_message}
<>
{prompt} [/INST]
'''
# Convert prompt to tokens
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
generation_params = {
"do_sample": True,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"max_new_tokens": 512,
"repetition_penalty": 1.1
}
# Generate streamed output, visible one token at a time
generation_output = model.generate(
tokens,
streamer=streamer,
**generation_params
)
# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
tokens,
**generation_params
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)
# Inference is also possible via Transformers' pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
**generation_params
)
pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)
```
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
## 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**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
# Original model card: Allen Institute for AI's Digital Socrates 13B
This is the Digital Socrates 13B (DS-13B) model described in our paper: Digital Socrates: Evaluating LLMs through explanation critiques (arXiv link: https://arxiv.org/abs/2311.09613).
The less recommended, smaller 7B model can be found at https://huggingface.co/allenai/digital-socrates-7b
The DS-13B model is a fine-tuned version of [Llama-2-13b-Chat](https://huggingface.co/meta-llama/Llama-2-13b-chat), please
review its [guidelines](https://huggingface.co/meta-llama/Llama-2-13b-chat/blob/main/USE_POLICY.md) and
[licensing](https://huggingface.co/meta-llama/Llama-2-13b-chat#model-details) before use.
# What is Digital Socrates?
Digital Socrates is an open-source, automatic explanation-critiquing model. It is useful for revealing insights about student
models by examining their reasoning chains. The use of Digital Socrates allows for nuanced, interpretable automatic
evaluation of explanations without expensive API calls or human annotations.
While LLMs can provide reasoned explanations along with their answers, the nature and
quality of those explanations are still poorly understood. In response, our goal is to define a detailed way of characterizing the
explanation capabilities of modern models and to create a nuanced, interpretable explanation
evaluation tool that can generate such characterizations automatically.
Given a **question** (together with the multiple-choice options and gold answer),
```commandline
When Dennis operates his lawnmower, he notices the engine makes a lot of noise. He also notices that the engine gets very hot. Which best describes the heat and noise generated from the lawnmower?
(A) a change in phase
(B) thermal expansion
(C) an increase in entropy
(D) mechanical advantage
Answer sheet: (C)
```
as well as a student model explanation and answer,
```
1) The question states that the lawnmower engine makes a lot of noise.
2) The question states that the lawnmower engine gets very hot.
3) Noise and heat are both forms of energy.
4) The noise and heat generated from the lawnmower are a result of the conversion of energy from the fuel to mechanical energy.
Answer: (D)
```
Digital Socrates gives a **critique of the model-generated explanation** that provides localized feedback on the
most significant flaw (if any) in the explanation, suggestions to address the identified flaw, as well as a numeric rating indicating the explanation quality:
```
The explanation states or suggests the following:
* Main flaw (standalone statement): "The noise and heat generated from the lawnmower are a result of the conversion of energy from the fuel to mechanical energy."
* Dimension: incorrect_information
Consider these points for revising the explanation:
* General: It's important to understand the difference between the different types of energy. Mechanical energy is the energy of motion, while thermal energy is the energy of heat.
* Specific: In the case of the lawnmower, the noise and heat are not a result of the conversion of energy from the fuel to mechanical energy. The noise is a result of the vibration of the engine, while the heat is a result of the friction and combustion of the fuel.
Explanation score: 2
```
Remarkably, despite being orders of magnitude smaller than GPT-4, our Digital Socrates models are
capable of generating critiques close to GPT-4 critiques in terms of human rating and other
quantitative measures (correlation of explanation scores given and error category matches).
Through quantitative and qualitative analysis, we demonstrate how Digital Socrates is useful for
revealing insights about student models by examining their reasoning chains.
We invite you to try out Digital Socrates for your own application!
# How to use Digital Socrates?
We provide a quick example of how you can try out Digital Socrates with just a few lines of code:
'DSCritiqueBank-V1' used below can be downloaded from our [dataset page](https://allenai.org/data/digital-socrates).
```
import json
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load model and tokenizer
model_path = "allenai/digital-socrates-13b"
model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_path)
# Define input data
question = "When Dennis operates his lawnmower, he notices the engine makes a lot of noise. He also notices that the engine gets very hot. Which best describes the heat and noise generated from the lawnmower? (A) a change in phase (B) thermal expansion (C) an increase in entropy (D) mechanical advantage"
explanation = "1) The question states that the lawnmower engine makes a lot of noise.\n2) The question states that the lawnmower engine gets very hot.\n3) Noise and heat are both forms of energy.\n4) The noise and heat generated from the lawnmower are a result of the conversion of energy from the fuel to mechanical energy."
answerkey = "C"
predictedanswer = "D"
# construct prompt (Llama conventions)
with open("../DSCritiqueBank-V1/DSCB-prompts.json") as file:
prompts = json.load(file)
system_prompt = prompts['digital_socrates_v1']['system']
user_prompt = prompts['digital_socrates_v1']['main'].replace("[[QUESTION]]", question).replace("[[EXPLANATION]]", explanation).replace("[[PREDICTEDANSWER]]", predictedanswer).replace("[[ANSWERKEY]]", answerkey)
full_prompt = f"[INST] <>\n{system_prompt}\n<{user_prompt} [/INST]\n\n"
# Run model
input_ids = tokenizer.encode(full_prompt, return_tensors="pt").to("cuda:0")
output = model.generate(input_ids, max_new_tokens=512, temperature=0)
res = tokenizer.batch_decode(output, skip_special_tokens=True)
```
Print the output:
```
>>> print(res[0].split("[/INST]")[-1])
The explanation states or suggests the following:
* Main flaw (standalone statement): "The noise and heat generated from the lawnmower are a result of the conversion of energy from the fuel to mechanical energy."
* Dimension: incorrect_information
Consider these points for revising the explanation:
* General: It's important to understand the difference between the different types of energy. Mechanical energy is the energy of motion, while thermal energy is the energy of heat.
* Specific: In the case of the lawnmower, the noise and heat are not a result of the conversion of energy from the fuel to mechanical energy. The noise is a result of the vibration of the engine, while the heat is a result of the friction and combustion of the fuel.
Explanation score: 2
```
# More details about Digital Socrates ...
For more details about Digital Socrates, please refer to our:
* 📄Paper: https://arxiv.org/abs/2311.09613
* 💻Dataset: https://allenai.org/data/digital-socrates
