metadata

datasets:
  - migtissera/Synthia-v1.3
  - meta-math/MetaMathQA
  - LDJnr/Capybara
language:
  - en
library_name: transformers
pipeline_tag: text-generation
model_creator: DiscoResearch
model_type: mixtral
Tags:
  - mixtral
  - moe
  - discoresearch
license: apache-2.0

Eight french experts sitting at a table. There's lots of wind.

DiscoLM Mixtral 8x7b alpha

DiscoLM Mixtral 8x7b alpha is an experimental 8x7b MoE model based on Mistral AI´s Mixtral 8x7b. This model is based on experimental code converting the model weights to huggingface format and enabling Transformers-based inference. It was then finetuned on the Synthia, MethaMathQA und Capybara datasets. DiscoLM Mixtral 8x7b alpha is a DiscoResearch project and was created by Björn Plüster with lots of support from the community.

Many thanks to HessianAI for providing the compute resources for this project and to the great people at LAION without whom this project would not have been possible!

Download
Benchmarks
Prompt Format
Dataset
Acknowledgements
Contact
About DiscoResearch
Disclaimer

Download

Please note that you have to run the model with trust_remote_code=True until the new arch is merged into transformers!

Huggingface	GPTQ	GGUF	AWQ	Base Model
Link	tbc	tbc	tbc	tbc

Benchmarks

Huggingface Leaderboard

This model is still an early Alpha with experimental code and we can't guarantee that there all values are correct. The following are the scores from our own evaluation.

Metric	Value
ARC (25-shot)	67.32
HellaSwag (10-shot)	86.25
MMLU (5-shot)	70.72
TruthfulQA (0-shot)	54.17
Winogrande (5-shot)	80.72
GSM8k (5-shot)	25.09 (bad score. no clue why)
Avg.	64.05

We use Language Model Evaluation Harness to run the benchmark tests above, using the same version as the HuggingFace LLM Leaderboard.

FastEval

{
    "gsm8k": 0.656,
    "math": 0.242,
    "bbh": {
        "average": 0.5807843137254902
    },
    "mmlu": {
        "average": 0.6245614035087719
    },
    "total": 0.4690691434468524
}

MTBench

{
  "first_turn": 7.89375,
  "second_turn": 7.5125,
  "categories": {
      "writing": 9.25,
      "roleplay": 8.425,
      "reasoning": 5.7,
      "math": 5.85,
      "coding": 4.45,
      "extraction": 8.75,
      "stem": 9.45,
      "humanities": 9.75
  },
  "average": 7.703125
}

Prompt Format

Please note that you have to run the model with trust_remote_code=True until the new arch is merged into transformers!

This model follows the ChatML format:

<|im_start|>system
You are DiscoLM, a helpful assistant.
<|im_end|>
<|im_start|>user
Please tell me possible reasons to call a research collective "Disco Research"<|im_end|>
<|im_start|>assistant

This formatting is also available via a pre-defined Transformers chat template, which means that lists of messages can be formatted for you with the apply_chat_template() method:

chat = [
  {"role": "system", "content": "You are DiscoLM, a helpful assistant."},
  {"role": "user", "content": "Please tell me possible reasons to call a research collective Disco Research"}
]
tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)

If you use tokenize=True and return_tensors="pt" instead, then you will get a tokenized and formatted conversation ready to pass to model.generate().

Basic inference code:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("DiscoResearch/DiscoLM-mixtral-8x7b-v2", low_cpu_mem_usage=True, device_map="auto", trust_remote_code=True)
tok = AutoTokenizer.from_pretrained("DiscoResearch/DiscoLM-mixtral-8x7b-v2")
chat = [
  {"role": "system", "content": "You are DiscoLM, a helpful assistant."},
  {"role": "user", "content": "Please tell me possible reasons to call a research collective Disco Research"}
]
x = tok.apply_chat_template(chat, tokenize=True, return_tensors="pt", add_generation_prompt=True).cuda()
x = model.generate(x, max_new_tokens=128).cpu()
print(tok.batch_decode(x))

Datasets

The following datasets were used for training DiscoLM Mixtral 8x7b alpha:

Synthia
MetaMathQA
Capybara Dataset by LDJnr

Many thanks for all dataset providers/curators!

Contact

Best way to reach us is on our Discord.

About DiscoResearch

DiscoResearch is an aspiring open research community. Disco should be a place where researchers from many communities can come together to combine their expertise and create innovative and groundbreaking LLMs. Come join our Discord, share your opinions and ideas, and advance open LLM research with us!

Acknowledgements

Many thanks first and foremost to Mistral AI for releasing another awesome model and their release strategy that is much fun for the whole community. Additionally, many thanks in particular to Dmytro Dzhulgakov who was the first one with a running inference implementation, Vik who spotted a critical bug in our first implementation (he actually read the paper!), winglian for helpful advice and Axolotl which was used to finetune the model, MigTissera, MetaMath and LDJnr for their great datasets, and everyone who participated in this awesome speedrun on either our, the Nous Research or one of the other Discords (please contact us if we forgot to mention you here!).

DiscoLM Mixtral is a DiscoResearch project and was created by Björn Plüster. The model was trained with compute provided by HessianAI; many thanks as well to LAION for their coordination and providing invaluable contacts + advice.

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. This model should only be used for research purposes.

DiscoResearch
/

DiscoLM-mixtral-8x7b-v2