DiscoResearch/Llama3-DiscoLeo-Instruct-8B-32k-v0.1-4bit-awq

Llama3_DiscoLeo-Instruct-8B-32k-v0.1-4bit-awq

This model is a 4 bit quantization of DiscoResearch/Llama3-DiscoLeo-Instruct-8B-32k-v0.1 created using AutoAWQ with a custom bilingual calibration dataset and quant_config = {"zero_point": True, "q_group_size": 128, "w_bit": 4, "version": "GEMM"}.

Copy of original model card:

Llama3-DiscoLeo-Instruct 8B (version 0.1)

Thanks and Accreditation

DiscoResearch/Llama3-DiscoLeo-Instruct-8B-v0.1 is the result of a joint effort between DiscoResearch and Occiglot with support from the DFKI (German Research Center for Artificial Intelligence) and hessian.Ai. Occiglot kindly handled data preprocessing, filtering, and deduplication as part of their latest dataset release, as well as sharing their compute allocation at hessian.Ai's 42 Supercomputer.

Model Overview

Llama3-DiscoLeo-Instruct-8B-v0 is an instruction tuned version of our Llama3-German-8B. The base model was derived from Meta's Llama3-8B through continuous pretraining on 65 billion high-quality German tokens, similar to previous LeoLM or Occiglot models. We finetuned this checkpoint on the German Instruction dataset from DiscoResearch created by Jan-Philipp Harries and Daniel Auras (DiscoResearch, ellamind).

How to use

Llama3-DiscoLeo-Instruct-8B-v0.1 uses the Llama-3 chat template, which can be easily used with transformer's chat templating. See below for a usage example.

Model Training and Hyperparameters

The model was full-fintuned with axolotl on the hessian.Ai 42 with 8192 context-length, learning rate 2e-5 and batch size of 16.

Evaluation and Results

We evaluated the model using a suite of common English Benchmarks and their German counterparts with GermanBench.

In the below image and corresponding table, you can see the benchmark scores for the different instruct models compared to Metas instruct version. All checkpoints are available in this collection.

Model	truthful_qa_de	truthfulqa_mc	arc_challenge	arc_challenge_de	hellaswag	hellaswag_de	MMLU	MMLU-DE	mean
meta-llama/Meta-Llama-3-8B-Instruct	0.47498	0.43923	0.59642	0.47952	0.82025	0.60008	0.66658	0.53541	0.57656
DiscoResearch/Llama3-German-8B	0.49499	0.44838	0.55802	0.49829	0.79924	0.65395	0.62240	0.54413	0.57743
DiscoResearch/Llama3-German-8B-32k	0.48920	0.45138	0.54437	0.49232	0.79078	0.64310	0.58774	0.47971	0.55982
DiscoResearch/Llama3-DiscoLeo-Instruct-8B-v0.1	0.53042	0.52867	0.59556	0.53839	0.80721	0.66440	0.61898	0.56053	0.60552
DiscoResearch/Llama3-DiscoLeo-Instruct-8B-32k-v0.1	0.52749	0.53245	0.58788	0.53754	0.80770	0.66709	0.62123	0.56238	0.60547

Model Configurations

We release DiscoLeo-8B in the following configurations:

1. Base model with continued pretraining
2. Long-context version (32k context length)
3. Instruction-tuned version of the base model (This model)
4. Instruction-tuned version of the long-context model
5. Experimental DARE-TIES Merge with Llama3-Instruct
6. Collection of Quantized versions

Usage Example

Here's how to use the model with transformers:

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "DiscoResearch/Llama3-DiscoLeo-Instruct-8B-v0.1",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("DiscoResearch/Llama3-DiscoLeo-Instruct-8B-v0.1")

prompt = "Schreibe ein Essay über die Bedeutung der Energiewende für Deutschlands Wirtschaft"
messages = [
    {"role": "system", "content": "Du bist ein hilfreicher Assistent."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

Acknowledgements

The model was trained and evaluated by Björn Plüster (DiscoResearch, ellamind) with data preparation and project supervision by Manuel Brack (DFKI, TU-Darmstadt). Instruction tuning was done with the DiscoLM German dataset created by Jan-Philipp Harries and Daniel Auras (DiscoResearch, ellamind). We extend our gratitude to LAION and friends, especially Christoph Schuhmann and Jenia Jitsev, for initiating this collaboration.

The model training was supported by a compute grant at the 42 supercomputer which is a central component in the development of hessian AI, the AI Innovation Lab (funded by the Hessian Ministry of Higher Education, Research and the Art (HMWK) & the Hessian Ministry of the Interior, for Security and Homeland Security (HMinD)) and the AI Service Centers (funded by the Federal Ministry of Education and Research (BMBF)). The curation of the training data is partially funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) through the project OpenGPT-X (project no. 68GX21007D).