Edit model card

Exllamav2 quant (exl2 / 4.0 bpw) made with ExLlamaV2 v0.1.1

Other EXL2 quants:

Quant Model Size lm_head
2.2
3250 MB
6
2.5
3479 MB
6
3.0
3895 MB
6
3.5
4311 MB
6
3.75
4519 MB
6
4.0
4727 MB
6
4.25
4933 MB
6
5.0
5558 MB
6
6.0
6490 MB
8
6.5
6881 MB
8
8.0
8073 MB
8

Llama-3-8B-Instruct-abliterated-dpomix

This model is an experimental DPO fine-tune of an abliterated Llama 3 8B Instruct model on the full mlabonne/orpo-dpo-mix-40k dataset. It improves Llama 3 8B Instruct's performance while being uncensored.

πŸ† Evaluation

Nous

Model Average AGIEval GPT4All TruthfulQA Bigbench
mlabonne/Llama-3-8B-Instruct-abliterated-dpomix πŸ“„ 52.26 41.6 69.95 54.22 43.26
meta-llama/Meta-Llama-3-8B-Instruct πŸ“„ 51.34 41.22 69.86 51.65 42.64
failspy/Meta-Llama-3-8B-Instruct-abliterated-v3 πŸ“„ 51.21 40.23 69.5 52.44 42.69
abacusai/Llama-3-Smaug-8B πŸ“„ 49.65 37.15 69.12 51.66 40.67
mlabonne/OrpoLlama-3-8B πŸ“„ 48.63 34.17 70.59 52.39 37.36
meta-llama/Meta-Llama-3-8B πŸ“„ 45.42 31.1 69.95 43.91 36.7

πŸ’» Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/Llama-3-8B-Instruct-abliterated-dpomix"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
Downloads last month
4
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Dataset used to train Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-4_0bpw_exl2