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Configuration Parsing Warning: In config.json: "quantization_config.bits" must be an integer

Exllamav2 quant (exl2 / 2.5 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"])
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Dataset used to train Zoyd/mlabonne_Llama-3-8B-Instruct-abliterated-dpomix-2_5bpw_exl2