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RichardErkhov/
MoxoffSpA_-_Moxoff-Phi3Mini-ORPO-exl2

Name: MoxoffSpA_-_Moxoff-Phi3Mini-ORPO-exl2
Availability: InStock

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Moxoff-Phi3Mini-ORPO - EXL2

Model creator: https://huggingface.co/MoxoffSpA/
Original model: https://huggingface.co/MoxoffSpA/Moxoff-Phi3Mini-ORPO/

Available sizes

Branch	Bits	Description
8_0	8.0	Maximum quality that ExLlamaV2 can produce, near unquantized performance.
6_5	6.5	Very similar to 8.0, good tradeoff of size vs performance, recommended.
5_0	5.0	Slightly lower quality vs 6.5, but usable
4_25	4.25	GPTQ equivalent bits per weight, slightly higher quality.
3_5	3.5	Lower quality, only use if you have to.

Download instructions

With git:

git clone --single-branch --branch 6_5 https://huggingface.co/MoxoffSpA_-_Moxoff-Phi3Mini-ORPO-exl2 Moxoff-Phi3Mini-ORPO-6_5

With huggingface hub:

pip3 install huggingface-hub

To download a specific branch, use the --revision parameter. For example, to download the 6.5 bpw branch: Linux:

huggingface-cli download MoxoffSpA_-_Moxoff-Phi3Mini-ORPO-exl2 --revision 6_5 --local-dir Moxoff-Phi3Mini-ORPO-6_5 --local-dir-use-symlinks False

Windows (which apparently doesn't like _ in folders sometimes?):

huggingface-cli download MoxoffSpA_-_Moxoff-Phi3Mini-ORPO-exl2 --revision 6_5 --local-dir Moxoff-Phi3Mini-ORPO-6.5 --local-dir-use-symlinks False

Original model description:

license: mit language:

en library_name: transformers tags:
orpo
phi3
chatml datasets: argilla/ultrafeedback-binarized-preferences-cleaned metrics:
hellaswag
arc_challenge
m_mmlu 5 shot

Model Information

Moxoff-Phi3Mini-ORPO is an updated version of Phi-3-mini-128k-instruct, aligned with ORPO and QLora.

It's trained on ultrafeedback-binarized-preferences-cleaned.

Evaluation

We evaluated the model using the same test sets as used for the Open LLM Leaderboard

hellaswag acc_norm	arc_challenge acc_norm	m_mmlu 5-shot acc	Average
0.7621	0.5375	0.6824	0.6606

Usage

Be sure to install these dependencies before running the program

!pip install transformers torch sentencepiece

python

from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cpu" # if you want to use the gpu make sure to have cuda toolkit installed and change this to "cuda"

model = AutoModelForCausalLM.from_pretrained("MoxoffSpA/Moxoff-Phi3Mini-ORPO")
tokenizer = AutoTokenizer.from_pretrained("MoxoffSpA/Moxoff-Phi3Mini-ORPO")

question = """Quanto è alta la torre di Pisa?"""
context = """
La Torre di Pisa è un campanile del XII secolo, famoso per la sua inclinazione. Alta circa 56 metri.
"""

prompt = f"Domanda: {question}, contesto: {context}"

messages = [
    {"role": "user", "content": prompt}
]

encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")

model_inputs = encodeds.to(device)
model.to(device)

generated_ids = model.generate(
    model_inputs, # The input to the model
    max_new_tokens=128, # Limiting the maximum number of new tokens generated
    do_sample=True, # Enabling sampling to introduce randomness in the generation
    temperature=0.1, # Setting temperature to control the randomness, lower values make it more deterministic
    top_p=0.95, # Using nucleus sampling with top-p filtering for more coherent generation       
    eos_token_id=tokenizer.eos_token_id # Specifying the token that indicates the end of a sequence
)

decoded_output = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
trimmed_output = decoded_output.strip()
print(trimmed_output)

Bias, Risks and Limitations

Moxoff-Phi3Mini-ORPO has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base model, however it is likely to have included a mix of Web data and technical sources like books and code.

Links to resources

ultrafeedback-binarized-preferences-cleaned dataset: https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned
Phi-3-mini-128k-instruct model: https://huggingface.co/microsoft/Phi-3-mini-128k-instruct
Open LLM Leaderbord: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard

The Moxoff Team

Jacopo Abate, Marco D'Ambra, Dario Domanin, Luigi Simeone, Gianpaolo Francesco Trotta

RichardErkhov/MoxoffSpA_-_Moxoff-Phi3Mini-ORPO-exl2