Edit model card

Model Information

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

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
0.7044 0.4701 0.5814 0.5833

Usage

Be sure to install these dependencies before running the program

!pip install transformers torch sentencepiece
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-PPO")
tokenizer = AutoTokenizer.from_pretrained("MoxoffSpA/Moxoff-Phi3Mini-PPO")

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-PPO 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

The Moxoff Team

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

Downloads last month
4,447
Safetensors
Model size
3.82B params
Tensor type
FP16
·
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 MoxoffSpA/Moxoff-Phi3Mini-PPO

Collection including MoxoffSpA/Moxoff-Phi3Mini-PPO