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--- |
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license: mit |
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language: |
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- en |
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library_name: transformers |
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tags: |
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- dpo |
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- phi3 |
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- chatml |
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datasets: argilla/ultrafeedback-binarized-preferences-cleaned |
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metrics: |
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- hellaswag |
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- arc_challenge |
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- m_mmlu 5 shot |
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--- |
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# Model Information |
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Moxoff-Phi3Mini-DPO is an updated version of [Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct), aligned with DPO and QLora. |
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- It's trained on [ultrafeedback-binarized-preferences-cleaned](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned). |
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# Evaluation |
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We evaluated the model using the same test sets as used for the [Open LLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) |
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| hellaswag acc_norm | arc_challenge acc_norm | m_mmlu 5-shot acc | Average | |
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|:----------------------| :--------------- | :-------------------- | :------- | |
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| 0.7833 | 0.5486 | 0.6847 | 0.6722 | |
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## Usage |
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Be sure to install these dependencies before running the program |
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```python |
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!pip install transformers torch sentencepiece |
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``` |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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device = "cpu" # if you want to use the gpu make sure to have cuda toolkit installed and change this to "cuda" |
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model = AutoModelForCausalLM.from_pretrained("MoxoffSpA/Moxoff-Phi3Mini-DPO") |
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tokenizer = AutoTokenizer.from_pretrained("MoxoffSpA/Moxoff-Phi3Mini-DPO") |
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question = """Quanto è alta la torre di Pisa?""" |
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context = """ |
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La Torre di Pisa è un campanile del XII secolo, famoso per la sua inclinazione. Alta circa 56 metri. |
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""" |
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prompt = f"Domanda: {question}, contesto: {context}" |
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messages = [ |
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{"role": "user", "content": prompt} |
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] |
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encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt") |
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model_inputs = encodeds.to(device) |
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model.to(device) |
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generated_ids = model.generate( |
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model_inputs, # The input to the model |
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max_new_tokens=128, # Limiting the maximum number of new tokens generated |
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do_sample=True, # Enabling sampling to introduce randomness in the generation |
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temperature=0.1, # Setting temperature to control the randomness, lower values make it more deterministic |
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top_p=0.95, # Using nucleus sampling with top-p filtering for more coherent generation |
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eos_token_id=tokenizer.eos_token_id # Specifying the token that indicates the end of a sequence |
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) |
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decoded_output = tokenizer.decode(generated_ids[0], skip_special_tokens=True) |
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trimmed_output = decoded_output.strip() |
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print(trimmed_output) |
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``` |
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## Bias, Risks and Limitations |
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Moxoff-Phi3Mini-DPO has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of |
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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 |
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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 |
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like books and code. |
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## Links to resources |
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- ultrafeedback-binarized-preferences-cleaned dataset: https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned |
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- Phi-3-mini-128k-instruct model: https://huggingface.co/microsoft/Phi-3-mini-128k-instruct |
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- Open LLM Leaderbord: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard |
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## The Moxoff Team |
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Jacopo Abate, Marco D'Ambra, Dario Domanin, Luigi Simeone, Gianpaolo Francesco Trotta |
