Phiter

Phiter is a merge of the following models using LazyMergekit:

• rhysjones/phi-2-orange
• cognitivecomputations/dolphin-2_6-phi-2

Thanks to the great Maxime Labonne we have evaluation results on YALL.

The model tops all other phi-2 finetunes on the leaderboard, even most MoE implementations like Phixtral(Date: 27th February 2024)

License: MIT

This model wouldn't have been possible without the support of:

Maxime Labonne - he helped me troubleshoot the merge process

brittlewis12 - helped me troubleshooting the creation of GGUF files

Prompt template: ChatML

<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

GGUF: Phiter-GGUF

🧩 Configuration

models:
  - model: mixedbread-ai/phi-2
    # no parameters necessary for base model
  - model: rhysjones/phi-2-orange
    parameters:
      density: 0.5
      weight: 0.5
  - model: cognitivecomputations/dolphin-2_6-phi-2
    parameters:
      density: 0.5
      weight: 0.3
merge_method: ties
base_model: mixedbread-ai/phi-2
parameters:
  normalize: true
dtype: float16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Venkman42/Phiter"
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"])