abacaj_-_phi-2-super-gguf

Quantization made by Richard Erkhov.

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phi-2-super - GGUF

• Model creator: https://huggingface.co/abacaj/
• Original model: https://huggingface.co/abacaj/phi-2-super/

Original model description:

license: mit license_link: https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE language:

• en widget:
• text: Hello who are you? example_title: Identity
• text: What can you do? example_title: Capabilities
• text: Create a fastapi endpoint to retrieve the weather given a zip code. example_title: Coding tags:
• convAI
• conversational pipeline_tag: text-generation model-index:
• name: phi-2-super results:

  IFEval

  • task: type: text-generation name: Text Generation dataset: name: Instruction Following Eval type: wis-k/instruction-following-eval metrics:
    • type: acc name: prompt_level_loose_acc value: 0.2717 source: name: LightEval url: https://github.com/huggingface/lighteval

---

Phi-2-super (SFT + cDPO)

Base Model: microsoft/phi-2

How to run inference:

import transformers
import torch

if __name__ == "__main__":
  model_name = "abacaj/phi-2-super"
  tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
  
  model = (
      transformers.AutoModelForCausalLM.from_pretrained(
          model_name,
      )
      .to("cuda:0")
      .eval()
  )
  
  messages = [
      {"role": "user", "content": "Hello, who are you?"}
  ]
  inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
  input_ids_cutoff = inputs.size(dim=1)
  
  with torch.no_grad():
      generated_ids = model.generate(
          input_ids=inputs,
          use_cache=True,
          max_new_tokens=512,
          temperature=0.2,
          top_p=0.95,
          do_sample=True,
          eos_token_id=tokenizer.eos_token_id,
          pad_token_id=tokenizer.pad_token_id,
      )
  
  completion = tokenizer.decode(
      generated_ids[0][input_ids_cutoff:],
      skip_special_tokens=True,
  )
  
  print(completion)

Chat template

The model uses the same chat template as found in Mistral instruct models:

text = "<|endoftext|>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!<|endoftext|> "
"[INST] Do you have mayonnaise recipes? [/INST]"

You don't need to do it manually if you use the HF transformers tokenizer:

  messages = [
      {"role": "user", "content": "Hello, who are you?"},
      {"role": "assistant": "content": "I am ..."}
  ]
  inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)

MT-bench / heval