README.md

metadata

base_model: freecs/phine-2-v0
datasets:
  - vicgalle/alpaca-gpt4
inference: false
license: unknown
model_creator: freecs
model_name: phine-2-v0
pipeline_tag: text-generation
quantized_by: afrideva
tags:
  - gguf
  - ggml
  - quantized
  - q2_k
  - q3_k_m
  - q4_k_m
  - q5_k_m
  - q6_k
  - q8_0

freecs/phine-2-v0-GGUF

Quantized GGUF model files for phine-2-v0 from freecs

Name	Quant method	Size
phine-2-v0.fp16.gguf	fp16	5.56 GB
phine-2-v0.q2_k.gguf	q2_k	1.17 GB
phine-2-v0.q3_k_m.gguf	q3_k_m	1.48 GB
phine-2-v0.q4_k_m.gguf	q4_k_m	1.79 GB
phine-2-v0.q5_k_m.gguf	q5_k_m	2.07 GB
phine-2-v0.q6_k.gguf	q6_k	2.29 GB
phine-2-v0.q8_0.gguf	q8_0	2.96 GB

Original Model Card:

---

Model Card: Phine-2-v0

Overview

• Model Name: Phine-2
• Base Model: Phi-2 (Microsoft model)
• Created By: GR
• Donations Link: Click Me

Code Usage

To try Phine, use the following Python code snippet:

#######################
'''
Name: Phine Inference
License: MIT
'''
#######################


##### Dependencies

""" IMPORTANT: Uncomment the following line if you are in a Colab/Notebook environment """

#!pip install gradio einops accelerate bitsandbytes transformers

#####

import gradio as gr
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import random
import re

def cut_text_after_last_token(text, token):

    last_occurrence = text.rfind(token)

    if last_occurrence != -1:
        result = text[last_occurrence + len(token):].strip()
        return result
    else:
        return None


class _SentinelTokenStoppingCriteria(transformers.StoppingCriteria):

    def __init__(self, sentinel_token_ids: torch.LongTensor,
                 starting_idx: int):
        transformers.StoppingCriteria.__init__(self)
        self.sentinel_token_ids = sentinel_token_ids
        self.starting_idx = starting_idx

    def __call__(self, input_ids: torch.LongTensor,
                 _scores: torch.FloatTensor) -> bool:
        for sample in input_ids:
            trimmed_sample = sample[self.starting_idx:]

            if trimmed_sample.shape[-1] < self.sentinel_token_ids.shape[-1]:
                continue

            for window in trimmed_sample.unfold(
                    0, self.sentinel_token_ids.shape[-1], 1):
                if torch.all(torch.eq(self.sentinel_token_ids, window)):
                    return True
        return False





model_path = 'freecs/phine-2-v0'

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, load_in_4bit=False, torch_dtype=torch.float16).to(device) #remove .to() if load_in_4/8bit = True

sys_message = "You are an AI assistant named Phine developed by FreeCS.org. You are polite and smart." #System Message

def phine(message, history, temperature, top_p, top_k, repetition_penalty):



    n = 0
    context = ""
    if history and len(history) > 0:

        for x in history:
          for h in x:
            if n%2 == 0:
              context+=f"""\n<|prompt|>{h}\n"""
            else:
              context+=f"""<|response|>{h}"""
            n+=1
    else:

        context = ""




    prompt = f"""\n<|system|>{sys_message}"""+context+"\n<|prompt|>"+message+"<|endoftext|>\n<|response|>"
    tokenized = tokenizer(prompt, return_tensors="pt").to(device)


    stopping_criteria_list = transformers.StoppingCriteriaList([
        _SentinelTokenStoppingCriteria(
            sentinel_token_ids=tokenizer(
                "<|endoftext|>",
                add_special_tokens=False,
                return_tensors="pt",
            ).input_ids.to(device),
            starting_idx=tokenized.input_ids.shape[-1])
    ])

        
    token = model.generate(**tokenized,
                        stopping_criteria=stopping_criteria_list,
                        do_sample=True,
                        max_length=2048, temperature=temperature, top_p=top_p, top_k = top_k, repetition_penalty = repetition_penalty
                           )

    completion = tokenizer.decode(token[0], skip_special_tokens=False)
    token = "<|response|>"
    res = cut_text_after_last_token(completion, token)
    return res.replace('<|endoftext|>', '')
demo = gr.ChatInterface(phine,
                          additional_inputs=[
                              gr.Slider(0.1, 2.0, label="temperature", value=0.5),
                              gr.Slider(0.1, 2.0, label="Top P", value=0.9),
                              gr.Slider(1, 500, label="Top K", value=50),
                              gr.Slider(0.1, 2.0, label="Repetition Penalty", value=1.15)
                          ]
                          )

if __name__ == "__main__":
    demo.queue().launch(share=True, debug=True) #If debug=True causes problems you can set it to False

Contact

For inquiries, collaboration opportunities, or additional information, reach out to me on Twitter: gr.

Disclaimer

As of now, I have not applied Reinforcement Learning from Human Feedback (RLHF). Due to this, the model may generate unexpected or potentially unethical outputs.

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