---
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](https://huggingface.co/freecs/phine-2-v0) from [freecs](https://huggingface.co/freecs)


| Name | Quant method | Size |
| ---- | ---- | ---- |
| [phine-2-v0.fp16.gguf](https://huggingface.co/afrideva/phine-2-v0-GGUF/resolve/main/phine-2-v0.fp16.gguf) | fp16 | 5.56 GB  |
| [phine-2-v0.q2_k.gguf](https://huggingface.co/afrideva/phine-2-v0-GGUF/resolve/main/phine-2-v0.q2_k.gguf) | q2_k | 1.17 GB  |
| [phine-2-v0.q3_k_m.gguf](https://huggingface.co/afrideva/phine-2-v0-GGUF/resolve/main/phine-2-v0.q3_k_m.gguf) | q3_k_m | 1.48 GB  |
| [phine-2-v0.q4_k_m.gguf](https://huggingface.co/afrideva/phine-2-v0-GGUF/resolve/main/phine-2-v0.q4_k_m.gguf) | q4_k_m | 1.79 GB  |
| [phine-2-v0.q5_k_m.gguf](https://huggingface.co/afrideva/phine-2-v0-GGUF/resolve/main/phine-2-v0.q5_k_m.gguf) | q5_k_m | 2.07 GB  |
| [phine-2-v0.q6_k.gguf](https://huggingface.co/afrideva/phine-2-v0-GGUF/resolve/main/phine-2-v0.q6_k.gguf) | q6_k | 2.29 GB  |
| [phine-2-v0.q8_0.gguf](https://huggingface.co/afrideva/phine-2-v0-GGUF/resolve/main/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](https://twitter.com/gr_username)
- **Donations Link:** [Click Me](https://www.buymeacoffee.com/gr.0)

## Code Usage

To try Phine, use the following Python code snippet:

```python
#######################
'''
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](https://twitter.com/gr_username).

## 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.

---