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
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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freecs/phine-2-v0