Unrecognized configuration class
ValueError: Unrecognized configuration class <class 'transformers_modules.microsoft.phi-2.d3186761bf5c4409f7679359284066c25ab668ee.configuration_phi.PhiConfig'> for this kind of AutoModel: TFAutoModelForCausalLM.Model type should be one of BertConfig, CamembertConfig, CTRLConfig, GPT2Config, GPT2Config, GPTJConfig, OpenAIGPTConfig, OPTConfig, RemBertConfig, RobertaConfig, RobertaPreLayerNormConfig, RoFormerConfig, TransfoXLConfig, XGLMConfig, XLMConfig, XLMRobertaConfig, XLNetConfig.
Hello @mrizwanse!
Could you please provide the code you are using to load the model?
Regards,
Gustavo.
Importing Python packages
from environs import Env
Importing HuggingFace packages
from transformers import TFAutoModelForCausalLM, AutoTokenizer, pipeline
Importing LangChain packages
from langchain.llms.huggingface_pipeline import HuggingFacePipeline
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
---------------------------------------------------------------------------
Read environment variables from .env file
env = Env()
env.read_env(".env")
HuggingFace Hub API token
HUGGINGFACEHUB_API_TOKEN = env.str("HUGGINGFACEHUB_API_TOKEN")
---------------------------------------------------------------------------
1. Image to Text (Image Captioning)
def img2txt(img_url):
image_to_text = pipeline(
task="image-to-text", model="Salesforce/blip-image-captioning-base"
)
text = image_to_text(img_url)[0]["generated_text"]
# print("\n\n", text)
return text
2. LLM to generate story
def generate_story(scenario):
template = """
You are a story teller;
You can generate a short story based on a simple narrative, the story should be no more than 100 words;
CONTEXT: {scenario}
STORY:
"""
prompt = PromptTemplate(template=template, input_variables=["scenario"])
model_id = "microsoft/phi-2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = TFAutoModelForCausalLM.from_pretrained(
model_id, trust_remote_code=True
)
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=70,
)
story_llm = HuggingFacePipeline(pipeline=pipe)
llm_chain = LLMChain(prompt=prompt, llm=story_llm, verbose=True)
story = llm_chain.run(scenario)
print("\n\n", story)
return story
generated_text = img2txt("images/rizwan.jpg")
generated_story = generate_story(generated_text)
Python
- 3.11.4
Python pip
- 23.3.1
TensorFlow
- 2.15.0
Trransformers
- 4.35.2
Environs
- 9.5.0
PIL
- 10.1.0
LangChain
- 0.0.345
There is an ongoing PR which should mitigate such an issue: https://github.com/huggingface/transformers/pull/28163.
We will use it to update Phi-2's code.