How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("question-answering", model="Joshi-Aryan/llama2_test_wikidata")
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Joshi-Aryan/llama2_test_wikidata")
model = AutoModelForCausalLM.from_pretrained("Joshi-Aryan/llama2_test_wikidata")
Quick Links

#Usage

pip install transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch

model = "Joshi-Aryan/llama-2-7b-miniguanaco"
prompt = "What is a large language model?"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)
  
sequences = pipeline(
    f'[INST] {prompt} [/INST]',
    do_sample=True,
    top_k=10,
    num_return_sequences=1,
    eos_token_id=tokenizer.eos_token_id,
    max_length=200,
)
for seq in sequences:
    print(f"Result: {seq['generated_text']}")

  
Downloads last month
1
Inference Providers NEW
This model isn't deployed by any Inference Provider. 馃檵 Ask for provider support