metadata
library_name: peft
tags:
- autotrain
- meta-llama
- meta-llama/Llama-2-7b-hf
inference: false
widget:
- text: >
instruction: "If you are a doctor, please answer the medical questions
based on the patient's description."
input: "Hi, I had a subarachnoid bleed and coiling of brain aneurysm last
year. I am having some major bilateral temple pain along with numbness
that comes and goes in my left arm/hand/fingers. I have had headaches
since the aneurysm, but this is different. Also, my moods have been
horrible for the past few weeks."
response: ''
pipeline_tag: text-generation
base_model: meta-llama/Llama-2-7b-hf
llama-2-7b-hf model finetuned for medical consultation. Works on T4 GPU (16GB VRAM), as well as CPU (32GB RAM)
To run on GPU :
import transformers
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from torch import cuda, bfloat16
base_model_id = 'meta-llama/Llama-2-7b-chat-hf'
device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'
bnb_config = transformers.BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type='nf4',
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=bfloat16
)
hf_auth = "your-huggingface-access-token"
model_config = transformers.AutoConfig.from_pretrained(
base_model_id,
use_auth_token=hf_auth
)
model = transformers.AutoModelForCausalLM.from_pretrained(
base_model_id,
trust_remote_code=True,
config=model_config,
quantization_config=bnb_config,
device_map='auto',
use_auth_token=hf_auth
)
config = PeftConfig.from_pretrained("Ashishkr/llama-2-medical-consultation")
model = PeftModel.from_pretrained(model, "Ashishkr/llama-2-medical-consultation").to(device)
model.eval()
print(f"Model loaded on {device}")
tokenizer = transformers.AutoTokenizer.from_pretrained(
base_model_id,
use_auth_token=hf_auth
)
def llama_generate(
model: AutoModelForCausalLM,
tokenizer: AutoTokenizer,
prompt: str,
max_new_tokens: int = 128,
temperature: float = 0.92):
inputs = tokenizer(
[prompt],
return_tensors="pt",
return_token_type_ids=False,
).to(
device
)
# Check if bfloat16 is supported, otherwise use float16
dtype_to_use = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
with torch.autocast("cuda", dtype=dtype_to_use):
response = model.generate(
**inputs,
max_new_tokens=max_new_tokens,
temperature=temperature,
return_dict_in_generate=True,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
)
decoded_output = tokenizer.decode(
response["sequences"][0],
skip_special_tokens=True,
)
return decoded_output[len(prompt) :]
prompt = """
instruction: "If you are a doctor, please answer the medical questions based on the patient's description." \n
input: "Hi, I had a subarachnoid bleed and coiling of brain aneurysm last year.
I am having some major bilateral temple pain along with numbness that comes and
goes in my left arm/hand/fingers. I have had headaches since the aneurysm,
but this is different. Also, my moods have been horrible for the past few weeks.\n
response: """
# You can use the function as before
response = llama_generate(
model,
tokenizer,
prompt,
max_new_tokens=100,
temperature=0.92,
)
print(response)
To run on CPU
import torch
import transformers
from torch import cuda, bfloat16
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
base_model_id = 'meta-llama/Llama-2-7b-chat-hf'
device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'
bnb_config = transformers.BitsAndBytesConfig(
llm_int8_enable_fp32_cpu_offload = True
)
import torch
hf_auth = "YOUR-HUGGINGFACE-ACCESS-TOKEN"
model_config = transformers.AutoConfig.from_pretrained(
base_model_id,
use_auth_token=hf_auth
)
model = transformers.AutoModelForCausalLM.from_pretrained(
base_model_id,
trust_remote_code=True,
config=model_config,
quantization_config=bnb_config,
# device_map='auto',
use_auth_token=hf_auth
)
config = PeftConfig.from_pretrained("Ashishkr/llama-2-medical-consultation")
model = PeftModel.from_pretrained(model, "Ashishkr/llama-2-medical-consultation").to(device)
model.eval()
print(f"Model loaded on {device}")
tokenizer = transformers.AutoTokenizer.from_pretrained(
base_model_id,
use_auth_token=hf_auth
)
def llama_generate(
model: AutoModelForCausalLM,
tokenizer: AutoTokenizer,
prompt: str,
max_new_tokens: int = 128,
temperature: float = 0.92):
inputs = tokenizer(
[prompt],
return_tensors="pt",
return_token_type_ids=False,
).to(
device
)
# Check if bfloat16 is supported, otherwise use float16
dtype_to_use = torch.float32
with torch.autocast("cuda", dtype=dtype_to_use):
response = model.generate(
**inputs,
max_new_tokens=max_new_tokens,
temperature=temperature,
return_dict_in_generate=True,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
)
decoded_output = tokenizer.decode(
response["sequences"][0],
skip_special_tokens=True,
)
return decoded_output[len(prompt) :]
prompt = """
instruction: "If you are a doctor, please answer the medical questions based on the patient's description." \n
input: "Hi, I had a subarachnoid bleed and coiling of brain aneurysm last year.
I am having some major bilateral temple pain along with numbness that comes and
goes in my left arm/hand/fingers. I have had headaches since the aneurysm,
but this is different. Also, my moods have been horrible for the past few weeks.\n
response: """
# You can use the function as before
response = llama_generate(
model,
tokenizer,
prompt,
max_new_tokens=100,
temperature=0.92,
)
print(response)