Text Generation
Transformers
Safetensors
English
gemma2
Biomedical
conversational
text-generation-inference
Instructions to use liyinghong/DFPO-Gemma2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use liyinghong/DFPO-Gemma2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="liyinghong/DFPO-Gemma2") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("liyinghong/DFPO-Gemma2") model = AutoModelForCausalLM.from_pretrained("liyinghong/DFPO-Gemma2") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use liyinghong/DFPO-Gemma2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "liyinghong/DFPO-Gemma2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "liyinghong/DFPO-Gemma2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/liyinghong/DFPO-Gemma2
- SGLang
How to use liyinghong/DFPO-Gemma2 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "liyinghong/DFPO-Gemma2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "liyinghong/DFPO-Gemma2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "liyinghong/DFPO-Gemma2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "liyinghong/DFPO-Gemma2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use liyinghong/DFPO-Gemma2 with Docker Model Runner:
docker model run hf.co/liyinghong/DFPO-Gemma2
Enhancing Biomedical Named Entity Recognition and Relation Extraction with RAG-ICL and DFPO
Use with transformers
See the snippet below for usage with Transformers:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = 'liyinghong/DFPO-Gemma2'
tokenizer = AutoTokenizer.from_pretrained(mode_path, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(mode_path,
torch_dtype=torch.bfloat16,
device_map="auto")
def predict(user_input):
messages = [
{"role": "user", "content": f"{user_input}"},
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
terminators = [tokenizer.eos_token_id]
with torch.no_grad():
outputs = model.generate(
input_ids,
max_new_tokens=512,
eos_token_id=terminators,
do_sample=True,
temperature=0.5,
top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
return tokenizer.decode(response, skip_special_tokens=True)
prompt = """Extract and list the names of all chemicals mentioned in the following text. Provide the output as a single Python list containing the chemicals names as strings.
Do not output anything except for the extracted information. Do not add any clarifying information.\n\n"""
prompt += """Input: Naloxone reverses the antihypertensive effect of clonidine. In unanesthetized, spontaneously hypertensive rats the decrease in blood pressure and heart rate produced by intravenous clonidine, 5 to 20 micrograms/kg, was inhibited or reversed by nalozone, 0.2 to 2 mg/kg. The hypotensive effect of 100 mg/kg alpha-methyldopa was also partially reversed by naloxone. Naloxone alone did not affect either blood pressure or heart rate. In brain membranes from spontaneously hypertensive rats clonidine, 10(-8) to 10(-5) M, did not influence stereoselective binding of [3H]-naloxone (8 nM), and naloxone, 10(-8) to 10(-4) M, did not influence clonidine-suppressible binding of [3H]-dihydroergocryptine (1 nM). These findings indicate that in spontaneously hypertensive rats the effects of central alpha-adrenoceptor stimulation involve activation of opiate receptors. As naloxone and clonidine do not appear to interact with the same receptor site, the observed functional antagonism suggests the release of an endogenous opiate by clonidine or alpha-methyldopa and the possible role of the opiate in the central control of sympathetic tone.
Output:"""
predict(prompt)
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