liyinghong/BioQwen
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How to use liyinghong/BioQwen-0.5B with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="liyinghong/BioQwen-0.5B")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("liyinghong/BioQwen-0.5B")
model = AutoModelForCausalLM.from_pretrained("liyinghong/BioQwen-0.5B")
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]:]))How to use liyinghong/BioQwen-0.5B with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "liyinghong/BioQwen-0.5B"
# 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/BioQwen-0.5B",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/liyinghong/BioQwen-0.5B
How to use liyinghong/BioQwen-0.5B with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "liyinghong/BioQwen-0.5B" \
--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/BioQwen-0.5B",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'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/BioQwen-0.5B" \
--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/BioQwen-0.5B",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'How to use liyinghong/BioQwen-0.5B with Docker Model Runner:
docker model run hf.co/liyinghong/BioQwen-0.5B
For model inference, please refer to the following example code:
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
transformers.logging.set_verbosity_error()
max_length = 512
model_path = 'yueqingyou/BioQwen-0.5B'
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(model_path, device_map='auto', torch_dtype=torch.bfloat16, attn_implementation='flash_attention_2').eval()
def predict(prompt):
zh_system = "你是千问生物智能助手,一个专注于生物领域的先进人工智能。"
en_system = "You are BioQwen, an advanced AI specializing in the field of biology."
english_count, chinese_count = 0, 0
for char in prompt:
if '\u4e00' <= char <= '\u9fff':
chinese_count += 1
elif 'a' <= char.lower() <= 'z':
english_count += 1
lang = 'zh' if chinese_count > english_count else 'en'
messages = [
{"role": "system", "content": zh_system if lang == 'zh' else en_system},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to('cuda')
with torch.no_grad():
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=max_length,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
do_sample=True,
top_p = 0.9,
temperature = 0.3,
repetition_penalty = 1.1
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
return response.strip()
prompt = 'I am suffering from irregular periods. I am currently taking medication Levothyroxine 50. My T3 is 0.87 ng/mL, T4 is 8.30 ug/dL, TSH is 2.43 uIU/mL. I am 34 years old, weigh 75 kg, and 5 feet tall. Please advice.'
print(f'Question:\t{prompt}\n\nAnswer:\t{predict(prompt)}')
For more detailed information and code, please refer to GitHub.