yahma/alpaca-cleaned
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How to use malhajar/phi-2-chat with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="malhajar/phi-2-chat", trust_remote_code=True) # Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("malhajar/phi-2-chat", trust_remote_code=True, dtype="auto")How to use malhajar/phi-2-chat with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "malhajar/phi-2-chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "malhajar/phi-2-chat",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/malhajar/phi-2-chat
How to use malhajar/phi-2-chat with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "malhajar/phi-2-chat" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "malhajar/phi-2-chat",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "malhajar/phi-2-chat" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "malhajar/phi-2-chat",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use malhajar/phi-2-chat with Docker Model Runner:
docker model run hf.co/malhajar/phi-2-chat
malhajar/phi-2-chat is a finetuned version of phi-2 using SFT Training.
This model can answer information in a chat format as it is finetuned specifically on instructions specifically alpaca-cleaned
Mohamad Alhajar microsoft/phi-2### Instruction:
<prompt> (without the <>)
### Response:
Use the code sample provided in the original post to interact with the model.
from transformers import AutoTokenizer,AutoModelForCausalLM
model_id = "malhajar/phi-2-chat"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
torch_dtype=torch.float16,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_id)
question: "Türkiyenin en büyük şehir nedir?"
# For generating a response
prompt = '''
### Instruction: {question} ### Response:
'''
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
output = model.generate(inputs=input_ids,max_new_tokens=512,pad_token_id=tokenizer.eos_token_id,top_k=50, do_sample=True,repetition_penalty=1.3
top_p=0.95,trust_remote_code=True,)
response = tokenizer.decode(output[0])
print(response)