vicgalle/alpaca-gpt4
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How to use KathirKs/phi-2_alpaca_52k with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="KathirKs/phi-2_alpaca_52k", trust_remote_code=True) # Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("KathirKs/phi-2_alpaca_52k", trust_remote_code=True, dtype="auto")How to use KathirKs/phi-2_alpaca_52k with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KathirKs/phi-2_alpaca_52k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "KathirKs/phi-2_alpaca_52k",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/KathirKs/phi-2_alpaca_52k
How to use KathirKs/phi-2_alpaca_52k with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "KathirKs/phi-2_alpaca_52k" \
--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": "KathirKs/phi-2_alpaca_52k",
"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 "KathirKs/phi-2_alpaca_52k" \
--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": "KathirKs/phi-2_alpaca_52k",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use KathirKs/phi-2_alpaca_52k with Docker Model Runner:
docker model run hf.co/KathirKs/phi-2_alpaca_52k
This is a finetuned version microsoft phi - 2. This is finetuned using the alpaca dataset.
num_train_epochs=1
per_device_train_batch_size=1
gradient_accumulation_steps=8
optim="paged_adamw_32bit"
logging_steps=25
learning_rate=2e-4
weight_decay=0.001
max_grad_norm=0.3
warmup_ratio=0.03
lr_scheduler_type="cosine"
Use the below format
Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction:{prompt} ### Response:{generated_output}