TokenBender/code_instructions_122k_alpaca_style
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How to use TitleOS/CodePhi2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="TitleOS/CodePhi2", trust_remote_code=True) # Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("TitleOS/CodePhi2", trust_remote_code=True, dtype="auto")How to use TitleOS/CodePhi2 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TitleOS/CodePhi2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "TitleOS/CodePhi2",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/TitleOS/CodePhi2
How to use TitleOS/CodePhi2 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "TitleOS/CodePhi2" \
--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": "TitleOS/CodePhi2",
"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 "TitleOS/CodePhi2" \
--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": "TitleOS/CodePhi2",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use TitleOS/CodePhi2 with Docker Model Runner:
docker model run hf.co/TitleOS/CodePhi2
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 "TitleOS/CodePhi2" \
--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": "TitleOS/CodePhi2",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'CodePhi2 is finetuning of the Microsoft Phi-2 LLM with 2.7 billion parameters. It was finetuned on TokenBender's code_instructions_122k_alpaca_style. The end goal was to increase Phi-2's coding ability while imbuing the Alpaca format.
CodePhi2 has been finetuned on the Alpaca instruction format, and as such should be prompted like below:
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Response:
If you are using transformers>=4.36.0, always load the model with trust_remote_code=True to prevent side-effects.
Base model
microsoft/phi-2
Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "TitleOS/CodePhi2" \ --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": "TitleOS/CodePhi2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'