Instructions to use vivecccccc/phi-2_kqa-program with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vivecccccc/phi-2_kqa-program with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="vivecccccc/phi-2_kqa-program", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("vivecccccc/phi-2_kqa-program", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use vivecccccc/phi-2_kqa-program with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vivecccccc/phi-2_kqa-program"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vivecccccc/phi-2_kqa-program",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/vivecccccc/phi-2_kqa-program

SGLang

How to use vivecccccc/phi-2_kqa-program 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 "vivecccccc/phi-2_kqa-program" \
    --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": "vivecccccc/phi-2_kqa-program",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "vivecccccc/phi-2_kqa-program" \
        --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": "vivecccccc/phi-2_kqa-program",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use vivecccccc/phi-2_kqa-program with Docker Model Runner:
```
docker model run hf.co/vivecccccc/phi-2_kqa-program
```

_saves_phi-2_full

This model is a fine-tuned version of phi-2 on the kqa_parsed-tree_train_complex.json dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 7e-06
train_batch_size: 4
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3.0
mixed_precision_training: Native AMP

Training results

Framework versions

Transformers 4.36.2
Pytorch 1.11.0+cu113
Datasets 2.16.1
Tokenizers 0.15.0

Downloads last month: 7

Safetensors

Model size

3B params

Tensor type

F32

Model tree for vivecccccc/phi-2_kqa-program

Base model

microsoft/phi-2

Finetuned

(409)

this model