Instructions to use KexuanShi/sft_gemma2_2b_code_layerdrop_p001 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KexuanShi/sft_gemma2_2b_code_layerdrop_p001 with Transformers:

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

pipe = pipeline("text-generation", model="KexuanShi/sft_gemma2_2b_code_layerdrop_p001")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KexuanShi/sft_gemma2_2b_code_layerdrop_p001")
model = AutoModelForCausalLM.from_pretrained("KexuanShi/sft_gemma2_2b_code_layerdrop_p001", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KexuanShi/sft_gemma2_2b_code_layerdrop_p001 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KexuanShi/sft_gemma2_2b_code_layerdrop_p001"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KexuanShi/sft_gemma2_2b_code_layerdrop_p001",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/KexuanShi/sft_gemma2_2b_code_layerdrop_p001

SGLang

How to use KexuanShi/sft_gemma2_2b_code_layerdrop_p001 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 "KexuanShi/sft_gemma2_2b_code_layerdrop_p001" \
    --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": "KexuanShi/sft_gemma2_2b_code_layerdrop_p001",
		"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 "KexuanShi/sft_gemma2_2b_code_layerdrop_p001" \
        --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": "KexuanShi/sft_gemma2_2b_code_layerdrop_p001",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use KexuanShi/sft_gemma2_2b_code_layerdrop_p001 with Docker Model Runner:
```
docker model run hf.co/KexuanShi/sft_gemma2_2b_code_layerdrop_p001
```

sft_gemma2_2b_code_layerdrop_p001 / training_args.bin

KexuanShi

Upload final checkpoint

ce0ef06 verified about 1 month ago

History Blame Contribute Delete

5.84 kB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 2d4b1170bbad8e9624b37a0622efdd9aa17c33ea1aacdfad3b82b5199891474f
Size of remote file:: 5.84 kB
SHA256:: 5868a44fc0c9e92774223928a0300fb214e4cd4452f1a18df47a1ed4905e5236

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.