Instructions to use KexuanShi/sft_gemma2_2b_code_layerdrop_p005 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KexuanShi/sft_gemma2_2b_code_layerdrop_p005 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_p005")

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

tokenizer = AutoTokenizer.from_pretrained("KexuanShi/sft_gemma2_2b_code_layerdrop_p005")
model = AutoModelForCausalLM.from_pretrained("KexuanShi/sft_gemma2_2b_code_layerdrop_p005", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KexuanShi/sft_gemma2_2b_code_layerdrop_p005 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_p005"
# 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_p005",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/KexuanShi/sft_gemma2_2b_code_layerdrop_p005

SGLang

How to use KexuanShi/sft_gemma2_2b_code_layerdrop_p005 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_p005" \
    --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_p005",
		"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_p005" \
        --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_p005",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use KexuanShi/sft_gemma2_2b_code_layerdrop_p005 with Docker Model Runner:
```
docker model run hf.co/KexuanShi/sft_gemma2_2b_code_layerdrop_p005
```

sft_gemma2_2b_code_layerdrop_p005 / README.md

KexuanShi

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---
library_name: transformers
model_name: sft_gemma2_2b_code_layerdrop_p005
tags:
- generated_from_trainer
- sft
- trl
- hf_jobs
licence: license
---
# Model Card for sft_gemma2_2b_code_layerdrop_p005
This model is a fine-tuned version of [None](https://huggingface.co/None).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
 
This model was trained with SFT.
### Framework versions
- TRL: 1.13.0
- Transformers: 5.17.0
- Pytorch: 2.13.0
- Datasets: 5.0.1
- Tokenizers: 0.23.2
## Citations
Cite TRL as:
    
```bibtex
@software{vonwerra2020trl,
  title   = {{TRL: Transformers Reinforcement Learning}},
  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
  license = {Apache-2.0},
  url     = {https://github.com/huggingface/trl},
  year    = {2020}
}
```

	---
	library_name: transformers
	model_name: sft_gemma2_2b_code_layerdrop_p005
	tags:
	- generated_from_trainer
	- sft
	- trl
	- hf_jobs
	licence: license
	---

	# Model Card for sft_gemma2_2b_code_layerdrop_p005

	This model is a fine-tuned version of [None](https://huggingface.co/None).
	It has been trained using [TRL](https://github.com/huggingface/trl).

	## Quick start

	```python
	from transformers import pipeline

	question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
	generator = pipeline("text-generation", model="None", device="cuda")
	output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
	print(output["generated_text"])
	```

	## Training procedure





	This model was trained with SFT.

	### Framework versions

	- TRL: 1.13.0
	- Transformers: 5.17.0
	- Pytorch: 2.13.0
	- Datasets: 5.0.1
	- Tokenizers: 0.23.2

	## Citations



	Cite TRL as:

	```bibtex
	@software{vonwerra2020trl,
	title = {{TRL: Transformers Reinforcement Learning}},
	author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
	license = {Apache-2.0},
	url = {https://github.com/huggingface/trl},
	year = {2020}
	}
	```