Instructions to use rkumar1999/DeepSeek-V2-Lite-Chat-deepseek-prover with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rkumar1999/DeepSeek-V2-Lite-Chat-deepseek-prover with Transformers:

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

pipe = pipeline("text-generation", model="rkumar1999/DeepSeek-V2-Lite-Chat-deepseek-prover")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rkumar1999/DeepSeek-V2-Lite-Chat-deepseek-prover", dtype="auto")

PEFT
How to use rkumar1999/DeepSeek-V2-Lite-Chat-deepseek-prover with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rkumar1999/DeepSeek-V2-Lite-Chat-deepseek-prover with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rkumar1999/DeepSeek-V2-Lite-Chat-deepseek-prover"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rkumar1999/DeepSeek-V2-Lite-Chat-deepseek-prover",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rkumar1999/DeepSeek-V2-Lite-Chat-deepseek-prover

SGLang

How to use rkumar1999/DeepSeek-V2-Lite-Chat-deepseek-prover 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 "rkumar1999/DeepSeek-V2-Lite-Chat-deepseek-prover" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rkumar1999/DeepSeek-V2-Lite-Chat-deepseek-prover",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "rkumar1999/DeepSeek-V2-Lite-Chat-deepseek-prover" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rkumar1999/DeepSeek-V2-Lite-Chat-deepseek-prover",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rkumar1999/DeepSeek-V2-Lite-Chat-deepseek-prover with Docker Model Runner:
```
docker model run hf.co/rkumar1999/DeepSeek-V2-Lite-Chat-deepseek-prover
```

Model Card for DeepSeek-V2-Lite-Chat-deepseek-prover

This model is a fine-tuned version of deepseek-ai/DeepSeek-V2-Lite on the deepseek-ai/DeepSeek-Prover-V1 dataset. It has been trained using TRL.

Quick start

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="rkumar1999/DeepSeek-V2-Lite-Chat-deepseek-prover", 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: 0.22.2
Transformers: 4.56.1
Pytorch: 2.8.0+cu128
Datasets: 4.0.0
Tokenizers: 0.22.0

Citations

Cite TRL as:

@misc{vonwerra2022trl,
    title        = {{TRL: Transformer Reinforcement Learning}},
    author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
    year         = 2020,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{https://github.com/huggingface/trl}}
}

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DeepSeek-V2-Lite-Chat-deepseek-prover