Instructions to use realtreetune/deepseekmath-7b-sft-GSM8K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use realtreetune/deepseekmath-7b-sft-GSM8K with Transformers:

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

pipe = pipeline("text-generation", model="realtreetune/deepseekmath-7b-sft-GSM8K")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("realtreetune/deepseekmath-7b-sft-GSM8K")
model = AutoModelForMultimodalLM.from_pretrained("realtreetune/deepseekmath-7b-sft-GSM8K")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use realtreetune/deepseekmath-7b-sft-GSM8K with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "realtreetune/deepseekmath-7b-sft-GSM8K"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "realtreetune/deepseekmath-7b-sft-GSM8K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/realtreetune/deepseekmath-7b-sft-GSM8K

SGLang

How to use realtreetune/deepseekmath-7b-sft-GSM8K 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 "realtreetune/deepseekmath-7b-sft-GSM8K" \
    --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": "realtreetune/deepseekmath-7b-sft-GSM8K",
		"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 "realtreetune/deepseekmath-7b-sft-GSM8K" \
        --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": "realtreetune/deepseekmath-7b-sft-GSM8K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use realtreetune/deepseekmath-7b-sft-GSM8K with Docker Model Runner:
```
docker model run hf.co/realtreetune/deepseekmath-7b-sft-GSM8K
```

deepseekmath-7b-sft-GSM8K / model-00003-of-00003.safetensors

realtreetune

Upload LlamaForCausalLM

66bd62e verified almost 2 years ago

Download

History Blame Contribute Delete

3.85 GB

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:: 8b215d9a3f72fec9af2d36782197031df6b4ab51350debf7a6061634510dd261
Size of remote file:: 3.85 GB
SHA256:: 4d8cc41731e3dd6cc20cc345dbed66d7c39bc88e776a38968c49d7c0e5174346

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