Instructions to use lvwerra/starcoderbase-gsm8k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lvwerra/starcoderbase-gsm8k with Transformers:

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

pipe = pipeline("text-generation", model="lvwerra/starcoderbase-gsm8k")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lvwerra/starcoderbase-gsm8k")
model = AutoModelForCausalLM.from_pretrained("lvwerra/starcoderbase-gsm8k", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lvwerra/starcoderbase-gsm8k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lvwerra/starcoderbase-gsm8k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lvwerra/starcoderbase-gsm8k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/lvwerra/starcoderbase-gsm8k

SGLang

How to use lvwerra/starcoderbase-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 "lvwerra/starcoderbase-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": "lvwerra/starcoderbase-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 "lvwerra/starcoderbase-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": "lvwerra/starcoderbase-gsm8k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use lvwerra/starcoderbase-gsm8k with Docker Model Runner:
```
docker model run hf.co/lvwerra/starcoderbase-gsm8k
```

starcoderbase-triviaqa

This model is baesed on https://huggingface.co/bigcode/starcoderbase and is fine-tuned on the GSM8K dataset using reinforcement learning via TRL's TextEnvironment (https://github.com/huggingface/trl/pull/424).

Out of Scope Use

Replacing human expertise

Bias, Risks, and Limitations

May generate answers that are incorrect or misleading.
May copy answers from the training data verbatim.
May generate language that is hateful or promotes discrimination (example).
May generate language that is offensive to direct or indirect users or to people or groups mentioned.

Recommendations

Answers should be validated through the use of external sources.
Disparities between the data contributors and the direct and indirect users of the technology should inform developers in assessing what constitutes an appropriate use case.
Further research is needed to attribute model generations to sources in the training data, especially in cases where the model copies answers from the training data.

Downloads last month: 6

Safetensors

Model size

16B params

Tensor type

F32

lvwerra
/

starcoderbase-gsm8k

starcoderbase-triviaqa

Out of Scope Use

Bias, Risks, and Limitations

Recommendations

Dataset used to train lvwerra/starcoderbase-gsm8k

Space using lvwerra/starcoderbase-gsm8k 1