Text Generation
Transformers
PyTorch
Safetensors
gpt_bigcode
code
Eval Results (legacy)
text-generation-inference
Instructions to use nuprl/MultiPL-T-StarCoderBase_1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nuprl/MultiPL-T-StarCoderBase_1b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nuprl/MultiPL-T-StarCoderBase_1b")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nuprl/MultiPL-T-StarCoderBase_1b") model = AutoModelForCausalLM.from_pretrained("nuprl/MultiPL-T-StarCoderBase_1b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nuprl/MultiPL-T-StarCoderBase_1b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nuprl/MultiPL-T-StarCoderBase_1b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nuprl/MultiPL-T-StarCoderBase_1b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/nuprl/MultiPL-T-StarCoderBase_1b
- SGLang
How to use nuprl/MultiPL-T-StarCoderBase_1b 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 "nuprl/MultiPL-T-StarCoderBase_1b" \ --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": "nuprl/MultiPL-T-StarCoderBase_1b", "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 "nuprl/MultiPL-T-StarCoderBase_1b" \ --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": "nuprl/MultiPL-T-StarCoderBase_1b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use nuprl/MultiPL-T-StarCoderBase_1b with Docker Model Runner:
docker model run hf.co/nuprl/MultiPL-T-StarCoderBase_1b
Commit History
-
jl_full_1b-epoch6
06ad8a5 -
jl_full_1b-epoch5
5246a0d -
jl_full_1b-epoch4
f0fa65e -
jl_full_1b-epoch3
86010ab -
jl_full_1b-epoch2
a81b229 -
jl_full_1b-epoch1
fce7e5e
-
rkt-subset-epoch7
fba8015 -
rkt-subset-epoch6
f78c171 -
rkt-subset-epoch5
f57df91 -
rkt-subset-epoch4
c9c02e8 -
rkt-subset-epoch3
de40ca8 -
rkt-subset-epoch2
fb7825b -
rkt-subset-epoch1
8b0399e
-
rkt-full-epoch5
2cdc541 -
rkt-full-epoch4
9612971 -
rkt-full-epoch3
c68968a -
rkt-full-epoch2
687c194 -
rkt-full-epoch1
93979c6 -
equal-mix-epoch4
a0d5e42 -
equal-mix-epoch3
ae10018 -
equal-mix-epoch2
3b63f8c -
equal-mix-epoch1
eab52de -
unequal-mix-epoch4
8bb03df -
unequal-mix-epoch3
e30787c -
unequal-mix-epoch2
597f177 -
unequal-mix-epoch1
dda3050
-
ocaml_full_1b-epoch7
1f07e68 -
ocaml_subset_1b-epoch7
d59c7eb -
ocaml_subset_1b-epoch6
9d77249 -
ocaml_full_1b-epoch6
e8a24f9 -
ocaml_subset_1b-epoch5
8caa08e -
ocaml_full_1b-epoch5
b60fefb -
ocaml_subset_1b-epoch4
a879596 -
ocaml_full_1b-epoch4
a5c29b8 -
ocaml_full_1b-epoch3
dfaca6c -
ocaml_subset_1b-epoch3
d92bf64 -
ocaml_full_1b-epoch2
adfd24b -
ocaml_subset_1b-epoch2
edb0a09 -
ocaml_full_1b-epoch1
c2511fa -
ocaml_subset_1b-epoch1
e90c2a9 -
ocaml_stack-epoch3
3922910 -
ocaml_stack-epoch2
602ec4d -
rkt_stack-epoch3
f72693d -
rkt_stack-epoch2
b66a6b6 -
ocaml_stack-epoch1
ec39848 -
lua_stack-epoch3
33022a6 -
lua_stack-epoch2
c08ba56 -
rkt_stack-epoch1
75b3d6d -
lua_stack-epoch1
5a34651
-
full_lua-epoch6
03fc93f