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")# 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") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- 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
lua-25k-epoch1
Browse files- config.json +39 -0
- generation_config.json +7 -0
- pytorch_model.bin +3 -0
config.json
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{
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"_name_or_path": "checkpoint_1966",
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"activation_function": "gelu_pytorch_tanh",
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"architectures": [
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"GPTBigCodeForCausalLM"
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],
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"attention_softmax_in_fp32": true,
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"attn_pdrop": 0.1,
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"bos_token_id": 0,
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"embd_pdrop": 0.1,
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"eos_token_id": 0,
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"inference_runner": 0,
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"initializer_range": 0.02,
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"layer_norm_epsilon": 1e-05,
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"max_batch_size": null,
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"max_sequence_length": null,
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"model_type": "gpt_bigcode",
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"multi_query": true,
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"n_embd": 2048,
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"n_head": 16,
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"n_inner": 8192,
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"n_layer": 24,
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"n_positions": 8192,
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"pad_key_length": true,
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"pre_allocate_kv_cache": false,
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"resid_pdrop": 0.1,
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"scale_attention_softmax_in_fp32": true,
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"scale_attn_weights": true,
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"summary_activation": null,
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"summary_first_dropout": 0.1,
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"summary_proj_to_labels": true,
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"summary_type": "cls_index",
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"summary_use_proj": true,
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"torch_dtype": "float32",
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"transformers_version": "4.28.1",
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"use_cache": false,
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"validate_runner_input": true,
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"vocab_size": 49152
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 0,
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"eos_token_id": 0,
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"transformers_version": "4.28.1",
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"use_cache": false
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:9df470304ac833283c4e0357181a704115f16c615ac86e86a4c042cd0a143d6b
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size 4548920521
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