Instructions to use yujiepan/starcoder-tiny-random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yujiepan/starcoder-tiny-random with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="yujiepan/starcoder-tiny-random")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("yujiepan/starcoder-tiny-random") model = AutoModelForCausalLM.from_pretrained("yujiepan/starcoder-tiny-random") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use yujiepan/starcoder-tiny-random with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "yujiepan/starcoder-tiny-random" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "yujiepan/starcoder-tiny-random", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/yujiepan/starcoder-tiny-random
- SGLang
How to use yujiepan/starcoder-tiny-random 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 "yujiepan/starcoder-tiny-random" \ --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": "yujiepan/starcoder-tiny-random", "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 "yujiepan/starcoder-tiny-random" \ --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": "yujiepan/starcoder-tiny-random", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use yujiepan/starcoder-tiny-random with Docker Model Runner:
docker model run hf.co/yujiepan/starcoder-tiny-random
upload logs
Browse files- config.json +39 -0
- generation_config.json +6 -0
- merges.txt +0 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +26 -0
- tokenizer.json +0 -0
- tokenizer_config.json +31 -0
- vocab.json +0 -0
config.json
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{
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"_name_or_path": "bigcode/starcoderbase-7b",
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"activation_function": "gelu",
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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": 32,
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"n_head": 8,
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"n_inner": 128,
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"n_layer": 1,
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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": "4.30.2",
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"use_cache": true,
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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": "4.30.2"
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}
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merges.txt
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:676a1c581a2131c2d405c953e6d30f998ff53c12519d3a967be05b73b319651c
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size 7388769
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special_tokens_map.json
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{
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"additional_special_tokens": [
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"<|endoftext|>",
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"<fim_prefix>",
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"<fim_middle>",
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"<fim_suffix>",
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"<fim_pad>",
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"<filename>",
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"<gh_stars>",
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"<issue_start>",
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"<issue_comment>",
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"<issue_closed>",
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"<jupyter_start>",
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"<jupyter_text>",
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"<jupyter_code>",
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"<jupyter_output>",
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"<empty_output>",
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"<commit_before>",
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"<commit_msg>",
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"<commit_after>",
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"<reponame>"
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],
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"bos_token": "<|endoftext|>",
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"eos_token": "<|endoftext|>",
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"unk_token": "<|endoftext|>"
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}
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tokenizer.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"additional_special_tokens": [
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"<|endoftext|>",
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"<fim_prefix>",
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"<fim_middle>",
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"<fim_suffix>",
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"<fim_pad>",
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"<filename>",
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"<gh_stars>",
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"<issue_start>",
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"<issue_comment>",
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"<issue_closed>",
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"<jupyter_start>",
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"<jupyter_text>",
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"<jupyter_code>",
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"<jupyter_output>",
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"<empty_output>",
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"<commit_before>",
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"<commit_msg>",
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"<commit_after>",
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"<reponame>"
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],
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"bos_token": "<|endoftext|>",
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"clean_up_tokenization_spaces": true,
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"eos_token": "<|endoftext|>",
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"model_max_length": 1000000000000000019884624838656,
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"tokenizer_class": "GPT2Tokenizer",
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"unk_token": "<|endoftext|>",
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"vocab_size": 49152
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}
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vocab.json
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