Instructions to use transformersbook/codeparrot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use transformersbook/codeparrot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="transformersbook/codeparrot")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("transformersbook/codeparrot") model = AutoModelForCausalLM.from_pretrained("transformersbook/codeparrot") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use transformersbook/codeparrot with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "transformersbook/codeparrot" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "transformersbook/codeparrot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/transformersbook/codeparrot
- SGLang
How to use transformersbook/codeparrot 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 "transformersbook/codeparrot" \ --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": "transformersbook/codeparrot", "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 "transformersbook/codeparrot" \ --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": "transformersbook/codeparrot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use transformersbook/codeparrot with Docker Model Runner:
docker model run hf.co/transformersbook/codeparrot
add mistral changes
Browse files- config.json +3 -8
- pytorch_model.bin +2 -2
config.json
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{
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"_num_labels": 1,
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"activation_function": "gelu_new",
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"architectures": [
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"GPT2LMHeadModel"
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"embd_pdrop": 0.1,
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"eos_token_id": 50256,
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"gradient_checkpointing": false,
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"id2label": {
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"0": "LABEL_0"
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},
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"initializer_range": 0.02,
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"label2id": {
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"LABEL_0": 0
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},
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"layer_norm_epsilon": 1e-05,
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"model_type": "gpt2",
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"n_ctx": 1024,
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"n_layer": 48,
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"n_positions": 1024,
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"output_past": true,
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"resid_pdrop": 0.1,
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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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}
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},
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"torch_dtype": "float32",
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"transformers_version": "4.
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"use_cache": true,
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"vocab_size": 32768
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}
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{
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"activation_function": "gelu_new",
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"architectures": [
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"GPT2LMHeadModel"
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"embd_pdrop": 0.1,
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"eos_token_id": 50256,
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"gradient_checkpointing": false,
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"initializer_range": 0.02,
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"layer_norm_epsilon": 1e-05,
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"model_type": "gpt2",
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"n_ctx": 1024,
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"n_layer": 48,
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"n_positions": 1024,
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"output_past": true,
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"reorder_and_upcast_attn": true,
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"resid_pdrop": 0.1,
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"scale_attn_by_layer_idx": 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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}
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},
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"torch_dtype": "float32",
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"transformers_version": "4.11.0.dev0",
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"use_cache": true,
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"vocab_size": 32768
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}
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pytorch_model.bin
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:f700cb9a50ef29578ed0b7d885e8b49208fbca9bdb9247ba9852ea6866d92e8b
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size 6169094681
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