Text Generation
Transformers
PyTorch
TensorBoard
llama
Generated from Trainer
text-generation-inference
Instructions to use simlamkr1/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use simlamkr1/output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="simlamkr1/output")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("simlamkr1/output") model = AutoModelForCausalLM.from_pretrained("simlamkr1/output") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use simlamkr1/output with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "simlamkr1/output" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "simlamkr1/output", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/simlamkr1/output
- SGLang
How to use simlamkr1/output 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 "simlamkr1/output" \ --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": "simlamkr1/output", "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 "simlamkr1/output" \ --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": "simlamkr1/output", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use simlamkr1/output with Docker Model Runner:
docker model run hf.co/simlamkr1/output
Training in progress, epoch 1
Browse files- .gitignore +1 -0
- runs/Sep24_16-49-57_35f86f63965d/events.out.tfevents.1695574211.35f86f63965d.4470.0 +3 -0
- special_tokens_map.json +24 -0
- tokenizer.json +0 -0
- tokenizer_config.json +33 -0
- training_args.bin +3 -0
.gitignore
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runs/Sep24_16-49-57_35f86f63965d/events.out.tfevents.1695574211.35f86f63965d.4470.0
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special_tokens_map.json
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{
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"bos_token": {
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"content": "<s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": "</s>",
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"unk_token": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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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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"bos_token": {
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"__type": "AddedToken",
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"content": "<s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"clean_up_tokenization_spaces": false,
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"eos_token": {
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"__type": "AddedToken",
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"legacy": false,
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": null,
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"padding_side": "right",
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"sp_model_kwargs": {},
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"tokenizer_class": "LlamaTokenizer",
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"unk_token": {
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"__type": "AddedToken",
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"content": "<unk>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:3ff88c0efe62f4b128a2dec1fe65c92b75d1f198d491f9a69b167855d83f6f33
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size 3899
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