Instructions to use jpllm/TBM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jpllm/TBM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jpllm/TBM")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jpllm/TBM") model = AutoModelForCausalLM.from_pretrained("jpllm/TBM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jpllm/TBM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jpllm/TBM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jpllm/TBM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jpllm/TBM
- SGLang
How to use jpllm/TBM 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 "jpllm/TBM" \ --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": "jpllm/TBM", "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 "jpllm/TBM" \ --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": "jpllm/TBM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jpllm/TBM with Docker Model Runner:
docker model run hf.co/jpllm/TBM
Training in progress, epoch 1
Browse files- config.json +1 -1
- generation_config.json +1 -1
- model.safetensors +1 -1
- tokenizer_config.json +1 -1
- training_args.bin +2 -2
config.json
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"summary_type": "cls_index",
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"summary_use_proj": true,
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"tie_word_embeddings": true,
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"transformers_version": "5.
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"use_cache": false,
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"vocab_size": 50257,
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"window_size": 256
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"summary_type": "cls_index",
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"summary_use_proj": true,
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"tie_word_embeddings": true,
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"transformers_version": "5.13.0",
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"use_cache": false,
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"vocab_size": 50257,
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"window_size": 256
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generation_config.json
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"transformers_version": "5.
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"use_cache": true
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"transformers_version": "5.13.0",
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"use_cache": true
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}
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model.safetensors
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tokenizer_config.json
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"bos_token": "<|endoftext|>",
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"eos_token": "<|endoftext|>",
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"errors": "replace",
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"is_local":
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"local_files_only": false,
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"model_max_length": 2048,
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"pad_token": "<|endoftext|>",
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"bos_token": "<|endoftext|>",
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"eos_token": "<|endoftext|>",
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"errors": "replace",
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"is_local": false,
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"local_files_only": false,
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"model_max_length": 2048,
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"pad_token": "<|endoftext|>",
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training_args.bin
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size 5265
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