Instructions to use CLMBR/superlative-quantifier-transformer-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLMBR/superlative-quantifier-transformer-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CLMBR/superlative-quantifier-transformer-1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("CLMBR/superlative-quantifier-transformer-1") model = AutoModelForCausalLM.from_pretrained("CLMBR/superlative-quantifier-transformer-1") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use CLMBR/superlative-quantifier-transformer-1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CLMBR/superlative-quantifier-transformer-1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CLMBR/superlative-quantifier-transformer-1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/CLMBR/superlative-quantifier-transformer-1
- SGLang
How to use CLMBR/superlative-quantifier-transformer-1 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 "CLMBR/superlative-quantifier-transformer-1" \ --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": "CLMBR/superlative-quantifier-transformer-1", "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 "CLMBR/superlative-quantifier-transformer-1" \ --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": "CLMBR/superlative-quantifier-transformer-1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use CLMBR/superlative-quantifier-transformer-1 with Docker Model Runner:
docker model run hf.co/CLMBR/superlative-quantifier-transformer-1
Training in progress, epoch 0, checkpoint
Browse files- checkpoint-2900160/config.json +28 -0
- checkpoint-2900160/generation_config.json +7 -0
- checkpoint-2900160/optimizer.pt +3 -0
- checkpoint-2900160/pytorch_model.bin +3 -0
- checkpoint-2900160/rng_state.pth +3 -0
- checkpoint-2900160/scheduler.pt +3 -0
- checkpoint-2900160/special_tokens_map.json +5 -0
- checkpoint-2900160/tokenizer.json +0 -0
- checkpoint-2900160/tokenizer_config.json +12 -0
- checkpoint-2900160/trainer_state.json +0 -0
- checkpoint-2900160/training_args.bin +3 -0
checkpoint-2900160/config.json
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{
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"_remove_final_layer_norm": false,
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"activation_function": "relu",
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"architectures": [
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"OPTForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 2,
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"do_layer_norm_before": true,
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"dropout": 0.1,
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"enable_bias": true,
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"eos_token_id": 2,
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"ffn_dim": 768,
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"hidden_size": 768,
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"init_std": 0.02,
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"layer_norm_elementwise_affine": true,
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"layerdrop": 0.0,
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"max_position_embeddings": 512,
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"model_type": "opt",
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"num_attention_heads": 8,
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"num_hidden_layers": 8,
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"pad_token_id": 1,
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"torch_dtype": "float32",
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"transformers_version": "4.33.3",
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"use_cache": true,
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"vocab_size": 50002,
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"word_embed_proj_dim": 768
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}
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checkpoint-2900160/generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 2,
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"eos_token_id": 2,
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"pad_token_id": 1,
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"transformers_version": "4.33.3"
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}
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checkpoint-2900160/optimizer.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:e389c98da8a561c7434e7b7cab5371d8e16fd346ed71ac4d9cfe60c3bf46a072
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size 537476677
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checkpoint-2900160/pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:e97b009d2c686b4baa691bed038d3b4814c8a1344fb47b46cdc042c9ea0b34e7
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size 268727709
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checkpoint-2900160/rng_state.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:ebebf9e8fe34b30a8c35c2535a377d18311afd174820faae188b84cfdd506e4e
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size 14575
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checkpoint-2900160/scheduler.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:f7ab1469a6a40c86fdc48ed8c946cd3cd9e747d28b364baed33c2adc8e58e01b
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size 627
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checkpoint-2900160/special_tokens_map.json
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{
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"eos_token": "<eos>",
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"pad_token": "<pad>",
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"unk_token": "<unk>"
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}
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checkpoint-2900160/tokenizer.json
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checkpoint-2900160/tokenizer_config.json
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{
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"clean_up_tokenization_spaces": true,
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"eos_token": "<eos>",
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"max_length": null,
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"model_max_length": 512,
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"pad_to_multiple_of": null,
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"pad_token": "<pad>",
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"pad_token_type_id": 0,
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"padding_side": "right",
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"tokenizer_class": "PreTrainedTokenizerFast",
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"unk_token": "<unk>"
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}
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checkpoint-2900160/trainer_state.json
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checkpoint-2900160/training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:982c78495d577bdcdbc1945175d06328ead1c14cedb437fc9a0ed5104ebdc88b
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size 4283
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