Instructions to use BigSalmon/Points with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BigSalmon/Points with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="BigSalmon/Points")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BigSalmon/Points") model = AutoModelForCausalLM.from_pretrained("BigSalmon/Points") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use BigSalmon/Points with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BigSalmon/Points" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BigSalmon/Points", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/BigSalmon/Points
- SGLang
How to use BigSalmon/Points 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 "BigSalmon/Points" \ --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": "BigSalmon/Points", "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 "BigSalmon/Points" \ --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": "BigSalmon/Points", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use BigSalmon/Points with Docker Model Runner:
docker model run hf.co/BigSalmon/Points
Initial commit
Browse files- 1642612803.271465/events.out.tfevents.1642612803.13806ba8c1fa.90.1 +3 -0
- config.json +39 -0
- events.out.tfevents.1642612803.13806ba8c1fa.90.0 +3 -0
- merges.txt +0 -0
- pytorch_model.bin +3 -0
- tokenizer.json +0 -0
- training_args.bin +3 -0
- vocab.json +0 -0
1642612803.271465/events.out.tfevents.1642612803.13806ba8c1fa.90.1
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config.json
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{
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"_name_or_path": "BigSalmon/InformalToFormalLincoln16",
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"activation_function": "gelu_new",
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"architectures": [
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"GPT2LMHeadModel"
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],
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"attn_pdrop": 0.1,
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"bos_token_id": 50256,
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"embd_pdrop": 0.1,
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"eos_token_id": 50256,
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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_embd": 1280,
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"n_head": 20,
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"n_inner": null,
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"n_layer": 36,
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"n_positions": 1024,
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"reorder_and_upcast_attn": false,
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"resid_pdrop": 0.1,
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"scale_attn_by_inverse_layer_idx": false,
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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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"task_specific_params": {
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"text-generation": {
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"do_sample": true,
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"max_length": 50
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}
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},
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"torch_dtype": "float32",
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"transformers_version": "4.16.0.dev0",
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"use_cache": true,
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"vocab_size": 50257
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}
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events.out.tfevents.1642612803.13806ba8c1fa.90.0
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version https://git-lfs.github.com/spec/v1
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oid sha256:c3cbb51fc29bcddef11b0cb3b246a38fd3826e13e24379fecf4b8e2ea69c99ec
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size 3915
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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:eeca76fe79816edbac8fc423fc8eff17da9109e5703b176d2c73fd481142a1b0
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size 3134053001
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tokenizer.json
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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:ca6af95473ce0e09b98aba7a90c1000594943d64d6a9930b9f0db42a094f1a58
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size 2991
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vocab.json
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