Text Generation
Transformers
Safetensors
English
medical
epidemiology
public-health
CDC
MMWR
llama-3.1
lora
Instructions to use BryanTegomoh/EpiBrief-MMWR-LM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BryanTegomoh/EpiBrief-MMWR-LM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="BryanTegomoh/EpiBrief-MMWR-LM")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BryanTegomoh/EpiBrief-MMWR-LM", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use BryanTegomoh/EpiBrief-MMWR-LM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BryanTegomoh/EpiBrief-MMWR-LM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BryanTegomoh/EpiBrief-MMWR-LM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/BryanTegomoh/EpiBrief-MMWR-LM
- SGLang
How to use BryanTegomoh/EpiBrief-MMWR-LM 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 "BryanTegomoh/EpiBrief-MMWR-LM" \ --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": "BryanTegomoh/EpiBrief-MMWR-LM", "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 "BryanTegomoh/EpiBrief-MMWR-LM" \ --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": "BryanTegomoh/EpiBrief-MMWR-LM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use BryanTegomoh/EpiBrief-MMWR-LM with Docker Model Runner:
docker model run hf.co/BryanTegomoh/EpiBrief-MMWR-LM
Upload folder using huggingface_hub
Browse files- adapter_config.json +31 -0
- adapter_model.safetensors +3 -0
- checkpoint_complete +0 -0
- weights.tar +3 -0
adapter_config.json
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{
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"alpha_pattern": {},
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"auto_mapping": null,
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"base_model_name_or_path": null,
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"bias": "none",
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"corda_config": null,
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"eva_config": null,
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"exclude_modules": null,
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"fan_in_fan_out": false,
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"inference_mode": false,
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"init_lora_weights": true,
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"layer_replication": null,
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 32,
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"lora_bias": false,
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"lora_dropout": 0,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 32,
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"rank_pattern": {},
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"revision": null,
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"target_modules": "all-linear",
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"task_type": "CAUSAL_LM",
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"trainable_token_indices": null,
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"use_dora": false,
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"use_rslora": false
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}
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:6edcc009037011f1fb98e9e0a8ab8c6f248b9d1249fd53422cf73d22f205e357
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size 176303496
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checkpoint_complete
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weights.tar
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version https://git-lfs.github.com/spec/v1
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oid sha256:f0dae2d37b2dfda2dc7e520184acae29d099436d2d67791cb273b0b66d6b0724
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size 176312320
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