Text Generation
Transformers
Safetensors
PyTorch
llama
facebook
meta
llama-3
text-generation-inference
fbgemm_fp8
Instructions to use sarkoB6273/forme with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sarkoB6273/forme with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="sarkoB6273/forme")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("sarkoB6273/forme") model = AutoModelForCausalLM.from_pretrained("sarkoB6273/forme") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use sarkoB6273/forme with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sarkoB6273/forme" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sarkoB6273/forme", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/sarkoB6273/forme
- SGLang
How to use sarkoB6273/forme 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 "sarkoB6273/forme" \ --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": "sarkoB6273/forme", "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 "sarkoB6273/forme" \ --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": "sarkoB6273/forme", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use sarkoB6273/forme with Docker Model Runner:
docker model run hf.co/sarkoB6273/forme
| { | |
| "_name_or_path": "meta-llama/Meta-Llama-3.1-405B-FP8", | |
| "architectures": [ | |
| "LlamaForCausalLM" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 128000, | |
| "eos_token_id": 128001, | |
| "hidden_act": "silu", | |
| "hidden_size": 16384, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 53248, | |
| "max_position_embeddings": 131072, | |
| "mlp_bias": false, | |
| "model_type": "llama", | |
| "num_attention_heads": 128, | |
| "num_hidden_layers": 126, | |
| "num_key_value_heads": 16, | |
| "pretraining_tp": 1, | |
| "quantization_config": { | |
| "activation_scale_ub": 1200.0, | |
| "modules_to_not_convert": [ | |
| "lm_head", | |
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