Instructions to use Username1/Mourinho with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Username1/Mourinho with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Username1/Mourinho") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Username1/Mourinho") model = AutoModelForCausalLM.from_pretrained("Username1/Mourinho") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Username1/Mourinho with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Username1/Mourinho" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Username1/Mourinho", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Username1/Mourinho
- SGLang
How to use Username1/Mourinho 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 "Username1/Mourinho" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Username1/Mourinho", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "Username1/Mourinho" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Username1/Mourinho", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Username1/Mourinho with Docker Model Runner:
docker model run hf.co/Username1/Mourinho
add model
Browse files- config.json +4 -4
- pytorch_model.bin +2 -2
config.json
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{
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"_name_or_path": "microsoft/DialoGPT-
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"activation_function": "gelu_new",
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"architectures": [
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"GPT2LMHeadModel"
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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":
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"n_head":
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"n_inner": null,
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"n_layer":
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"n_positions": 1024,
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"resid_pdrop": 0.1,
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"scale_attn_weights": true,
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{
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"_name_or_path": "microsoft/DialoGPT-large",
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"activation_function": "gelu_new",
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"architectures": [
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"GPT2LMHeadModel"
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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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"resid_pdrop": 0.1,
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"scale_attn_weights": true,
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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:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:b23e9eab2b440e18612a2ab19505a7bf128c346a441804c21bcedd1076fca4ac
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size 3134045897
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