Text Generation
Transformers
Safetensors
mistral
Mistral_Star
Mistral_Quiet
Mistral
Mixtral
Question-Answer
Token-Classification
Sequence-Classification
SpydazWeb-AI
chemistry
biology
legal
code
climate
medical
text-generation-inference
custom_code
Instructions to use LeroyDyer/_Spydaz_Web_AI_MistralStar_V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LeroyDyer/_Spydaz_Web_AI_MistralStar_V2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LeroyDyer/_Spydaz_Web_AI_MistralStar_V2", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("LeroyDyer/_Spydaz_Web_AI_MistralStar_V2", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("LeroyDyer/_Spydaz_Web_AI_MistralStar_V2", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use LeroyDyer/_Spydaz_Web_AI_MistralStar_V2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LeroyDyer/_Spydaz_Web_AI_MistralStar_V2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LeroyDyer/_Spydaz_Web_AI_MistralStar_V2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LeroyDyer/_Spydaz_Web_AI_MistralStar_V2
- SGLang
How to use LeroyDyer/_Spydaz_Web_AI_MistralStar_V2 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 "LeroyDyer/_Spydaz_Web_AI_MistralStar_V2" \ --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": "LeroyDyer/_Spydaz_Web_AI_MistralStar_V2", "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 "LeroyDyer/_Spydaz_Web_AI_MistralStar_V2" \ --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": "LeroyDyer/_Spydaz_Web_AI_MistralStar_V2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LeroyDyer/_Spydaz_Web_AI_MistralStar_V2 with Docker Model Runner:
docker model run hf.co/LeroyDyer/_Spydaz_Web_AI_MistralStar_V2
Update config.json
Browse files- config.json +1 -1
config.json
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"attention_dropout": 0.0,
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"auto_map": {
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"AutoConfig": "LeroyDyer/_Spydaz_Web_AI_MistralStar_V2--modeling_mistral.
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"AutoModel": "LeroyDyer/_Spydaz_Web_AI_MistralStar_V2--modeling_mistral.MistralModel",
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"AutoModelForCausalLM": "LeroyDyer/_Spydaz_Web_AI_MistralStar_V2--modeling_mistral.MistralStarForCausalLM"
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},
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],
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"attention_dropout": 0.0,
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"auto_map": {
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"AutoConfig": "LeroyDyer/_Spydaz_Web_AI_MistralStar_V2--modeling_mistral.MistralConfig",
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"AutoModel": "LeroyDyer/_Spydaz_Web_AI_MistralStar_V2--modeling_mistral.MistralModel",
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"AutoModelForCausalLM": "LeroyDyer/_Spydaz_Web_AI_MistralStar_V2--modeling_mistral.MistralStarForCausalLM"
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},
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