Text Generation
Transformers
TensorBoard
Safetensors
mistral
Generated from Trainer
text-generation-inference
Instructions to use Fredithefish/mistral_tiny_3epoch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fredithefish/mistral_tiny_3epoch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Fredithefish/mistral_tiny_3epoch")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Fredithefish/mistral_tiny_3epoch") model = AutoModelForCausalLM.from_pretrained("Fredithefish/mistral_tiny_3epoch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Fredithefish/mistral_tiny_3epoch with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Fredithefish/mistral_tiny_3epoch" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Fredithefish/mistral_tiny_3epoch", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Fredithefish/mistral_tiny_3epoch
- SGLang
How to use Fredithefish/mistral_tiny_3epoch 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 "Fredithefish/mistral_tiny_3epoch" \ --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": "Fredithefish/mistral_tiny_3epoch", "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 "Fredithefish/mistral_tiny_3epoch" \ --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": "Fredithefish/mistral_tiny_3epoch", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Fredithefish/mistral_tiny_3epoch with Docker Model Runner:
docker model run hf.co/Fredithefish/mistral_tiny_3epoch
- Xet hash:
- e8a75cb98e6537ba8bd90fe50029fc098b5231359b0af3b59c7ede920f5b02b0
- Size of remote file:
- 4.92 kB
- SHA256:
- 3299677a55c55a7a162b8dcf3d55bd7f7f95edae1bff1184ae0628f78ab5d121
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