Instructions to use tridm/IELTS_Grader with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tridm/IELTS_Grader with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tridm/IELTS_Grader")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("tridm/IELTS_Grader") model = AutoModelForCausalLM.from_pretrained("tridm/IELTS_Grader", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use tridm/IELTS_Grader with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tridm/IELTS_Grader" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tridm/IELTS_Grader", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/tridm/IELTS_Grader
- SGLang
How to use tridm/IELTS_Grader 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 "tridm/IELTS_Grader" \ --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": "tridm/IELTS_Grader", "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 "tridm/IELTS_Grader" \ --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": "tridm/IELTS_Grader", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Desktop
- Docker Model Runner
How to use tridm/IELTS_Grader with Docker Model Runner:
docker model run hf.co/tridm/IELTS_Grader
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Download README.md from tridm/IELTS_Grader: direct link, hf CLI and curl.
- Browser
- Download file 562 Bytes
-
https://huggingface.co/tridm/IELTS_Grader/resolve/main/README.md
- Command line
-
hf download hf://tridm/IELTS_Grader/README.md
-
curl -L -o README.md https://huggingface.co/tridm/IELTS_Grader/resolve/main/README.md
562 Bytes
metadata
base_model: unsloth/mistral-7b
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
license: apache-2.0
language:
- en
Uploaded finetuned model
- Developed by: tridm
- License: apache-2.0
- Finetuned from model : unsloth/mistral-7b
This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.
