Text Generation
Transformers
PyTorch
qwen2
trl
sft
text-generation-inference
4-bit precision
bitsandbytes
Instructions to use Darshan03/Edu-Model-v7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Darshan03/Edu-Model-v7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Darshan03/Edu-Model-v7")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Darshan03/Edu-Model-v7") model = AutoModelForCausalLM.from_pretrained("Darshan03/Edu-Model-v7", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Darshan03/Edu-Model-v7 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Darshan03/Edu-Model-v7" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Darshan03/Edu-Model-v7", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Darshan03/Edu-Model-v7
- SGLang
How to use Darshan03/Edu-Model-v7 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 "Darshan03/Edu-Model-v7" \ --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": "Darshan03/Edu-Model-v7", "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 "Darshan03/Edu-Model-v7" \ --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": "Darshan03/Edu-Model-v7", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Darshan03/Edu-Model-v7 with Docker Model Runner:
docker model run hf.co/Darshan03/Edu-Model-v7
Download adapter_model.bin from Darshan03/Edu-Model-v7: direct link, hf CLI and curl.
- Browser
- Download file 1.31 GB
-
https://huggingface.co/Darshan03/Edu-Model-v7/resolve/main/adapter_model.bin
- Command line
-
hf download hf://Darshan03/Edu-Model-v7/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/Darshan03/Edu-Model-v7/resolve/main/adapter_model.bin
1.31 GB
- Xet hash:
- 68393a42d8ddb25757199dacae5c16d26b50c3bc5dbc7d65f58bbbe93a607ec2
- Size of remote file:
- 1.31 GB
- SHA256:
- 28908e2f6977adb8e08da89ef796be14e903490360058a97101fb276cd052601
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