Text Generation
Transformers
Safetensors
llama
trl
dpo
Generated from Trainer
text-generation-inference
Instructions to use stojchet/d7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stojchet/d7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="stojchet/d7")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("stojchet/d7") model = AutoModelForCausalLM.from_pretrained("stojchet/d7", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use stojchet/d7 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "stojchet/d7" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "stojchet/d7", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/stojchet/d7
- SGLang
How to use stojchet/d7 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 "stojchet/d7" \ --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": "stojchet/d7", "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 "stojchet/d7" \ --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": "stojchet/d7", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use stojchet/d7 with Docker Model Runner:
docker model run hf.co/stojchet/d7
Download training_args.bin from stojchet/d7: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/stojchet/d7/resolve/main/training_args.bin
- Command line
-
hf download hf://stojchet/d7/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/stojchet/d7/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- 8851ef7c9e2a66825ab07f95504e5479fe09b7a5b3bab3aed6651301f518877e
- Size of remote file:
- 5.24 kB
- SHA256:
- 449c1f3e4d7519eb5b534d6a84ea6cc449e08be9cdfdddb3ad0bbbadf8c587f5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.