Text Generation
Transformers
Safetensors
falcon
custom_code
text-generation-inference
4-bit precision
bitsandbytes
Instructions to use Sk4467/axolotl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sk4467/axolotl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Sk4467/axolotl", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Sk4467/axolotl", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("Sk4467/axolotl", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Sk4467/axolotl with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Sk4467/axolotl" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Sk4467/axolotl", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Sk4467/axolotl
- SGLang
How to use Sk4467/axolotl 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 "Sk4467/axolotl" \ --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": "Sk4467/axolotl", "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 "Sk4467/axolotl" \ --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": "Sk4467/axolotl", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Sk4467/axolotl with Docker Model Runner:
docker model run hf.co/Sk4467/axolotl
Download training_args.bin from Sk4467/axolotl: direct link, hf CLI and curl.
- Browser
- Download file 4.67 kB
-
https://huggingface.co/Sk4467/axolotl/resolve/main/training_args.bin
- Command line
-
hf download hf://Sk4467/axolotl/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Sk4467/axolotl/resolve/main/training_args.bin
4.67 kB
- Xet hash:
- 49c2e21de2e7d1bc0f8ea78681bcb02d9162134b25f53ffacc62c18e580b8481
- Size of remote file:
- 4.67 kB
- SHA256:
- fda1c201d4421e62c6c3e254f573845491c892813c662e00951d388305f67944
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