Text Generation
Transformers
Safetensors
Japanese
llama
japanese
llama-2
Powered by AWS Trainium
text-generation-inference
Instructions to use stockmark/stockmark-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stockmark/stockmark-13b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="stockmark/stockmark-13b")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("stockmark/stockmark-13b") model = AutoModelForCausalLM.from_pretrained("stockmark/stockmark-13b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use stockmark/stockmark-13b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "stockmark/stockmark-13b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "stockmark/stockmark-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/stockmark/stockmark-13b
- SGLang
How to use stockmark/stockmark-13b 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 "stockmark/stockmark-13b" \ --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": "stockmark/stockmark-13b", "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 "stockmark/stockmark-13b" \ --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": "stockmark/stockmark-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use stockmark/stockmark-13b with Docker Model Runner:
docker model run hf.co/stockmark/stockmark-13b
Commit History
-
Update README.md (#1)
Verified signature- Update README.md (5b58b6d789ffe33c9a6b55cf59f03e7a503767a4) Co-authored-by: Kamran Khan <kamranjkhan@users.noreply.huggingface.co>77a620e
-
Update notebooks/LoRA.ipynb
Verified signaturea9ecf73
-
Upload inferentia2.ipynb
Verified signaturefdff80b
-
Update README.md
1853074
-
Update README.md
852fd3e
-
Update README.md
07808f6 -
Update notebooks/LoRA.ipynb
edb49d9 -
Delete notebooks/QLoRA.ipynb
1803d3a -
Update notebooks/QLoRA.ipynb
8f53238
-
Update notebooks/QLoRA.ipynb
26274a2 -
Update notebooks/QLoRA.ipynb
29c55fc -
Create QLoRA.ipynb
ccc5a6c -
Create notebooks/LoRA.ipynb
8fbcc3a -
Update README.md
a1660f4 -
Update README.md
5294e31 -
Update README.md
96ed3d4 -
Update README.md
6851ff6 -
Update README.md
0ae4abc
-
Upload LlamaForCausalLM
d9f6dad
-
Update tokenizer_config.json
5c40319
-
Upload tokenizer
719e61e -
initial commit
bc6d1cb