Instructions to use LSX-UniWue/LLaMmlein_1B_prerelease with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LSX-UniWue/LLaMmlein_1B_prerelease with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="LSX-UniWue/LLaMmlein_1B_prerelease")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LSX-UniWue/LLaMmlein_1B_prerelease")
model = AutoModelForCausalLM.from_pretrained("LSX-UniWue/LLaMmlein_1B_prerelease", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LSX-UniWue/LLaMmlein_1B_prerelease with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LSX-UniWue/LLaMmlein_1B_prerelease"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LSX-UniWue/LLaMmlein_1B_prerelease",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LSX-UniWue/LLaMmlein_1B_prerelease

SGLang

How to use LSX-UniWue/LLaMmlein_1B_prerelease 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 "LSX-UniWue/LLaMmlein_1B_prerelease" \
    --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": "LSX-UniWue/LLaMmlein_1B_prerelease",
		"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 "LSX-UniWue/LLaMmlein_1B_prerelease" \
        --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": "LSX-UniWue/LLaMmlein_1B_prerelease",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LSX-UniWue/LLaMmlein_1B_prerelease with Docker Model Runner:
```
docker model run hf.co/LSX-UniWue/LLaMmlein_1B_prerelease
```

LLaMmlein_1B_prerelease / README.md

JanPf

Update README.md

8130e22 verified 11 months ago

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metadata

datasets:
  - togethercomputer/RedPajama-Data-V2
  - LSX-UniWue/LLaMmlein-Dataset
language:
  - de
pipeline_tag: text-generation
library_name: transformers
license: other
new_version: LSX-UniWue/LLaMmlein_1B

LLäMmlein 1B

This is a German Tinyllama 1B language model trained from scratch using the Tinyllama codebase on the German portion of RedPajama V2. Find more details on our page and our preprint!

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("LSX-UniWue/LLaMmlein_1B")

tokenizer = AutoTokenizer.from_pretrained("LSX-UniWue/LLaMmlein_1B")

Evaluation

We evaluated our results on the SuperGLEBer benchmark. Data Take Down