Instructions to use konstantindobler/mistral7b-de-tokenizer-swap-pure-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use konstantindobler/mistral7b-de-tokenizer-swap-pure-bf16 with Transformers:

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

pipe = pipeline("text-generation", model="konstantindobler/mistral7b-de-tokenizer-swap-pure-bf16")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("konstantindobler/mistral7b-de-tokenizer-swap-pure-bf16")
model = AutoModelForCausalLM.from_pretrained("konstantindobler/mistral7b-de-tokenizer-swap-pure-bf16", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use konstantindobler/mistral7b-de-tokenizer-swap-pure-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "konstantindobler/mistral7b-de-tokenizer-swap-pure-bf16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "konstantindobler/mistral7b-de-tokenizer-swap-pure-bf16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/konstantindobler/mistral7b-de-tokenizer-swap-pure-bf16

SGLang

How to use konstantindobler/mistral7b-de-tokenizer-swap-pure-bf16 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 "konstantindobler/mistral7b-de-tokenizer-swap-pure-bf16" \
    --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": "konstantindobler/mistral7b-de-tokenizer-swap-pure-bf16",
		"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 "konstantindobler/mistral7b-de-tokenizer-swap-pure-bf16" \
        --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": "konstantindobler/mistral7b-de-tokenizer-swap-pure-bf16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use konstantindobler/mistral7b-de-tokenizer-swap-pure-bf16 with Docker Model Runner:
```
docker model run hf.co/konstantindobler/mistral7b-de-tokenizer-swap-pure-bf16
```

konstantindobler commited on Jun 26, 2024

Commit

2b1d5e7

verified ·

1 Parent(s): 7dc3335

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +45 -0

README.md ADDED Viewed

	@@ -0,0 +1,45 @@

+---
+language: de
+license: apache-2.0
+datasets: oscar-corpus/OSCAR-2301
+---
+# mistral7b-de-tokenizer-swap-pure-bf16
+Mistral-7B-v0.1 adapted to German as part of our study on efficient language adaptation: "Language Adaptation on a Tight Academic Compute Budget: Tokenizer Swapping Works and Pure bfloat16 Is Enough".
+Code: https://github.com/konstantinjdobler/tight-budget-llm-adaptation
+Paper: https://openreview.net/forum?id=VYfJaHeVod
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("konstantindobler/mistral7b-de-tokenizer-swap-pure-bf16")
+model = AutoModelForCausalLM.from_pretrained("konstantindobler/mistral7b-de-tokenizer-swap-pure-bf16")
+# Use model and tokenizer as usual
+```
+## Details
+The model is based on [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) and was adapted to German.
+The original tokenizer was replaced by a language-specific German tokenizer with a vocabulary of 32768 tokens. The new embeddings were initialized with [FOCUS](https://github.com/konstantinjdobler/focus).
+The model was then trained on 8 billion German tokens from [oscar-corpus/OSCAR-2301](https://huggingface.co/oscar-corpus/OSCAR-2301) with pure bfloat16 precision (no mixed precision). More details and hyperparameters can be found [in the paper](https://openreview.net/forum?id=VYfJaHeVod).
+## Disclaimer
+The web-scale dataset used for pretraining and tokenizer training ([oscar-corpus/OSCAR-2301](https://huggingface.co/oscar-corpus/OSCAR-2301)) might contain personal and sensitive information.
+Such behavior needs to be assessed carefully before any real-world deployment of the models.
+## Citation
+Please cite as follows:
+```bibtex
+@inproceedings{dobler2024language,
+    title={Language Adaptation on a Tight Academic Compute Budget: Tokenizer Swapping Works and Pure bfloat16 Is Enough},
+    author={Konstantin Dobler and Gerard de Melo},
+    booktitle={2nd Workshop on Advancing Neural Network Training: Computational Efficiency, Scalability, and Resource Optimization (WANT@ICML 2024)},
+    year={2024},
+    url={https://openreview.net/forum?id=VYfJaHeVod}
+}
+```