Instructions to use cross-ling-know/llama2-7b-wiki2-mixed-lang-sentence with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cross-ling-know/llama2-7b-wiki2-mixed-lang-sentence with Transformers:

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

pipe = pipeline("text-generation", model="cross-ling-know/llama2-7b-wiki2-mixed-lang-sentence")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cross-ling-know/llama2-7b-wiki2-mixed-lang-sentence")
model = AutoModelForCausalLM.from_pretrained("cross-ling-know/llama2-7b-wiki2-mixed-lang-sentence", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cross-ling-know/llama2-7b-wiki2-mixed-lang-sentence with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cross-ling-know/llama2-7b-wiki2-mixed-lang-sentence"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cross-ling-know/llama2-7b-wiki2-mixed-lang-sentence",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/cross-ling-know/llama2-7b-wiki2-mixed-lang-sentence

SGLang

How to use cross-ling-know/llama2-7b-wiki2-mixed-lang-sentence 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 "cross-ling-know/llama2-7b-wiki2-mixed-lang-sentence" \
    --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": "cross-ling-know/llama2-7b-wiki2-mixed-lang-sentence",
		"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 "cross-ling-know/llama2-7b-wiki2-mixed-lang-sentence" \
        --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": "cross-ling-know/llama2-7b-wiki2-mixed-lang-sentence",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use cross-ling-know/llama2-7b-wiki2-mixed-lang-sentence with Docker Model Runner:
```
docker model run hf.co/cross-ling-know/llama2-7b-wiki2-mixed-lang-sentence
```

llama2-7b-wiki2-mixed-lang-sentence / model-00004-of-00006.safetensors

yangsibo

Upload LlamaForCausalLM

0671931 verified about 2 years ago

Download

History Blame Contribute Delete

4.86 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 8d85175831a5b1d973747751d625eb48b52e6a5b4c8500228aafd1dec4264069
Size of remote file:: 4.86 GB
SHA256:: ba1af5c422cdca6ee4ac116c073c06e2a972da8e14b0d91504e49fd6f45051be

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.