Instructions to use Billyyy/extended_llama_mongolian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Billyyy/extended_llama_mongolian with Transformers:

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

pipe = pipeline("text-generation", model="Billyyy/extended_llama_mongolian")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Billyyy/extended_llama_mongolian")
model = AutoModelForCausalLM.from_pretrained("Billyyy/extended_llama_mongolian", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Billyyy/extended_llama_mongolian with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Billyyy/extended_llama_mongolian

SGLang

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

Docker Model Runner
How to use Billyyy/extended_llama_mongolian with Docker Model Runner:
```
docker model run hf.co/Billyyy/extended_llama_mongolian
```

extended_llama_mongolian / model-00001-of-00002.safetensors

Billyyy

Upload LlamaForCausalLM

5f5a4aa verified over 1 year ago

Download

History Blame Contribute Delete

4.99 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:: 5453d341feb885001c9d0762824a2ca8cc4ddd293d6b49eed769d63010225d2a
Size of remote file:: 4.99 GB
SHA256:: 2b53d9e5e6e3f79ff2322d64add601d428afe1f596697bc03bf6fddacf7bafb8

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