Instructions to use llm-stacking/G_learn_width with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use llm-stacking/G_learn_width with Transformers:

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

pipe = pipeline("text-generation", model="llm-stacking/G_learn_width")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("llm-stacking/G_learn_width")
model = AutoModelForCausalLM.from_pretrained("llm-stacking/G_learn_width")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use llm-stacking/G_learn_width with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/llm-stacking/G_learn_width

SGLang

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

Docker Model Runner
How to use llm-stacking/G_learn_width with Docker Model Runner:
```
docker model run hf.co/llm-stacking/G_learn_width
```

Tongxu Luo commited on May 25, 2024

Commit

56805f1

verified ·

1 Parent(s): 7632e61

Update config.json

Browse files

Files changed (1) hide show

config.json +0 -1

config.json CHANGED Viewed

@@ -1,5 +1,4 @@
 {
-  "_name_or_path": "/aifs4su/data/tongxuluo/TinyLlama/tiny_LLaMA_410M_wo_gqa/2024-03-20-22-29-04/1.1B",
   "architectures": [
     "LlamaForCausalLM"
   ],

 {
   "architectures": [
     "LlamaForCausalLM"
   ],