Instructions to use llmware/bling-tiny-llama-ov with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use llmware/bling-tiny-llama-ov with Transformers:

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

pipe = pipeline("text-generation", model="llmware/bling-tiny-llama-ov")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("llmware/bling-tiny-llama-ov")
model = AutoModelForCausalLM.from_pretrained("llmware/bling-tiny-llama-ov")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use llmware/bling-tiny-llama-ov with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/llmware/bling-tiny-llama-ov

SGLang

How to use llmware/bling-tiny-llama-ov 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 "llmware/bling-tiny-llama-ov" \
    --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": "llmware/bling-tiny-llama-ov",
		"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 "llmware/bling-tiny-llama-ov" \
        --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": "llmware/bling-tiny-llama-ov",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use llmware/bling-tiny-llama-ov with Docker Model Runner:
```
docker model run hf.co/llmware/bling-tiny-llama-ov
```

doberst commited on Sep 24, 2024

Commit

595a3f3

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1 Parent(s): 1e4fd71

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hash_record_sha256.json +10 -0

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	@@ -0,0 +1,10 @@

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+ "openvino_model.bin": "25cedbf7c49069b622be05fe439550c919aa66f51e9958aa7c963373fbd75cc5",
+ "openvino_model.xml": "83047d63b39a23b10fbe3b1bd888831616f647fd89d4aa35f0ea194b4ead4b93",
+ "openvino_tokenizer.bin": "9ec38b843ff6c229ec650d242feaad2c9e4fa694c69d5e20a8e68b95a878c7ad",
+ "openvino_tokenizer.xml": "058c5e731258524da7e4f961040e71d70034fc2a100ae65c365d16cf6cfdfa28",
+ "tokenizer.model": "9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347",
+ "time_stamp": "2024-09-24_010847"
+}