Instructions to use llmware/bling-tiny-llama-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use llmware/bling-tiny-llama-v0 with Transformers:

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

pipe = pipeline("text-generation", model="llmware/bling-tiny-llama-v0")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("llmware/bling-tiny-llama-v0")
model = AutoModelForCausalLM.from_pretrained("llmware/bling-tiny-llama-v0")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use llmware/bling-tiny-llama-v0 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-v0"
# 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-v0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/llmware/bling-tiny-llama-v0

SGLang

How to use llmware/bling-tiny-llama-v0 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-v0" \
    --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-v0",
		"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-v0" \
        --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-v0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use llmware/bling-tiny-llama-v0 with Docker Model Runner:
```
docker model run hf.co/llmware/bling-tiny-llama-v0
```

doberst commited on Dec 29, 2023

Commit

775111d

1 Parent(s): f4d9e55

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@@ -59,8 +59,8 @@ Any model can provide inaccurate or incomplete information, and should be used i
 The fastest way to get started with BLING is through direct import in transformers:
     from transformers import AutoTokenizer, AutoModelForCausalLM
-    tokenizer = AutoTokenizer.from_pretrained("bling-tiny-llama-v0", trust_remote_code=True)
-    model = AutoModelForCausalLM.from_pretrained("bling-tiny-llama-v0", trust_remote_code=True)
 Please refer to the generation_test .py files in the Files repository, which includes 200 samples and script to test the model.  The **generation_test_llmware_script.py** includes built-in llmware capabilities for fact-checking, as well as easy integration with document parsing and actual retrieval to swap out the test set for RAG workflow consisting of business documents.

 The fastest way to get started with BLING is through direct import in transformers:
     from transformers import AutoTokenizer, AutoModelForCausalLM
+    tokenizer = AutoTokenizer.from_pretrained("bling-tiny-llama-v0")
+    model = AutoModelForCausalLM.from_pretrained("bling-tiny-llama-v0")
 Please refer to the generation_test .py files in the Files repository, which includes 200 samples and script to test the model.  The **generation_test_llmware_script.py** includes built-in llmware capabilities for fact-checking, as well as easy integration with document parsing and actual retrieval to swap out the test set for RAG workflow consisting of business documents.