Instructions to use nm-testing/TinyLlama-1.1B-compressed-tensors-kv-cache-scheme with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nm-testing/TinyLlama-1.1B-compressed-tensors-kv-cache-scheme with Transformers:

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

pipe = pipeline("text-generation", model="nm-testing/TinyLlama-1.1B-compressed-tensors-kv-cache-scheme")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nm-testing/TinyLlama-1.1B-compressed-tensors-kv-cache-scheme")
model = AutoModelForCausalLM.from_pretrained("nm-testing/TinyLlama-1.1B-compressed-tensors-kv-cache-scheme", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nm-testing/TinyLlama-1.1B-compressed-tensors-kv-cache-scheme with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nm-testing/TinyLlama-1.1B-compressed-tensors-kv-cache-scheme"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nm-testing/TinyLlama-1.1B-compressed-tensors-kv-cache-scheme",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nm-testing/TinyLlama-1.1B-compressed-tensors-kv-cache-scheme

SGLang

How to use nm-testing/TinyLlama-1.1B-compressed-tensors-kv-cache-scheme 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 "nm-testing/TinyLlama-1.1B-compressed-tensors-kv-cache-scheme" \
    --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": "nm-testing/TinyLlama-1.1B-compressed-tensors-kv-cache-scheme",
		"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 "nm-testing/TinyLlama-1.1B-compressed-tensors-kv-cache-scheme" \
        --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": "nm-testing/TinyLlama-1.1B-compressed-tensors-kv-cache-scheme",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nm-testing/TinyLlama-1.1B-compressed-tensors-kv-cache-scheme with Docker Model Runner:
```
docker model run hf.co/nm-testing/TinyLlama-1.1B-compressed-tensors-kv-cache-scheme
```

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

This model is outdated and uses very old kv-cache quant scheme. It should be removed from HF-Hub once we are certain it is not used anywhere else. I have removed it from vLLM's CI in this PR: https://github.com/vllm-project/vllm/pull/30141

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Safetensors

Model size

1B params

Tensor type

I64

F32

I32