Instructions to use RedHatAI/MiniChat-3B-pruned50-quant-ds with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/MiniChat-3B-pruned50-quant-ds with Transformers:

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

pipe = pipeline("text-generation", model="RedHatAI/MiniChat-3B-pruned50-quant-ds")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RedHatAI/MiniChat-3B-pruned50-quant-ds")
model = AutoModelForCausalLM.from_pretrained("RedHatAI/MiniChat-3B-pruned50-quant-ds")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use RedHatAI/MiniChat-3B-pruned50-quant-ds with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/MiniChat-3B-pruned50-quant-ds"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/MiniChat-3B-pruned50-quant-ds",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RedHatAI/MiniChat-3B-pruned50-quant-ds

SGLang

How to use RedHatAI/MiniChat-3B-pruned50-quant-ds 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 "RedHatAI/MiniChat-3B-pruned50-quant-ds" \
    --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": "RedHatAI/MiniChat-3B-pruned50-quant-ds",
		"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 "RedHatAI/MiniChat-3B-pruned50-quant-ds" \
        --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": "RedHatAI/MiniChat-3B-pruned50-quant-ds",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RedHatAI/MiniChat-3B-pruned50-quant-ds with Docker Model Runner:
```
docker model run hf.co/RedHatAI/MiniChat-3B-pruned50-quant-ds
```

mwitiderrick commited on Dec 5, 2023

Commit

0484553

1 Parent(s): 68dc6a4

Update README.md

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@@ -70,7 +70,6 @@ pip install -e "sparseml[transformers]"
 python sparseml/src/sparseml/transformers/sparsification/obcq/obcq.py GeneZC/MiniChat-3B open_platypus --recipe recipe.yaml --save True
 python sparseml/src/sparseml/transformers/sparsification/obcq/export.py --task text-generation --model_path obcq_deployment
 cp deployment/model.onnx deployment/model-orig.onnx
-python onnx_kv_inject.py --input-file deployment/model-orig.onnx --output-file deployment/model.onnx
 ```
 Run this kv-cache injection to speed up the model at inference by caching the Key and Value states:
 ```python

 python sparseml/src/sparseml/transformers/sparsification/obcq/obcq.py GeneZC/MiniChat-3B open_platypus --recipe recipe.yaml --save True
 python sparseml/src/sparseml/transformers/sparsification/obcq/export.py --task text-generation --model_path obcq_deployment
 cp deployment/model.onnx deployment/model-orig.onnx
 ```
 Run this kv-cache injection to speed up the model at inference by caching the Key and Value states:
 ```python