Instructions to use liminerity/Mistral-quiet-star-ascii-demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use liminerity/Mistral-quiet-star-ascii-demo with Transformers:

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

pipe = pipeline("text-generation", model="liminerity/Mistral-quiet-star-ascii-demo")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("liminerity/Mistral-quiet-star-ascii-demo")
model = AutoModelForCausalLM.from_pretrained("liminerity/Mistral-quiet-star-ascii-demo", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use liminerity/Mistral-quiet-star-ascii-demo with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/liminerity/Mistral-quiet-star-ascii-demo

SGLang

How to use liminerity/Mistral-quiet-star-ascii-demo 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 "liminerity/Mistral-quiet-star-ascii-demo" \
    --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": "liminerity/Mistral-quiet-star-ascii-demo",
		"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 "liminerity/Mistral-quiet-star-ascii-demo" \
        --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": "liminerity/Mistral-quiet-star-ascii-demo",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Desktop
Docker Model Runner
How to use liminerity/Mistral-quiet-star-ascii-demo with Docker Model Runner:
```
docker model run hf.co/liminerity/Mistral-quiet-star-ascii-demo
```

gate369 commited on Mar 30, 2024

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@@ -16,7 +16,7 @@ base_model: liminerity/Mistral-quiet-star
 -so i made this based off the doom mistral model. i gotta say just wow..
 - heres a conversation i had with the model:
 - prompt: "You are a helpful assistant who thinks before responding"
-- user: "hi there. id like to build a time machine. how might i go about achieving this?"
 - assistant: "|startthought| Black holes are regions in space where the gravitational pull is so strong that
               nothing, not even light, can escape once it passes a certain boundary called the event horizon.
               They are formed from the collapse of extremely massive stars. |endthought|

 -so i made this based off the doom mistral model. i gotta say just wow..
 - heres a conversation i had with the model:
 - prompt: "You are a helpful assistant who thinks before responding"
+- user: "what does a blackhole look like?"
 - assistant: "|startthought| Black holes are regions in space where the gravitational pull is so strong that
               nothing, not even light, can escape once it passes a certain boundary called the event horizon.
               They are formed from the collapse of extremely massive stars. |endthought|