Instructions to use RTTP/AptimizeAI-Mistral-7b-HPP1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RTTP/AptimizeAI-Mistral-7b-HPP1 with Transformers:

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

pipe = pipeline("text-generation", model="RTTP/AptimizeAI-Mistral-7b-HPP1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RTTP/AptimizeAI-Mistral-7b-HPP1")
model = AutoModelForCausalLM.from_pretrained("RTTP/AptimizeAI-Mistral-7b-HPP1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RTTP/AptimizeAI-Mistral-7b-HPP1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RTTP/AptimizeAI-Mistral-7b-HPP1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RTTP/AptimizeAI-Mistral-7b-HPP1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RTTP/AptimizeAI-Mistral-7b-HPP1

SGLang

How to use RTTP/AptimizeAI-Mistral-7b-HPP1 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 "RTTP/AptimizeAI-Mistral-7b-HPP1" \
    --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": "RTTP/AptimizeAI-Mistral-7b-HPP1",
		"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 "RTTP/AptimizeAI-Mistral-7b-HPP1" \
        --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": "RTTP/AptimizeAI-Mistral-7b-HPP1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RTTP/AptimizeAI-Mistral-7b-HPP1 with Docker Model Runner:
```
docker model run hf.co/RTTP/AptimizeAI-Mistral-7b-HPP1
```

AptimizeAI-Mistral-7b-HPP1 / model.safetensors

dontjandra

Upload MistralForCausalLM

e8f2393 verified about 2 years ago

Download

History Blame Contribute Delete

4.65 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: bcd2c64933388cdb37398e869e888e202e45a008d103495e1724917a14390e9f
Size of remote file:: 4.65 GB
SHA256:: e15fdd7e52daecc1a1bd742d92a64267f821de951e5875a24d79c0f7d34657e3

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.