Instructions to use JDWebProgrammer/Mistral-CultriX-slerp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JDWebProgrammer/Mistral-CultriX-slerp with Transformers:

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

pipe = pipeline("text-generation", model="JDWebProgrammer/Mistral-CultriX-slerp")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JDWebProgrammer/Mistral-CultriX-slerp")
model = AutoModelForCausalLM.from_pretrained("JDWebProgrammer/Mistral-CultriX-slerp")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use JDWebProgrammer/Mistral-CultriX-slerp with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/JDWebProgrammer/Mistral-CultriX-slerp

SGLang

How to use JDWebProgrammer/Mistral-CultriX-slerp 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 "JDWebProgrammer/Mistral-CultriX-slerp" \
    --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": "JDWebProgrammer/Mistral-CultriX-slerp",
		"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 "JDWebProgrammer/Mistral-CultriX-slerp" \
        --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": "JDWebProgrammer/Mistral-CultriX-slerp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use JDWebProgrammer/Mistral-CultriX-slerp with Docker Model Runner:
```
docker model run hf.co/JDWebProgrammer/Mistral-CultriX-slerp
```

JDWebProgrammer commited on Feb 23, 2024

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d658cf9

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@@ -30,7 +30,10 @@ Research & Development for AutoSynthetix AI
 🐦 Twitter(X) https://twitter.com/jdwebprogrammer
-* License is the same as the model derivatives.
 Mistral-CultriX-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
 * [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)

 🐦 Twitter(X) https://twitter.com/jdwebprogrammer
+* License includes the license of the model derivatives:
+ - MergeKit LGPL-3.0 https://github.com/arcee-ai/mergekit?tab=LGPL-3.0-1-ov-file#readme
+ - Mistral Apache 2.0 https://huggingface.co/mistralai/Mistral-7B-v0.1
+ - CultriX Apache 2.0 https://huggingface.co/CultriX/NeuralTrix-7B-dpo
 Mistral-CultriX-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
 * [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)