Not-For-All-Audiences

Instructions to use cgato/Thespis-Mistral-7b-Alpha-v0.7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cgato/Thespis-Mistral-7b-Alpha-v0.7 with Transformers:

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

pipe = pipeline("text-generation", model="cgato/Thespis-Mistral-7b-Alpha-v0.7")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cgato/Thespis-Mistral-7b-Alpha-v0.7")
model = AutoModelForCausalLM.from_pretrained("cgato/Thespis-Mistral-7b-Alpha-v0.7", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cgato/Thespis-Mistral-7b-Alpha-v0.7 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cgato/Thespis-Mistral-7b-Alpha-v0.7"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cgato/Thespis-Mistral-7b-Alpha-v0.7",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/cgato/Thespis-Mistral-7b-Alpha-v0.7

SGLang

How to use cgato/Thespis-Mistral-7b-Alpha-v0.7 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 "cgato/Thespis-Mistral-7b-Alpha-v0.7" \
    --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": "cgato/Thespis-Mistral-7b-Alpha-v0.7",
		"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 "cgato/Thespis-Mistral-7b-Alpha-v0.7" \
        --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": "cgato/Thespis-Mistral-7b-Alpha-v0.7",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use cgato/Thespis-Mistral-7b-Alpha-v0.7 with Docker Model Runner:
```
docker model run hf.co/cgato/Thespis-Mistral-7b-Alpha-v0.7
```

cgato commited on Dec 8, 2023

Commit

b59ddba

1 Parent(s): 06c86f8

Update README.md

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@@ -23,9 +23,6 @@ It uses a 40/60 Split of Assistant and RP data, and includes items from the belo
 * Augmental Dataset
 As a result of adding this additional Assistant data, the model shows greatly improved instruction following and coherence during multiturn chats.
-The downside of both the additional Assistant data and the choice of base mode is that its definitely more censored.
-## TLDR Model has more assistant data, its smarter but more censored.


23	* Augmental Dataset
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25	As a result of adding this additional Assistant data, the model shows greatly improved instruction following and coherence during multiturn chats.



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