Instructions to use migtissera/Tess-M-Creative-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use migtissera/Tess-M-Creative-v1.0 with Transformers:

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

pipe = pipeline("text-generation", model="migtissera/Tess-M-Creative-v1.0")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("migtissera/Tess-M-Creative-v1.0")
model = AutoModelForCausalLM.from_pretrained("migtissera/Tess-M-Creative-v1.0", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use migtissera/Tess-M-Creative-v1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "migtissera/Tess-M-Creative-v1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "migtissera/Tess-M-Creative-v1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/migtissera/Tess-M-Creative-v1.0

SGLang

How to use migtissera/Tess-M-Creative-v1.0 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 "migtissera/Tess-M-Creative-v1.0" \
    --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": "migtissera/Tess-M-Creative-v1.0",
		"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 "migtissera/Tess-M-Creative-v1.0" \
        --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": "migtissera/Tess-M-Creative-v1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use migtissera/Tess-M-Creative-v1.0 with Docker Model Runner:
```
docker model run hf.co/migtissera/Tess-M-Creative-v1.0
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Note:

This version is experimental and have been depracated. Please use the stable release Tess-M-v1.3: https://huggingface.co/migtissera/Tess-M-v1.3

Tess

Tess, short for Tessoro/Tessoso, is a general purpose Large Language Model series. Tess-M series is trained on the Yi-34B-200K base.

Tess-M-Creative is an AI most suited for creative tasks, such as writing, role play, design and exploring novel concepts. While it has been trained on STEM, its reasoning capabilities may lag state-of-the-art. Please download Tess-M-STEM series for reasoning, logic and STEM related tasks.

Prompt Format:

SYSTEM: <ANY SYSTEM CONTEXT>
USER: What is the relationship between Earth's atmosphere, magnetic field and gravity?
ASSISTANT:

Downloads last month: 51

Safetensors

Model size

34B params

Tensor type

F16

Model tree for migtissera/Tess-M-Creative-v1.0

Quantizations

5 models

migtissera
/

Tess-M-Creative-v1.0

Note:

Tess

Prompt Format:

Model tree for migtissera/Tess-M-Creative-v1.0

Spaces using migtissera/Tess-M-Creative-v1.0 2