Instructions to use Downtown-Case/Tess-2.0-RPMerge-SlerpMerge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Downtown-Case/Tess-2.0-RPMerge-SlerpMerge with Transformers:

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

pipe = pipeline("text-generation", model="Downtown-Case/Tess-2.0-RPMerge-SlerpMerge")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Downtown-Case/Tess-2.0-RPMerge-SlerpMerge")
model = AutoModelForCausalLM.from_pretrained("Downtown-Case/Tess-2.0-RPMerge-SlerpMerge")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Downtown-Case/Tess-2.0-RPMerge-SlerpMerge with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Downtown-Case/Tess-2.0-RPMerge-SlerpMerge"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Downtown-Case/Tess-2.0-RPMerge-SlerpMerge",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Downtown-Case/Tess-2.0-RPMerge-SlerpMerge

SGLang

How to use Downtown-Case/Tess-2.0-RPMerge-SlerpMerge 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 "Downtown-Case/Tess-2.0-RPMerge-SlerpMerge" \
    --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": "Downtown-Case/Tess-2.0-RPMerge-SlerpMerge",
		"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 "Downtown-Case/Tess-2.0-RPMerge-SlerpMerge" \
        --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": "Downtown-Case/Tess-2.0-RPMerge-SlerpMerge",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Downtown-Case/Tess-2.0-RPMerge-SlerpMerge with Docker Model Runner:
```
docker model run hf.co/Downtown-Case/Tess-2.0-RPMerge-SlerpMerge
```

Just a quick slerp merge of 2 Yi 200K models.

Tess20-RPMerge-Merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

migtissera_Tess-2.0-Yi-34B-200K
brucethemoose_Yi-34B-200K-RPMerge

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: /home/alpha/Models/Raw/brucethemoose_Yi-34B-200K-RPMerge
  - model: /home/alpha/Models/Raw/migtissera_Tess-2.0-Yi-34B-200K
merge_method: slerp
base_model: /home/alpha/Models/Raw/migtissera_Tess-2.0-Yi-34B-200K
parameters:
  int8_mask: false
  t:
    - value: 0.5
dtype: bfloat16
tokenizer_source: model:/home/alpha/Models/Raw/brucethemoose_Yi-34B-200K-RPMerge

Downloads last month: 2

Safetensors

Model size

34B params

Tensor type

BF16

Downtown-Case
/

Tess-2.0-RPMerge-SlerpMerge

Tess20-RPMerge-Merge

Merge Details

Merge Method

Models Merged

Configuration

Space using Downtown-Case/Tess-2.0-RPMerge-SlerpMerge 1