Instructions to use SonaliSN/MergedModelTest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SonaliSN/MergedModelTest with Transformers:

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

pipe = pipeline("text-generation", model="SonaliSN/MergedModelTest")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SonaliSN/MergedModelTest")
model = AutoModelForCausalLM.from_pretrained("SonaliSN/MergedModelTest", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SonaliSN/MergedModelTest with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/SonaliSN/MergedModelTest

SGLang

How to use SonaliSN/MergedModelTest 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 "SonaliSN/MergedModelTest" \
    --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": "SonaliSN/MergedModelTest",
		"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 "SonaliSN/MergedModelTest" \
        --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": "SonaliSN/MergedModelTest",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SonaliSN/MergedModelTest with Docker Model Runner:
```
docker model run hf.co/SonaliSN/MergedModelTest
```

base_model:

EmbeddedLLM/Mistral-7B-Merge-14-v0
janai-hq/trinity-v1 library_name: transformers tags:
mergekit
merge

merge

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

Merge Details

Merge Method

This model was merged using the TIES merge method using EmbeddedLLM/Mistral-7B-Merge-14-v0 as a base.

Models Merged

The following models were included in the merge:

janai-hq/trinity-v1

Configuration

The following YAML configuration was used to produce this model:


models:
  - model: EmbeddedLLM/Mistral-7B-Merge-14-v0
    parameters:
      density: 0.5
      weight: 0.5
  - model: janai-hq/trinity-v1
    parameters:
      density: 0.5
      weight: 0.3
merge_method: ties
base_model: EmbeddedLLM/Mistral-7B-Merge-14-v0
parameters:
  normalize: true
dtype: float16

Downloads last month: 13

Safetensors

Model size

7B params

Tensor type

F16

Paper for SonaliSN/MergedModelTest

Resolving Interference When Merging Models

Paper • 2306.01708 • Published Jun 2, 2023 • 19