Instructions to use martyn/solar-megamerge-dare-10.7b-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use martyn/solar-megamerge-dare-10.7b-v1 with Transformers:

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

pipe = pipeline("text-generation", model="martyn/solar-megamerge-dare-10.7b-v1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("martyn/solar-megamerge-dare-10.7b-v1")
model = AutoModelForCausalLM.from_pretrained("martyn/solar-megamerge-dare-10.7b-v1", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use martyn/solar-megamerge-dare-10.7b-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "martyn/solar-megamerge-dare-10.7b-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "martyn/solar-megamerge-dare-10.7b-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/martyn/solar-megamerge-dare-10.7b-v1

SGLang

How to use martyn/solar-megamerge-dare-10.7b-v1 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 "martyn/solar-megamerge-dare-10.7b-v1" \
    --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": "martyn/solar-megamerge-dare-10.7b-v1",
		"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 "martyn/solar-megamerge-dare-10.7b-v1" \
        --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": "martyn/solar-megamerge-dare-10.7b-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use martyn/solar-megamerge-dare-10.7b-v1 with Docker Model Runner:
```
docker model run hf.co/martyn/solar-megamerge-dare-10.7b-v1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

solar megamerge 10.7b

The following models were merged with DARE using https://github.com/martyn/safetensors-merge-supermario

Mergelist

models:
  - model: upstage/SOLAR-10.7B-v1.0
  - model: upstage/SOLAR-10.7B-Instruct-v1.0
    parameters:
      weight: 0.20
      density: 0.8
  - model: kyujinpy/SOLAR-Platypus-10.7B-v1
    parameters:
      weight: 0.19
      density: 0.75
  - model: We-Want-GPU/SOLAR-10.7B-orca-alpaca-gpt4-math
    parameters:
      weight: 0.18
      density: 0.75
  - model: maywell/Synatra-10.7B-v0.4
    parameters:
      weight: 0.18
      density: 0.7
  - model: kyujinpy/SOLAR-Platypus-10.7B-v2
    parameters:
      weight: 0.17
      density: 0.7
  - model: Sao10K/Frostwind-10.7B-v1
    parameters:
      weight: 0.16
      density: 0.65
  - model: rishiraj/meow
    parameters:
      weight: 0.15
      density: 0.6

Merge command

python3 hf_merge.py mergelist.yaml solar-1

Notes

in the yaml: p=weight and lambda=1/density

Downloads last month: 86

Safetensors

Model size

11B params

Tensor type

F32

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Quantizations

2 models

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Collection including martyn/solar-megamerge-dare-10.7b-v1

Dare LLM Merges

Collection

These are large language models merged through my implementation of Super Mario DARE merge. • 10 items • Updated Dec 31, 2023 • 2