Instructions to use martyn/llama2-megamerge-dare-13b-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use martyn/llama2-megamerge-dare-13b-v2 with Transformers:

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

pipe = pipeline("text-generation", model="martyn/llama2-megamerge-dare-13b-v2")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("martyn/llama2-megamerge-dare-13b-v2")
model = AutoModelForCausalLM.from_pretrained("martyn/llama2-megamerge-dare-13b-v2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use martyn/llama2-megamerge-dare-13b-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "martyn/llama2-megamerge-dare-13b-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "martyn/llama2-megamerge-dare-13b-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/martyn/llama2-megamerge-dare-13b-v2

SGLang

How to use martyn/llama2-megamerge-dare-13b-v2 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/llama2-megamerge-dare-13b-v2" \
    --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/llama2-megamerge-dare-13b-v2",
		"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/llama2-megamerge-dare-13b-v2" \
        --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/llama2-megamerge-dare-13b-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use martyn/llama2-megamerge-dare-13b-v2 with Docker Model Runner:
```
docker model run hf.co/martyn/llama2-megamerge-dare-13b-v2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

llama 13b mega merge v2

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

Mergelist

meta-llama/Llama-2-13b-hf
ajibawa-2023/Code-13B
ajibawa-2023/Python-Code-13B
meta-math/MetaMath-13B-V1.0
rombodawg/LosslessMegaCoder-llama2-13b-mini
NousResearch/Nous-Hermes-Llama2-13b
allenai/digital-socrates-13b
migtissera/Synthia-13B
Gryphe/MythoLogic-L2-13b
allenai/tulu-2-dpo-13b
FPHam/Free_Sydney_13b_HF
FPHam/Free_Sydney_V2_13b_HF
FPHam/Sydney_Overthinker_13b_HF
KoboldAI/LLaMA2-13B-Psyfighter2
Undi95/Unholy-v1-12L-13B
athirdpath/Eileithyia-13B
athirdpath/Orca-2-13b-Alpaca-Uncensored

Merge command

python hf_merge.py mergelist.txt 13b-merge-v2 -p 0.11 -lambda 2.1

Notes

seems to generalize instruct styles
p and lambda are still a guess

Downloads last month: 62

Safetensors

Model size

13B params

Tensor type

F32

Model tree for martyn/llama2-megamerge-dare-13b-v2

Quantizations

1 model

Collection including martyn/llama2-megamerge-dare-13b-v2

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