Instructions to use chargoddard/Chronorctypus-Limarobormes-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chargoddard/Chronorctypus-Limarobormes-13b with Transformers:

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

pipe = pipeline("text-generation", model="chargoddard/Chronorctypus-Limarobormes-13b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("chargoddard/Chronorctypus-Limarobormes-13b")
model = AutoModelForCausalLM.from_pretrained("chargoddard/Chronorctypus-Limarobormes-13b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use chargoddard/Chronorctypus-Limarobormes-13b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/chargoddard/Chronorctypus-Limarobormes-13b

SGLang

How to use chargoddard/Chronorctypus-Limarobormes-13b 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 "chargoddard/Chronorctypus-Limarobormes-13b" \
    --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": "chargoddard/Chronorctypus-Limarobormes-13b",
		"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 "chargoddard/Chronorctypus-Limarobormes-13b" \
        --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": "chargoddard/Chronorctypus-Limarobormes-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

Five different instruction-tuned models (which I'm sure are intuitively obvious from the name) merged using the methodology described in Resolving Interference When Merging Models.

In theory this should retain more of the capabilites of the constituent models than a straight linear merge would. In my testing, it feels quite capable.

Base model used for the merge: TheBloke/Llama-2-13B-fp16

Models merged in:

Works quite well with Alpaca-style prompts:

### Instruction:

...

### Response:

The script I used to perform the merge is available here.

The command that produced this model:

python ties_merge.py TheBloke/Llama-2-13B-fp16 ./Chronorctypus-Limarobormes-13b --merge elinas/chronos-13b-v2 --merge Open-Orca/OpenOrca-Platypus2-13B --merge Oniichat/limarp-13b-merged --merge jondurbin/airoboros-l2-13b-gpt4-1.4.1 --merge NousResearch/Nous-Hermes-Llama2-13b --cuda

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	49.88
ARC (25-shot)	59.9
HellaSwag (10-shot)	82.75
MMLU (5-shot)	58.45
TruthfulQA (0-shot)	51.9
Winogrande (5-shot)	74.43
GSM8K (5-shot)	3.87
DROP (3-shot)	17.89