Instructions to use R136a1/ReMM-v2.2-L2-13B-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use R136a1/ReMM-v2.2-L2-13B-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="R136a1/ReMM-v2.2-L2-13B-exl2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("R136a1/ReMM-v2.2-L2-13B-exl2")
model = AutoModelForCausalLM.from_pretrained("R136a1/ReMM-v2.2-L2-13B-exl2")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use R136a1/ReMM-v2.2-L2-13B-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "R136a1/ReMM-v2.2-L2-13B-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "R136a1/ReMM-v2.2-L2-13B-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/R136a1/ReMM-v2.2-L2-13B-exl2

SGLang

How to use R136a1/ReMM-v2.2-L2-13B-exl2 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 "R136a1/ReMM-v2.2-L2-13B-exl2" \
    --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": "R136a1/ReMM-v2.2-L2-13B-exl2",
		"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 "R136a1/ReMM-v2.2-L2-13B-exl2" \
        --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": "R136a1/ReMM-v2.2-L2-13B-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use R136a1/ReMM-v2.2-L2-13B-exl2 with Docker Model Runner:
```
docker model run hf.co/R136a1/ReMM-v2.2-L2-13B-exl2
```

EXL2 Quantization of Undi95's's ReMM-v2.2-L2-13B.

Model details

Quantized at 5.33bpw

Prompt Format

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:

Original model card:

Re:MythoMax v2.2 (ReMM v2.2) is a recreation trial of the original MythoMax-L2-B13 with updated models.

This merge use SLERP merging method to merge ReML v2.2 and Huginn v1.2.

Explaination :

- ReML-v2.2: (Chronos-Beluga v2/Hermes/Airboros 2.2)
=> Keeping The-Face-Of-Goonery/Chronos-Beluga-v2-13bfp16
=> Replacing jondurbin/airoboros-l2-13b-2.2 by jondurbin/airoboros-l2-13b-2.2.1 (last version)
=> Keeping NousResearch/Nous-Hermes-Llama2-13b

With that :
- ReMM-v2.2: (ReML/Huginn v1.2)
=> Replacing ReMM by the one above (ReML v2.1)
=> Keeping The-Face-Of-Goonery/Huginn-13b-v1.2 (hottest)

Description

This repo contains fp16 files of ReMM v2.1, a recreation of the original MythoMax, but updated and merged with SLERP.

Models used

The-Face-Of-Goonery/Chronos-Beluga-v2-13bfp16
jondurbin/airoboros-l2-13b-2.2.1
NousResearch/Nous-Hermes-Llama2-13b
The-Face-Of-Goonery/Huginn-13b-v1.2
ReML-v2.1-L2-13B (Private recreation trial of an updated Mythologic-L2-13B)

Prompt template: Alpaca

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:

Special thanks to Sushi.

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