Instructions to use Undi95/ReMM-SLERP-L2-13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Undi95/ReMM-SLERP-L2-13B with Transformers:

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

pipe = pipeline("text-generation", model="Undi95/ReMM-SLERP-L2-13B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Undi95/ReMM-SLERP-L2-13B")
model = AutoModelForCausalLM.from_pretrained("Undi95/ReMM-SLERP-L2-13B", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Undi95/ReMM-SLERP-L2-13B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Undi95/ReMM-SLERP-L2-13B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Undi95/ReMM-SLERP-L2-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Undi95/ReMM-SLERP-L2-13B

SGLang

How to use Undi95/ReMM-SLERP-L2-13B 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 "Undi95/ReMM-SLERP-L2-13B" \
    --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": "Undi95/ReMM-SLERP-L2-13B",
		"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 "Undi95/ReMM-SLERP-L2-13B" \
        --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": "Undi95/ReMM-SLERP-L2-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Undi95/ReMM-SLERP-L2-13B with Docker Model Runner:
```
docker model run hf.co/Undi95/ReMM-SLERP-L2-13B
```

cherry0328 commited on Jan 7

Commit

6fe7533

verified ·

1 Parent(s): 4c21dc6

Update README.md

Browse files

The model is fantastic! We would like to contribute by updating the README to include the base_model information. This is to address the missing model card details. Thank you for your consideration!

Files changed (1) hide show

README.md +6 -1

README.md CHANGED Viewed

@@ -1,5 +1,10 @@
 ---
 license: cc-by-nc-4.0
 ---
 Re:MythoMax (ReMM) is a recreation trial of the original [MythoMax-L2-B13](https://huggingface.co/Gryphe/MythoMax-L2-13b) with updated models.
@@ -70,4 +75,4 @@ Detailed results can be found [here](https://huggingface.co/datasets/open-llm-le
 | TruthfulQA (0-shot)   | 51.97   |
 | Winogrande (5-shot)   | 75.22   |
 | GSM8K (5-shot)        | 9.17        |
-| DROP (3-shot)         | 20.76         |

 ---
 license: cc-by-nc-4.0
+base_model:
+- TheBloke/Llama-2-13B-fp16
+- jondurbin/airoboros-l2-13b-2.1
+- NousResearch/Nous-Hermes-Llama2-13b
+- Undi95/ReML-L2-13B
 ---
 Re:MythoMax (ReMM) is a recreation trial of the original [MythoMax-L2-B13](https://huggingface.co/Gryphe/MythoMax-L2-13b) with updated models.
 | TruthfulQA (0-shot)   | 51.97   |
 | Winogrande (5-shot)   | 75.22   |
 | GSM8K (5-shot)        | 9.17        |
+| DROP (3-shot)         | 20.76         |