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

Libraries

How to use reeducator/bluemoonrp-13b with Transformers:

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

pipe = pipeline("text-generation", model="reeducator/bluemoonrp-13b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("reeducator/bluemoonrp-13b")
model = AutoModelForCausalLM.from_pretrained("reeducator/bluemoonrp-13b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use reeducator/bluemoonrp-13b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/reeducator/bluemoonrp-13b

SGLang

How to use reeducator/bluemoonrp-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 "reeducator/bluemoonrp-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": "reeducator/bluemoonrp-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 "reeducator/bluemoonrp-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": "reeducator/bluemoonrp-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use reeducator/bluemoonrp-13b with Docker Model Runner:
```
docker model run hf.co/reeducator/bluemoonrp-13b
```

reeducator commited on May 7, 2023

Commit

94c60f8

1 Parent(s): 95554e4

Update readme

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@@ -7,10 +7,8 @@ language:
 ## General
 Bluemoon roleplay finetune of LLaMA 13B (2 roleplayers only).
-*Note.* This is an intermediate version which has not been trained for sufficiently long to reach a satisfactory final loss value. The repository will be updated later with a model trained over additional epochs.
 ## Models
-Two models are provided, labeled (1) `4k-epoch6` and (2) `epoch3`. In case of the (1), the training is extended over more epochs to reduce the high training loss observed in (2). This release also tests a longer 4k context token size achieved with AliBi.
 *GGML 4-bit for llama.cpp*<br/>

 ## General
 Bluemoon roleplay finetune of LLaMA 13B (2 roleplayers only).
 ## Models
+Two models are provided, labeled (1) `4k-epoch6` and (2) `epoch3` (other branch). In case of the (1), the training is extended over more epochs to reduce the high training loss observed in (2). This release also tests a longer 4k context token size achieved with AliBi.
 *GGML 4-bit for llama.cpp*<br/>