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

Libraries

How to use Dans-DiscountModels/Dans-RetroRodeo-13b with Transformers:

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

pipe = pipeline("text-generation", model="Dans-DiscountModels/Dans-RetroRodeo-13b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Dans-DiscountModels/Dans-RetroRodeo-13b")
model = AutoModelForCausalLM.from_pretrained("Dans-DiscountModels/Dans-RetroRodeo-13b")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Dans-DiscountModels/Dans-RetroRodeo-13b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Dans-DiscountModels/Dans-RetroRodeo-13b

SGLang

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

Docker Model Runner
How to use Dans-DiscountModels/Dans-RetroRodeo-13b with Docker Model Runner:
```
docker model run hf.co/Dans-DiscountModels/Dans-RetroRodeo-13b
```

PocketDoc commited on Sep 20, 2023

Commit

0375cd3

1 Parent(s): 670bf96

Update README.md

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@@ -119,7 +119,7 @@ Once outside, I headed straight for the nearest tavern. There, I met up with my
 Now, I'm waiting impatiently for them to arrive. I wonder what kind of adventures lie ahead for me...
 ```
-### Some quick and  dirty training details:
 - [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="150" height="24"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
 - Sequence length: 4096
 - \# of epochs: 4
@@ -129,11 +129,11 @@ Now, I'm waiting impatiently for them to arrive. I wonder what kind of adventure
 - PEFT R/A: 32/32
-### Credits:
-#### Holodeck-1:
 Thank you to Mr. Seeker and the Kobold AI team for the wonderful model Holodeck-1
 [Holodeck-1 Huggingface page](https://huggingface.co/KoboldAI/LLAMA2-13B-Holodeck-1)
-#### Skein Text Adventure Data:
 Thank you to the [Kobold AI](https://huggingface.co/KoboldAI) community for curating the Skein dataset, which is pivotal to this model's capabilities.

 Now, I'm waiting impatiently for them to arrive. I wonder what kind of adventures lie ahead for me...
 ```
+# Some quick and  dirty training details:
 - [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="150" height="24"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
 - Sequence length: 4096
 - \# of epochs: 4
 - PEFT R/A: 32/32
+# Credits:
+### Holodeck-1:
 Thank you to Mr. Seeker and the Kobold AI team for the wonderful model Holodeck-1
 [Holodeck-1 Huggingface page](https://huggingface.co/KoboldAI/LLAMA2-13B-Holodeck-1)
+### Skein Text Adventure Data:
 Thank you to the [Kobold AI](https://huggingface.co/KoboldAI) community for curating the Skein dataset, which is pivotal to this model's capabilities.