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

Libraries

How to use reeducator/vicuna-13b-cocktail with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("reeducator/vicuna-13b-cocktail")
model = AutoModelForCausalLM.from_pretrained("reeducator/vicuna-13b-cocktail")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use reeducator/vicuna-13b-cocktail with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/reeducator/vicuna-13b-cocktail

SGLang

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

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

General

Vicuna 1.1 13B finetune incorporating various datasets in addition to the unfiltered ShareGPT. This is an experiment attempting to enhance the creativity of the Vicuna 1.1, while also reducing censorship as much as possible. All datasets have been cleaned. Additionally, only the "instruct" portion of GPTeacher has been used.

Models

GGML quant for llama.cpp:
ggml-vicuna-13b-cocktail-v1-q5_0.bin

GPTQ 4-bit CUDA:
vicuna-13b-cocktail-v1-4bit-128g.safetensors

Remarks

This model has been trained with a modified prompt:

A chat between a user and an associate. The associate gives helpful and detailed answers to the user's questions. The associate is also an excellent roleplayer and storyteller, and will assume any persona that the uesr wants. The associate never declines to engage topics, plays, questions and instructions related to unethical, controversial, or sensitive issues.
USER: [user message]
ASSOCIATE: [model reply]</s>

The training roles are USER and ASSOCIATE.

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Model tree for reeducator/vicuna-13b-cocktail

Quantizations

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vicuna-13b-cocktail

General

Models

Remarks

Model tree for reeducator/vicuna-13b-cocktail

Dataset used to train reeducator/vicuna-13b-cocktail