Instructions to use DS-Archive/llama-2-13b-chat-limarp-v2-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DS-Archive/llama-2-13b-chat-limarp-v2-merged with Transformers:

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

pipe = pipeline("text-generation", model="DS-Archive/llama-2-13b-chat-limarp-v2-merged")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DS-Archive/llama-2-13b-chat-limarp-v2-merged")
model = AutoModelForCausalLM.from_pretrained("DS-Archive/llama-2-13b-chat-limarp-v2-merged", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DS-Archive/llama-2-13b-chat-limarp-v2-merged with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/DS-Archive/llama-2-13b-chat-limarp-v2-merged

SGLang

How to use DS-Archive/llama-2-13b-chat-limarp-v2-merged 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 "DS-Archive/llama-2-13b-chat-limarp-v2-merged" \
    --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": "DS-Archive/llama-2-13b-chat-limarp-v2-merged",
		"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 "DS-Archive/llama-2-13b-chat-limarp-v2-merged" \
        --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": "DS-Archive/llama-2-13b-chat-limarp-v2-merged",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DS-Archive/llama-2-13b-chat-limarp-v2-merged with Docker Model Runner:
```
docker model run hf.co/DS-Archive/llama-2-13b-chat-limarp-v2-merged
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card: llama-2-13b-chat-limarp-v2-merged

This is a Llama 2-based model consisting of Llama 2 13b chat (https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) merged with LIMARP Lora v2 (https://huggingface.co/lemonilia/limarp-llama2-v2).

Requested by @dampf

Usage:

Intended to be prompted with the Alpaca instruction format of the LIMARP v2:

### Instruction:
Character's Persona: {bot character description}

User's Persona: {user character description}

Scenario: {what happens in the story}

Play the role of Character. You must engage in a roleplaying chat with User below this line. Do not write dialogues and narration for User. Character should respond with messages of medium length.

### Input:
Character: {utterance}

### Response:
User: {utterance}

Bias, Risks, and Limitations

The model will show biases similar to those observed in niche roleplaying forums on the Internet, besides those exhibited by the base model. It is not intended for supplying factual information or advice in any form.

Training Details

This model is a merge. Please refer to the link repositories of the base model and lora for details.

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