Instructions to use diffnamehard/Psyfighter2-Noromaid-ties-13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use diffnamehard/Psyfighter2-Noromaid-ties-13B with Transformers:

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

pipe = pipeline("text-generation", model="diffnamehard/Psyfighter2-Noromaid-ties-13B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("diffnamehard/Psyfighter2-Noromaid-ties-13B")
model = AutoModelForCausalLM.from_pretrained("diffnamehard/Psyfighter2-Noromaid-ties-13B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use diffnamehard/Psyfighter2-Noromaid-ties-13B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "diffnamehard/Psyfighter2-Noromaid-ties-13B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "diffnamehard/Psyfighter2-Noromaid-ties-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/diffnamehard/Psyfighter2-Noromaid-ties-13B

SGLang

How to use diffnamehard/Psyfighter2-Noromaid-ties-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 "diffnamehard/Psyfighter2-Noromaid-ties-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": "diffnamehard/Psyfighter2-Noromaid-ties-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 "diffnamehard/Psyfighter2-Noromaid-ties-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": "diffnamehard/Psyfighter2-Noromaid-ties-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use diffnamehard/Psyfighter2-Noromaid-ties-13B with Docker Model Runner:
```
docker model run hf.co/diffnamehard/Psyfighter2-Noromaid-ties-13B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Merge of KoboldAI/LLaMA2-13B-Psyfighter2 and NeverSleep/Noromaid-13b-v0.1.1

.yaml file for mergekit

models:
  - model: LLaMA2-13B-Psyfighter2
  - model: Noromaid-13b-v0.1.1
    parameters:
      density: 0.65
      weight: [0, 0.3, 0.7, 1]
merge_method: ties
base_model: LLaMA2-13B-Psyfighter2
parameters:
  normalize: true
  int8_mask: true
dtype: float16

Metric	Value
Avg.	59.47
ARC (25-shot)	61.86
HellaSwag (10-shot)	84.58
MMLU (5-shot)	57.04
TruthfulQA (0-shot)	50.66
Winogrande (5-shot)	75.37
GSM8K (5-shot)	27.29

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Safetensors

Model size

13B params

Tensor type

F16

Model tree for diffnamehard/Psyfighter2-Noromaid-ties-13B

Quantizations

1 model