Instructions to use DaveGergern/PsyfighterTwo-ErebusThree-SlerpThree with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DaveGergern/PsyfighterTwo-ErebusThree-SlerpThree with Transformers:

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

pipe = pipeline("text-generation", model="DaveGergern/PsyfighterTwo-ErebusThree-SlerpThree")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DaveGergern/PsyfighterTwo-ErebusThree-SlerpThree")
model = AutoModelForCausalLM.from_pretrained("DaveGergern/PsyfighterTwo-ErebusThree-SlerpThree")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DaveGergern/PsyfighterTwo-ErebusThree-SlerpThree with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/DaveGergern/PsyfighterTwo-ErebusThree-SlerpThree

SGLang

How to use DaveGergern/PsyfighterTwo-ErebusThree-SlerpThree 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 "DaveGergern/PsyfighterTwo-ErebusThree-SlerpThree" \
    --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": "DaveGergern/PsyfighterTwo-ErebusThree-SlerpThree",
		"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 "DaveGergern/PsyfighterTwo-ErebusThree-SlerpThree" \
        --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": "DaveGergern/PsyfighterTwo-ErebusThree-SlerpThree",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DaveGergern/PsyfighterTwo-ErebusThree-SlerpThree with Docker Model Runner:
```
docker model run hf.co/DaveGergern/PsyfighterTwo-ErebusThree-SlerpThree
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

slices:
- sources:
  - model: KoboldAI/LLaMA2-13B-Psyfighter2
    layer_range:
    - 0
    - 32
  - model: KoboldAI/LLaMA2-13B-Erebus-v3
    layer_range:
    - 0
    - 32
merge_method: slerp
base_model: KoboldAI/LLaMA2-13B-Psyfighter2
parameters:
  t:
  - filter: self_attn
    value:
    - 0
    - 0.5
    - 0.3
    - 0.7
    - 1
  - filter: mlp
    value:
    - 1
    - 0.5
    - 0.7
    - 0.3
    - 0
  - value: 0.5
dtype: bfloat16

Downloads last month: 1

Safetensors

Model size

10B params

Tensor type

BF16

Model tree for DaveGergern/PsyfighterTwo-ErebusThree-SlerpThree

KoboldAI/LLaMA2-13B-Erebus-v3

KoboldAI/LLaMA2-13B-Psyfighter2

Merge model

this model

Quantizations

2 models