Instructions to use intervitens-archive/WinterGoddess_1.4x-70B-L2-4.65bpw-h6-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use intervitens-archive/WinterGoddess_1.4x-70B-L2-4.65bpw-h6-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="intervitens-archive/WinterGoddess_1.4x-70B-L2-4.65bpw-h6-exl2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("intervitens-archive/WinterGoddess_1.4x-70B-L2-4.65bpw-h6-exl2")
model = AutoModelForCausalLM.from_pretrained("intervitens-archive/WinterGoddess_1.4x-70B-L2-4.65bpw-h6-exl2")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use intervitens-archive/WinterGoddess_1.4x-70B-L2-4.65bpw-h6-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "intervitens-archive/WinterGoddess_1.4x-70B-L2-4.65bpw-h6-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "intervitens-archive/WinterGoddess_1.4x-70B-L2-4.65bpw-h6-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/intervitens-archive/WinterGoddess_1.4x-70B-L2-4.65bpw-h6-exl2

SGLang

How to use intervitens-archive/WinterGoddess_1.4x-70B-L2-4.65bpw-h6-exl2 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 "intervitens-archive/WinterGoddess_1.4x-70B-L2-4.65bpw-h6-exl2" \
    --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": "intervitens-archive/WinterGoddess_1.4x-70B-L2-4.65bpw-h6-exl2",
		"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 "intervitens-archive/WinterGoddess_1.4x-70B-L2-4.65bpw-h6-exl2" \
        --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": "intervitens-archive/WinterGoddess_1.4x-70B-L2-4.65bpw-h6-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use intervitens-archive/WinterGoddess_1.4x-70B-L2-4.65bpw-h6-exl2 with Docker Model Runner:
```
docker model run hf.co/intervitens-archive/WinterGoddess_1.4x-70B-L2-4.65bpw-h6-exl2
```

Quantized using 200 samples of 4096 tokens from PIPPA dataset.

Original model link: WinterGoddess-1.4x-70B-L2

Original model README below.

Winter Goddess - A 70B L2 Model for General use, or for Roleplay.

I wanted a Smart Model that is Capable of following Instructions, while being able to (e)RP effectively. Sort of like 1.3, but better.

I merged some models as a base, and had tuned on top of it afterwards.

I personally think this mogs Euryale 1.3, but ymmv.

Prompt Format - Alpaca

### Instruction:
<Prompt>

### Response:

### Instruction:
<Prompt>

### Input:
<Insert Context Here>

### Response:

42. A 25-year-old female has been struck in the right eye with a pipe. She has a ruptured right globe, an orbital fracture and no other obvious injury. You should bandage:

A) The right eye tightly
B) Both eyes loosely
C) The right eye loosely
D) Both eyes tightly

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