Instructions to use impossibleexchange/seenoevil with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use impossibleexchange/seenoevil with Transformers:

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

pipe = pipeline("text-generation", model="impossibleexchange/seenoevil")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("impossibleexchange/seenoevil")
model = AutoModelForCausalLM.from_pretrained("impossibleexchange/seenoevil")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use impossibleexchange/seenoevil with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "impossibleexchange/seenoevil"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "impossibleexchange/seenoevil",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/impossibleexchange/seenoevil

SGLang

How to use impossibleexchange/seenoevil 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 "impossibleexchange/seenoevil" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "impossibleexchange/seenoevil",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "impossibleexchange/seenoevil" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "impossibleexchange/seenoevil",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use impossibleexchange/seenoevil with Docker Model Runner:
```
docker model run hf.co/impossibleexchange/seenoevil
```

impossibleexchange commited on Jul 15, 2024

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----
-license: other
-language:
-- en
----
-![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642265bc01c62c1e4102dc36/MY9tjLnEG5hOQOyKk06PK.jpeg)
-# Hathor_Tahsin [v-0.85] is designed to seamlessly integrate the qualities of creativity, intelligence, and robust performance.
-# Quants available Thanks to Bartowski <3: [GGUF Here](https://huggingface.co/bartowski/Hathor_Tahsin-L3-8B-v0.85-GGUF) [5bpw exl2 Here](https://huggingface.co/Nitral-AI/Hathor_Tahsin-L3-8B-v0.85-5bpw-exl2)
-# Recomended ST Presets: [Hathor Presets(Updated)](https://huggingface.co/Nitral-AI/Hathor_Presets/tree/main)
----
-# Note: Hathor_Tahsin [v0.85] is trained on 3 epochs of Private RP, STEM (Intruction/Dialogs), Opus instructons, mixture light/classical novel data, roleplaying chat pairs over llama 3 8B instruct.
-# Additional Note's: (Based on Hathor_Fractionate-v0.5 instead of Hathor_Aleph-v0.72, should be less repetitive than either 0.72 or 0.8)