Instructions to use ChaoticNeutrals/Sekhmet_Bet-L3.1-8B-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ChaoticNeutrals/Sekhmet_Bet-L3.1-8B-v0.2 with Transformers:

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

pipe = pipeline("text-generation", model="ChaoticNeutrals/Sekhmet_Bet-L3.1-8B-v0.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ChaoticNeutrals/Sekhmet_Bet-L3.1-8B-v0.2")
model = AutoModelForCausalLM.from_pretrained("ChaoticNeutrals/Sekhmet_Bet-L3.1-8B-v0.2", device_map="auto")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ChaoticNeutrals/Sekhmet_Bet-L3.1-8B-v0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ChaoticNeutrals/Sekhmet_Bet-L3.1-8B-v0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ChaoticNeutrals/Sekhmet_Bet-L3.1-8B-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ChaoticNeutrals/Sekhmet_Bet-L3.1-8B-v0.2

SGLang

How to use ChaoticNeutrals/Sekhmet_Bet-L3.1-8B-v0.2 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 "ChaoticNeutrals/Sekhmet_Bet-L3.1-8B-v0.2" \
    --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": "ChaoticNeutrals/Sekhmet_Bet-L3.1-8B-v0.2",
		"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 "ChaoticNeutrals/Sekhmet_Bet-L3.1-8B-v0.2" \
        --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": "ChaoticNeutrals/Sekhmet_Bet-L3.1-8B-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ChaoticNeutrals/Sekhmet_Bet-L3.1-8B-v0.2 with Docker Model Runner:
```
docker model run hf.co/ChaoticNeutrals/Sekhmet_Bet-L3.1-8B-v0.2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Sekhmet_Bet [v-0.2] - Designed to provide robust solutions to complex problems while offering support and insightful guidance.

GGUF Quant's available thanks to: Reiterate3680 <3 GGUF Here

Additional GGUF Quant's available thanks to: Bartowski <3 GGUF Here

EXL2 Quant: 5bpw Exl2 Here

Recomended ST Presets: Sekhmet Presets(Same as Hathor's)

Training Note: Sekhmet_Bet [v0.2] is trained on: 1 epoch of Private - Hathor_0.85 Instructions, small subset of creative writing data, roleplaying chat pairs over Sekhmet_Aleph-L3.1-8B-v0.1

Additional Note's: This model was quickly assembled to provide users with a relatively uncensored alternative to L3.1 Instruct, featuring extended context capabilities. (As I will soon be on a short hiatus) The learning rate for this model was set rather low. Therefore, I do not expect it to match the performance levels demonstrated by Hathor versions 0.5, 0.85, or 1.0.

Downloads last month: 25

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for ChaoticNeutrals/Sekhmet_Bet-L3.1-8B-v0.2

Quantizations

6 models

ChaoticNeutrals
/

Sekhmet_Bet-L3.1-8B-v0.2