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
```

Will quant this (and others) when all the llamacpp fixes are in

by bartowski - opened Jul 25, 2024

Discussion

bartowski

Jul 25, 2024

Since there's still pending fixes (https://github.com/ggerganov/llama.cpp/pull/8676) will be holding off on most llama 3.1 quants, just so you know :)

Nitral-AI

The Chaotic Neutrals org Jul 25, 2024

Since there's still pending fixes (https://github.com/ggerganov/llama.cpp/pull/8676) will be holding off on most llama 3.1 quants, just so you know :)

Appreciate the heads up my dude!

Joseph717171

Jul 25, 2024

This comment has been hidden

Reiterate3680

Jul 26, 2024

I made some experimental quants with the PR pulled in. Koboldcpp frankenfork has support for models with the PR.
I ran the BABIlong 32k qa2 dataset prompts on the broken quant and new, the unfixed one just repeats tokens but the new quant indeed at least produces sane responses at Q5. I've updated the linked repo to point to those

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