Instructions to use JayhC/L3-ChaoticSoliloquy-v2-4x8B-test-8bpw-h8-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JayhC/L3-ChaoticSoliloquy-v2-4x8B-test-8bpw-h8-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="JayhC/L3-ChaoticSoliloquy-v2-4x8B-test-8bpw-h8-exl2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("JayhC/L3-ChaoticSoliloquy-v2-4x8B-test-8bpw-h8-exl2")
model = AutoModelForMultimodalLM.from_pretrained("JayhC/L3-ChaoticSoliloquy-v2-4x8B-test-8bpw-h8-exl2")
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 Settings

vLLM

How to use JayhC/L3-ChaoticSoliloquy-v2-4x8B-test-8bpw-h8-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JayhC/L3-ChaoticSoliloquy-v2-4x8B-test-8bpw-h8-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JayhC/L3-ChaoticSoliloquy-v2-4x8B-test-8bpw-h8-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/JayhC/L3-ChaoticSoliloquy-v2-4x8B-test-8bpw-h8-exl2

SGLang

How to use JayhC/L3-ChaoticSoliloquy-v2-4x8B-test-8bpw-h8-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 "JayhC/L3-ChaoticSoliloquy-v2-4x8B-test-8bpw-h8-exl2" \
    --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": "JayhC/L3-ChaoticSoliloquy-v2-4x8B-test-8bpw-h8-exl2",
		"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 "JayhC/L3-ChaoticSoliloquy-v2-4x8B-test-8bpw-h8-exl2" \
        --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": "JayhC/L3-ChaoticSoliloquy-v2-4x8B-test-8bpw-h8-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use JayhC/L3-ChaoticSoliloquy-v2-4x8B-test-8bpw-h8-exl2 with Docker Model Runner:
```
docker model run hf.co/JayhC/L3-ChaoticSoliloquy-v2-4x8B-test-8bpw-h8-exl2
```

8bpw/h8 exl2 quantization of xxx777xxxASD/L3-ChaoticSoliloquy-v2-4x8B-test using default exllamav2 calibration dataset.

ORIGINAL CARD:

(Maybe i'll change the waifu picture later.)

TRY 1.5 or 1.0 INSTEAD AND CHECK IF THEY WORK BETTER, I DIDN'T TEST THIS VERSION BEFORE PUBLISHING

GGUF quants

Experimental RP-oriented MoE, the idea was to get a model that would be equal to or better than the Mixtral 8x7B and it's finetunes in RP/ERP tasks.

The model has totally 25B parameters, of which ~13B are active.

Please feedback me if it's more stable than the previous version

Llama 3 ChaoticSoliloquy-v2-4x8B test

base_model: L3_ChaosMaid_8B
gate_mode: random
dtype: bfloat16
experts_per_token: 2
experts:
  - source_model: ChaoticNeutrals_Poppy_Porpoise-0.72-L3-8B
  - source_model: L3_ChaosMaid_8B
  - source_model: openlynn_Llama-3-Soliloquy-8B-v2
  - source_model: Sao10K_L3-Solana-8B-v1

Models used

Difference

Update from ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B to ChaoticNeutrals/Poppy_Porpoise-0.72-L3-8B
Update from openlynn/Llama-3-Soliloquy-8B to openlynn/Llama-3-Soliloquy-8B-v2
Change - NeverSleep/Llama-3-Lumimaid-8B-v0.1 to L3-ChaosMaid-8B

L3 ChaosMaid-8B

models:
  - model: jeiku_Chaos_RP_l3_8B
    # No parameters necessary for base model
  - model: NeverSleep_Llama-3-Lumimaid-8B-v0.1
    parameters:
      density: 0.5
      weight: 0.5
merge_method: dare_ties
base_model: jeiku_Chaos_RP_l3_8B
parameters:
  int8_mask: true
dtype: bfloat16

Vision

llama3_mmproj

Prompt format: Llama 3

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