Instructions to use R136a1/Ayam-2x8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use R136a1/Ayam-2x8B with Transformers:

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

pipe = pipeline("text-generation", model="R136a1/Ayam-2x8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("R136a1/Ayam-2x8B")
model = AutoModelForCausalLM.from_pretrained("R136a1/Ayam-2x8B")
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 R136a1/Ayam-2x8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "R136a1/Ayam-2x8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "R136a1/Ayam-2x8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/R136a1/Ayam-2x8B

SGLang

How to use R136a1/Ayam-2x8B 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 "R136a1/Ayam-2x8B" \
    --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": "R136a1/Ayam-2x8B",
		"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 "R136a1/Ayam-2x8B" \
        --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": "R136a1/Ayam-2x8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use R136a1/Ayam-2x8B with Docker Model Runner:
```
docker model run hf.co/R136a1/Ayam-2x8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

art by myself using AI

Ayam 2x8B

Another MoE, this time using L3.
Recipe: Sao's Stheno-v3.2 + L3 8B Instruct.

This model is intended for personal use but I think it's really good and worth sharing. Stheno-v3.2 is, as you probably know well, very good. In creative writing, RP and ERP it's far better than L3 Instruct and to be honest it's the best L3 finetunes I've tried so far so yeah I liked it very much. But while playing with it, I feel like the model is (a bit) dumber than L3 Instruct. It can't understand complex scenario well and confused in multi-char scenario, at least that what I was experiencing. So yeah, I tried to improve its intelligence while still preserving its creativity.

Why MoE not merge?
Well... 2 model working together is always better than merging it into one. And (surprisingly) the result is far exceeded my expectations.
And I think merging models sometimes can damage It's quality.

Testing condition (using SillyTavern):
Context and Instruct: Llama 3 Instruct.
Sampler:

Temperature : 1.15
MinP : 0.075
TopK : 50
Other is disabled.

Switch

BF16 - GGUF

Downloads last month: 23

Safetensors

Model size

14B params

Tensor type

BF16

Model tree for R136a1/Ayam-2x8B

Quantizations

3 models