Instructions to use Isotonic/Mixnueza-Chat-6x32M-MoE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Isotonic/Mixnueza-Chat-6x32M-MoE with Transformers:

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

pipe = pipeline("text-generation", model="Isotonic/Mixnueza-Chat-6x32M-MoE")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Isotonic/Mixnueza-Chat-6x32M-MoE")
model = AutoModelForCausalLM.from_pretrained("Isotonic/Mixnueza-Chat-6x32M-MoE")
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 Isotonic/Mixnueza-Chat-6x32M-MoE with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Isotonic/Mixnueza-Chat-6x32M-MoE"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Isotonic/Mixnueza-Chat-6x32M-MoE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Isotonic/Mixnueza-Chat-6x32M-MoE

SGLang

How to use Isotonic/Mixnueza-Chat-6x32M-MoE 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 "Isotonic/Mixnueza-Chat-6x32M-MoE" \
    --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": "Isotonic/Mixnueza-Chat-6x32M-MoE",
		"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 "Isotonic/Mixnueza-Chat-6x32M-MoE" \
        --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": "Isotonic/Mixnueza-Chat-6x32M-MoE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Isotonic/Mixnueza-Chat-6x32M-MoE with Docker Model Runner:
```
docker model run hf.co/Isotonic/Mixnueza-Chat-6x32M-MoE
```

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Mixnueza-6x32M-MoE

Mixnueza-6x32M-MoE is a Mixure of Experts (MoE) made with the following models using LazyMergekit:

6 X Felladrin/Minueza-32M-Chat
Num Experts Per Token : 3
Evaluation Results

💻 Usage

from transformers import pipeline

generate = pipeline("text-generation", "Isotonic/Mixnueza-6x32M-MoE")

messages = [
    {
        "role": "system",
        "content": "You are a helpful assistant who answers the user's questions with details and curiosity.",
    },
    {
        "role": "user",
        "content": "What are some potential applications for quantum computing?",
    },
]

prompt = generate.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

output = generate(
    prompt,
    max_new_tokens=256,
    do_sample=True,
    temperature=0.65,
    top_k=35,
    top_p=0.55,
    repetition_penalty=1.176,
)

print(output[0]["generated_text"])

Downloads last month: 92

Safetensors

Model size

83.9M params

Tensor type

F32

Datasets used to train Isotonic/Mixnueza-Chat-6x32M-MoE

Collection including Isotonic/Mixnueza-Chat-6x32M-MoE

🧪⛙ Merged Models

Collection

A collection of merged models. • 11 items • Updated Sep 25, 2024 • 2