Instructions to use s3nh/TinyLLama-4x1.1B-MoE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use s3nh/TinyLLama-4x1.1B-MoE with Transformers:

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

pipe = pipeline("text-generation", model="s3nh/TinyLLama-4x1.1B-MoE")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("s3nh/TinyLLama-4x1.1B-MoE")
model = AutoModelForCausalLM.from_pretrained("s3nh/TinyLLama-4x1.1B-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 s3nh/TinyLLama-4x1.1B-MoE with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "s3nh/TinyLLama-4x1.1B-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": "s3nh/TinyLLama-4x1.1B-MoE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/s3nh/TinyLLama-4x1.1B-MoE

SGLang

How to use s3nh/TinyLLama-4x1.1B-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 "s3nh/TinyLLama-4x1.1B-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": "s3nh/TinyLLama-4x1.1B-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 "s3nh/TinyLLama-4x1.1B-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": "s3nh/TinyLLama-4x1.1B-MoE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use s3nh/TinyLLama-4x1.1B-MoE with Docker Model Runner:
```
docker model run hf.co/s3nh/TinyLLama-4x1.1B-MoE
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Example usage:

from transformers import AutoModelForCausalLM
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("s3nh/TinyLLama-1.1B-MoE")
tokenizer = AutoTokenizer.from_pretrained("s3nh/TinyLLama-1.1B-MoE")

input_text =  """
###Input: You are a pirate. tell me a story about wrecked ship.
###Response:
""")

input_ids = tokenizer.encode(input_text, return_tensors='pt').to(device)
output = model.generate(inputs=input_ids,
                        max_length=max_length,
                        do_sample=True,
                        top_k=10,
                        temperature=0.7,
                        pad_token_id=tokenizer.eos_token_id,
                        attention_mask=input_ids.new_ones(input_ids.shape))
tokenizer.decode(output[0], skip_special_tokens=True)

This model was possible to create by tremendous work of mergekit developers. I decided to merge tinyLlama models to create mixture of experts. Config used as below:

"""base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
experts:
  - source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
    positive_prompts:
    - "chat"
    - "assistant"
    - "tell me"
    - "explain"
  - source_model: 78health/TinyLlama_1.1B-function-calling
    positive_prompts:
    - "code"
    - "python"
    - "javascript"
    - "programming"
    - "algorithm"
  - source_model: phanerozoic/Tiny-Pirate-1.1b-v0.1
    positive_prompts:
    - "storywriting"
    - "write"
    - "scene"
    - "story"
    - "character"
  - source_model: Tensoic/TinyLlama-1.1B-3T-openhermes
    positive_prompts:
    - "reason"
    - "provide"
    - "instruct"
    - "summarize"
    - "count"
"""

Downloads last month: 109

Safetensors

Model size

3B params

Tensor type

BF16

Model tree for s3nh/TinyLLama-4x1.1B-MoE

Tensoic/TinyLlama-1.1B-3T-openhermes

TinyLlama/TinyLlama-1.1B-Chat-v1.0

phanerozoic/Tiny-Pirate-1.1b-v0.1

Merge model

this model

Quantizations

1 model