Instructions to use SE6446/Tiny-llamix_2x1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SE6446/Tiny-llamix_2x1B with Transformers:

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

pipe = pipeline("text-generation", model="SE6446/Tiny-llamix_2x1B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SE6446/Tiny-llamix_2x1B")
model = AutoModelForCausalLM.from_pretrained("SE6446/Tiny-llamix_2x1B")
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 SE6446/Tiny-llamix_2x1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SE6446/Tiny-llamix_2x1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SE6446/Tiny-llamix_2x1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SE6446/Tiny-llamix_2x1B

SGLang

How to use SE6446/Tiny-llamix_2x1B 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 "SE6446/Tiny-llamix_2x1B" \
    --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": "SE6446/Tiny-llamix_2x1B",
		"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 "SE6446/Tiny-llamix_2x1B" \
        --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": "SE6446/Tiny-llamix_2x1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SE6446/Tiny-llamix_2x1B with Docker Model Runner:
```
docker model run hf.co/SE6446/Tiny-llamix_2x1B
```

Tiny-llama

Model Description

Tiny llamix is a model built from TinyLlama using Charles Goddard's mergekit on the mixtral branch. Though techincally a mixtral model it can be plugged into most llama implementation (Maybe...). The model uses Tiny-llama's tokenizer and works on the same prompt format.

This model is a proof-of-concept and might not yield necessarily better outputs. (IDK haven't tested it...)

Configuration

base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
gate_mode: hidden
dtype: bfloat16 
experts:
  - source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
    positive_prompts:
      - "M1"
  - source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
    positive_prompts:
     - "M2"

Usage

It can be used like any other model

from transformers import AutoModelForCausalLM, AutoTokenizer
#load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("SE6446/Tiny-llamix").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("SE6446/Tiny-llamix")
#write and tokenize prompt
instruction = '''<|system|>\nYou are a chatbot who can help code!</s>
<|user|> Write me a function to calculate the first 10 digits of the fibonacci sequence in Python and print it out to the CLI.</s>
<|assistant|>'''
inputs = tokenizer(instruction, return_tensors="pt", return_attention_mask=False).to("cuda")

#generate
outputs = model.generate(**inputs, max_length=200)

#print
text = tokenizer.batch_decode(outputs)[0]
print(text)

Acknowledgements

To Charles Goddard for creating the tool and for explaining it in his blog in a way a buffoon like me could understand.

To TinyLlama for providing the model as open source!

Downloads last month: 9

Safetensors

Model size

2B params

Tensor type

F32