Instructions to use srinivasbilla/tinymix-8x1b-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use srinivasbilla/tinymix-8x1b-chat with Transformers:

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

pipe = pipeline("text-generation", model="srinivasbilla/tinymix-8x1b-chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("srinivasbilla/tinymix-8x1b-chat")
model = AutoModelForCausalLM.from_pretrained("srinivasbilla/tinymix-8x1b-chat")
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 srinivasbilla/tinymix-8x1b-chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "srinivasbilla/tinymix-8x1b-chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "srinivasbilla/tinymix-8x1b-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/srinivasbilla/tinymix-8x1b-chat

SGLang

How to use srinivasbilla/tinymix-8x1b-chat 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 "srinivasbilla/tinymix-8x1b-chat" \
    --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": "srinivasbilla/tinymix-8x1b-chat",
		"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 "srinivasbilla/tinymix-8x1b-chat" \
        --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": "srinivasbilla/tinymix-8x1b-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use srinivasbilla/tinymix-8x1b-chat with Docker Model Runner:
```
docker model run hf.co/srinivasbilla/tinymix-8x1b-chat
```

TinyMix-8x1b-Chat

This is a MoE-ification of TinyLlama/TinyLlama-1.1B-Chat-v1.0 using the Mixtral branch of mergekit

The Goal was to MoE-fy the TinyLlama model and then use this as a base model to finetune from. The intuition being finetuning 8x1b should give better performance than finetuning 1b by itself.

More work coming!

Chat Template

def make_prompt(instruction):
  return f"<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n"

llm.generate(make_prompt('What is quantum tunneling?'))

Mergekit Config

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: [""]
  - source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
    positive_prompts: [""]
  - source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
    positive_prompts: [""]
  - source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
    positive_prompts: [""]
  - source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
    positive_prompts: [""]
  - source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
    positive_prompts: [""]
  - source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
    positive_prompts: [""]
  - source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
    positive_prompts: [""]

Eval

Thanks to u/mhenrichsen for the HellaSwag score

|  Tasks  |Version|Filter|n-shot| Metric |Value |   |Stderr|

|---------|-------|------|-----:|--------|-----:|---|-----:|

|hellaswag|Yaml   |none  |     0|acc     |0.4657|±  |0.0050|

|         |       |none  |     0|acc\_norm|0.6042|±  |0.0049|

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Safetensors

Model size

6B params

Tensor type

BF16