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

Libraries

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

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

pipe = pipeline("text-generation", model="srinivasbilla/tinymix-8x1b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use srinivasbilla/tinymix-8x1b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "srinivasbilla/tinymix-8x1b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "srinivasbilla/tinymix-8x1b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use srinivasbilla/tinymix-8x1b 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" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "srinivasbilla/tinymix-8x1b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "srinivasbilla/tinymix-8x1b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

TinyMix-8x1b

This is a MoE-ification of TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T using the Mixtral branch of mergekit

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

More work coming!

Inference Template

This is a merge of the base model, so treat it like a completion.

llm.generate('Quantum Tunneling is')

Mergekit Config

base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
gate_mode: hidden
dtype: bfloat16
experts:
  - source_model: /TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
    positive_prompts: [""]
  - source_model: /TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
    positive_prompts: [""]
  - source_model: /TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
    positive_prompts: [""]
  - source_model: /TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
    positive_prompts: [""]
  - source_model: /TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
    positive_prompts: [""]
  - source_model: /TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
    positive_prompts: [""]
  - source_model: /TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
    positive_prompts: [""]
  - source_model: /TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
    positive_prompts: [""]

Eval

Thanks to u/mhenrichsen for thr HellaSwag score

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

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

|hellaswag|Yaml   |none  |     0|acc     |0.4659|±  |0.0050|

|         |       |none  |     0|acc\_norm|0.6044|±  |0.0049|

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Safetensors

Model size

6B params

Tensor type

BF16