Instructions to use Blackroot/TensorProduct-Microllama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Blackroot/TensorProduct-Microllama with Transformers:

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

pipe = pipeline("text-generation", model="Blackroot/TensorProduct-Microllama")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Blackroot/TensorProduct-Microllama", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Blackroot/TensorProduct-Microllama with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Blackroot/TensorProduct-Microllama

SGLang

How to use Blackroot/TensorProduct-Microllama 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 "Blackroot/TensorProduct-Microllama" \
    --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": "Blackroot/TensorProduct-Microllama",
		"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 "Blackroot/TensorProduct-Microllama" \
        --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": "Blackroot/TensorProduct-Microllama",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Blackroot/TensorProduct-Microllama with Docker Model Runner:
```
docker model run hf.co/Blackroot/TensorProduct-Microllama
```

Add model card metadata: pipeline tag, library name, link to paper, and link to code repository.

by nielsr HF Staff - opened Feb 17, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

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README.md +10 -0

README.md CHANGED Viewed

@@ -1,3 +1,9 @@
 From scratch pretraining on english only no synthetic data, no code, 3 epochs of 1 gig of data for the ~125M param model.
 Test network using [Tensor Product Attention](https://arxiv.org/abs/2501.06425). Other than some alterations to the attention, such as 16 heads insted of 9 and using TPA, this is the same setup as https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct
@@ -21,3 +27,7 @@ One of the primary reported benefits for TPA are for inference which are not rea
 - Final Train Perplexity: 20.95
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/637f3b03932a61b89aefbf5c/8iTSQFvwgbn5or6LdNT9G.png)

+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+---
 From scratch pretraining on english only no synthetic data, no code, 3 epochs of 1 gig of data for the ~125M param model.
 Test network using [Tensor Product Attention](https://arxiv.org/abs/2501.06425). Other than some alterations to the attention, such as 16 heads insted of 9 and using TPA, this is the same setup as https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct
 - Final Train Perplexity: 20.95
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/637f3b03932a61b89aefbf5c/8iTSQFvwgbn5or6LdNT9G.png)
+# Code
+The code is available at: https://github.com/tensorgi/T6.