Instructions to use Thox-ai/ThoxMicroprocessor-8M-Base-TinyStories with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Thox-ai/ThoxMicroprocessor-8M-Base-TinyStories with Transformers:

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

pipe = pipeline("text-generation", model="Thox-ai/ThoxMicroprocessor-8M-Base-TinyStories", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Thox-ai/ThoxMicroprocessor-8M-Base-TinyStories", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Thox-ai/ThoxMicroprocessor-8M-Base-TinyStories with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Thox-ai/ThoxMicroprocessor-8M-Base-TinyStories

SGLang

How to use Thox-ai/ThoxMicroprocessor-8M-Base-TinyStories 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 "Thox-ai/ThoxMicroprocessor-8M-Base-TinyStories" \
    --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": "Thox-ai/ThoxMicroprocessor-8M-Base-TinyStories",
		"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 "Thox-ai/ThoxMicroprocessor-8M-Base-TinyStories" \
        --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": "Thox-ai/ThoxMicroprocessor-8M-Base-TinyStories",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Thox-ai/ThoxMicroprocessor-8M-Base-TinyStories with Docker Model Runner:
```
docker model run hf.co/Thox-ai/ThoxMicroprocessor-8M-Base-TinyStories
```

ThoxMicroprocessor-8M-Base-TinyStories

Tiny THOX Decoder (Architecture-Validation Checkpoint) — thoxMicroprocessor-8M is an attention-dominant tiny decoder architecture designed by THOX for local-first routing, structured extraction, device control, and compact on-device languag

Safetensors weights

Find your model: THOX Model Factory · Collection: Research & From-Scratch Models

Your AI. Your Data. Your Rules.™

Summary

ThoxMicroprocessor-8M is an attention-dominant tiny decoder architecture designed by THOX for local-first routing, structured extraction, device control, and compact on-device language workloads. This checkpoint is an architecture-validation build, not a product model. It was trained only on TinyStories (simple synthetic children's stories): it has no routing, tool-call, device-control, or THOX domain capability.

Highlights

ThoxMicroprocessor-8M is an attention-dominant tiny decoder architecture designed by THOX for local-first routing, structured extraction, device control, and compact on-device language workloads.

Specifications


Base model	See card body
Parameters	See card body
Formats	Safetensors weights
License	Apache-2.0 as declared for this repo. Training data is TinyStories (CDLA-Sharing-1.0); share-alike implications are under review with the other TinyStories-based THOX models.
This repository contains	Full weights and tokenizer in this repo

Files

File	Size	Purpose
`model.safetensors`	32,400,144 B	Model weights (safetensors)
`tokenizer.json`	561,388 B	Tokenizer files
`modeling_thox_microprocessor.py`	15,337 B	Code
`configuration_thox_microprocessor.py`	4,832 B	Code
`tokenization_thox_microprocessor.py`	2,151 B	Code
`.gitattributes`	1,519 B	Repository file
`config.json`	1,030 B	Model configuration
`tokenizer_config.json`	220 B	Tokenizer files
`generation_config.json`	146 B	Generation configuration
`special_tokens_map.json`	100 B	Tokenizer files

Architecture

Property	Value
Parameters	8,093,520
Non-embedding parameters	5,472,080
d_model	320
Layers	21
Attention heads / KV heads	8 / 2
Context	1024
QK RMS normalization	True
Sandwich normalization	True
Blockwise Walsh-Hadamard mixer	True
Architecture version	thox-microprocessor-v0.2
Trained tokens	98.3M (TinyStories)

Weight payload at INT4 (group size 64, including per-group scales and the fp32 1-D tensors): 4.45 MB (4.40 effective bits per parameter). That is the raw weight payload, not peak runtime RAM; device qualification must additionally measure code, allocator overhead, activations, and KV cache.

Run it

from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Thox-ai/ThoxMicroprocessor-8M-Base-TinyStories", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Thox-ai/ThoxMicroprocessor-8M-Base-TinyStories", trust_remote_code=True)

The remote-code reference implementation recomputes the prefix on each decoding step rather than maintaining a KV cache; it is optimised for portability across transformers versions. Use the thox_microprocessor package for cached decoding.

Platforms

Platform / device	Run path
Hugging Face	Download the builds listed under Files from this repo (see Run it)

Intended use

Architecture research and validation for THOX's tiny-model line.
Out of scope: any production or domain use (this checkpoint has no domain capability), and medical, legal, financial, safety-critical, or other high-stakes decisions without human review.

Training data and procedure

Trained by THOX from scratch as part of the THOX small-model research line. The corpus and procedure are as described in this card at release; no external base model was used.

Evaluation

Held-out loss 1.6956 / perplexity 5.4499 on the TinyStories validation split (2,095,104 target tokens). THOX INT4 quantization costs +0.0366 held-out loss at 4.45 MB; INT4 with an INT8 embedding costs +0.0162 at 5.76 MB. No THOX GATE49 result is published for this model.

Limitations

Trained only on simple synthetic children's stories; outputs outside that distribution are meaningless.
Small models make factual mistakes and can produce confident but wrong output; verify important answers.
Quantization trades accuracy for size.

Provenance

Published by THOX.ai as Thox-ai/ThoxMicroprocessor-8M-Base-TinyStories. THOX from-scratch research line; no upstream base model. This card was conformed to THOX Model Card Standard v1.0 on 2026-10-31 (frontmatter, tags, header, files, and section structure); the technical content is as published at release.

License

Apache-2.0 as declared for this repo. Training data is TinyStories (CDLA-Sharing-1.0); share-alike implications are under review with the other TinyStories-based THOX models.

THOX.ai — Your AI. Your Data. Your Rules.™

Citation

@misc{thoxai2026thoxmicroprocessor8mbasetinystories,
      title={ThoxMicroprocessor-8M-Base-TinyStories},
      author={THOX.ai},
      year={2026},
      url={https://huggingface.co/Thox-ai/ThoxMicroprocessor-8M-Base-TinyStories}
}

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Model size

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Tensor type

F32

Collection including Thox-ai/ThoxMicroprocessor-8M-Base-TinyStories

THOX Research & From-Scratch Models

Collection

THOX research models: the from-scratch ThoxLLM and thox-micro lines, 1-bit experiments, ThoxNeedle, the kernel model and speech. • 14 items • Updated 1 day ago • 1