Instructions to use Thox-ai/thox-micro-125m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Thox-ai/thox-micro-125m with Transformers:

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

pipe = pipeline("text-generation", model="Thox-ai/thox-micro-125m")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Thox-ai/thox-micro-125m")
model = AutoModelForCausalLM.from_pretrained("Thox-ai/thox-micro-125m", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Thox-ai/thox-micro-125m with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Thox-ai/thox-micro-125m:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Thox-ai/thox-micro-125m:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Thox-ai/thox-micro-125m:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Thox-ai/thox-micro-125m:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Thox-ai/thox-micro-125m:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Thox-ai/thox-micro-125m:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Thox-ai/thox-micro-125m:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Thox-ai/thox-micro-125m:Q4_K_M

Use Docker

docker model run hf.co/Thox-ai/thox-micro-125m:Q4_K_M

LM Studio
Jan

vLLM

How to use Thox-ai/thox-micro-125m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Thox-ai/thox-micro-125m"
# 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/thox-micro-125m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Thox-ai/thox-micro-125m:Q4_K_M

SGLang

How to use Thox-ai/thox-micro-125m 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/thox-micro-125m" \
    --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/thox-micro-125m",
		"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/thox-micro-125m" \
        --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/thox-micro-125m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use Thox-ai/thox-micro-125m with Ollama:
```
ollama run hf.co/Thox-ai/thox-micro-125m:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use Thox-ai/thox-micro-125m with Docker Model Runner:
```
docker model run hf.co/Thox-ai/thox-micro-125m:Q4_K_M
```

Lemonade

How to use Thox-ai/thox-micro-125m with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Thox-ai/thox-micro-125m:Q4_K_M

Run and chat with the model

lemonade run user.thox-micro-125m-Q4_K_M

List all available models

lemonade list

Atomic Chat
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

thox-micro-125m

Your AI. Your Data. Your Rules.

From-scratch 123M Llama-architecture base/completion model for edge and on-device use.

What this is

Trained from scratch (THOX-owned) — Apache-2.0.
Base/completion model, not an instruction-tuned assistant.
Best validation loss 2.6099 (internal eval).

Architecture (from config)

Field	Value
Architecture	LlamaForCausalLM
Layers	12
Hidden size	768
Attention heads	12
KV heads	12 (full MHA, no GQA)
FFN / intermediate	2048
Vocab	50,304 (GPT-2 r50k)
Max context	1024
Tied embeddings	yes
dtype	float16

Intended use

On-device / edge text generation within the THOX stack. Not a safety-aligned public assistant unless deployed behind THOX guardrails.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("Thox-ai/thox-micro-125m")
model = AutoModelForCausalLM.from_pretrained("Thox-ai/thox-micro-125m")

Files

File	Size	SHA-256
`model.safetensors`	247.19 MB (247,186,856 B)	`d0a7cbd9bd2ca32e7150bb6b61ed78d935b64b0de046baca2ed036c86461cc49`
`thox-micro-125m.Q4_0.gguf`	81.32 MB (81,318,848 B)	`cbd9d0ff263140a7c11c85b7a5689d7f9e83b9ddc3f82130f5c8dc4706a3a503`
`thox-micro-125m.Q4_K_M.gguf`	84.66 MB (84,664,256 B)	`51e3442728ae2ce339416f8afc745c00da6b65f278d20f85bcea98d7fc8c6bbb`
`thox-micro-125m.f16.gguf`	248.99 MB (248,987,840 B)	`1fa8b56e5e338e4bce48deabc8cbd63a7462620764e9d52976671cd2ebddda2f`

Use with ThoxOS

Download the file once and run it fully offline on a ThoxOS host (or any Linux/macOS/Windows machine):

pip install -U "huggingface_hub[cli]"
hf download Thox-ai/thox-micro-125m thox-micro-125m.Q4_K_M.gguf --local-dir ./thox-micro-125m
llama-cli -m ./thox-micro-125m/thox-micro-125m.Q4_K_M.gguf -p "Hello" -n 128

Ollama (pulls straight from the Hub):

ollama run hf.co/Thox-ai/thox-micro-125m:Q4_K_M

LM Studio: search Thox-ai/thox-micro-125m in the model browser (or lms get Thox-ai/thox-micro-125m), pick thox-micro-125m.Q4_K_M.gguf, and load it.

Use with MeshStack

Serve the model as an OpenAI-compatible endpoint on any MeshStack node, then register that endpoint with your MeshStack coordinator (protocol openai, base URL http://<node-ip>:8080/v1; or protocol ollama at http://<node-ip>:11434 if you serve it with Ollama).

llama-server -m ./thox-micro-125m/thox-micro-125m.Q4_K_M.gguf --host 0.0.0.0 --port 8080 -c 4096
curl http://<node-ip>:8080/v1/chat/completions -H 'Content-Type: application/json' \
  -d '{"messages":[{"role":"user","content":"Hello"}]}'

Device fit (by file size only)

thox-micro-125m.Q4_0.gguf (81.32 MB): Small (<600 MB): single-board computers with ≥1 GB RAM, phones, any laptop/desktop.
thox-micro-125m.Q4_K_M.gguf (84.66 MB): Small (<600 MB): single-board computers with ≥1 GB RAM, phones, any laptop/desktop.
thox-micro-125m.f16.gguf (248.99 MB): Small (<600 MB): single-board computers with ≥1 GB RAM, phones, any laptop/desktop.

RAM needed ≈ file size + context (KV cache) + runtime overhead. These tiers are derived from file size alone; no throughput, TOPS or battery figures are claimed here.

Licence and attribution

Trained from scratch by THOX.ai LLC; Apache-2.0.

Out of scope

Medical, legal, financial, safety-critical or other high-stakes decisions without human review.
Unattended control of physical devices or actuators without a confirmation step.
Any use prohibited by the upstream licence or acceptable-use policy.

Limitations and bias

Small and quantized models make factual mistakes and can produce confident but wrong output; verify important answers.
Behaviour and bias reflect the training data, which is mostly English web/instruction text; quality in other languages is lower unless stated above.
Quantization trades accuracy for size; lower-bit files degrade more.

THOX.ai LLC — Your AI. Your Data. Your Rules.™ · thox.ai · Contact: tommy@thox.ai

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Safetensors

Model size

0.1B params

Tensor type

F16

Model tree for Thox-ai/thox-micro-125m

Finetunes

1 model

Quantizations

2 models

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THOX edge device models

Collection

Key/Air/Clip/Mini role + Route + LLM-327M + Edge + micro. Local-first. Claims HOLD. • 17 items • Updated Sep 24

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thox-micro-125m