Instructions to use ForgeWorks/ForgePlex-M2-9M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ForgeWorks/ForgePlex-M2-9M with Transformers:

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

pipe = pipeline("text-generation", model="ForgeWorks/ForgePlex-M2-9M", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ForgeWorks/ForgePlex-M2-9M", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ForgeWorks/ForgePlex-M2-9M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ForgeWorks/ForgePlex-M2-9M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ForgeWorks/ForgePlex-M2-9M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ForgeWorks/ForgePlex-M2-9M

SGLang

How to use ForgeWorks/ForgePlex-M2-9M 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 "ForgeWorks/ForgePlex-M2-9M" \
    --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": "ForgeWorks/ForgePlex-M2-9M",
		"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 "ForgeWorks/ForgePlex-M2-9M" \
        --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": "ForgeWorks/ForgePlex-M2-9M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ForgeWorks/ForgePlex-M2-9M with Docker Model Runner:
```
docker model run hf.co/ForgeWorks/ForgePlex-M2-9M
```

We would like to thank Axiomic Labs for allowing us to use their TrainWork framework to train this model.

ForgePlex-M2-9M

ForgePlex-M2-9M is a ~9.95M-parameter decoder-only language model from ForgeWorks. Our second attempt at creating a <10m parameter model. We're proud of this product, while M1 was a promising start, M2 shows what we can do.

Q&A

What was the motivation behind M2?

"That is an excellent question. To be completely honest. George Mallory was asked why he wanted to climb Everest. He said, “Because it’s there.” I just see M2 as a mountain to climb"

Who is your competition?

"I don't believe I have competition, i'm not in this to "win". Though I would be lying if didn't say I was concerned about KSLM, mainly due to their targetted abuse towards my team and I, and they have stated KSLM-ZBT1's is being made to "Crush ForgePlex" but I am happy to work with anyone and everyone :)"

Metric	Value
Unique parameters	9,949,698
Intelligence Index (train-time)	8.51
HellaSwag	27.05%
ARC (combined)	29.78%
PIQA	56.86%
ArithMark-3	34.50%
License	Apache-2.0

Architecture

GQA + NeoX-style RoPE + RMSNorm + SwiGLU, with Qwen3.5-style attention output gates and refresh gates on inject layers [5, 10] (kernel 9). XSA is off. Weights keep training key layout (no Llama remapping).

Component	Details
Position encoding	RoPE (theta=5,000, NeoX even/odd)
Normalization	RMSNorm (eps=1e-6)
Feed-forward	SwiGLU (intermediate 707)
Attention	GQA — 8Q / 2KV, head_dim=32 + attn output gate
Refresh	Layers 5, 10, kernel 9
Bias	None
Embedding	Weight tying
Depth × width	11 layers × 256 hidden
Context	1024 tokens
Vocab	4,096 custom BPE

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = r"C:\slm\ForgePlex-M2-9M"
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    trust_remote_code=True,
    torch_dtype=torch.float32,
    device_map="auto",
)

prompt = "Once upon a time"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.inference_mode():
    out = model.generate(**inputs, max_new_tokens=80, do_sample=False)
print(tokenizer.decode(out[0], skip_special_tokens=True))

Or run python usage.py from this folder.

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Safetensors

Model size

9.95M params

Tensor type

F32