Text Generation
Transformers
Safetensors
English
forgeplex_m2
language-model
forgeplex
forgeworks
rope
swiglu
gqa
attn-output-gate
refresh-gate
custom_code
Instructions to use ForgeWorks/ForgePlex-M2-9M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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)# pip install -U transformers accelerate # 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
Download M2-Complete.jpg from ForgeWorks/ForgePlex-M2-9M: direct link, hf CLI and curl.
- Browser
- Download file 300 kB
-
https://huggingface.co/ForgeWorks/ForgePlex-M2-9M/resolve/main/M2-Complete.jpg
- Command line
-
hf download hf://ForgeWorks/ForgePlex-M2-9M/M2-Complete.jpg
-
curl -L -o M2-Complete.jpg https://huggingface.co/ForgeWorks/ForgePlex-M2-9M/resolve/main/M2-Complete.jpg
300 kB

- Xet hash:
- 0e2c83b4a98860f6f98230cfd3387caa2ed1fdeb069b97d1d343e899f8d90f98
- Size of remote file:
- 300 kB
- SHA256:
- 3afba94945c5503e21c516166aa7e8730bc798f10dbebbec7ebc379c5aad0628
·
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