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 model.safetensors from ForgeWorks/ForgePlex-M2-9M: direct link, hf CLI and curl.
- Browser
- Download file 39.8 MB
-
https://huggingface.co/ForgeWorks/ForgePlex-M2-9M/resolve/main/model.safetensors
- Command line
-
hf download hf://ForgeWorks/ForgePlex-M2-9M/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/ForgeWorks/ForgePlex-M2-9M/resolve/main/model.safetensors
39.8 MB
- Xet hash:
- ea91371bb92db1d933f09de08cde7d5bd15d21aeee3f9bf7fdfb8ab0434278ad
- Size of remote file:
- 39.8 MB
- SHA256:
- 2993a54673b3087f566e262fe1114ad60fda497194ff925c9433ae5e1b62474c
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