Instructions to use ForgeWorks/ForgePlex-M2.2-10M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ForgeWorks/ForgePlex-M2.2-10M with Transformers:

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

pipe = pipeline("text-generation", model="ForgeWorks/ForgePlex-M2.2-10M", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ForgeWorks/ForgePlex-M2.2-10M", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ForgeWorks/ForgePlex-M2.2-10M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ForgeWorks/ForgePlex-M2.2-10M"
# 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-M2.2-10M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ForgeWorks/ForgePlex-M2.2-10M

SGLang

How to use ForgeWorks/ForgePlex-M2.2-10M 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-M2.2-10M" \
    --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-M2.2-10M",
		"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-M2.2-10M" \
        --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-M2.2-10M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ForgeWorks/ForgePlex-M2.2-10M with Docker Model Runner:
```
docker model run hf.co/ForgeWorks/ForgePlex-M2.2-10M
```

Jsandero commited on 7 days ago

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@@ -16,6 +16,8 @@ tags:
 pipeline_tag: text-generation
 ---
 # ForgePlex-M2.2-10M
 **ForgePlex-M2.2-10M** is the definitive M2: the final and most refined release of the ForgePlex M2 architecture from **ForgeWorks**. Same ~9.99M-parameter (unique, tied embeddings) decoder-only design as M2 and M2.1, pushed as far as we could take it. Packaged as a Hugging Face custom causal LM (**requires `trust_remote_code=True`**).

 pipeline_tag: text-generation
 ---
+<p align="center"><img src="assets/title.jpg" width="650"></p>
 # ForgePlex-M2.2-10M
 **ForgePlex-M2.2-10M** is the definitive M2: the final and most refined release of the ForgePlex M2 architecture from **ForgeWorks**. Same ~9.99M-parameter (unique, tied embeddings) decoder-only design as M2 and M2.1, pushed as far as we could take it. Packaged as a Hugging Face custom causal LM (**requires `trust_remote_code=True`**).