Instructions to use caid-technologies/parti-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use caid-technologies/parti-base with Transformers:

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

pipe = pipeline("text-generation", model="caid-technologies/parti-base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("caid-technologies/parti-base")
model = AutoModelForCausalLM.from_pretrained("caid-technologies/parti-base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use caid-technologies/parti-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "caid-technologies/parti-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "caid-technologies/parti-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/caid-technologies/parti-base

SGLang

How to use caid-technologies/parti-base 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 "caid-technologies/parti-base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "caid-technologies/parti-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "caid-technologies/parti-base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "caid-technologies/parti-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use caid-technologies/parti-base with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for caid-technologies/parti-base to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for caid-technologies/parti-base to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for caid-technologies/parti-base to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="caid-technologies/parti-base",
    max_seq_length=2048,
)

Docker Model Runner
How to use caid-technologies/parti-base with Docker Model Runner:
```
docker model run hf.co/caid-technologies/parti-base
```

Hudeani commited on 16 days ago

Commit

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1 Parent(s): 9539557

Update README.md

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@@ -21,7 +21,7 @@ library_name: transformers
 # Parti Base — Qwen2.5-3B
-**Parti turns any hardware idea as natural language prompt into hardware design & plan.**
 Tell it what you want to build — *"a compact desk clock with an e-ink display and a remote"* —
 and it gives back a structured blueprint: the parts list, how the parts connect, step-by-step
@@ -153,7 +153,7 @@ above). *Figures are from held-out testing and are being finalized for the curre
 ```bibtex
 @misc{parti_base,
-  title  = {Parti Base: Qwen2.5-3B for structured hardware design & plan generation},
   author = {Caid Technologies},
   year   = {2026},
   howpublished = {\url{https://huggingface.co/caid-technologies}}

 # Parti Base — Qwen2.5-3B
+**Parti turns natural language prompts into hardware designs and plans.**
 Tell it what you want to build — *"a compact desk clock with an e-ink display and a remote"* —
 and it gives back a structured blueprint: the parts list, how the parts connect, step-by-step
 ```bibtex
 @misc{parti_base,
+  title  = {Parti Base: Qwen2.5-3B for structured hardware generation},
   author = {Caid Technologies},
   year   = {2026},
   howpublished = {\url{https://huggingface.co/caid-technologies}}