Instructions to use TreeSoft/Prism-1-Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TreeSoft/Prism-1-Mini with Transformers:

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

pipe = pipeline("text-generation", model="TreeSoft/Prism-1-Mini")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TreeSoft/Prism-1-Mini")
model = AutoModelForCausalLM.from_pretrained("TreeSoft/Prism-1-Mini", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TreeSoft/Prism-1-Mini with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TreeSoft/Prism-1-Mini"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TreeSoft/Prism-1-Mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TreeSoft/Prism-1-Mini

SGLang

How to use TreeSoft/Prism-1-Mini 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 "TreeSoft/Prism-1-Mini" \
    --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": "TreeSoft/Prism-1-Mini",
		"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 "TreeSoft/Prism-1-Mini" \
        --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": "TreeSoft/Prism-1-Mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TreeSoft/Prism-1-Mini with Docker Model Runner:
```
docker model run hf.co/TreeSoft/Prism-1-Mini
```

alexutzusoft commited on Jul 19

Commit

d891a80

verified ·

1 Parent(s): bfaad8b

Updated README

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README.md +5 -10

README.md CHANGED Viewed

@@ -91,18 +91,13 @@ prompt.
 ## How to Get Started
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-model_id = "TreeSoft/Prism-1-Mini"
-tok = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="bfloat16", device_map="auto")
-messages = [{"role": "user", "content": "You must respond in exactly one sentence. Explain relativity."}]
-inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
-out = model.generate(inputs, max_new_tokens=256)
-print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
-```
 ## Training Details

 ## How to Get Started
+The easiest way to run Prism is through the official **Prism** repository:
+**→ https://github.com/treesoft-ai/prism**
+It ships a ready-to-go CLI for chatting with the model, running one-shot
+prompts, and everything else — just clone it, point it at Prism 1 Mini, and go.
+Head over there to get started and run it locally.
 ## Training Details