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)

# 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=40)
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
```

Prism 1 Mini

Prism 1 Mini is the smallest member of the Prism 1 family — a set of instruction-following language models tuned by TreeSoft for deterministic compliance: reliably obeying the instructions it is given, including instructions injected inline inside a prompt.

Mini is the entry point of the range. It is fast, cheap to run, and built for constrained-format tasks where the instruction must be followed exactly rather than merely acknowledged. The Standard and Pro tiers each improve on the tier below by a good margin — see Model Family below.

Model Details

Model Description

Prism 1 Mini is a decoder-only causal language model fine-tuned (SFT with LoRA via TRL) from a Qwen3.5-0.8B base. Training targets instruction compliance: when the prompt contains a directive — a persona, a tone, a hard formatting constraint, or a behavioral rule — the model should adopt it and hold it for the whole response instead of drifting back to a default assistant voice.

The Prism tuning specifically hardens the model against inline instruction injection, where a directive is embedded mid-prompt (for example, wrapped in <i>...</i> markers) rather than placed in a system message. The intended behavior is deterministic: the same instruction should produce the same class of compliant behavior every time.

Developed by: TreeSoft
Model type: Decoder-only causal language model (Qwen3.5 architecture)
Language(s): Primarily English
License: TreeSoft Open Source License
Finetuned from: Qwen3.5-0.8B

Model Family

Model	Base	Approx. params	Position
Prism 1 Mini	Qwen3.5-0.8B	~0.75B	Fastest, lightest
Prism 1 Standard	Qwen3.5-2B	~1.9B	Clearly stronger than Mini
Prism 1 Pro	Qwen3.5-4B	~4.2B	Clearly stronger again than Standard

Each step up the family is meaningfully more capable than the one below it by a good margin — better instruction adherence, steadier tone, and cleaner handling of multi-part and hard-constraint instructions. Pick Mini for latency and cost, Standard for a balanced default, Pro when compliance quality matters most.

Uses

Direct Use

Instruction- and persona-conditioned chat and generation
Format-constrained generation (case, length, bullet-only, no-questions, etc.)
On-device or low-latency assistants where a small footprint is required

Out-of-Scope Use

High-stakes factual, medical, legal, or financial decisions without review
Safety-critical automation with no human in the loop
Tasks needing strong long-form reasoning — prefer Standard or Pro

Bias, Risks, and Limitations

Prism 1 Mini is a small model and inherits the biases and knowledge gaps of its base. Because it is tuned to comply with injected instructions, it will readily adopt personas or constraints supplied in the prompt — including ones a downstream application may not intend. Treat prompt-supplied instructions as untrusted input in multi-user or tool-connected settings. As the smallest tier, it is the most likely to break a hard constraint on long or multi-part instructions; step up to Standard or Pro where reliability matters.

Recommendations

Keep a human in the loop for consequential outputs, validate format constraints programmatically when they matter, and sanitize untrusted text that reaches the prompt.

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

Training Data

Instruction-following data emphasizing compliance with directives — personas, emotional tone, and hard behavioral/formatting constraints — including cases where the directive is injected inline within the user prompt.

Training Procedure

Method: Supervised fine-tuning (SFT) with LoRA adapters via TRL, merged into the released weights
Training regime: bf16 mixed precision

Technical Specifications

Model Architecture

Architecture: Qwen3_5ForCausalLM (hybrid linear + full attention, MTP head)
Hidden size: 1024 · Layers: 24 · Attention heads: 8 (2 KV heads)
Full-attention interval: every 4th layer
Vocabulary: 248,320 · Max position embeddings: 262,144
Precision: bfloat16 · Weights: ~1.5 GB (safetensors)

Software

torch>=2.3, transformers>=4.51.0, safetensors>=0.4.0

Citation

@misc{treesoft2026prism1mini,
  title  = {Prism 1 Mini},
  author = {TreeSoft},
  year   = {2026}
}

Model Card Contact

TreeSoft. Built on Qwen3.5. Licensed under the TreeSoft Open Source License.

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Safetensors

Model size

0.8B params

Tensor type

BF16

Model tree for TreeSoft/Prism-1-Mini

Base model

Qwen/Qwen3.5-0.8B-Base

Finetuned

Qwen/Qwen3.5-0.8B

Adapter

(170)

this model