| --- |
| base_model: Qwen/Qwen3.5-4B |
| library_name: transformers |
| pipeline_tag: text-generation |
| tags: |
| - prism |
| - instruction-following |
| - deterministic-compliance |
| - sft |
| - lora |
| license: other |
| license_name: treesoft-open-source-license |
| --- |
| |
| # Prism 1 Pro |
|
|
|  |
|
|
| **Prism 1 Pro** is the flagship 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. |
|
|
| Pro is the top tier. It follows instructions the most reliably of the three |
| models, holding personas, tone, and hard constraints most consistently and |
| handling stacked, multi-part directives most cleanly. It improves on **Standard** |
| by a good margin, just as Standard clearly beats **Mini** — see *Model Family* |
| below. Choose Pro when compliance quality matters more than raw speed or |
| footprint. |
|
|
| ## Model Details |
|
|
| ### Model Description |
|
|
| Prism 1 Pro is a decoder-only causal language model fine-tuned (SFT with LoRA |
| via TRL) from a Qwen3.5-4B 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. Pro delivers this the most dependably in the |
| family. |
|
|
| - **Developed by:** TreeSoft |
| - **Model type:** Decoder-only causal language model (Qwen3.5 architecture) |
| - **Language(s):** Primarily English |
| - **License:** TreeSoft Open Source License |
| - **Finetuned from:** Qwen3.5-4B |
|
|
| ### 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 | Balanced default — clearly beats Mini | |
| | **Prism 1 Pro** | Qwen3.5-4B | ~4.2B | Flagship — clearly beats 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. Pro sits at the top: the most |
| reliable compliance in the range. |
|
|
| ## Uses |
|
|
| ### Direct Use |
|
|
| - Instruction- and persona-conditioned chat and generation |
| - Format-constrained generation (case, length, bullet-only, no-questions, etc.) |
| - Applications where dependable, repeatable instruction adherence is the priority |
|
|
| ### Out-of-Scope Use |
|
|
| - High-stakes factual, medical, legal, or financial decisions without review |
| - Safety-critical automation with no human in the loop |
|
|
| ## Bias, Risks, and Limitations |
|
|
| Prism 1 Pro inherits the biases and knowledge gaps of its base. Because it is |
| tuned to comply with injected instructions — and does so more effectively than |
| the smaller tiers — it will readily adopt personas or constraints supplied in |
| the prompt, including ones a downstream application may not intend. This makes |
| treating prompt-supplied instructions as untrusted input especially important in |
| multi-user or tool-connected settings: the stronger the compliance, the more |
| faithfully an injected directive is carried out. |
|
|
| ### 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 Pro, 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: 2560 · Layers: 32 · Attention heads: 16 (4 KV heads) |
| - Full-attention interval: every 4th layer |
| - Vocabulary: 248,320 · Max position embeddings: 262,144 |
| - Precision: bfloat16 · Weights: ~8.4 GB (safetensors) |
|
|
| ### Software |
|
|
| `torch>=2.3`, `transformers>=4.51.0`, `safetensors>=0.4.0` |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{treesoft2026prism1pro, |
| title = {Prism 1 Pro}, |
| author = {TreeSoft}, |
| year = {2026} |
| } |
| ``` |
|
|
| ## Model Card Contact |
|
|
| TreeSoft. Built on Qwen3.5. Licensed under the TreeSoft Open Source License. |
|
|
