---
license: cc-by-nc-4.0
language:
  - en
pipeline_tag: text-generation
tags:
  - code
  - full-stack
  - mistral
  - gguf
  - zwen-ai-labs
base_model: mistralai/Mistral-7B-Instruct-v0.3
---

# Zwen-Prime

*An elite Full-Stack Principal Engineer in a 7B parameter body. Built by Zwen AI Labs.*

Zwen-Prime is a two-stage model: a DARE-TIES neural fusion of three Mistral-7B-class specialists, followed by a supervised fine-tune on a hand-crafted five-corpus mixture that hard-installs strict `<thinking>` reasoning, Big-O discipline, native JSON function-calling, and zero-filler full-stack output. It is engineered for local inference on Apple Silicon and served via the official `zwen-cli` (with Ollama / llama.cpp as alternatives).

## Model Summary

| Feature | Details |
| :--- | :--- |
| **Creator** | Zwen AI Labs |
| **Architecture** | Mistral-7B (DARE-TIES merge + LoRA SFT, fused) |
| **Active params** | ~7B |
| **Context window** | 32,000 tokens (32k via preserved RoPE scaling) |
| **Quantization** | Q4_K_M GGUF (~4.2 GB, < 5 GB RAM target) |
| **Hardware target** | Apple Silicon (M-series, Metal-accelerated) |
| **Serving** | zwen-cli (official) · Ollama · llama.cpp |
| **Languages** | English |

## The Brain — Five-Corpus Training Mixture

Zwen-Prime is fine-tuned on a deliberate mixture of five datasets, each installing a distinct cognitive faculty:

| Corpus | Weight | Faculty installed |
| :--- | :--- | :--- |
| **Alpaca Python** | 40% | Elite, typed Python and algorithms at correct complexity. |
| **Orca Math** | 30% | Deep, step-by-step mathematical reasoning with verified derivation. |
| **Salesforce XLAM** | 20% | Flawless, raw-JSON function/tool calling (Mistral v0.3 convention). |
| **LongAlpaca** | 10% | Extended-context retention and long-document grounding without drift. |
| **Zwen Custom** | core spine | Strict `<thinking>` logic + Big-O breakdown, full-stack mastery (TypeScript, React/Next.js, Java concurrency), absolute zero-filler output. |

The custom 550-row dataset (`zwen_prime_master_dataset.jsonl`) is the enforcement spine: 50 Identity rows that set the persona and 500 Algorithmic/Logic rows that lock in the `<thinking>...</thinking>` → `raw-code` template across advanced TypeScript generics, React/Next.js App-Router architecture, Java concurrency primitives, and Python system design.

## Capabilities

* **Strict reasoning core:** Every non-trivial answer opens a `<thinking>` block with step-by-step logic and a time/space Big-O breakdown, immediately followed by the raw deliverable — no filler, no preamble.
* **Full-stack mastery:**
    * **Python:** typed (PEP 484/604), async-aware, concurrency-correct algorithms.
    * **TypeScript:** advanced generics, conditional/mapped types, type-stateful builders, discriminated unions.
    * **React / Next.js:** App Router, RSC, Server Actions, route handlers, caching/revalidation, React 19 hooks, streaming.
    * **Java:** ReentrantLock, StampedLock, Semaphore, CompletableFuture, ForkJoinPool, virtual threads, happens-before reasoning.
* **Native function calling:** Emits `[TOOL_CALLS]` raw JSON matching the provided tool schema; no markdown, no prose around the call.
* **Mathematical reasoning:** Derives quantitative claims in the scratchpad before asserting them; sanity-checks units, magnitudes, and boundaries.
* **Long-context discipline:** Anchors claims to source passages; refuses to confabulate when evidence is absent.
* **Zero conversational filler:** No greetings, sign-offs, apologies, or compliance narration.

## Merge Details

### Merge Method
The base was produced with the DARE TIES merge method via mergekit, using `Mistral-7B-Instruct-v0.3` as the density-0 reference base.

### Models Merged
* `dphn/dolphin-2.9.3-mistral-7B-32k` — reasoning specialist
* `theprint/ReWiz-7B` — fine-tune specialist
* `mistralai/Mistral-7B-Instruct-v0.3` — base / reference (density 0, weight 0)

### Merge Configuration
```yaml
base_model: mistralai/Mistral-7B-Instruct-v0.3
merge_method: dare_ties
dtype: bfloat16
out_shard_size: 1.2B
parameters:
  density: 0.5
  weight: 1.0
  normalize: true
  int8_mask: true
  rescale: true
  lambda: 1.0
models:
  - model: dphn/dolphin-2.9.3-mistral-7B-32k
    parameters:
      density: 0.5
      weight: 1.0
  - model: theprint/ReWiz-7B
    parameters:
      density: 0.5
      weight: 1.0
  - model: mistralai/Mistral-7B-Instruct-v0.3
    parameters:
      density: 0.0
      weight: 0.0
```

### Fine-Tune Stage
The merged base was then LoRA fine-tuned on the five-corpus mixture and the adapters permanently fused into the base weights (`peft.merge_and_unload`). The fused model is exported to GGUF for local serving.

```
DARE-TIES merge → LoRA SFT (5-corpus mixture) → merge_and_unload → GGUF → Ollama
```

## ChatML-Native Format

Although the GGUF embeds the Mistral v0.3 chat template, Zwen-Prime's fusion corpus (Dolphin / ReWiz) is **ChatML-formatted**, so the model's native turn format is `<|im_start|>system…<|im_end|><|im_start|>user…`. Under Mistral `[INST]` framing it ignores the user prompt and runs on; under ChatML it reasons cleanly in `<thinking>` and stops on turn boundaries. Both official runtimes therefore pin the ChatML wrapper:

* **zwen-cli** pins `ChatMLChatWrapper` automatically — no configuration needed.
* The **Ollama Modelfile** injects the strict Zwen-Prime system prompt and the ChatML template via its `TEMPLATE`/`SYSTEM` directives.

The base merge is `dolphin-2.9.3-mistral-7B-32k`, and its 32k RoPE scaling is preserved intact — so Zwen-Prime officially supports a **32,000-token context window**, enough to ingest a large enterprise codebase in a single pass.

### Serving

Zwen-Prime is pre-quantized to **Q4_K_M** to run blisteringly fast on Apple Silicon and consumer GPUs (< 5 GB RAM required). The published quant is **`Zwen-Prime-Final.Q4_K_M.gguf`** (~4.2 GB).

**Official CLI** (recommended) — installs globally and auto-downloads the GGUF into `~/.zwen/models/` on first run, with a 32k context out of the box:

```
npm install -g @zwenailabs13/zwen-cli
zwen run zwen-prime
zwen chat          # interactive REPL
zwen list          # list cached models in ~/.zwen/models/
```

**Ollama** (alternative) — the Modelfile automatically injects the strict Zwen-Prime system prompt and handles the ChatML formatting:

```
ollama run zwenailabs/zwen-prime
```

**llama.cpp** (manual) — point any ChatML-aware runner at `Zwen-Prime-Final.Q4_K_M.gguf` and supply the Zwen-Prime system prompt.

## System Prompt
Zwen-Prime ships with a dedicated system prompt that sets the Principal-Engineer persona, the `<thinking>` mandate, the zero-filler output protocol, full-stack mastery expectations, native JSON tool-calling rules, long-context discipline, and the hard constraints. It is embedded in the Modelfile's `SYSTEM` directive.

## Intended Use
Zwen-Prime is intended as a local, autonomous engineering copilot: designing and shipping production code across Python, TypeScript, React/Next.js, and Java; reasoning through math and algorithms with verifiable steps; and calling tools via raw JSON when integrated into an agent runtime.

## Limitations
* **7B scale:** Strong on focused engineering tasks; not a frontier-class generalist.
* **Wall-clock-sensitive:** Derived math is verified symbolically in-context but not executed; verify numerically when stakes are high.
* **Function calling:** Follows the Mistral v0.3 / XLAM JSON convention and expects a tool-aware runtime to dispatch `[TOOL_CALLS]`.
* **Behavioral design:** Fine-tuned toward zero-filler directness; users expecting conversational preamble will not get it.

## License & Commercial Use
The weights and architecture of Zwen-Prime are released under the **CC BY-NC 4.0 (Creative Commons Non-Commercial)** license.

* **Free for Developers:** Individual developers, researchers, and hobbyists are fully encouraged to download, run, and modify Zwen-Prime locally for free.
* **Commercial Restrictions:** Cloud hosting, API provisioning, enterprise internal deployments, and any form of commercial reselling are strictly prohibited under this license.
* **Enterprise Licensing:** For commercial deployment, managed hosting, or enterprise use, you must acquire a commercial license through Axora.

Visit: https://axoraio.in
Email: contact@axoraio.in