---
license: apache-2.0
base_model: marketeam/Qwen-Marketing
library_name: transformers
pipeline_tag: text-generation
language:
- en
tags:
- marketing
- content-generation
- social-media
- structured-output
- json
- qlora
---

# Qwen-Marketing-S1 — reliable JSON social-media content generation

**Qwen-Marketing-S1** is an 8B instruction model specialised for one job and does
it near-perfectly: turn a **brand profile + campaign brief** into a **clean,
schema-valid JSON array of 6–8 platform-ready social posts** — each with a
caption, content type, hashtags, an image (media) prompt, and short reasoning.

It emits **bare JSON directly** — no markdown fences, no chain-of-thought
leakage, no malformed structure — so it drops into a production pipeline with no
post-processing.

## Why it exists

General instruction models are strong writers but unreliable at *structured
output*: a few percent of the time they wrap JSON in fences, leak reasoning, or
return the wrong shape/count — which breaks any automated pipeline. This model
closes that reliability gap for the marketing-content-plan task.

## Results (100 held-out scenarios, judge-independent hard metrics)

| Metric | Baseline | **Qwen-Marketing-S1** |
|---|---|---|
| **Aggregate score** | 0.954 | **0.999** |
| Parses as JSON | 0.95 | **1.00** |
| Correct array shape | 0.95 | **1.00** |
| Post count 6–8 | 0.93 | **1.00** |
| Posts with all keys | 0.94 | **1.00** |
| Valid platforms | 0.95 | **1.00** |
| Valid content types | 0.95 | **1.00** |
| Hashtags 5–15 | 0.94 | **0.99** |
| Caption within platform limit | 0.94 | **1.00** |

Every gate reaches 100%: the outputs the baseline broke (invalid JSON on ~5%,
wrong post count on ~7%) drop to **zero**.

## Output schema

Each element of the returned array:

```json
{
  "platform": "instagram",
  "content_type": "carousel",
  "caption": "...",
  "hashtags": ["...", "..."],
  "media_prompt": "a prompt for an image model",
  "reasoning": "why this post fits the brief"
}
```

Valid `platform`: instagram, twitter/x, linkedin, facebook, tiktok.
Valid `content_type`: text, image, video, carousel, reel.

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "AbdulrahmanOmar/qwen-marketing-s1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, torch_dtype=torch.bfloat16, device_map="auto"
)

messages = [
    {"role": "system", "content": "You are a senior content creator within an AI marketing platform. Output the deliverable immediately as one valid JSON array and nothing else."},
    {"role": "user", "content": "Create a social media content calendar for a specialty coffee roaster launching a summer single-origin Ethiopian bean. Platforms: instagram, tiktok. Create 6-8 posts."},
]
inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
out = model.generate(inputs, max_new_tokens=2048, do_sample=False)
print(tokenizer.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
```

Trained with `enable_thinking=False`; keep thinking mode off at inference.

## How it was trained

- **Knowledge distillation → QLoRA SFT → merged** into a standalone model.
- **Teacher:** a 14B instruction model (`Qwen/Qwen2.5-14B-Instruct-AWQ`) generated
  content plans over 1,400 synthetic brand/campaign scenarios; only schema-valid
  outputs were kept as training targets (1,321 pairs; 94.4% valid).
- **Fine-tuning:** QLoRA (4-bit NF4 base), LoRA on all attention + MLP
  projections, **assistant-only loss**, then merged to fp16 for release.

| Setting | Value |
|---|---|
| LoRA rank / alpha / dropout | 16 / 32 / 0.05 |
| Epochs | 3 |
| Learning rate / schedule | 2e-4 / cosine, 3% warmup |
| Max sequence length | 2560 |
| Effective batch size | 16 |
| Optimizer | paged AdamW 8-bit |
| Training examples | 1,321 |

## Limitations

- Purpose-built for the JSON content-plan schema above — not a general chat model.
- English marketing scenarios only; evaluated in-distribution on synthetic data.
- Can still produce off-brand copy or unverified claims — keep a human in the loop.

## License

Apache-2.0.

## Base model

Fine-tuned from `marketeam/Qwen-Marketing` (Apache-2.0).