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Model: AbdulrahmanOmar/qwen-marketing-s1
Source: Original Platform

.gitattributes

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README.md

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+---
+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).

added_tokens.json

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+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- set dummy_placeholder = '[[dummy_message]]' %}
+        {%- if message.content == dummy_placeholder %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- else %}
+            {%- if message.reasoning_content is string %}
+                {%- set reasoning_content = message.reasoning_content %}
+            {%- else %}
+                {%- if '</think>' in content %}
+                    {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                    {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+                {%- endif %}
+            {%- endif %}
+            {%- if loop.index0 > ns.last_query_index %}
+                {%- if loop.last or (not loop.last and reasoning_content) %}
+                    {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+                {%- else %}
+                    {{- '<|im_start|>' + message.role + '\n' + content }}
+                {%- endif %}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 40960,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.6",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+  "_name_or_path": "AbdulrahmanOmar/qwen-marketing-s1",
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
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+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.57.6"
+}

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special_tokens_map.json

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