LinkedQwen2.5-14B-Instruct: Fine-tuned LinkedIn Post Generator

Model Details

• Model Name: jacobpwarren/LinkedQwen2.5-14B-Instruct
• Base Model: Qwen2.5-14B-Instruct
• Framework: Built using the Emulate Framework
• License: Apache-2.0 (inherited from base model)
• Fine-tuning Focus: LinkedIn post generation with custom writing style parameters
• Languages Supported: English (primary), plus 28+ languages from base model

Model Description

LinkedQwen2.5-14B-Instruct is a specialized language model fine-tuned on the Qwen2.5-14B-Instruct base for generating high-quality LinkedIn posts with customizable writing styles. The model can produce various post structures while adhering to specific writing patterns, tones, and stylistic features derived from analysis of successful LinkedIn content.

Emulate Framework Implementation

This model is a practical demonstration of the Emulate Framework in action - a methodology that transforms expert knowledge and workflows into defensible AI capabilities that preserve unique competitive advantages. Unlike generic implementations, LinkedQwen2.5-14B-Instruct was built by:

1. Reverse-engineering expert workflows rather than starting with available data
2. Capturing writing style fingerprints including sentence structure, vocabulary patterns, and narrative flow
3. Developing differentiated features that preserve the unique elements of successful LinkedIn content
4. Creating end-to-end automation that completes valuable processes rather than just providing information The model represents the full execution of the Emulate process, including workflow decomposition, expert style fingerprinting, feature engineering, and systematic validation against business metrics.

To learn more about fine-tuning competitively differentiated models, visit https://emulateframework.ai.

Intended Use

This model is designed for:

• Content creators seeking to generate professional LinkedIn posts
• Marketing professionals developing social media content
• Individuals looking to improve their LinkedIn presence with stylistically consistent posts
• Teams wanting to maintain brand voice across LinkedIn communications

Base Model: Qwen2.5-14B-Instruct

The fine-tuning builds upon Qwen2.5-14B-Instruct, which features:

• 14.7B parameters (13.1B non-embedding)
• Causal language model architecture with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
• 48 layers with 40 attention heads for Q and 8 for KV
• Context length support up to 131,072 tokens with generation capability of 8,192 tokens
• Significant improvements in instruction following and generating structured outputs

Fine-tuning Dataset

The model was fine-tuned on a dataset of high-performing LinkedIn posts, analyzed for various writing style features including:

• Sentence structure patterns
• Vocabulary richness
• Line break usage
• Punctuation patterns
• Bullet styles
• Topic flow and shifts
• Narrative structure
• Pacing and sentiment arcs

Prompt Template

The model accepts stylized prompts in the following format:

# Request
Create a LinkedIn post that **[structure type]** **on the topic of**: `[topic]`

### Key Message
```
[opinion] [context]
```

### Writing Constraints
- **Suggested Post Length**: [max_length]
- **Emoji Usage**: [emoji_usage]
- **Tone**: [tone]

### Writing Style Features
- **Sentence Structure**: [sentence_structure_description]
- **Vocabulary Usage**: [vocabulary_usage_description]
- **Common Phrases**: [common_phrases_description]
- **Section Divider**: [divider_style]
- **Line Break Usage**: [line_break_description]
- **Punctuation**: [punctuation_description]
- **Bullet Styles**: [bullet_styles_description]
- **Topic Shifts**: [topic_shifts_description]
- **Narrative Flow**: [narrative_flow_description]
- **Pacing**: [pacing_description]
- **Sentiment Arc**: [sentiment_arc_description]
- **Profanity Level**: [profanity]

Parameters and Variables

Required Parameters

Parameter	Type	Description	Possible Values
structure	String	Post structure type	"instructional", "reflective", "inspirational", "controversial", "insightful", "comparative", "announcement"
topic	Short text	Main subject of the post	Open-ended
opinion	Short text	The user's viewpoint	Open-ended
context	Long text	Background information for the post	Open-ended
max_length	String	Target length for the post	"Up to 750 characters long", "Between 750 and 1,500 characters long", "Between 1,500 and 3,000 characters long"
emoji_usage	String	Level of emoji inclusion	"none", "very low", "low", "medium", "high", "extreme"
tone	String	Overall emotional register	"adventurous", "artistic", "assertive", "authoritative", "bold", "bright", "calm", "capable", "caring", "casual", "charming", "cheerful", "clever", "cocky", "colorful", "comfortable", "conversational", "creative", "daring", "delightful", "detailed", "dramatic", "dry", "eccentric", "elegant", "endearing", "energetic", "engaging", "exciting", "fabulous", "fancy", "fierce", "formal", "friendly", "fun", "futuristic", "glamorous", "honorable", "industrial", "informative", "inspiring", "intense", "inviting", "lively", "natural", "no-nonsense", "persuasive", "playful", "powerful", "professional", "quirky", "rebellious", "reliable", "sarcastic", "savvy", "scholarly", "secure", "serious", "silly", "sleek", "smart", "soothing", "sophisticated", "stable", "stimulating", "strong", "swanky", "tasteful", "thoughtful", "trustworthy", "unconventional", "unique", "upbeat", "versatile", "whimsical", "witty"

Optional Writing Style Features

Feature	Type	Description
sentence_structure	Array	Lengths of sentences, analyzed for patterns and described
vocabulary_usage	Float	Ratio of unique words to total words, described in qualitative terms
common_phrases	Array	Distinctive phrases identified in the writing style
divider_style	String	Character pattern used to separate sections
line_breaks	Integer	Count of line breaks, with qualitative description
punctuation_usage	Object	Frequency of various punctuation marks
bullet_styles	String	Type of bullet point formatting
topic_shifts	Array	Indicators of subject changes throughout the post
flow	Array	Narrative progression patterns
pacing	String	Rhythm of the content delivery
sentiment_arc	String	Emotional progression pattern
profanity	String	Level of profanity allowed

Style Feature Descriptions

Sentence Structure

Based on analysis of sentence lengths, classified as:

• "Short sentences, suggesting brevity and conciseness."
• "Long and complex sentences, indicating a detailed and elaborate style."
• "A mix of short and long sentences, showing a balanced style."

Vocabulary Usage

Calculated as a ratio of unique words to total words:

• 50% unique: "A rich vocabulary, showcasing extensive language use and depth."
• 35% unique: "A developed vocabulary, indicating a wide range of language and expression."
• 25% unique: "A normal vocabulary, reflecting a balanced and versatile use of language."
• 15% unique: "A conservative vocabulary, suggesting a focused and deliberate choice of words."
• ≤15% unique: "A very narrow vocabulary, highlighting a specific and targeted use of language."

Line Break Usage

Based on frequency and average density:

• No breaks: "No line breaks, indicating a continuous block of text."
• Many breaks: "Frequent line breaks, contributing to an easy-to-read structure."
• Few breaks: "Fewer line breaks, indicating a more compact writing style."
• Moderate: "A moderate number of line breaks, balancing readability and density."

Punctuation

Analysis of punctuation frequency relative to text length, with descriptions like:

• "Heavy use of periods/commas/exclamation marks/question marks/semicolons."
• "Regular use of periods/commas/exclamation marks/question marks/semicolons."
• "Standard punctuation usage."

Bullet Styles

Categorized as:

• Various symbol types: "-", "•", "#", "1.", "a.", etc.
• "Differing Emojis": Using various emojis as bullet points
• "EmojiBullets": Multiple emojis as bullets
• "Mixed Bullet Styles": Multiple formatting approaches

Topic Shifts

Based on semantic shift analysis between segments:

• Dynamic (>0.8): "Dynamic topic shifts, showing a highly versatile and engaging writing style."
• Regular (>0.6): "Regular topic shifts, reflecting a balanced and varied approach."
• Moderate (>0.4): "Moderate topic shifts, indicating a well-rounded but focused narrative."
• Conservative (>0.2): "Conservative topic shifts, suggesting a cautious approach to topic changes."
• Consistent (≤0.2): "Consistent topic focus, highlighting a deep and thorough exploration of subjects."

Narrative Flow

Captured as a sequence of content structure types:

• "Introduction/Setup"
• "Conflict/Resolution Point"
• "Introduction/Development"
• "Transition/Reflection"

Pacing

Classified as:

• "Fast"
• "Slow"
• "Variable"
• "Dynamic"
• "Moderate"
• "Short/Not Enough Data" (if <3 sentences)

Sentiment Arc

Progression of emotional tone:

• "Upward Trend": Increasingly positive
• "Downward Trend": Increasingly negative
• "Stable": Consistent emotional tone
• "Complex/Variable": Multiple shifts
• "Short/Not Enough Data for Arc": Insufficient for analysis

Limitations

• The model inherits the limitations of the base Qwen2.5-14B-Instruct model
• Style analysis is most accurate for English-language content
• Certain combinations of style parameters may produce inconsistent results
• Performance may vary for highly technical or specialized industry topics
• Not all writing style features may be represented in generated output with equal fidelity

Ethical Considerations

• The model should not be used to generate misleading or false professional information
• Users should verify factual claims in generated content before publishing on LinkedIn
• Consideration should be given to professional norms and cultural sensitivities in different industries and regions

Citation

@misc{LinkedQwen2.5-14B-Instruct,
    title = {LinkedQwen2.5-14B-Instruct: Fine-tuned LinkedIn Post Generator},
    url = {https://huggingface.co/jacobpwarren/LinkedQwen2.5-14B-Instruct},
    author = {Jacob Warren},
    year = {2025},
    month = {April}
}

@misc{qwen2.5,
    title = {Qwen2.5: A Party of Foundation Models},
    url = {https://qwenlm.github.io/blog/qwen2.5/},
    author = {Qwen Team},
    month = {September},
    year = {2024}
}