somieee20/llama-linkedin

Llama-3.1-8B-LinkedIn-Finetune

This model is a QLoRA fine-tuned version of Meta-Llama 3.1 8B Instruct, trained on a curated dataset of 6,200 viral-style LinkedIn posts inspired by top creators like Lara Acosta, Matt Gray, and Mischa G.
It is optimized for high-conversion, authentic, and emotionally resonant content generation — particularly for founders, creators, and professionals looking to grow influence and inbound leads on LinkedIn.

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🧠 Model Details

Model Description

• Base model: meta-llama/Meta-Llama-3.1-8B-Instruct
• Fine-tuning method: QLoRA (8-bit quantization, rank = 64)
• Context length: 4096 tokens
• Training samples: 6,200 (95 % train / 5 % eval)
• Hardware: NVIDIA H100 80 GB GPU
• Precision: bfloat16 with 8-bit loading
• Epochs: 5
• Optimizer: AdamW (Torch Implementation)
• Learning rate: 3e-4 with cosine schedule
• LoRA target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj, lm_head

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📊 Training Configuration

Category	Value
Batch size (train/eval)	8 / 8
Gradient accumulation	2
Warmup ratio	0.05
Weight decay	0.01
Max grad norm	1.0
Dropout (LoRA)	0.05
Learning rate scheduler	Cosine
Save/eval frequency	Every 200 steps
Total checkpoints kept	5
Logging	W&B (llama-3.1-8b-finetune)
Optimizer	AdamW (β₁ = 0.9, β₂ = 0.999, ε = 1e-8)
Quantization	8-bit with llm_int8_threshold = 6.0
Mixed precision	bf16 (True)

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💾 Dataset

The dataset consists of 6000+ real viral posts from top creators, expanded synthetically to ~6,200 samples using generative augmentation for tone, structure, and narrative diversity.
Each post follows the LinkedIn-native storytelling format (hook → story → lesson → CTA) with labeled stylistic attributes such as authenticity, pacing, and emotional tone.

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⚙️ Use Cases

✅ Direct Use

• Generating high-quality LinkedIn posts or storytelling templates.
• Writing thought leadership content for founders or coaches.
• Producing outbound copy for cold outreach that feels “human.”

🔧 Downstream Use

• Integrate into marketing automation tools or content CRMs.
• Use as a base for persona-tuned agents (e.g., “Matt-style writer”).
• Fine-tune further for niche B2B verticals (e.g., SaaS, AI, VC).

🚫 Out-of-Scope

• Political or sensitive opinion generation.
• Automated spam or fake persona creation.

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🧩 How to Use

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "somieee20/llama-3.1-8b-linkedin"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

prompt = "Write a viral LinkedIn post about learning from startup failures."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=300, temperature=0.8, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))