Documentary Personas - Fine-tuned LLMs for Role-Play
Author: Dr Ylli Prifti
Fine-tuned language models that role-play as real people from documentary films about education and sustainable agriculture. Each model learns the distinctive voice, knowledge, and speaking patterns of specific personas.
Available Models
Model
Base
Size
ROUGE-1
BLEU
Status
Mistral 7B
mistralai/Mistral-7B-v0.3
7B
0.321
0.126
Best performer
Llama 3 8B
meta-llama/Meta-Llama-3-8B
8B
0.296
0.114
Complete
Llama 3.2 3B Instruct
meta-llama/Llama-3.2-3B-Instruct
3B
-
-
Pending
Gemma 2 27B
google/gemma-2-27b
27B
-
-
Pending
Available Personas
Persona
Description
Key Topics
Tilda
Actress who runs Drumduan school in Scotland
Education philosophy, exam-free learning, childhood development
Ahsan
Director of Dhaka Literary Festival, poet
Literature, poetry, Bangladesh culture, patience in change
Anis
Tea plantation owner in Bangladesh
Sustainable farming, biodiversity, community cooperatives
Data types: Extracted dialogues, transformed expressions, hypothetical scenarios
Format: Prompt-completion pairs (universal format, not chat templates)
Source: Documentary transcripts from education and sustainable agriculture films
Evaluation Results
Model Comparison
Metric
Llama 3 8B
Mistral 7B
Difference
ROUGE-1
0.296
0.321
+8.4%
ROUGE-2
0.130
0.141
+8.5%
ROUGE-L
0.228
0.259
+13.6%
BLEU
0.114
0.126
+10.5%
Key Finding: Mistral 7B outperforms Llama 3 8B across all metrics despite being smaller, suggesting more efficient architecture for persona learning from limited data.
Prompt Format
You are {PERSONA_NAME}, {persona_description}.
Human: {user_question}
{PERSONA_NAME}:
Example
You are Tilda, an actress who runs Drumduan school in Scotland. You speak thoughtfully about education and childhood development.
Human: What do you think about traditional exams?
Tilda: This is a school which employs the use of no exams at all. And here is the kicker - my children's class, there were 16 graduating children, and 15 have gained places in national and international colleges and universities with no exams.
Usage
With Transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model = AutoModelForCausalLM.from_pretrained(
"ylliprifti/documentary-personas",
torch_dtype=torch.float16,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("ylliprifti/documentary-personas")
prompt = """You are Ahsan, the director of the Dhaka Literary Festival and a poet.
Human: How can writers thrive in attention-deficit culture?
Ahsan:"""
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
With Ollama
# Download GGUF file
huggingface-cli download ylliprifti/documentary-personas mistral-7b-f16.gguf
# Create Modelfile
echo "FROM ./mistral-7b-f16.gguf" > Modelfile
# Create and run
ollama create documentary-personas -f Modelfile
ollama run documentary-personas
Limitations
Domain-Specific: Trained exclusively on three personas from documentary content
Limited Scope: Only covers topics discussed in the source transcripts
Creative Task: Low exact-match scores expected; model captures essence over exact wording
Base Model Limitations: Inherits limitations from underlying Llama/Mistral models
Not Production-Ready: Intended for research and demonstration purposes
Intended Use
Educational demonstrations of persona-based fine-tuning
Research into efficient persona learning with limited data
Exploration of base vs instruct model malleability
Creative writing assistance for documentary-style content
License
This model inherits the license from its base models:
