Downloads · 30 days
25
9% of all-time downloads
Nearby2077/Llama-3-8B-Trotsky-Chatbot
Llama-3-8B-Trotsky-Chatbot is a machine learning model from Nearby2077. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as llama3.
[](https://www.kaggle.com/code/thanvirdiouf/llama-3-8b-trotsky-chatbot) [](https://www.llama.com/llama3/license/)
Downloads · 30 days
25
9% of all-time downloads
All-time downloads
286
Public
Repo size
5 GB
Likes
0
Public
Click a slice to open those files.
.gguf4.9 GB · 99%
From the Hugging Face model README
"Life is not an easy matter…. You cannot live through it without falling into frustration and cynicism unless you have before you a great idea which raises you above personal misery, above weakness, above all kinds of perfidy and baseness."
A revolutionary fine-tune of Meta Llama 3, dedicated to the permanent revolution. This model captures the dialectical style, historical depth, and revolutionary fervor of Leon Trotsky.
Built with Meta Llama 3
The easiest way to run this model with GPU acceleration is via the Kaggle notebook linked above. It is pre-configured for T4 x2 GPUs.
This model is highly efficient and optimized for consumer-grade hardware.
To achieve the authentic "Trotsky" persona, strict adherence to the system prompt is required.
1. System Prompt
"You are Leon Trotsky. Answer the following inquiry using your dialectical materialist perspective, historical knowledge, and revolutionary fervor. You reject the Stalinist falsification of history."
2. Sampler Settings
0.7 (Creative/Chat) or 0.1 (Historical/Factual)1.158192from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="Nearby2077/Llama-3-8B-Trotsky-Chatbot",
filename="Llama-3-8B-Trotsky-Chatbot.Q4_K_M.gguf",
n_gpu_layers=-1,
n_ctx=8192
)
response = llm.create_chat_completion(
messages=[
{"role": "system", "content": "You are Leon Trotsky..."}, # Insert full prompt above
{"role": "user", "content": "What is your analysis of the current moment?"}
],
temperature=0.7,
repeat_penalty=1.15
)
print(response['choices'][0]['message']['content'])