MLMLML/README.md

license, base_model, tags, language, pipeline_tag

license	base_model	tags	language	pipeline_tag
agpl-3.0	unsloth/DeepSeek-R1-0528-Qwen3-8B	marxism-leninism grpo llama-cpp ollama political-education marxism communism political-extremism	en	text-generation

MLMLML - Machine Learning Marxist-Leninist Models of Language

A GRPO fine-tuned language model for Marxist-Leninist political education and analysis.

Model Description

This model is fine-tuned from unsloth/DeepSeek-R1-0528-Qwen3-8B using Group Relative Policy Optimization (GRPO) on a curated dataset of Marxist-Leninist Q&A pairs from ProleWiki.

The training rewards:

• Ideological firmness: Clear positions grounded in material analysis
• Coherence: Self-consistent, well-structured responses
• Accuracy: Faithful to Marxist-Leninist theory and historical evidence

The training penalizes:

• "Both-sidesing" and false balance
• Hedging and evasive language
• Bourgeois framing and ahistorical claims

Writing Style

Following Chairman Mao's guidance in "Oppose Stereotyped Party Writing":

• Vigorous, lively, fresh and forceful - never drab or stereotyped
• Audience-aware - "When shooting an arrow, one must aim at the target"
• Investigation-based - "No investigation, no right to speak"
• Clear positions - FOR and AGAINST, using scientific argument

Usage

Download and Convert to GGUF

# Clone the repo
git lfs install
git clone https://huggingface.co/percyraskova/MLMLML
cd MLMLML

# Convert to GGUF (requires llama.cpp)
python ~/llama.cpp/convert_hf_to_gguf.py . --outfile MLMLML-F16.gguf --outtype f16

# Quantize to Q4_K_M
~/llama.cpp/build/bin/llama-quantize MLMLML-F16.gguf MLMLML-Q4_K_M.gguf Q4_K_M

# Create Ollama model
ollama create mlmlml -f Modelfile
ollama run mlmlml

Direct with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("percyraskova/MLMLML")
tokenizer = AutoTokenizer.from_pretrained("percyraskova/MLMLML")

inputs = tokenizer("What is imperialism?", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0]))

Training Details

• Base model: unsloth/DeepSeek-R1-0528-Qwen3-8B
• Method: GRPO (Group Relative Policy Optimization)
• Dataset: ProleWiki Q&A pairs (~4500 samples)
• Epochs: 2
• Hardware: NVIDIA A100 80GB

Limitations

This model is designed for educational purposes about Marxist-Leninist theory and analysis. It takes clear ideological positions and is not intended to be "neutral" on class struggle, imperialism, or other questions where Marxism-Leninism has definite answers.

License

Apache 2.0

Citation

If you use this model, please cite ProleWiki as the source of training data.