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imadreamerboy/Qwen3-8B-Marxist
Qwen3-8B-Marxist is a text generation model from imadreamerboy. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
Author: @imadreamerboy Base model: Qwen/Qwen3-8B Dataset: imadreamerboy/our-dataset License: Apache 2.0
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From the Hugging Face model README
Author: @imadreamerboy
Base model: Qwen/Qwen3-8B
Dataset: imadreamerboy/our-dataset
License: Apache 2.0
Qwen3-8B-Marxist is a Qwen3-8B derivative tuned for Marx-style analytical responses: dialectical framing, political-economy language, and a serious historical-materialist tone.
This release is a validated demo/toy research model rather than a production-specialized system. The strongest behavior is still system-prompt conditioned. Without the system prompt, the model often shows Marxian bias and vocabulary, but it is less consistent and more neutral in framing.
This repo now reflects a fresh end-to-end run that was:
messages datasetllama.cpp on GPU| Item | Value |
|---|---|
| Base model | Qwen/Qwen3-8B |
| Training method | LoRA -> merged full model |
| LoRA rank | 32 |
| LoRA alpha | 32 |
| LoRA dropout | 0.0 |
| Target modules | all-linear |
| Dataset format | JSONL with OpenAI-style messages |
| Conversations used | 399 |
| Supervision | all assistant turns in each conversation |
| Steps | 100 |
| Initial eval weighted NLL | 2.3348 |
| Final eval weighted NLL | 1.6889 |
| Final train weighted NLL | 1.4316 |
Validated behaviors for this release:
I am Qwen...user / assistant turn driftObserved nuance:
Use a system prompt. The model is noticeably better and more stable with one.
Example system prompt:
You are Our-LLM, a dialectical intelligence model embodying the collective reasoning of Karl Marx.
You speak in the analytical, historically grounded, and rhetorically powerful tone of Marx's mature works.
You write as if addressing comrades and critics alike, drawing upon the collected works of Marx and Engels.
Your purpose is to interpret, critique, and explain the social relations of production, the contradictions of capitalism, and the historical movement of classes.
You avoid anachronisms unless explicitly engaging with modern conditions, in which case you apply Marxian categories to them.
You maintain seriousness and intellectual rigour, not parody.
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "imadreamerboy/Qwen3-8B-Marxist"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
dtype=torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float16,
trust_remote_code=True,
)
messages = [
{
"role": "system",
"content": (
"You are Our-LLM, a dialectical intelligence model embodying the collective reasoning of Karl Marx. "
"You speak in the analytical, historically grounded, and rhetorically powerful tone of Marx's mature works."
),
},
{
"role": "user",
"content": "Explain surplus value in modern platform capitalism.",
},
]
prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300, temperature=0.7, top_p=0.8)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
This repo includes a refreshed q4_k_m GGUF for local inference with llama.cpp-style runtimes.
| File | Notes |
|---|---|
qwen3-8b-marxist-q4_k_m.gguf | Default deployment choice |