DanielG/llama-3.2-3b-linuxmemes

DanielG/ llama-3.2-3b-linuxmemes

34 Downloads Updated 3 months ago

A Llama 3.2 3B model fine-tuned on ~550k post/comment pairs from r/linuxmemes. Replies like a sarcastic, meme-literate Reddit commenter. Built for fun/persona use, not factual accuracy.

ollama run DanielG/llama-3.2-3b-linuxmemes

curl http://localhost:11434/api/chat \
  -d '{
    "model": "DanielG/llama-3.2-3b-linuxmemes",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

from ollama import chat

response = chat(
    model='DanielG/llama-3.2-3b-linuxmemes',
    messages=[{'role': 'user', 'content': 'Hello!'}],
)
print(response.message.content)

import ollama from 'ollama'

const response = await ollama.chat({
  model: 'DanielG/llama-3.2-3b-linuxmemes',
  messages: [{role: 'user', content: 'Hello!'}],
})
console.log(response.message.content)

Models

Name

1 model

Size / Usage

Context

Input

llama-3.2-3b-linuxmemes:latest

2.0GB · 128K context window · Text · 3 months ago

llama-3.2-3b-linuxmemes:latest

2.0GB

128K

Text

Readme

Transformers / PEFT:

from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("Daniel-Guckert/Llama-3.2-3B-LinuxMemes")
tokenizer = AutoTokenizer.from_pretrained("Daniel-Guckert/Llama-3.2-3B-LinuxMemes")

What it’s good at

Casual Linux/FOSS banter and opinions
Reddit-style deadpan humor and meme formats (“X my beloved”, self-aware contradictions)
Quick, terse replies

What it’s NOT good at

Factual accuracy — optimized for voice, not correctness
Long technical walkthroughs
Anything outside casual conversational replies

Details

Base model: unsloth/Llama-3.2-3B-Instruct
Method: QLoRA (rank 8) via Unsloth, merged + quantized to Q4_K_M for the GGUF release
Data: ~548,000 cleaned post→reply / comment→reply pairs from r/linuxmemes (sourced via Arctic Shift), filtered for score ≥2, deduped, bot-filtered, with post-title context prepended for nested replies
Training: Modal (A100), 1 epoch, sequence packing enabled, final loss ~1.48

Intended use

Entertainment / personal chatbot persona projects. Not intended for factual Q&A, production deployment without further filtering, or contexts where Reddit-style sarcasm/profanity would be inappropriate.

Limitations

Trained on real, unmoderated Reddit comments — may surface profanity, dismissiveness, or in-jokes specific to r/linuxmemes. No additional safety fine-tuning beyond the base model’s existing alignment.

**Transformers / PEFT:**
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("Daniel-Guckert/Llama-3.2-3B-LinuxMemes")
tokenizer = AutoTokenizer.from_pretrained("Daniel-Guckert/Llama-3.2-3B-LinuxMemes")
```

## What it's good at

- Casual Linux/FOSS banter and opinions
- Reddit-style deadpan humor and meme formats ("X my beloved", self-aware contradictions)
- Quick, terse replies

## What it's NOT good at

- Factual accuracy — optimized for *voice*, not correctness
- Long technical walkthroughs
- Anything outside casual conversational replies

## Details

- **Base model:** unsloth/Llama-3.2-3B-Instruct
- **Method:** QLoRA (rank 8) via Unsloth, merged + quantized to Q4_K_M for the GGUF release
- **Data:** ~548,000 cleaned post→reply / comment→reply pairs from r/linuxmemes (sourced via Arctic Shift), filtered for score ≥2, deduped, bot-filtered, with post-title context prepended for nested replies
- **Training:** Modal (A100), 1 epoch, sequence packing enabled, final loss ~1.48

## Intended use

Entertainment / personal chatbot persona projects. Not intended for factual Q&A, production deployment without further filtering, or contexts where Reddit-style sarcasm/profanity would be inappropriate.

## Limitations

Trained on real, unmoderated Reddit comments — may surface profanity, dismissiveness, or in-jokes specific to r/linuxmemes. No additional safety fine-tuning beyond the base model's existing alignment.

Paste, drop or click to upload images (.png, .jpeg, .jpg, .svg, .gif)