LLaMA-1

Qwen2-0.5B Reddit LoRA Adapter

Repo: iko-01/LLaMA-1
Base model: Qwen/Qwen2-0.5B
Adapter type: LoRA (via LLaMA-Factory + QLoRA)
Intended use: Simulating casual, Reddit-style comments, discussions, and thread replies

Model Description

This is a LoRA adapter fine-tuned on top of Qwen2-0.5B using a filtered subset of Reddit posts & comments from the Dolma dataset (v1.6 Reddit portion).

The model is trained to generate informal, conversational text typical of Reddit threads — including sarcasm, memes references, casual opinions, upvotes/downvotes vibe, and natural thread continuations.

Despite the repository name (LLaMA-1), this is not a LLaMA model — it is purely Qwen2 architecture.

Key Characteristics

• Extremely lightweight (only ~0.5B base + small LoRA adapter)
• Runs comfortably on consumer GPUs, laptops, or even decent CPUs
• Fast inference (very suitable for local prototyping, chatbots, Reddit simulators, etc.)
• Casual / internet / meme-friendly tone

Training Details

• Framework: LLaMA-Factory

• Training method: QLoRA (4-bit base quantization + LoRA)

• Dataset size: ~6,000 high-quality, deduplicated Reddit samples

• Hardware: Google Colab T4 (single GPU)

• Training duration: ≈ 30 minutes

• Hyperparameters:

  Parameter	Value
  LoRA rank (r)	32
  LoRA alpha	64
  Learning rate	2e-4
  Batch size	2
  Gradient accumulation	16
  Epochs	3
  Optimizer	AdamW
  Warmup ratio	0.03

Usage

pip install -U transformers peft torch accelerate bitsandbytes  # bitsandbytes optional but recommended

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model_id = "Qwen/Qwen2-0.5B"
adapter_id    = "iko-01/LLaMA-1"

# Load base model
model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)

# Apply LoRA adapter
model = PeftModel.from_pretrained(model, adapter_id)
model.eval()

tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)

# Example prompt
prompt = """Continue this r/AskReddit thread:

After playing for 50 hours I finally"""

messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    out = model.generate(
        **inputs,
        max_new_tokens=120,
        temperature=0.75,
        top_p=0.92,
        repetition_penalty=1.08,
        do_sample=True
    )

response = tokenizer.decode(out[0][len(inputs.input_ids[0]):], skip_special_tokens=True)
print(response)

Example Outputs

Prompt:
Complete this r/gaming discussion: After playing for 50 hours I finally

Typical model output:
...realized the main story is mid but the side content is actually peak. The open world exploration in the frozen north hits different. Spent like 6 hours just fishing and upgrading my house and I don't even feel bad about it lmao. Anyone else 100% the fishing minigame before the final boss?

Limitations & Responsible Use

• Model size — Being a 0.5B model, it has limited world knowledge, reasoning depth, and coherence over very long contexts compared to 7B+ models.
• Reddit bias — The training data comes from Reddit → expect informal language, slang, sarcasm, exaggeration, memes, controversial/hot-take opinions, and sometimes toxic phrasing.
• Hallucinations — Can confidently generate plausible but incorrect facts, especially outside popular Reddit topics.
• Not for production / sensitive use — Not suitable for factual Q&A, customer support, education, legal/medical advice, or any high-stakes application.
• English only — The fine-tune was done exclusively on English Reddit content.

Use this model mainly for creative, entertainment, or research purposes (e.g. generating synthetic discussion data, building Reddit-style bots, style transfer experiments).

Citation / Thanks

If you use this adapter in your work, feel free to mention:

Fine-tuned with LLaMA-Factory on Qwen2-0.5B using Reddit data from Dolma.

Big thanks to the Qwen team, LLaMA-Factory contributors, and AllenAI (Dolma dataset).

Happy hacking! 🚀