Havoc999/tiny-openhermes

🤖 Tiny OpenHermes — LoRA Fine-Tuned on OpenHermes-2.5

Fine-tuned TinyLlama/TinyLlama-1.1B-Chat-v1.0 on the teknium/OpenHermes-2.5 dataset using LoRA (rank 32) via TRL's SFTTrainer on Kaggle Dual T4 GPU.

LoRA rank 32 LoRA alpha 64 Epochs 1 Peak LR 2e-4 Effective batch 64 (4/GPU × 2 GPUs × 8 accum) Precision float16 Hardware Kaggle Dual T4 (2 × 16 GiB)

⚠️ Limitations English-primary (OpenHermes-2.5 is predominantly English)

May hallucinate facts — verify important claims

1.1 B parameter model: complex multi-step reasoning can fail

Not RLHF-aligned for safety beyond TinyLlama's base alignment

Benchmark Results

The model was evaluated using standard NLP benchmarks via the Language Model Evaluation Harness. It demonstrates moderate baseline capabilities in everyday physical reasoning but requires improvement in complex scientific knowledge and multi-step reasoning.

Benchmark	Tasks (Samples)	Metric	Raw Score (acc)	Normalized Score (acc_norm)
PIQA (Physical Commonsense)	1,838	Accuracy	72.58%	72.03%
HellaSwag (Commonsense Reasoning)	10,042	Accuracy	44.69%	59.20%
ARC-Challenge (Advanced Science)	1,172	Accuracy	25.43%	29.69%
MMLU (mathemaatics)	1531	Accuracy	26.13%	26.13%

🚀 Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "Havoc999/tiny-openhermes", torch_dtype=torch.float16
).cuda()
tok = AutoTokenizer.from_pretrained("Havoc999/tiny-openhermes")

prompt = "<|user|>\nExplain gravity simply.</s>\n<|assistant|>\n"
ids = tok(prompt, return_tensors="pt").input_ids.cuda()
out = model.generate(ids, max_new_tokens=200, temperature=0.7, do_sample=True)
print(tok.decode(out[0, ids.shape:], skip_special_tokens=True))