初始化项目，由ModelHub XC社区提供模型

Model: Havoc999/tiny-openhermes
Source: Original Platform

README.md

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+---
+language:
+- en
+license: apache-2.0
+datasets:
+- teknium/OpenHermes-2.5
+tags:
+- instruction-tuning
+- chatbot
+- trl
+- openhermes
+pipeline_tag: text-generation
+---
+
+# 🤖 Tiny OpenHermes — LoRA Fine-Tuned on OpenHermes-2.5
+
+Fine-tuned **[TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0)** on the
+[teknium/OpenHermes-2.5](https://huggingface.co/datasets/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
+
+```python
+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))