samarthraina/qwen2.5-7b-openhermes-v1
09
license: apache-2.0 base_model: Qwen/Qwen2.5-7B datasets:
- teknium/OpenHermes-2.5 tags:
- sft
- instruction-tuning
- qwen
- lora
- fine-tuned language:
- en pipeline_tag: text-generation ---
Qwen2.5-7B OpenHermes V1
A fine-tuned version of Qwen/Qwen2.5-7B on the OpenHermes-2.5 dataset for instruction following.
Model Details
Training Configuration
LoRA Target Modules
- qproj, kproj, vproj, oproj
- gateproj, upproj, down_proj
Training Results
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("samarthraina/qwen2.5-7b-openhermes-v1")
tokenizer = AutoTokenizer.from_pretrained("samarthraina/qwen2.5-7b-openhermes-v1")
messages = [
{"role": "user", "content": "Explain quantum computing in simple terms."}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Hardware
- GPU: NVIDIA RTX 4090 (48GB)
- Training Time: ~21 hours
Limitations
- Trained on 250k samples (subset of full OpenHermes-2.5)
- Single epoch training
- Conservative hyperparameters (lr=2e-5, r=32)
- May not perform optimally on tasks outside the training distribution
License
This model is released under the Apache 2.0 license, following the base model's license.
Citation
If you use this model, please cite:
@misc{qwen2.5-7b-openhermes-v1,
author = {Samarth Raina},
title = {Qwen2.5-7B OpenHermes V1},
year = {2026},
publisher = {HuggingFace},
url = {https://huggingface.co/samarthraina/qwen2.5-7b-openhermes-v1}
}