samarthraina/qwen2.5-7b-openhermes-v1

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

Parameter	Value
Base Model	Qwen/Qwen2.5-7B
Training Method	LoRA (Low-Rank Adaptation)
Dataset	teknium/OpenHermes-2.5
Dataset Size	250,000 samples
Final Eval Loss	0.5643

Training Configuration

Hyperparameter	Value
LoRA Rank (r)	32
LoRA Alpha	64
LoRA Dropout	0.05
Learning Rate	2e-5
LR Scheduler	Cosine
Batch Size	4
Gradient Accumulation	2
Effective Batch Size	8
Max Sequence Length	1536
Epochs	1
Precision	bfloat16
Optimizer	AdamW

LoRA Target Modules

• q_proj, k_proj, v_proj, o_proj
• gate_proj, up_proj, down_proj

Training Results

Step	Eval Loss
500	0.5733
5,000	0.5803
10,000	0.5662
15,000	0.5651
21,000	0.5643

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}
}