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qwen7b-marine
naga080898/qwen7b-marine
qwen7b-marine is a machine learning model from naga080898. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
This repository contains LoRA adapters for the Maritime-LLM project, trained via progressive continual pretraining on maritime domain data.
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Updated Dec 13, 2025
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At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Dec 13, 2025
- Updated
- Dec 13, 2025
- SHA
- 7c056576
Base models
Model card
From the Hugging Face model README
Qwen 7B Maritime (Marine) Adapters
This repository contains LoRA adapters for the Maritime-LLM project, trained via progressive continual pretraining on maritime domain data.
📦 Available Checkpoints
All checkpoints are stored in subfolders within this repository.
| Description | Subfolder Name | Training Steps |
|---|---|---|
| Phase 1a Short Context (2,157 steps) | phase1a-short-ckpt2157 | 2157 |
| Phase 1a Short Context (4,314 steps) | phase1a-short-ckpt4314 | 4314 |
| Phase 1a Short Context (6,471 steps) | phase1a-short-ckpt6471 | 6471 |
| Phase 1a Short Context (8,628 steps - Final) | phase1a-short-ckpt8628 | 8628 |
| Phase 1b Medium Context (872 steps) | phase1b-medium-ckpt872 | 872 |
🚀 Usage
You can load any specific checkpoint by specifying the subfolder argument.
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
# 1. Load Base Model
base_model_id = "Qwen/Qwen2.5-7B-Instruct"
base_model = AutoModelForCausalLM.from_pretrained(
base_model_id,
device_map="auto",
torch_dtype="auto"
)
# 2. Load Specific Adapter Checkpoint
# Example: Loading Phase 1a Final Checkpoint
adapter_id = "naga080898/qwen7b-marine"
subfolder = "phase1a-short-ckpt8628"
model = PeftModel.from_pretrained(
base_model,
adapter_id,
subfolder=subfolder
)
# 3. Inference
tokenizer = AutoTokenizer.from_pretrained(base_model_id)
prompt = "Explain the safety procedure for enclosing space entry:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Training Phases
- Phase 1a (Short Context): Foundation maritime knowledge training (Short context window).
- Phase 1b (Medium Context): Extended context training with longer documents.
- License
- apache-2.0
- Created
- Dec 13, 2025
- Updated
- Dec 13, 2025