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PROJECT: Intelligent Analysis Model for
Metal Matrix Composites
(Fine‑tuned from Qwen 2.5‑7B Instruct)
- INTRODUCTION
This model is a domain‑specific fine‑tuned version of the Qwen 2.5‑7B Instruct
large language model, trained on a proprietary multi‑modal dataset of Metal
Matrix Composites (MMC). After fine‑tuning, the model acquires the following
core capabilities:
• Image Analysis – semantic understanding and feature description of
microstructure images (SEM/TEM/metallographic).
• Data Analysis – mining correlations from structured/unstructured data,
including processing parameters and composition ratios.
• Performance Prediction – predicting mechanical/physical properties
(e.g., tensile strength, elastic modulus, thermal conductivity) from
given inputs (composition, process, testing environment).
This model is intended for materials R&D support, process optimisation,
failure analysis, and other industrial or academic scenarios.
- MODEL INFORMATION
Base model : Qwen 2.5‑7B Instruct (Qwen/Qwen2.5‑7B‑Instruct)
Parameters : 7 billion (7B)
Context length : 128K tokens (inherited from base)
Fine‑tuning type : Full parameter fine‑tuning / LoRA [choose one]
Framework : [e.g., Hugging Face Transformers + PEFT / DeepSpeed]
Model precision : [FP16 / BF16 / INT8]
Model file format: [safetensors / bin]
- FINE‑TUNING DATASET
Data source : [in‑house dataset / public dataset + manual annotation]
- FUNCTIONAL EXAMPLES (Input → Output)
-
Image Analysis
Input: [image file] + instruction “Describe the distribution morphology of
the reinforcement phase in this SEM image.”
Output: “The reinforcement phase exhibits a network‑like distribution,
particle sizes around 2–5 μm, with local agglomeration...”
-
Data Analysis
Input: “SiC volume fraction 15%, ageing temperature 180°C, duration 8h.
Analyse the trend of these parameters on hardness.”
Output: “At 15% volume fraction, increasing ageing temperature from 160°C
to 180°C improves hardness by about 8%...”
-
Performance Prediction
Input: “Al‑7Si‑0.3Mg alloy, with 10% SiC, T6 treatment. Predict room‑
temperature tensile strength.”
Output: “Predicted tensile strength: 345 ± 12 MPa (based on regression
output).”
-
QUICK START (Inference)
Requirements:
- Python 3.10+
- torch >= 2.0.0
- transformers >= 4.37.0
- [other dependencies, e.g., peft, accelerate, pillow]
Example loading code (Python):
from transformers import AutoModelForCausalLM, AutoTokenizer
from PIL import Image
import torch
model_name = "your_model_path" # local path or Hugging Face ID
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="auto",
torch_dtype=torch.bfloat16,
trust_remote_code=True
)
Text‑only conversation
prompt = "User question"
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
For image analysis (requires a vision encoder if fine‑tuned with visual module)
Please refer to your actual multi‑modal adaptation method.
- TRAINING DETAILS (optional)
Hardware : [e.g., 8 × A100 80GB]
Training time : [~X hours]
Optimiser : [AdamW]
Learning rate : [1e‑5 ~ 5e‑5]
Batch size : [per_device 4, gradient accumulation 8]
Loss function : [cross‑entropy / additional MSE for regression head]
Special techniques: [Flash Attention, gradient checkpointing, etc.]
- LIMITATIONS AND DISCLAIMER
• Prediction results are for research reference only and should not be used
for actual process decision‑making.
• The model has limited extrapolation capability for extreme compositions or
abnormal processes.
• Image analysis relies on input image quality; blurry or non‑standard images
may lead to misjudgements.
• This model is intended for non‑commercial research purposes only. Please
comply with the base model's open‑source license before use.
- ACKNOWLEDGEMENTS AND CITATION
This work is based on the Qwen open‑source community and the support of
[your funding/partners].
If you use this model in your work, please cite:
@article{...your paper or technical report...}
Last updated: 2026‑07‑30
Contact: [your email or project homepage]
