Instructions to use CYHcyh66/AI_Material_mechanics_assistant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CYHcyh66/AI_Material_mechanics_assistant with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="CYHcyh66/AI_Material_mechanics_assistant")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CYHcyh66/AI_Material_mechanics_assistant")
model = AutoModelForCausalLM.from_pretrained("CYHcyh66/AI_Material_mechanics_assistant", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CYHcyh66/AI_Material_mechanics_assistant with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CYHcyh66/AI_Material_mechanics_assistant"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CYHcyh66/AI_Material_mechanics_assistant",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/CYHcyh66/AI_Material_mechanics_assistant

SGLang

How to use CYHcyh66/AI_Material_mechanics_assistant with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "CYHcyh66/AI_Material_mechanics_assistant" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CYHcyh66/AI_Material_mechanics_assistant",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "CYHcyh66/AI_Material_mechanics_assistant" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CYHcyh66/AI_Material_mechanics_assistant",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Desktop
Docker Model Runner
How to use CYHcyh66/AI_Material_mechanics_assistant with Docker Model Runner:
```
docker model run hf.co/CYHcyh66/AI_Material_mechanics_assistant
```

AI Material Mechanics Assistant

Overview

AI Material Mechanics Assistant is a Chinese-language assistant for mechanics-of-materials question answering, developed through LoRA-based instruction fine-tuning of a Qwen2.5-7B-family model.

The project explores domain adaptation for explaining mechanical concepts, interpreting formulas, and presenting worked solutions to textbook-style problems. It is intended for educational assistance and exploratory research, rather than independently verified engineering analysis.

This repository provides complete model weights, configuration files, and a tokenizer in Hugging Face Transformers format.

Model Details

Item	Description
Developer	Yanghao Chen / CYHcyh66
Primary language	Chinese
Domain	Mechanics of materials and introductory engineering mechanics
Base model family	Qwen2.5-7B
Adaptation method	LoRA-based instruction fine-tuning
Architecture	`Qwen2ForCausalLM`
Transformer layers	28
Stored weight precision	BF16
Release format	Four Safetensors weight shards, an index, configuration files, and tokenizer files

The architecture and layer count are recorded in config.json. The weight layout is described in model.safetensors.index.json.

This repository contains a complete checkpoint, not an adapter-only release. The exported configuration does not identify the exact upstream base-model repository or revision.

Associated Dataset

The associated public dataset is Material-mechanics.

Its published file, shujuji_demo.json, contains 238 records with three fields:

instruction: The requested task or guidance for the response.
input: The mechanics question or additional context; this field can be an empty string.
output: The accompanying explanation or worked answer.

The examples cover stress–strain relations, elastic constants, axial deformation, shear, torsion, beam bending, constitutive models, and energy methods. Tasks include conceptual questions, formula explanations, and numerical calculations.

The record count describes the public dataset file. The complete training selection and internal training–validation split are not documented in this model repository. Dataset answers should be checked before being reused as evaluation references.

Fine-Tuning and Reproducibility

The model was adapted using LoRA-based instruction fine-tuning. The exported configuration records Unsloth 2025.3.19 and Transformers 4.50.3; these are recorded software versions, not a tested minimum-version specification.

The public release contains the exported checkpoint but does not include a training script, LoRA configuration, or training logs. Exact training hyperparameters, hardware, and compute time are therefore not specified in this card. Their absence from the repository does not imply that they were never recorded elsewhere.

Usage Notes

The checkpoint and tokenizer are packaged for use with Hugging Face Transformers.

The published tokenizer_config.json does not define a chat template. The prompt format used during fine-tuning should be confirmed before adopting a generic chat-template inference example.

Likewise, the context-length setting in the exported configuration should not be interpreted as evidence of validated long-context performance on mechanics tasks.

Evaluation

The public repository does not provide quantitative held-out evaluation results, a reproducible comparison against the base model, or an evaluation script. This card therefore makes no measured claim of improved mechanics accuracy or expert-level performance.

Future evaluation should use questions excluded from training and assess conceptual correctness, formula selection, numerical accuracy, and unit consistency. Training examples and illustrative answers should not be presented as independent test results.

Intended Use and Limitations

Intended uses include reviewing mechanics terminology, discussing the assumptions behind formulas, and exploring explanations of textbook-style problems in Chinese.

Generated answers may contain incorrect formulas, arithmetic, units, or mechanical assumptions.
Dataset topic coverage does not establish reliable performance across those topics.
Important calculations should be independently checked against reliable references and calculations.
The model does not replace finite-element analysis, experimental validation, or professional engineering judgment.
It should not be used as the sole basis for safety-critical engineering decisions.

Related Resources

This model
Associated dataset: Material-mechanics — 238 published records.
Related model: AI Material Mechanics Assistant (Merged)
Related dataset: Material-mechanics-merge — 774 published records, linked from the related model's card.

Both model repositories contain complete weight files. Repository names and associated dataset sizes alone do not establish their training sequence or relative performance.

Citation

If you use this model, please cite the repository:

@misc{chen2026_ai_material_mechanics_assistant,
  author = {Chen, Yanghao},
  title = {AI Material Mechanics Assistant},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/CYHcyh66/AI_Material_mechanics_assistant}}
}

Contact

Please use the repository's Community tab for questions, feedback, or reports of incorrect mechanics explanations.

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Safetensors

Model size

8B params

Tensor type

BF16

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