初始化项目，由ModelHub XC社区提供模型

Model: tianchuang/Qwen-3-8B-RHEA-property-predictor
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- en
+metrics:
+- accuracy
+- recall
+- precision
+- r_squared
+- mse
+- mae
+base_model:
+- Qwen/Qwen3-8B
+tags:
+- text-generation-inference
+- materials-science
+- qwen3
+- classification
+- regression
+---
+# Model Overview
+
+This repository contains the weights for the **Qwen-3-8B-RHEA-property-predictor**, fine-tuned for refractory high entropy alloys property prediction and phase classification tasks. 
+
+## Training Details
+
+### Prompt Template
+Training prompts follow the template:
+
+**property prediction task**
+> “You are a materials science expert. Predict the {**property name**} for the following refractory high entropy alloy.” 
+
+property name includs density, hardness, compressive yield strength at room temperature, compressive strain at room temperature,
+compressive yield strength at 1073K, and compressive yield strength at 1273K
+
+**phase classification task**
+> “You are a materials science expert. Determine whether the given refractory high entropy alloy has {**single solution phase/intermetallic phase**}.” 
+
+Each query was formatted as:
+> “{**composition**} alloy prepared by {**process description text**}”
+
+### Hyperparameters & Settings
+* **Task:** Binary classification / Regression
+* **GPU:** 4 × NVIDIA GeForce RTX 3090
+* **Seed:** 42
+* **Final Epoch:** 4
+* **per-device batch size:** 2
+* **gradient accumulation:** 8 
+* **Training Objective:** Full fine-tuning with CrossEntropyLoss
+* **Sequence Length:** 1024 tokens
+* **Dataset:**
+  * Train: `train_data-all.jsonl`
+  * Validation: `val_data-all.jsonl`
+* **Dataset Source:** The datasets are available at (https://huggingface.co/datasets/tianchuang/RHEA-mechanical-property)
+
+---
+
+## Validation Metrics 
+
+Metrics are from model-calling evaluation.
+
+**Property prediction (val)**
+| Metric | YS task | Strain task | YS-1073 task | YS-1273 task | hardness task | density task |
+| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
+| R2 | 0.533 | 0.185 | 0.565 | 0.671 | 0.561 | 0.886 |
+| MAE | 237.4 | 7.73 | 192.1 | 117.1 | 76.3 | 0.46 |
+| RMSE | 324.8 | 9.93 | 251.4 | 149.1 | 109.6 | 0.69 |
+
+**Phase classification (val)**
+| Metric | SS task | IM task |
+| :--- | :--- | :--- |
+| Precision | 0.833 | 0.849 |
+| Recall | 0.847 | 0.865 |
+| F1 score | 0.840 | 0.857 |
+| Accuracy | 0.858 | 0.886 |
+
+---
+
+## How to Use
+
+You can load and run inference with this model using the `transformers` library. The model uses the ChatML prompt format.
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+
+# 1. Load model and tokenizer
+model_id = "tianchuang/Qwen-3-8B-RHEA-property-predictor"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+# 2. Prepare your input data
+instruction = "Is the material BaTiO3 likely synthesizable? Answer with P (positive) or N (negative)."
+input_text = ""  # Leave empty if no additional context is needed
+
+# 3. Format the input using the ChatML template
+if input_text:
+    prompt = f"<|im_start|>system\nYou are a materials science expert.<|im_end|>\n<|im_start|>user\n{instruction}\n{input_text}<|im_end|>\n<|im_start|>assistant\n"
+else:
+    prompt = f"<|im_start|>system\nYou are a materials science expert.<|im_end|>\n<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n"
+
+# 4. Tokenize and generate response
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+generation_config = GenerationConfig(
+    max_new_tokens=64,
+    do_sample=True,
+    temperature=0.6, 
+    top_p=0.9, 
+    top_k=50,
+    pad_token_id=tokenizer.eos_token_id,
+    eos_token_id=tokenizer.eos_token_id,
+)
+
+outputs = model.generate(**inputs, generation_config=generation_config)
+
+# 5. Decode and parse the prediction
+full_response = tokenizer.decode(outputs[0], skip_special_tokens=False)
+assistant_response = full_response.split("<|im_start|>assistant")[-1]
+clean_response = assistant_response.replace("<|im_end|>", "").strip()
+
+print(f"Prediction: {clean_response}")