初始化项目，由ModelHub XC社区提供模型

Model: ibm-research/PowerLM-3b
Source: Original Platform

README.md

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+---
+pipeline_tag: text-generation
+inference: false
+license: apache-2.0
+library_name: transformers
+model-index:
+- name: ibm/PowerLM-3b
+  results:
+  - task:
+      type: text-generation
+    dataset:
+      type: lm-eval-harness
+      name: ARC
+    metrics:
+    - name: accuracy-norm
+      type: accuracy-norm
+      value: 60.5
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: lm-eval-harness
+      name: BoolQ
+    metrics:
+    - name: accuracy
+      type: accuracy
+      value: 72.0
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: lm-eval-harness
+      name: Hellaswag
+    metrics:
+    - name: accuracy-norm
+      type: accuracy-norm
+      value: 74.6
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: lm-eval-harness
+      name: OpenBookQA
+    metrics:
+    - name: accuracy-norm
+      type: accuracy-norm
+      value: 43.6
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: lm-eval-harness
+      name: PIQA
+    metrics:
+    - name: accuracy-norm
+      type: accuracy-norm
+      value: 79.9
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: lm-eval-harness
+      name: Winogrande
+    metrics:
+    - name: accuracy-norm
+      type: accuracy-norm
+      value: 70.0
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: lm-eval-harness
+      name: MMLU (5 shot)
+    metrics:
+    - name: accuracy
+      type: accuracy
+      value: 49.2
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: lm-eval-harness
+      name: GSM8k (5 shot)
+    metrics:
+    - name: accuracy
+      type: accuracy
+      value: 34.9
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: lm-eval-harness
+      name: math (4 shot)
+    metrics:
+    - name: accuracy
+      type: accuracy
+      value: 15.2
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: bigcode-eval
+      name: humaneval
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 26.8
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: bigcode-eval
+      name: MBPP
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 33.6
+      verified: false
+---
+
+## Model Summary
+PowerLM-3B is a 3B state-of-the-art small language model trained with the Power learning rate scheduler. It is trained on a mix of open-source and proprietary datasets. PowerLM-3B has shown promising results compared to other models in the size categories across various benchmarks, including natural language multi-choices, code generation, and math reasoning.
+Paper: https://arxiv.org/abs/2408.13359
+
+## Usage
+Note: Requires installing HF transformers from source.
+
+### Generation
+This is a simple example of how to use **PowerLM-3b** model.
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+device = "cuda" # or "cpu"
+model_path = "ibm/PowerLM-3b"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+# drop device_map if running on CPU
+model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
+model.eval()
+# change input text as desired
+prompt = "Write a code to find the maximum value in a list of numbers."
+# tokenize the text
+input_tokens = tokenizer(prompt, return_tensors="pt")
+# transfer tokenized inputs to the device
+for i in input_tokens:
+    input_tokens[i] = input_tokens[i].to(device)
+# generate output tokens
+output = model.generate(**input_tokens, max_new_tokens=100)
+# decode output tokens into text
+output = tokenizer.batch_decode(output)
+# loop over the batch to print, in this example the batch size is 1
+for i in output:
+    print(i)
+```