初始化项目，由ModelHub XC社区提供模型

Model: UW-Madison-Lee-Lab/Llama-PRM800K
Source: Original Platform

README.md

@@ -0,0 +1,51 @@
+---
+library_name: transformers
+license: llama3.1
+base_model: meta-llama/Llama-3.1-8B-Instruct
+tags:
+- generated_from_trainer
+model-index:
+- name: Llama-PRM800K
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# Llama-PRM800K
+
+This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on [PRM800K](https://github.com/openai/prm800k/tree/main).
+
+## Get rewards
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+def get_tokenizer(model_id):
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    tokenizer.pad_token = tokenizer.eos_token  
+    tokenizer.padding_side = 'left' 
+    tokenizer.truncation_side = 'left'
+    return tokenizer
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+tokenizer = get_tokenizer('UW-Madison-Lee-Lab/Llama-PRM800K')
+model = AutoModelForCausalLM.from_pretrained('UW-Madison-Lee-Lab/Llama-PRM800K')
+candidate_tokens = [12, 10]
+model.to(device)
+
+question = 'Question: In Python 3, which of the following function convert a string to an int in python?\nA. short(x)\nB. float(x)\nC. integer(x [,base])\nD. double(x)\nE. int(x [,base])\nF. long(x [,base] )\nG. num(x)\nH. str(x)\nI. char(x)\nJ. digit(x [,base])'
+solution = ["To convert a string to an integer in Python 3, we use the built-in function int().",
+            "The int() function takes two arguments: the string to be converted and an optional base (default is 10, which is for decimal).",
+            "For example: int(\"123\", 10) converts the string \"123\" to the integer 123.",
+            "Looking at the options, we can see that the correct function is option E: int(x [,base]).",
+            "The answer is (E)."]
+input_text = question + ' \n\n' + ' \n\n\n\n'.join(solution) + ' \n\n\n\n' # solution steps are separated by ' \n\n\n\n'
+input_id = torch.tensor([tokenizer.encode(input_text)]).to(device)
+
+with torch.no_grad():
+    logits = model(input_id).logits[:,:,candidate_tokens]
+    scores = logits.softmax(dim=-1)[:,:,1] 
+    step_scores = scores[input_id == 23535]
+    step_probs  = step_scores.tolist()
+```