upload evluate code

Evaluate Codes/1-Qwen-7b.py

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+# %%
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers.generation import GenerationConfig
+import torch
+import json
+import os
+from tqdm import tqdm
+from functools import partial
+
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-7B-Chat", trust_remote_code=True)
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen-7B-Chat",
+    device_map="cuda:0",
+    trust_remote_code=True, fp16=True
+).eval()
+
+
+def generate_output(file_path, max_new_tokens=128, prefix=""):
+    with open(file_path, 'r', encoding='utf-8') as file:
+        json_data = json.load(file)
+    output = {}
+    for index in tqdm(range(len(json_data)), desc=file_path):
+        item = json_data[index]
+        instruction = item.get("instruction")
+        question = item.get("question")
+        answer = item.get("answer")
+
+        chat_input = instruction + "\n" + question
+        chat_input = chat_input[:2000]
+        prediction, history = model.chat(tokenizer, chat_input, history=[], max_new_tokens=64,
+                                         do_sample=False, use_cache=True,)
+                                         # prefix=prefix)
+        output[str(index)] = {
+            "origin_prompt": chat_input,
+            "prediction": prediction,
+            "refr": answer
+        }
+    return output
+
+
+folder_path = "../national_test"
+output_path = "./Qwen-7B-Chat"
+if not os.path.exists(output_path):
+    os.mkdir(output_path)
+id_to_task = {
+    "4-1": generate_output,
+    "4-2": generate_output,
+    "4-3": generate_output,
+    "4-4": generate_output,
+    "4-5": generate_output,
+}
+for filename in os.listdir(folder_path):
+    if filename.endswith('.json'):
+        task_name = filename.split(".")[0]
+        if task_name not in id_to_task:
+            continue
+        file_path = os.path.join(folder_path, filename)
+        if os.path.exists(os.path.join(output_path, filename)):
+            continue
+        output = id_to_task[task_name](file_path)
+        if not os.path.exists(output_path):
+            os.mkdir(output_path)
+        with open(os.path.join(output_path, filename), "w", encoding='utf-8') as file:
+            json.dump(output, file, ensure_ascii=False)
+
+# %%

Evaluate Codes/2-InternLM2-7b.py

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+# %%
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers.generation import GenerationConfig
+import torch
+import json
+import os
+from tqdm import tqdm
+from functools import partial
+
+tokenizer = AutoTokenizer.from_pretrained("internlm/internlm2-chat-7b", trust_remote_code=True)
+
+model = AutoModelForCausalLM.from_pretrained(
+    "internlm/internlm2-chat-7b",
+    device_map="cuda:1",
+    trust_remote_code=True, torch_dtype=torch.float16
+).eval()
+
+
+def generate_output(file_path, max_new_tokens=128, prefix=""):
+    with open(file_path, 'r', encoding='utf-8') as file:
+        json_data = json.load(file)
+    output = {}
+    for index in tqdm(range(len(json_data)), desc=file_path):
+        item = json_data[index]
+        instruction = item.get("instruction")
+        question = item.get("question")
+        answer = item.get("answer")
+
+        chat_input = instruction + "\n" + question
+        chat_input = chat_input[:2000]
+        prediction, history = model.chat(tokenizer, chat_input, history=[], max_new_tokens=64,
+                                         do_sample=False, use_cache=True,)
+                                         # prefix=prefix)
+        output[str(index)] = {
+            "origin_prompt": chat_input,
+            "prediction": prediction,
+            "refr": answer
+        }
+    return output
+
+
+folder_path = "../national_test"
+output_path = "./InternLM2-7B-Chat"
+if not os.path.exists(output_path):
+    os.mkdir(output_path)
+id_to_task = {
+    "4-1": generate_output,
+    "4-2": generate_output,
+    "4-3": generate_output,
+    "4-4": generate_output,
+    "4-5": generate_output,
+}
+for filename in os.listdir(folder_path):
+    if filename.endswith('.json'):
+        task_name = filename.split(".")[0]
+        if task_name not in id_to_task:
+            continue
+        file_path = os.path.join(folder_path, filename)
+        if os.path.exists(os.path.join(output_path, filename)):
+            continue
+        output = id_to_task[task_name](file_path)
+        if not os.path.exists(output_path):
+            os.mkdir(output_path)
+        with open(os.path.join(output_path, filename), "w", encoding='utf-8') as file:
+            json.dump(output, file, ensure_ascii=False)
+
+# %%

Evaluate Codes/3-Baichuan2-7b.py

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+# %%
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers.generation import GenerationConfig
+import torch
+import json
+import os
+from tqdm import tqdm
+from functools import partial
+
+tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/Baichuan2-7B-Chat", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    "baichuan-inc/Baichuan2-7B-Chat",
+    device_map="cuda:2",
+    trust_remote_code=True, torch_dtype=torch.float16
+).eval()
+model.generation_config = GenerationConfig.from_pretrained("baichuan-inc/Baichuan2-7B-Chat")
+
+
+def generate_output(file_path, max_new_tokens=128, prefix=""):
+    with open(file_path, 'r', encoding='utf-8') as file:
+        json_data = json.load(file)
+    output = {}
+    for index in tqdm(range(len(json_data)), desc=file_path):
+        item = json_data[index]
+        instruction = item.get("instruction")
+        question = item.get("question")
+        answer = item.get("answer")
+
+        chat_input = instruction + "\n" + question
+        chat_input = chat_input[:2000]
+
+        messages = []
+        messages.append({"role": "user", "content": chat_input})
+        prediction = model.chat(tokenizer, messages)
+
+        # prefix=prefix)
+        output[str(index)] = {
+            "origin_prompt": chat_input,
+            "prediction": prediction,
+            "refr": answer
+        }
+    return output
+
+
+folder_path = "../national_test"
+output_path = "./Baichuan2-7B-Chat"
+if not os.path.exists(output_path):
+    os.mkdir(output_path)
+id_to_task = {
+    "4-1": generate_output,
+    "4-2": generate_output,
+    "4-3": generate_output,
+    "4-4": generate_output,
+    "4-5": generate_output,
+}
+for filename in os.listdir(folder_path):
+    if filename.endswith('.json'):
+        task_name = filename.split(".")[0]
+        if task_name not in id_to_task:
+            continue
+        file_path = os.path.join(folder_path, filename)
+        if os.path.exists(os.path.join(output_path, filename)):
+            continue
+        output = id_to_task[task_name](file_path)
+        if not os.path.exists(output_path):
+            os.mkdir(output_path)
+        with open(os.path.join(output_path, filename), "w", encoding='utf-8') as file:
+            json.dump(output, file, ensure_ascii=False)
+
+# %%

Evaluate Codes/4-ChatGLM2-6b.py

@@ -0,0 +1,65 @@
+# %%
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers.generation import GenerationConfig
+import torch
+import json
+import os
+from tqdm import tqdm
+from functools import partial
+
+tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    "THUDM/chatglm2-6b",
+    device_map="cuda:4",
+    trust_remote_code=True, torch_dtype=torch.float16
+).eval()
+
+
+def generate_output(file_path, max_new_tokens=128, prefix=""):
+    with open(file_path, 'r', encoding='utf-8') as file:
+        json_data = json.load(file)
+    output = {}
+    for index in tqdm(range(len(json_data)), desc=file_path):
+        item = json_data[index]
+        instruction = item.get("instruction")
+        question = item.get("question")
+        answer = item.get("answer")
+
+        chat_input = instruction + "\n" + question
+        chat_input = chat_input[:2000]
+        prediction, history = model.chat(tokenizer, chat_input, history=[])
+
+        output[str(index)] = {
+            "origin_prompt": chat_input,
+            "prediction": prediction,
+            "refr": answer
+        }
+    return output
+
+
+folder_path = "../national_test"
+output_path = "./ChatGLM2-6B-Chat"
+if not os.path.exists(output_path):
+    os.mkdir(output_path)
+id_to_task = {
+    "4-1": generate_output,
+    "4-2": generate_output,
+    "4-3": generate_output,
+    "4-4": generate_output,
+    "4-5": generate_output,
+}
+for filename in os.listdir(folder_path):
+    if filename.endswith('.json'):
+        task_name = filename.split(".")[0]
+        if task_name not in id_to_task:
+            continue
+        file_path = os.path.join(folder_path, filename)
+        if os.path.exists(os.path.join(output_path, filename)):
+            continue
+        output = id_to_task[task_name](file_path)
+        if not os.path.exists(output_path):
+            os.mkdir(output_path)
+        with open(os.path.join(output_path, filename), "w", encoding='utf-8') as file:
+            json.dump(output, file, ensure_ascii=False)
+
+# %%

Evaluate Codes/5-fuzimingcha-7b.py

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+# %%
+from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModel
+from transformers.generation import GenerationConfig
+import torch
+import json
+import os
+from tqdm import tqdm
+from functools import partial
+
+# 设置 HTTP 代理
+os.environ['http_proxy'] = 'http://127.0.0.1:7890'
+
+# 设置 HTTPS 代理
+os.environ['https_proxy'] = 'http://127.0.0.1:7890'
+
+# 设置GPU
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+
+# 验证环境变量是否设置成功
+print(os.environ['http_proxy'])
+print(os.environ['https_proxy'])
+
+tokenizer = AutoTokenizer.from_pretrained("SDUIRLab/fuzi-mingcha-v1_0", trust_remote_code=True)
+model = AutoModel.from_pretrained("SDUIRLab/fuzi-mingcha-v1_0", trust_remote_code=True).half().cuda()
+
+
+def generate_output(file_path, max_new_tokens=128, prefix=""):
+    with open(file_path, 'r', encoding='utf-8') as file:
+        json_data = json.load(file)
+    output = {}
+    for index in tqdm(range(len(json_data)), desc=file_path):
+        item = json_data[index]
+        instruction = item.get("instruction")
+        question = item.get("question")
+        answer = item.get("answer")
+
+        chat_input = instruction + "\n" + question
+        chat_input = chat_input[:2000]
+        prediction, history = model.chat(tokenizer, chat_input, history=[])
+        output[str(index)] = {
+            "origin_prompt": chat_input,
+            "prediction": prediction,
+            "refr": answer
+        }
+    return output
+
+
+folder_path = "../national_test"
+output_path = "./Fuzi-Mingcha"
+if not os.path.exists(output_path):
+    os.mkdir(output_path)
+id_to_task = {
+    "4-1": generate_output,
+    "4-2": generate_output,
+    "4-3": generate_output,
+    "4-4": generate_output,
+    "4-5": generate_output,
+}
+for filename in os.listdir(folder_path):
+    if filename.endswith('.json'):
+        task_name = filename.split(".")[0]
+        if task_name not in id_to_task:
+            continue
+        file_path = os.path.join(folder_path, filename)
+        if os.path.exists(os.path.join(output_path, filename)):
+            continue
+        output = id_to_task[task_name](file_path)
+        if not os.path.exists(output_path):
+            os.mkdir(output_path)
+        with open(os.path.join(output_path, filename), "w", encoding='utf-8') as file:
+            json.dump(output, file, ensure_ascii=False)
+
+# %%