初始化项目，由ModelHub XC社区提供模型

Model: BoscoTheDog/bitnet_b1_58-large_q8_0_gguf
Source: Original Platform

eval_task.py

@@ -0,0 +1,63 @@
+import os
+import json
+import argparse
+import torch
+import random
+import glog
+
+from lm_eval import evaluator
+from eval_utils import LMEvalAdaptor
+from .tokenization_bitnet import BitnetTokenizer
+from .modeling_bitnet import BitnetForCausalLM
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--seed', default=0, type=int)
+parser.add_argument('--hf_path', default='1bitLLM/bitnet_b1_58-3B', type=str)
+parser.add_argument('--batch_size', type=int, default=1, help='batch size')
+parser.add_argument("--tasks", type=str)
+parser.add_argument("--output_path", default=None, type=str)
+parser.add_argument('--num_fewshot', type=int, default=0)
+parser.add_argument('--ctx_size', default=2048, type=int)
+
+
+def main(args):
+    model_str = args.hf_path
+    model = BitnetForCausalLM.from_pretrained(
+        args.hf_path,
+        device_map='auto',
+        low_cpu_mem_usage=True, 
+        use_flash_attention_2=True,
+        torch_dtype=torch.float16,
+    ).half()
+
+    tokenizer = BitnetTokenizer.from_pretrained(args.hf_path, use_fast=False)
+    glog.info('loaded model!')
+
+    task_names = args.tasks.split(",")
+
+    lm_eval_model = LMEvalAdaptor(model_str, model, tokenizer, args.batch_size, args.ctx_size)
+    results = evaluator.simple_evaluate(
+        model=lm_eval_model,
+        tasks=task_names,
+        batch_size=args.batch_size,
+        no_cache=True,
+        num_fewshot=args.num_fewshot,
+    )
+
+    print(evaluator.make_table(results))
+
+    if args.output_path is not None:
+        os.makedirs(os.path.dirname(args.output_path), exist_ok=True)
+        # otherwise cannot save
+        results["config"]["model"] = args.hf_path
+        with open(args.output_path, "w") as f:
+            json.dump(results, f, indent=2)
+
+
+if __name__ == '__main__':
+    torch.set_grad_enabled(False)
+    args = parser.parse_args()
+    random.seed(args.seed)
+    torch.random.manual_seed(args.seed)
+    main(args)