初始化项目，由ModelHub XC社区提供模型

Model: llmware/slim-sql-1b-v0
Source: Original Platform

generation_test_sql_slim_hf.py

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+
+import time
+import os
+import json
+from werkzeug.utils import secure_filename
+import re
+import ast
+import sqlite3
+import random
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from llmware.models import ModelCatalog
+from llmware.prompts import Prompt
+
+def model_test_run_general():
+
+    t0 = time.time()
+
+    model_name = "llmware/slim-sql-1b-v0"
+
+    print("update: model_name - ", model_name)
+
+    custom_hf_model = AutoModelForCausalLM.from_pretrained(model_name,trust_remote_code=True)
+
+    hf_tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+    #   now, we have 'imported' our own custom 'instruct' model into llmware
+    model = ModelCatalog().load_hf_generative_model(custom_hf_model, hf_tokenizer, instruction_following=False,
+                                                    prompt_wrapper="human_bot")
+
+    model.temperature = 0.3
+    # run direct inference on model
+    print("\nupdate: Starting Generative Instruct Custom Fine-tuned Test")
+
+    t1 = time.time()
+
+    print("update: time loading model - ", t1 - t0)
+
+    fp = ""
+    fn = "sql_test_100_simple_s.jsonl"
+
+    opened_file = open(os.path.join(fp, fn), "r")
+
+    prompt_list = []
+
+    for i, rows in enumerate(opened_file):
+        # print("update: ", i, rows)
+        rows = json.loads(rows)
+        new_entry = {"question": rows["question"],
+                     "answer": rows["answer"],
+                     "context": rows["context"]}
+
+        prompt_list.append(new_entry)
+
+    random.shuffle(prompt_list)
+
+    total_response_output = []
+    perfect_match = 0
+
+    for i, entries in enumerate(prompt_list):
+        prompt = entries["question"]
+        context = re.sub("[\n\r]","", entries["context"])
+        context = re.sub("\s+", " ", context)
+        context = re.sub("\"", "", context)
+
+        answer = ""
+
+        if "answer" in entries:
+            answer = entries["answer"]
+
+        output = model.inference(prompt, add_context=context, add_prompt_engineering=True)
+
+        print("\nupdate: model question - ", prompt)
+
+        llm_response = re.sub("['\"]", "", output["llm_response"])
+        answer = re.sub("['\"]", "", answer)
+
+        print("update: model response - ", i, llm_response)
+        print("update: model gold answer - ", answer)
+
+        if llm_response.strip().lower() == answer.strip().lower():
+            perfect_match += 1
+            print("update: 100% MATCH")
+
+        print("update: perfect match accuracy - ", perfect_match / (i+1))
+
+        core_output = {"number": i,
+                       "llm_response": output["llm_response"],
+                       "gold_answer": answer,
+                       "prompt": prompt,
+                       "usage": output["usage"]}
+
+        total_response_output.append(core_output)
+
+    t2 = time.time()
+
+    print("update: total processing time: ", t2-t1)
+
+    return total_response_output
+
+output = model_test_run_general()