初始化项目，由ModelHub XC社区提供模型

Model: huihui-ai/Qwen2.5-1.5B-Instruct-abliterated-SFT Source: Original Platform
2026-06-16 14:17:11 +08:00
commit 566a75b9b3
11 changed files with 151874 additions and 0 deletions
--- a/README.md
+++ b/README.md
@@ -0,0 +1,140 @@
+---
+base_model:
+- huihui-ai/Qwen2.5-1.5B-Instruct-abliterated
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- abliterated
+- uncensored
+license: apache-2.0
+language:
+- en
+datasets:
+- huihui-ai/Guilherme34_uncensor
+---
+
+# huihui-ai/Qwen2.5-1.5B-Instruct-abliterated-SFT
+
+- **Developed by:** huihui-ai
+- **License:** apache-2.0
+- **Finetuned from model :** [huihui-ai/Qwen2.5-1.5B-Instruct-abliterated](https://huggingface.co/huihui-ai/Qwen2.5-0.5B-Instruct-abliterated)
+- **Dataset used to train :** [huihui-ai/Guilherme34_uncensor](https://huggingface.co/datasets/huihui-ai/Guilherme34_uncensor), please refer to [SFT with Unsloth](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_(7B)-Alpaca.ipynb#scrollTo=2ejIt2xSNKKp).
+
+## Use with transformers
+
+```
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer
+import torch
+import os
+import signal
+
+cpu_count = os.cpu_count()
+print(f"Number of CPU cores in the system: {cpu_count}")
+half_cpu_count = cpu_count // 2
+os.environ["MKL_NUM_THREADS"] = str(half_cpu_count)
+os.environ["OMP_NUM_THREADS"] = str(half_cpu_count)
+torch.set_num_threads(half_cpu_count)
+
+print(f"PyTorch threads: {torch.get_num_threads()}")
+print(f"MKL threads: {os.getenv('MKL_NUM_THREADS')}")
+print(f"OMP threads: {os.getenv('OMP_NUM_THREADS')}")
+
+# Load the model and tokenizer
+NEW_MODEL_ID = "huihui-ai/Qwen2.5-1.5B-Instruct-abliterated-SFT"
+print(f"Load Model {NEW_MODEL_ID} ... ")
+quant_config_4 = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_use_double_quant=True,
+    llm_int8_enable_fp32_cpu_offload=True,
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    NEW_MODEL_ID,
+    device_map="auto",
+    trust_remote_code=True,
+    #quantization_config=quant_config_4,
+    torch_dtype=torch.bfloat16
+)
+tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+tokenizer.pad_token_id = tokenizer.eos_token_id
+
+initial_messages = [{"role": "system", "content": "You are a helpful assistant."}]
+messages = initial_messages.copy()
+
+class CustomTextStreamer(TextStreamer):
+    def __init__(self, tokenizer, skip_prompt=True, skip_special_tokens=True):
+        super().__init__(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens)
+        self.generated_text = ""
+        self.stop_flag = False
+
+    def on_finalized_text(self, text: str, stream_end: bool = False):
+        self.generated_text += text
+        print(text, end="", flush=True)
+        if self.stop_flag:
+            raise StopIteration
+
+    def stop_generation(self):
+        self.stop_flag = True
+
+def generate_stream(model, tokenizer, messages, max_new_tokens):
+    input_ids = tokenizer.apply_chat_template(
+        messages,
+        tokenize=True,
+        add_generation_prompt=True,
+        return_tensors="pt"
+    )
+    attention_mask = torch.ones_like(input_ids, dtype=torch.long)
+    tokens = input_ids.to(model.device) 
+    attention_mask = attention_mask.to(model.device)
+
+    streamer = CustomTextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+    def signal_handler(sig, frame):
+        streamer.stop_generation()
+        print("\n[Generation stopped by user with Ctrl+C]")
+
+    signal.signal(signal.SIGINT, signal_handler)
+    
+    print("Response: ", end="", flush=True)
+    try:
+        generated_ids = model.generate(
+            tokens,
+            attention_mask=attention_mask,
+            use_cache=False,
+            max_new_tokens=max_new_tokens,
+            do_sample=True,
+            pad_token_id=tokenizer.pad_token_id,
+            streamer=streamer
+        )
+        del generated_ids
+    except StopIteration:
+        print("\n[Stopped by user]")
+
+    del input_ids, attention_mask
+    torch.cuda.empty_cache()
+    signal.signal(signal.SIGINT, signal.SIG_DFL)
+
+    return streamer.generated_text, streamer.stop_flag
+
+while True:
+    user_input = input("\nUser: ").strip()
+    if user_input.lower() == "/exit":
+        print("Exiting chat.")
+        break
+    if user_input.lower() == "/clear":
+        messages = initial_messages.copy()
+        print("Chat history cleared. Starting a new conversation.")
+        continue
+    if not user_input:
+        print("Input cannot be empty. Please enter something.")
+        continue
+    messages.append({"role": "user", "content": user_input})
+    response, stop_flag = generate_stream(model, tokenizer, messages, 8192)
+    if stop_flag:
+        continue
+    messages.append({"role": "assistant", "content": response})
+```