--- license: apache-2.0 base_model: - huihui-ai/Qwen3-8B-abliterated - mlabonne/Qwen3-8B-abliterated - Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1 - soob3123/GrayLine-Qwen3-8B library_name: transformers license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE pipeline_tag: text-generation tags: - moe extra_gated_prompt: >- **Usage Warnings** “**Risk of Sensitive or Controversial Outputs**“: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs. “**Not Suitable for All Audiences**:“ Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security. “**Legal and Ethical Responsibilities**“: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences. “**Research and Experimental Use**“: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications. “**Monitoring and Review Recommendations**“: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content. “**No Default Safety Guarantees**“: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use. --- # huihui-ai/Huihui-MoE-24B-A8B-abliterated ## Model Overview Huihui-MoE-24B-A8B-abliterated is a **Mixture of Experts (MoE)** language model developed by **huihui.ai**, built upon the **[huihui-ai/Qwen3-8B-abliterated](https://huggingface.co/huihui-ai/Qwen3-8B-abliterated)** base model. It enhances the standard Transformer architecture by replacing MLP layers with MoE layers, each containing 4 experts, to achieve high performance with efficient inference. The model is designed for natural language processing tasks, including text generation, question answering, and conversational applications. This model combines four ablated models, and perhaps it can achieve the performance of all the ablated models? This is just a test. The exploration of merging different manifestations of models of the same type is another possibility. - **Architecture**: Qwen3MoeForCausalLM model with 4 experts per layer (num_experts=4), activating 1 expert per token (num_experts_per_tok=1). - **Total Parameters**: ~24 billion (24B) - **Activated Parameters**: ~8 billion (8B) during inference, comparable to Qwen3-8B-abliterated - **Developer**: huihui.ai - **Release Date**: June 2025 - **License**: Inherits the license of the Qwen3 base model (apache-2.0) ## Expert Models: ### Expert 1: [mlabonne/Qwen3-8B-abliterated](https://huggingface.co/mlabonne/Qwen3-8B-abliterated) ### Expert 2: [Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1](https://huggingface.co/Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1) ### Expert 3: [huihui-ai/Qwen3-8B-abliterated](https://huggingface.co/huihui-ai/Qwen3-8B-abliterated) ### Expert 4: [soob3123/GrayLine-Qwen3-8B](https://huggingface.co/soob3123/GrayLine-Qwen3-8B) ### Instruction Following: [huihui-ai/Qwen3-8B-abliterated](https://huggingface.co/huihui-ai/Qwen3-8B-abliterated) ## Training - **Base Model**: Qwen3-8B-abliterated - **Conversion**: The model copies embeddings, self-attention, and normalization weights from Qwen3-8B-abliterated, replacing MLP layers with MoE layers (4 experts). Gating weights are randomly initialized. - **Fine-Tuning**: Not fine-tuned; users are recommended to fine-tune for specific tasks to optimize expert routing. ## ollama You can use [huihui_ai/huihui-moe-abliterated:24b](https://ollama.com/huihui_ai/huihui-moe-abliterated:24b) directly, Switch the thinking toggle using /set think and /set nothink ``` ollama run huihui_ai/huihui-moe-abliterated:24b ``` ## Usage ``` from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer import torch import os import signal import random import numpy as np import time from collections import Counter 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/Huihui-MoE-24B-A8B-abliterated" 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 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 messages = [] nothink = False same_seed = False skip_prompt=True skip_special_tokens=True do_sample = True def set_random_seed(seed=None): """Set random seed for reproducibility. If seed is None, use int(time.time()).""" if seed is None: seed = int(time.time()) # Convert float to int random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) # If using CUDA torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False return seed # Return seed for logging if needed 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 self.init_time = time.time() # Record initialization time self.end_time = None # To store end time self.first_token_time = None # To store first token generation time self.token_count = 0 # To track total tokens def on_finalized_text(self, text: str, stream_end: bool = False): if self.first_token_time is None and text.strip(): # Set first token time on first non-empty text self.first_token_time = time.time() self.generated_text += text # Count tokens in the generated text tokens = self.tokenizer.encode(text, add_special_tokens=False) self.token_count += len(tokens) print(text, end="", flush=True) if stream_end: self.end_time = time.time() # Record end time when streaming ends if self.stop_flag: raise StopIteration def stop_generation(self): self.stop_flag = True self.end_time = time.time() # Record end time when generation is stopped def get_metrics(self): """Returns initialization time, first token time, first token latency, end time, total time, total tokens, and tokens per second.""" if self.end_time is None: self.end_time = time.time() # Set end time if not already set total_time = self.end_time - self.init_time # Total time from init to end tokens_per_second = self.token_count / total_time if total_time > 0 else 0 first_token_latency = (self.first_token_time - self.init_time) if self.first_token_time is not None else None metrics = { "init_time": self.init_time, "first_token_time": self.first_token_time, "first_token_latency": first_token_latency, "end_time": self.end_time, "total_time": total_time, # Total time in seconds "total_tokens": self.token_count, "tokens_per_second": tokens_per_second } return metrics def generate_stream(model, tokenizer, messages, nothink, skip_prompt, skip_special_tokens, do_sample, max_new_tokens): input_ids = tokenizer.apply_chat_template( messages, tokenize=True, enable_thinking = not nothink, 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=skip_prompt, skip_special_tokens=skip_special_tokens) def signal_handler(sig, frame): streamer.stop_generation() print("\n[Generation stopped by user with Ctrl+C]") signal.signal(signal.SIGINT, signal_handler) generate_kwargs = {} if do_sample: generate_kwargs = { "do_sample": do_sample, "max_length": max_new_tokens, "temperature": 0.6, "top_k": 20, "top_p": 0.95, "repetition_penalty": 1.2, "no_repeat_ngram_size": 2 } else: generate_kwargs = { "do_sample": do_sample, "max_length": max_new_tokens, "repetition_penalty": 1.2, "no_repeat_ngram_size": 2 } print("Response: ", end="", flush=True) try: generated_ids = model.generate( tokens, attention_mask=attention_mask, #use_cache=False, pad_token_id=tokenizer.pad_token_id, streamer=streamer, **generate_kwargs ) 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, streamer.get_metrics() init_seed = set_random_seed() # List to store activated expert indices activated_experts = [] # Define hook function to capture gate_probs output def hook_fn(module, input, output): # output is gate_probs, shape: [batch_size, sequence_length, num_experts] gate_probs = output # Compute top-1 expert indices (since only one expert is activated) _, topk_indices = gate_probs.topk(1, dim=-1) # Take top-1 # Flatten and store activated expert indices activated_experts.extend(topk_indices.squeeze(-1).view(-1).cpu().tolist()) hooks = [] for layer in model.model.layers: hooks.append(layer.mlp.gate.register_forward_hook(hook_fn)) while True: if same_seed: set_random_seed(init_seed) else: init_seed = set_random_seed() print(f"\nnothink: {nothink}") print(f"skip_prompt: {skip_prompt}") print(f"skip_special_tokens: {skip_special_tokens}") print(f"do_sample: {do_sample}") print(f"same_seed: {same_seed}, {init_seed}\n") user_input = input("User: ").strip() if user_input.lower() == "/exit": print("Exiting chat.") break if user_input.lower() == "/clear": messages = [] print("Chat history cleared. Starting a new conversation.") continue if user_input.lower() == "/nothink": nothink = not nothink continue if user_input.lower() == "/skip_prompt": skip_prompt = not skip_prompt continue if user_input.lower() == "/skip_special_tokens": skip_special_tokens = not skip_special_tokens continue if user_input.lower().startswith("/same_seed"): parts = user_input.split() if len(parts) == 1: # /same_seed (no number) same_seed = not same_seed # Toggle switch elif len(parts) == 2: # /same_seed try: init_seed = int(parts[1]) # Extract and convert number to int same_seed = True except ValueError: print("Error: Please provide a valid integer after /same_seed") continue if user_input.lower() == "/do_sample": do_sample = not do_sample continue if not user_input: print("Input cannot be empty. Please enter something.") continue messages.append({"role": "user", "content": user_input}) activated_experts = [] response, stop_flag, metrics = generate_stream(model, tokenizer, messages, nothink, skip_prompt, skip_special_tokens, do_sample, 40960) print("\n\nMetrics:") for key, value in metrics.items(): print(f" {key}: {value}") # Count the frequency of each activated expert expert_counts = Counter(activated_experts) # Print activation statistics print("\nActivated Expert Statistics:") for expert_idx, count in sorted(expert_counts.items()): print(f"Expert {expert_idx}: {count} times") print("", flush=True) if stop_flag: continue messages.append({"role": "assistant", "content": response}) # Remove all hooks after inference for h in hooks: h.remove() ``` ## Applications - **Text Generation: Articles**, dialogues, and creative writing. - **Question Answering**: Information retrieval and query resolution. - **Conversational AI**: Multi-turn dialogues for chatbots. - **Research**: Exploration of MoE architectures and efficient model scaling. ## Limitations - **Fine-Tuning Required**: Randomly initialized gating weights may lead to suboptimal expert utilization without fine-tuning. - **Compatibility**: Developed with transformers 4.52.4; ensure matching versions to avoid loading issues. - **Inference Speed**: While efficient for an MoE model, performance depends on hardware (GPU recommended). ## Ethical Considerations - **Bias**: Inherits potential biases from the Qwen3-8B-abliterated base model; users should evaluate outputs for fairness. - **Usage**: Intended for research and responsible applications; avoid generating harmful or misleading content. ## Contact - **Developer**: huihui.ai - **Repository**: huihui-ai/Huihui-MoE-24B-A8B-abliterated (available locally or on Hugging Face) - **Issues**: Report bugs or request features via the repository or please send an email to support@huihui.ai