--- license: apache-2.0 base_model: - huihui-ai/Qwen3-1.7B-abliterated - mlabonne/Qwen3-1.7B-abliterated - Goekdeniz-Guelmez/Josiefied-Qwen3-1.7B-abliterated-v1 - UnfilteredAI/DAN-Qwen3-1.7B library_name: transformers license_link: https://huggingface.co/Qwen/Qwen3-1.7B/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-4.8B-A1.7B-abliterated ## Model Overview Huihui-MoE-4.8B-A1.7B-abliterated is a **Mixture of Experts (MoE)** language model developed by **huihui.ai**, built upon the **[huihui-ai/Qwen3-1.7B-abliterated](https://huggingface.co/huihui-ai/Qwen3-1.7B-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. ### Note This version does not support ollama because tie_word_embeddings=True results in the absence of lm_head parameters being saved; therefore, ollama cannot be used. If ollama support is required, please choose the latest version [huihui-ai/Huihui-MoE-5B-A1.7B-abliterated](https://huggingface.co/huihui-ai/Huihui-MoE-5B-A1.7B-abliterated). 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**: ~4.8 billion (4.8B) - **Activated Parameters**: ~1.72 billion (1.7B) during inference, comparable to Qwen3-1.7B-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/Qwen3-1.7B-abliterated](https://huggingface.co/mlabonne/Qwen3-1.7B-abliterated) ### Expert 2: [Goekdeniz-Guelmez/Josiefied-Qwen3-1.7B-abliterated-v1](https://huggingface.co/Goekdeniz-Guelmez/Josiefied-Qwen3-1.7B-abliterated-v1) ### Expert 3: [huihui-ai/Qwen3-1.7B-abliterated](https://huggingface.co/huihui-ai/Qwen3-1.7B-abliterated) ### Expert 4: [UnfilteredAI/DAN-Qwen3-1.7B](https://huggingface.co/UnfilteredAI/DAN-Qwen3-1.7B) ### Instruction Following: [huihui-ai/Qwen3-1.7B-abliterated](https://huggingface.co/huihui-ai/Qwen3-1.7B-abliterated) ## Training - **Base Model**: Qwen3-1.7B-abliterated - **Conversion**: The model copies embeddings, self-attention, and normalization weights from Qwen3-1.7B-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. ## 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-4.8B-A1.7B-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 Qwen3-1.7B-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-4.8B-A1.7B-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