huihui-ai/Huihui-MoE-1.2B-A0.6B

Model Overview

Huihui-MoE-1.2B-A0.6B is a Mixture of Experts (MoE) language model developed by huihui.ai, built upon the Qwen/Qwen3-0.6B base model. It enhances the standard Transformer architecture by replacing MLP layers with MoE layers, each containing 3 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

huihui-ai/Huihui-MoE-1B-A0.6B Because tie_word_embeddings=True, the parameters for the lm_head were not saved, which causes ollama to be unable to use it. Therefore, this version supports ollama.

• Architecture: Qwen3MoeForCausalLM model with 3 experts per layer (num_experts=3), activating 1 expert per token (num_experts_per_tok=1).
• Total Parameters: ~1.2 billion (1.2B)
• Activated Parameters: ~0.62 billion (0.6B) during inference, comparable to Qwen3-0.6B
• Developer: huihui.ai
• Release Date: June 2025
• License: Inherits the license of the Qwen3 base model (apache-2.0)

Expert Models:

Coding:

suayptalha/Qwen3-0.6B-Code-Expert

This model was fully fine-tuned with BF16 on first 20k rows of nvidia/OpenCodeReasoning dataset for 1 epoch.

Math:

suayptalha/Qwen3-0.6B-Math-Expert

This model was fully fine-tuned with BF16 on entire unsloth/OpenMathReasoning-mini dataset for 1 epoch.

Medical:

suayptalha/Qwen3-0.6B-Medical-Expert

This model was fully fine-tuned with BF16 on first 20k rows of FreedomIntelligence/medical-o1-reasoning-SFT dataset for 1 epoch.

Instruction Following:

Qwen/Qwen3-0.6B

Qwen/Qwen3-0.6B model was directly used for this expert, no fine-tune was applied.

Training

• Base Model: Qwen3-0.6B, pre-trained by the Qwen team, Experts, pre-trained by the Suayptalha team.
• Conversion: The model copies embeddings, self-attention, and normalization weights from Qwen3-0.6B, replacing MLP layers with MoE layers (3 experts). Gating weights are randomly initialized.
• Fine-Tuning: Not fine-tuned; users are recommended to fine-tune for specific tasks to optimize expert routing. The fine-tuned version is already available and can be referred to as huihui-ai/Huihui-MoE-1.2B-A0.6B-SFT.

ollama

You can use huihui_ai/huihui-moe:1.2b directly, Switch the thinking toggle using /set think and /set nothink

ollama run huihui_ai/huihui-moe:1.2b

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-1.2B-A0.6B"
print(f"Load Model {NEW_MODEL_ID} ... ")
quant_config_32 = BitsAndBytesConfig(
    load_in_32bit=True,
    bnb_32bit_compute_dtype=torch.bfloat16,
    bnb_32bit_use_double_quant=True,
    llm_int32_enable_fp32_cpu_offload=True,
)

model = AutoModelForCausalLM.from_pretrained(
    NEW_MODEL_ID,
    device_map="auto",
    trust_remote_code=True,
    #quantization_config=quant_config_32,
    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 <number>
            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, 320960)
    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 Qwen3-0.6B 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-1.2B-A0.6B (available locally or on Hugging Face)
• Issues: Report bugs or request features via the repository or please send an email to [email protected]

Acknowledgments

• Built upon the Qwen3-0.6B model by the Qwen team.
• Built upon the Experts model by the Suayptalha team.
• Powered by the Hugging Face transformers library.