README.md

---
license: apache-2.0
base_model:
- Qwen/Qwen3-0.6B
- suayptalha/Qwen3-0.6B-Code-Expert
- suayptalha/Qwen3-0.6B-Math-Expert
- suayptalha/Qwen3-0.6B-Medical-Expert
library_name: transformers
license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- moe
---
# huihui-ai/Huihui-MoE-1B-A0.6B

## Model Overview
Huihui-MoE-1B-A0.6B is a **Mixture of Experts (MoE)** language model developed by **huihui.ai**, built upon the **[Qwen/Qwen3-0.6B](https://huggingface.co/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.

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-1.2B-A0.6B](https://huggingface.co/huihui-ai/Huihui-MoE-1.2B-A0.6B).

 - **Architecture**: Qwen3MoeForCausalLM model with 3 experts per layer (num_experts=3), activating 1 expert per token (num_experts_per_tok=1).
 - **Total Parameters**: ~1.1 billion (1B)
 - **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](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/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-1B-A0.6B-SFT](https://huggingface.co/huihui-ai/Huihui-MoE-1B-A0.6B-SFT).

## Usage

```
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/Huihui-MoE-1B-A0.6B"
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()
enable_thinking = True
skip_prompt=True
skip_special_tokens=True

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, enable_thinking, skip_prompt, skip_special_tokens, max_new_tokens):
    input_ids = tokenizer.apply_chat_template(
        messages,
        tokenize=True,
        enable_thinking = enable_thinking,
        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)
    
    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("User: ").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 user_input.lower() == "/nothink":
        if enable_thinking:
            enable_thinking = False
            print("Thinking = False.")
        else:
            enable_thinking = True
            print("Thinking = True.")        
        continue
    if user_input.lower() == "/skip_prompt":
        if skip_prompt:
            skip_prompt = False
            print("skip_prompt = False.")
        else:
            skip_prompt = True
            print("skip_prompt = True.")        
        continue
    if user_input.lower() == "/skip_special_tokens":
        if skip_special_tokens:
            skip_special_tokens = False
            print("skip_special_tokens = False.")
        else:
            skip_special_tokens = True
            print("skip_special_tokens = True.")        
        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, enable_thinking, skip_prompt, skip_special_tokens, 14192)
    print("", flush=True)
    if stop_flag:
        continue
    messages.append({"role": "assistant", "content": response})

```

## 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-1B-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.