metadata
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-5B-A1.7B-abliterated
Model Overview
Huihui-MoE-5B-A1.7B-abliterated is a Mixture of Experts (MoE) language model developed by huihui.ai, built upon the 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
huihui-ai/Huihui-MoE-4.8B-A1.7B-abliterated 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.
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: ~5 billion (5B)
- 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
Expert 2:
Goekdeniz-Guelmez/Josiefied-Qwen3-1.7B-abliterated-v1
Expert 3:
huihui-ai/Qwen3-1.7B-abliterated
Expert 4:
Instruction Following:
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.
ollama
You can use huihui_ai/huihui-moe-abliterated:5b directly, Switch the thinking toggle using /set think and /set nothink
ollama run huihui_ai/huihui-moe-abliterated:5b
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-5B-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 <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, 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-5B-A1.7B-abliterated (available locally or on Hugging Face)
- Issues: Report bugs or request features via the repository or please send an email to [email protected]