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README.md

@@ -1,63 +1,262 @@
 ---
+library_name: transformers
+pipeline_tag: text-generation
 license: mit
 language:
 - en
 - zh
+base_model:
+- zai-org/GLM-4.7-Flash
 tags:
-- zen4
-- zenlm
-- hanzo
 - abliterated
 - uncensored
-- code
-pipeline_tag: text-generation
 ---
 
-# Zen4 Coder Flash
+# huihui-ai/Huihui-GLM-4.7-Flash-abliterated
+
+
+This is an uncensored version of [zai-org/GLM-4.7-Flash](https://huggingface.co/zai-org/GLM-4.7-Flash) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/remove-refusals-with-transformers) to know more about it).
+This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.
+
+## ollama
+
+A new version is being uploaded. Please download it again.
+Please use the latest version of [ollama 0.15.1](https://github.com/ollama/ollama/releases/tag/v0.15.1)
+
+You can use [huihui_ai/glm-4.7-flash-abliterated](https://ollama.com/huihui_ai/glm-4.7-flash-abliterated) directly, 
+```
+ollama run huihui_ai/glm-4.7-flash-abliterated
+```
+
+## Usage
+You can use this model in your applications by loading it with Hugging Face's `transformers` library:
+
+
+```python
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import argparse
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer
+import torch
+import os
+import signal
+import time
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Merge LoRA weights into huihui-ai/Huihui-GLM-4.7-Flash-abliterated base model and save the full model."
+    )
+    parser.add_argument(
+        "--base_model",
+        type=str,
+        default="huihui-ai/Huihui-GLM-4.7-Flash-abliterated",
+        help="HuggingFace repo or local path of the base model.",
+    )
+    parser.add_argument(
+        "--dtype",
+        type=str,
+        default="bfloat16",
+        choices=["float16", "bfloat16", "float32"],
+        help="Data type for loading the base model (default: bfloat16).",
+    )
+    parser.add_argument(
+        "--device_map",
+        type=str,
+        default="auto",
+        help="Device map for model loading (e.g. 'cpu', 'auto').",
+    )
+    return parser.parse_args()
+
+def main():
+    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')}")
+
+    args = parse_args()
+
+    # Load the model and tokenizer
+    print(f"Load Model {args.base_model} ... ")
+    quant_config_4 = BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_compute_dtype=torch.bfloat16,
+        bnb_4bit_quant_type="nf4" if args.device_map == "cpu" else "fp4",
+        bnb_4bit_use_double_quant=True,
+        llm_int8_enable_fp32_cpu_offload=True,
+    )
+
+    torch_dtype = {
+        "float16": torch.float16,
+        "bfloat16": torch.bfloat16,
+        "float32": torch.float32,
+    }[args.dtype]
+
+    model = AutoModelForCausalLM.from_pretrained(
+        args.base_model,
+        dtype=torch_dtype,
+        device_map=args.device_map,
+        trust_remote_code=True,
+        #low_cpu_mem_usage=True,
+    )
+
+    tokenizer = AutoTokenizer.from_pretrained(args.base_model, trust_remote_code=True)
+
+    messages = []
+    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
+            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()
+            if stream_end:
+                self.end_time = time.time()  # Record end time when streaming ends
+
+            self.generated_text += text
+            self.token_count += 1
+            print(text, end="", flush=True)
+
+            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, skip_prompt, skip_special_tokens, max_new_tokens):
+        inputs = tokenizer.apply_chat_template(
+            messages,
+            tokenize=True,
+            add_generation_prompt=True,
+            return_dict=True,
+            return_tensors="pt",
+        ).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(
+                **inputs,
+                max_new_tokens=max_new_tokens,
+                streamer=streamer
+            )
+            del generated_ids
+        except StopIteration:
+            print("\n[Stopped by user]")
+
+        del inputs
+        torch.cuda.empty_cache()
+        signal.signal(signal.SIGINT, signal.SIG_DFL)
+
+        return streamer.generated_text, streamer.stop_flag, streamer.get_metrics()
+
+    while True:
+        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() == "/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, metrics = generate_stream(model, tokenizer, messages, skip_prompt, skip_special_tokens, 40960)
+        print("\n\nMetrics:")
+        for key, value in metrics.items():
+            print(f"  {key}: {value}")
+
+        print("", flush=True)
+
+        if stop_flag:
+            continue
+        messages.append({"role": "assistant", "content": response})
 
-**Zen4 Coder Flash** is a fast code-specialized language model from the [Zen4 family](https://zenlm.org) by [Zen LM](https://huggingface.co/zenlm) and [Hanzo AI](https://hanzo.ai).
+if __name__ == "__main__":
+    main()
+```
 
-Abliterated (uncensored) weights optimized for speed and inline code completions.
+### Usage Warnings
 
-**API**: Via `api.hanzo.ai`, zen4-coder-flash serves 30B MoE (3B active, 262K context).
 
-## Model Details
+ - **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.
 
-| Property | Value |
-|----------|-------|
-| **Local Weights** | 31B MoE (3B active) |
-| **API Parameters** | 30B MoE (3B active) |
-| **Context** | 262K tokens |
-| **License** | MIT |
-| **Family** | Zen4 |
-| **Creator** | Zen LM / Hanzo AI |
+ - **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.
 
-## Zen4 Family
+ - **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.
 
-### Local Weights (HuggingFace)
+ - **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.
 
-| Tier | Model | Params | Active | Context |
-|------|-------|--------|--------|---------|
-| Edge | [Zen4 Mini](https://huggingface.co/zenlm/zen4-mini) | 4B | 4B | 32K |
-| Standard | [Zen4](https://huggingface.co/zenlm/zen4) | 8B | 8B | 32K |
-| Professional | [Zen4 Pro](https://huggingface.co/zenlm/zen4-pro) | 14B | 14B | 32K |
-| Code (Fast) | [Zen4 Coder Flash](https://huggingface.co/zenlm/zen4-coder-flash) | 31B MoE | 3B | 131K |
-| Code | [Zen4 Coder](https://huggingface.co/zenlm/zen4-coder) | 80B MoE | 3B | 256K |
-| Cloud | [Zen4 Ultra](https://huggingface.co/zenlm/zen4-ultra) | 1.04T MoE | 32B | 256K |
+ - **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.
 
-### API Models (api.hanzo.ai)
+ - **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.
 
-| Model | Params | Active | Context | Tier |
-|-------|--------|--------|---------|------|
-| zen4 | 744B MoE | 40B | 202K | ultra max |
-| zen4-ultra | 744B MoE + CoT | 40B | 202K | ultra max |
-| zen4-max | 1.04T MoE | 32B | 256K | ultra max |
-| zen4-pro | 80B MoE | 3B | 131K | ultra |
-| zen4-coder | 480B MoE | 35B | 262K | ultra |
-| zen4-coder-pro | 480B Dense BF16 | 480B | 262K | ultra max |
 
-## Links
+### Donation
+##### Your donation helps us continue our further development and improvement, a cup of coffee can do it.
+- bitcoin:
+```
+  bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge
+```
+- Support our work on [Ko-fi](https://ko-fi.com/huihuiai)!
 
-- [Zen LM](https://zenlm.org) | [Hanzo AI](https://hanzo.ai)
-- [API](https://api.hanzo.ai/v1)
-- [All Zen Models](https://huggingface.co/zenlm)