Rebrand README to Zen4 Coder Flash

README.md

@@ -1,262 +1,55 @@
 ---
-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
+base_model: huihui-ai/Huihui-GLM-4.7-Flash-abliterated
+pipeline_tag: text-generation
 ---
 
-# huihui-ai/Huihui-GLM-4.7-Flash-abliterated
+# Zen4 Coder Flash
 
+**31B MoE total, 3B active | 131K Context**
 
-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.
+Code-focused MoE model based on GLM-4.7-Flash with abliterated weights. 131K context for entire codebases. Tool calling and reasoning mode support.
 
-## ollama
+Part of the [Zen4 family](https://zenlm.org) by [Zen LM](https://huggingface.co/zenlm) and [Hanzo AI](https://hanzo.ai).
 
-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)
+## Model Details
 
-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
-```
+| Property | Value |
+|----------|-------|
+| **Parameters** | 31B MoE total, 3B active |
+| **Context** | 131K tokens |
+| **Base** | GLM-4.7-Flash (abliterated) |
+| **License** | MIT |
+| **Family** | Zen4 |
 
 ## 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
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-    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)
+model = AutoModelForCausalLM.from_pretrained("zenlm/zen4-coder-flash", torch_dtype="auto", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("zenlm/zen4-coder-flash")
 
-        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})
-
-if __name__ == "__main__":
-    main()
+messages = [{"role": "user", "content": "Hello"}]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer([text], return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
 
-### Usage Warnings
+## About Zen4
 
+The Zen4 family provides abliterated (uncensored) variants of the best open-source models, ranging from 4B to 1T+ parameters.
 
- - **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.
-
-
-### 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)!
-
+- **Website**: [zenlm.org](https://zenlm.org)
+- **Organization**: [Zen LM](https://huggingface.co/zenlm)
+- **Built by**: [Hanzo AI](https://hanzo.ai) (Techstars '17)