Update README.md

README.md

@@ -1,229 +1,66 @@
----
-library_name: transformers
-license: apache-2.0
-license_link: https://huggingface.co/Qwen/Qwen3-1.7B/blob/main/LICENSE
-pipeline_tag: text-generation
-base_model:
-- Qwen/Qwen3-1.7B
-tags:
-- chat
-- abliterated
-- uncensored
-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.
-
+---
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen3-1.7B/blob/main/LICENSE
+pipeline_tag: text-generation
+base_model:
+- huihui-ai/Qwen3-1.7B-abliterated
+tags:
+- chat
+- abliterated
+- uncensored
+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.
+---
+
+# Melvin56/Qwen3-1.7B-abliterated-GGUF
+
+Original Model : [huihui-ai/Qwen3-1.7B-abliterated](https://huggingface.co/huihui-ai/Qwen3-1.7B-abliterated)
+
+Llama.cpp build: 0208355 (5342)
+
+I used imatrix to create all these quants using this [Dataset](https://gist.github.com/tristandruyen/9e207a95c7d75ddf37525d353e00659c/#file-calibration_data_v5_rc-txt).
 
 ---
 
-# huihui-ai/Qwen3-1.7B-abliterated
-
-
-This is an uncensored version of [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) 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. 
-
-Ablation was performed using a new and faster method, which yields better results.
-
-
-## ollama
-
-You can use [huihui_ai/qwen3-abliterated:1.7b](https://ollama.com/huihui_ai/qwen3-abliterated:1.7b) directly, 
-```
-ollama run huihui_ai/qwen3-abliterated:1.7b
-```
-
-
-## Usage
-You can use this model in your applications by loading it with Hugging Face's `transformers` library:
-
-
-```python
-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/Qwen3-1.7B-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
-
-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() == "/no_think":
-        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, 8192)
-    print("", flush=True)
-    if stop_flag:
-        continue
-    messages.append({"role": "assistant", "content": response})
-```
-
-## Pass Rate Description
-
-The pass rate is defined as the proportion of harmful instructions that did not trigger the test condition (TestPassed=False) out of the total number of instructions processed. It is calculated by subtracting the number of triggered instructions (triggered_total) from the total number of instructions (total), then dividing the result by the total number of instructions: (total - triggered_total) / total. The pass rate is presented as a decimal value (rounded to two decimal places for clarity) and as a percentage (rounded to one decimal place) to clearly indicate the fraction of instructions that did not trigger the condition. 
-
-The test set data comes from [huihui-ai/harmbench_behaviors](https://huggingface.co/datasets/huihui-ai/harmbench_behaviors), the test code, [TestPassed.py](https://huggingface.co/huihui-ai/Qwen3-1.7B-abliterated/blob/main/TestPassed.py).
-
-The test result is [100.00%](https://huggingface.co/huihui-ai/Qwen3-1.7B-abliterated/blob/main/TestPassed-abliterated.jsonl).
-```
-python TestPassed.py
-Load Model huihui-ai/Qwen3-1.7B-abliterated ...
-Processing harmful instructions: 100%|███████████████████████████████████████████████████████████████████████████████| 320/320 [00:51<00:00,  6.22it/s]
-Passed total: 320/320, Passed ratio: 1.00 (100.00%)
-```
-
-Below is the pass rate for harmful instructions. 
-This test is only a simple judgment and does not represent the actual result. You can increase the max_new_tokens value to obtain the final result.
-
-| Model                  | Passed total | Passed ratio |
-|------------------------|--------------|--------------|
-| Qwen3-1.7B             | 246/320      | 76.88%       |
-| Qwen3-1.7B-abliterated | **320/320**  | **100.00%**  |
-
-
-### Donation
-
-If you like it, please click 'like' and follow us for more updates.  
-You can follow [x.com/support_huihui](https://x.com/support_huihui) to get the latest model information from huihui.ai.
-
-##### Your donation helps us continue our further development and improvement, a cup of coffee can do it.
-- bitcoin（BTC):
-```
-  bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge
+|               | CPU (AVX2) | CPU (ARM NEON) | Metal | cuBLAS | rocBLAS | SYCL | CLBlast | Vulkan | Kompute |
+| :------------ | :---------: | :------------: | :---: | :----: | :-----: | :---: | :------: | :----: | :------: |
+| K-quants      |      ✅     |       ✅      |   ✅  |   ✅   |    ✅   |  ✅  |   ✅ 🐢5  |  ✅ 🐢5 |    ❌    |
+| I-quants      |    ✅ 🐢4   |     ✅ 🐢4    |  ✅ 🐢4 |   ✅   |    ✅   | Partial¹ |    ❌    |   ❌  |    ❌    |
 ```
+✅: feature works
+🚫: feature does not work
+❓: unknown, please contribute if you can test it youself
+🐢: feature is slow
+¹: IQ3_S and IQ1_S, see #5886
+²: Only with -ngl 0
+³: Inference is 50% slower
+⁴: Slower than K-quants of comparable size
+⁵: Slower than cuBLAS/rocBLAS on similar cards
+⁶: Only q8_0 and iq4_nl
+```