Super-squash branch 'main' using huggingface_hub

.gitattributes

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+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
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+*.pt filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+library_name: transformers
+pipeline_tag: text-generation
+license: mit
+language:
+- en
+base_model:
+- miromind-ai/MiroThinker-v1.0-8B
+tags:
+- agent
+- open-source
+- miromind
+- deep-research
+- chat
+- abliterated
+- uncensored
+---
+
+# huihui-ai/Huihui-MiroThinker-v1.0-8B-abliterated
+
+
+This is an uncensored version of [miromind-ai/MiroThinker-v1.0-8B](https://huggingface.co/miromind-ai/MiroThinker-v1.0-8B) 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.
+
+## 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="Load HuggingFace model."
+    )
+    parser.add_argument(
+        "--base_model",
+        type=str,
+        default="huihui-ai/Huihui-MiroThinker-v1.0-8B-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,
+        #quantization_config=quant_config_4,
+        #attn_implementation="eager",
+    )
+
+    tokenizer = AutoTokenizer.from_pretrained(args.base_model, trust_remote_code=True)
+    tokenizer.padding_side = 'left'
+    tokenizer.pad_token = tokenizer.eos_token
+    tokenizer.pad_token_id = tokenizer.eos_token_id
+
+    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):
+        text = tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True,
+        )
+        inputs = tokenizer(
+            text,
+            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,
+                #pad_token_id=tokenizer.pad_token_id,
+                #eos_token_id=tokenizer.eos_token_id,
+                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()
+```
+
+### 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.
+
+
+### 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)!
+

added_tokens.json

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+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for forward_message in messages %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- set message = messages[index] %}
+    {%- set current_content = message.content if message.content is not none else '' %}
+    {%- set tool_start = '<tool_response>' %}
+    {%- set tool_start_length = tool_start|length %}
+    {%- set start_of_message = current_content[:tool_start_length] %}
+    {%- set tool_end = '</tool_response>' %}
+    {%- set tool_end_length = tool_end|length %}
+    {%- set start_pos = (current_content|length) - tool_end_length %}
+    {%- if start_pos < 0 %}
+        {%- set start_pos = 0 %}
+    {%- endif %}
+    {%- set end_of_message = current_content[start_pos:] %}
+    {%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set content = message.content %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in message.content %}
+                {%- set content = (message.content.split('</think>')|last).lstrip('\n') %}
+                {%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\n') %}
+                {%- set reasoning_content = (reasoning_content.split('<think>')|last).lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 262144,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.57.1"
+}

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+}