commit ff849edd3deee8d4380618c0e35f44e641ac4169
Author: ModelHub XC <noreply@modelhub.org.cn>
Date:   Wed May 20 15:40:43 2026 +0800

    初始化项目，由ModelHub XC社区提供模型
    
    Model: AI-ModelScope/deepthought-8b-llama-v0.01-alpha
    Source: Original Platform

diff --git a/.gitattributes b/.gitattributes
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diff --git a/README.md b/README.md
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+---
+license: llama3.1
+language:
+- en
+pipeline_tag: text-generation
+---
+# Deepthought-8B
+
+Deepthought-8B is a small and capable reasoning model built on LLaMA-3.1 8B, designed to make AI reasoning more transparent and controllable. Despite its relatively small size, it achieves sophisticated reasoning capabilities that rival much larger models.
+
+## Model Description
+
+Deepthought-8B is designed with a unique approach to problem-solving, breaking down its thinking into clear, distinct, documented steps. The model outputs its reasoning process in a structured JSON format, making it easier to understand and validate its decision-making process.
+
+### Key Features
+
+- **Transparent Reasoning**: Step-by-step documentation of the thought process
+- **Programmable Approach**: Customizable reasoning patterns without model retraining
+- **Test-time Compute Scaling**: Flexible reasoning depth based on task complexity
+- **Efficient Scale**: Runs on 16GB+ VRAM
+- **Structured Output**: JSON-formatted reasoning chains for easy integration
+
+Try out Deepthought-8B on our Ruliad interface: https://chat.ruliad.co
+
+## Technical Requirements
+
+- Python 3.6+
+- PyTorch
+- Transformers library
+- 16GB+ VRAM
+- Optional: Flash Attention 2 for improved performance
+
+## Installation
+
+```bash
+pip install torch transformers
+# Optional: Install Flash Attention 2 for better performance
+pip install flash-attn
+```
+
+## Usage
+
+1. First, set your HuggingFace token as an environment variable:
+```bash
+export HF_TOKEN=your_token_here
+export HF_HUB_ENABLE_HF_TRANSFER=1
+```
+
+2. Use the model in your Python code:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Initialize the model
+model_name = "ruliad/deepthought-8b-llama-v0.01-alpha"
+tokenizer = AutoTokenizer.from_pretrained(
+    model_name,
+    add_bos_token=False,
+    trust_remote_code=True,
+    padding="left",
+    torch_dtype=torch.bfloat16,
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    attn_implementation="flash_attention_2",  # Use "eager" (or omit) if flash_attn is not installed
+    use_cache=True,
+    trust_remote_code=True,
+)
+```
+
+3. Run the provided example script:
+```bash
+python deepthought_inference.py
+```
+
+## Example Output
+
+The model provides structured reasoning in JSON format:
+
+```json
+{
+  "step": 1,
+  "type": "problem_understanding",
+  "thought": "Understanding the user's objective for the task."
+}
+```
+
+Each reasoning chain includes multiple steps:
+1. Problem understanding
+2. Data gathering
+3. Analysis
+4. Calculation (when applicable)
+5. Verification
+6. Conclusion drawing
+7. Implementation
+
+## Performance
+
+Deepthought-8B demonstrates strong performance across various benchmarks:
+- Step-by-step problem-solving
+- Coding and mathematical tasks
+- Instruction following with transparent reasoning
+- Scalable performance with test-time compute
+
+## Limitations
+
+Current known limitations include:
+- Complex mathematical reasoning
+- Long-context processing
+- Edge case handling
+
+## License
+
+The model is available under a commercial license for enterprise use.
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@misc{Deepthought2024,
+  author = {Ruliad},
+  title = {Deepthought-8B: A Small and Capable Reasoning Model},
+  year = {2024},
+  publisher = {Ruliad}
+}
+```
+
+## Support
+
+For questions and feedback:
+- Twitter: @ruliad_ai
+- Email: team@ruliad.co
\ No newline at end of file
diff --git a/config.json b/config.json
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--- /dev/null
+++ b/config.json
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+{
+  "_name_or_path": "ruliad/deepthought-v0.02-ep3",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": 128256,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 8.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.46.1",
+  "use_cache": false,
+  "vocab_size": 128260
+}
diff --git a/configuration.json b/configuration.json
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--- /dev/null
+++ b/configuration.json
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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
\ No newline at end of file
diff --git a/deepthought_inference.py b/deepthought_inference.py
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index 0000000..8c541df
--- /dev/null
+++ b/deepthought_inference.py
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+import logging
+import os
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"  # Suppress TensorFlow logging
+os.environ["TF_ENABLE_ONEDNN_OPTS"] = "0" # Disable oneDNN optimizations
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+import warnings
+
+
+warnings.filterwarnings("ignore", message="A NumPy version >=")
+logging.basicConfig(level=logging.ERROR)
+logging.getLogger("transformers").setLevel(logging.ERROR)
+
+
+# Check if Flash Attention is available
+try:
+    import flash_attn  # noqa: F401
+    flash_attn_exists = True
+except ImportError:
+    flash_attn_exists = False
+
+
+# Define the DeepthoughtModel class
+class DeepthoughtModel:
+    def __init__(self):
+        self.model_name = "ruliad/deepthought-8b-llama-v0.01-alpha"
+        print(f"Loading model: {self.model_name}")
+
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            self.model_name,
+            add_bos_token=False,
+            trust_remote_code=True,
+            padding="left",
+            torch_dtype=torch.bfloat16,
+        )
+
+        self.model = AutoModelForCausalLM.from_pretrained(
+            self.model_name,
+            torch_dtype=torch.bfloat16,
+            device_map="auto",
+            attn_implementation=("flash_attention_2" if flash_attn_exists else "eager"),
+            use_cache=True,
+            trust_remote_code=True,
+        )
+
+    # Helper method to generate the initial prompt
+    def _get_initial_prompt(
+        self, query: str, system_message: str = None
+    ) -> str:
+        '''Helper method to generate the initial prompt format.'''
+        if system_message is None:
+            system_message = '''You are a superintelligent AI system, capable of comprehensive reasoning. When provided with <reasoning>, you must provide your logical reasoning chain to solve the user query. Be verbose with your outputs.'''
+
+        return f'''<|im_start|>system
+{system_message}<|im_end|>
+
+<|im_start|>user
+{query}<|im_end|>
+
+<|im_start|>reasoning
+<reasoning>
+[
+  {{
+    "step": 1,
+    "type": "problem_understanding",
+    "thought": "'''
+
+    # Method to generate reasoning given the prompt
+    def generate_reasoning(self, query: str, system_message: str = None) -> dict:
+        print('Generating reasoning...')
+
+        # Get and print prompt
+        prompt = self._get_initial_prompt(query, system_message)
+        print(prompt, end='')
+
+        # Tokenize the prompt
+        inputs = self.tokenizer(prompt, return_tensors='pt').input_ids.to(self.model.device)
+
+        try:
+
+            # Generate and stream reasoning
+            outputs = self.model.generate(
+                input_ids=inputs,
+                max_new_tokens=800,
+                do_sample=True,
+                temperature=0.2,
+                top_k=200,
+                top_p=1.0,
+                eos_token_id=self.tokenizer.eos_token_id,
+                streamer=TextStreamer(self.tokenizer, skip_prompt=True, skip_special_tokens=True),
+            )
+
+            # Get the reasoning string
+            generated_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+            return {
+                'raw_output': generated_text,
+                'success': True,
+                'error': None,
+                'initial_prompt': prompt,
+            }
+
+        except Exception as e:
+            logging.error(f'Error during generation: {e}')
+            return {
+                'raw_output': None,
+                'success': False,
+                'error': str(e),
+                'initial_prompt': None,
+            }
+
+    # Method to generate the final output
+    def generate_final_output(self, reasoning_output: dict) -> dict:
+
+        # Get the reasoning text and create the full prompt for the final output
+        reasoning_text = reasoning_output['raw_output'].replace(reasoning_output['initial_prompt'], '')
+        full_prompt = f'''{reasoning_text}<|im_end|>
+
+<|im_start|>assistant
+'''
+
+        print('Generating final response...')
+
+        # Tokenize the full prompt
+        inputs = self.tokenizer(full_prompt, return_tensors='pt').input_ids.to(self.model.device)
+
+        try:
+
+            # Generate and stream the final output
+            _ = self.model.generate(
+                input_ids=inputs,
+                max_new_tokens=400,
+                do_sample=True,
+                temperature=0.1,
+                top_k=50,
+                top_p=0.9,
+                eos_token_id=self.tokenizer.eos_token_id,
+                streamer=TextStreamer(self.tokenizer, skip_prompt=True, skip_special_tokens=True)
+            )
+
+            return {'success': True, 'error': None}
+
+        except Exception as e:
+            logging.error(f'Error during final generation: {e}')
+            return {'success': False, 'error': str(e)}
+
+
+def main():
+    model = DeepthoughtModel()
+
+    # Test queries
+    queries = [
+        "We want you to tell us the answer to life, the universe and everything. We'd really like an answer, something simple.",
+    ]
+
+    # Process each query at a time (because we are streaming)
+    for query in queries:
+        print(f'\nProcessing query: {query}')
+        print('='*50)
+
+        # Reasoning
+        reasoning_result = model.generate_reasoning(query)
+        if not reasoning_result['success']:
+            print(f'\nError in reasoning: {reasoning_result["error"]}')
+            print('='*50)
+            continue
+
+        print('-'*50)
+
+        # Final output
+        final_result = model.generate_final_output(reasoning_result)
+        if not final_result['success']:
+            print(f'\nError in final generation: {final_result["error"]}')
+
+        print('='*50)
+
+if __name__ == '__main__':
+    main()
diff --git a/generation_config.json b/generation_config.json
new file mode 100644
index 0000000..0484b99
--- /dev/null
+++ b/generation_config.json
@@ -0,0 +1,12 @@
+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.46.1"
+}
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+    "128256": {
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+    },
+    "128257": {
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+      "rstrip": false,
+      "single_word": false,
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+    }
+  },
+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content']|trim %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n    {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n    {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content']|trim %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n    {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n    {%- elif 'tool_calls' in message %}\n        {%- if not message.tool_calls|length == 1 %}\n            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n        {%- endif %}\n        {%- set tool_call = message.tool_calls[0].function %}\n        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n            {%- for arg_name, arg_val in tool_call.arguments | items %}\n                {{- arg_name + '=\"' + arg_val + '\"' }}\n                {%- if not loop.last %}\n                    {{- \", \" }}\n                {%- endif %}\n                {%- endfor %}\n            {{- \")\" }}\n        {%- else  %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n            {{- '\"parameters\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- \"}\" }}\n        {%- endif %}\n        {%- if builtin_tools is defined %}\n            {#- This means we're in ipython mode #}\n            {{- \"<|eom_id|>\" }}\n        {%- else %}\n            {{- \"<|eot_id|>\" }}\n        {%- endif %}\n    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \"<|eot_id|>\" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|im_end|>",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "pad_token": "<|end_of_text|>",
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}