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Model: AI-ModelScope/deepthought-8b-llama-v0.01-alpha Source: Original Platform
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136
README.md
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README.md
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---
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license: llama3.1
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language:
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- en
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pipeline_tag: text-generation
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---
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# Deepthought-8B
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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.
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## Model Description
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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.
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### Key Features
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- **Transparent Reasoning**: Step-by-step documentation of the thought process
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- **Programmable Approach**: Customizable reasoning patterns without model retraining
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- **Test-time Compute Scaling**: Flexible reasoning depth based on task complexity
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- **Efficient Scale**: Runs on 16GB+ VRAM
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- **Structured Output**: JSON-formatted reasoning chains for easy integration
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Try out Deepthought-8B on our Ruliad interface: https://chat.ruliad.co
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## Technical Requirements
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- Python 3.6+
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- PyTorch
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- Transformers library
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- 16GB+ VRAM
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- Optional: Flash Attention 2 for improved performance
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## Installation
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```bash
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pip install torch transformers
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# Optional: Install Flash Attention 2 for better performance
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pip install flash-attn
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```
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## Usage
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1. First, set your HuggingFace token as an environment variable:
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```bash
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export HF_TOKEN=your_token_here
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export HF_HUB_ENABLE_HF_TRANSFER=1
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```
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2. Use the model in your Python code:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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# Initialize the model
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model_name = "ruliad/deepthought-8b-llama-v0.01-alpha"
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tokenizer = AutoTokenizer.from_pretrained(
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model_name,
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add_bos_token=False,
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trust_remote_code=True,
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padding="left",
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torch_dtype=torch.bfloat16,
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)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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attn_implementation="flash_attention_2", # Use "eager" (or omit) if flash_attn is not installed
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use_cache=True,
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trust_remote_code=True,
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)
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```
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3. Run the provided example script:
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```bash
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python deepthought_inference.py
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```
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## Example Output
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The model provides structured reasoning in JSON format:
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```json
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{
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"step": 1,
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"type": "problem_understanding",
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"thought": "Understanding the user's objective for the task."
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}
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```
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Each reasoning chain includes multiple steps:
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1. Problem understanding
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2. Data gathering
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3. Analysis
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4. Calculation (when applicable)
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5. Verification
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6. Conclusion drawing
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7. Implementation
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## Performance
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Deepthought-8B demonstrates strong performance across various benchmarks:
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- Step-by-step problem-solving
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- Coding and mathematical tasks
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- Instruction following with transparent reasoning
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- Scalable performance with test-time compute
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## Limitations
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Current known limitations include:
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- Complex mathematical reasoning
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- Long-context processing
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- Edge case handling
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## License
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The model is available under a commercial license for enterprise use.
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## Citation
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If you use this model in your research, please cite:
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```bibtex
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@misc{Deepthought2024,
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author = {Ruliad},
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title = {Deepthought-8B: A Small and Capable Reasoning Model},
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year = {2024},
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publisher = {Ruliad}
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}
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```
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## Support
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For questions and feedback:
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- Twitter: @ruliad_ai
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- Email: team@ruliad.co
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36
config.json
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config.json
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{
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"_name_or_path": "ruliad/deepthought-v0.02-ep3",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
|
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"eos_token_id": 128256,
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"head_dim": 128,
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"hidden_act": "silu",
|
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"hidden_size": 4096,
|
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 131072,
|
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
|
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"pretraining_tp": 1,
|
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"rms_norm_eps": 1e-05,
|
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"rope_scaling": {
|
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"factor": 8.0,
|
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"high_freq_factor": 4.0,
|
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"low_freq_factor": 1.0,
|
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"original_max_position_embeddings": 8192,
|
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"rope_type": "llama3"
|
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},
|
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"rope_theta": 500000.0,
|
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"tie_word_embeddings": false,
|
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"torch_dtype": "bfloat16",
|
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"transformers_version": "4.46.1",
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"use_cache": false,
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"vocab_size": 128260
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}
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1
configuration.json
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configuration.json
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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deepthought_inference.py
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deepthought_inference.py
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import logging
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import os
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os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" # Suppress TensorFlow logging
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os.environ["TF_ENABLE_ONEDNN_OPTS"] = "0" # Disable oneDNN optimizations
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
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import warnings
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warnings.filterwarnings("ignore", message="A NumPy version >=")
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logging.basicConfig(level=logging.ERROR)
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logging.getLogger("transformers").setLevel(logging.ERROR)
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# Check if Flash Attention is available
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try:
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import flash_attn # noqa: F401
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flash_attn_exists = True
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except ImportError:
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flash_attn_exists = False
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# Define the DeepthoughtModel class
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class DeepthoughtModel:
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def __init__(self):
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self.model_name = "ruliad/deepthought-8b-llama-v0.01-alpha"
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print(f"Loading model: {self.model_name}")
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self.tokenizer = AutoTokenizer.from_pretrained(
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self.model_name,
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add_bos_token=False,
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trust_remote_code=True,
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padding="left",
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torch_dtype=torch.bfloat16,
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)
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self.model = AutoModelForCausalLM.from_pretrained(
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self.model_name,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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attn_implementation=("flash_attention_2" if flash_attn_exists else "eager"),
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use_cache=True,
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trust_remote_code=True,
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)
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# Helper method to generate the initial prompt
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def _get_initial_prompt(
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self, query: str, system_message: str = None
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) -> str:
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'''Helper method to generate the initial prompt format.'''
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if system_message is None:
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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.'''
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return f'''<|im_start|>system
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{system_message}<|im_end|>
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<|im_start|>user
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{query}<|im_end|>
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<|im_start|>reasoning
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<reasoning>
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[
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{{
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"step": 1,
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"type": "problem_understanding",
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"thought": "'''
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# Method to generate reasoning given the prompt
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def generate_reasoning(self, query: str, system_message: str = None) -> dict:
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print('Generating reasoning...')
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# Get and print prompt
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prompt = self._get_initial_prompt(query, system_message)
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print(prompt, end='')
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# Tokenize the prompt
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inputs = self.tokenizer(prompt, return_tensors='pt').input_ids.to(self.model.device)
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try:
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# Generate and stream reasoning
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outputs = self.model.generate(
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input_ids=inputs,
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max_new_tokens=800,
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do_sample=True,
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temperature=0.2,
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top_k=200,
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top_p=1.0,
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eos_token_id=self.tokenizer.eos_token_id,
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streamer=TextStreamer(self.tokenizer, skip_prompt=True, skip_special_tokens=True),
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)
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# Get the reasoning string
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generated_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
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|
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return {
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'raw_output': generated_text,
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'success': True,
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'error': None,
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'initial_prompt': prompt,
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}
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|
||||
except Exception as e:
|
||||
logging.error(f'Error during generation: {e}')
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return {
|
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'raw_output': None,
|
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'success': False,
|
||||
'error': str(e),
|
||||
'initial_prompt': None,
|
||||
}
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|
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# Method to generate the final output
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def generate_final_output(self, reasoning_output: dict) -> dict:
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|
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# Get the reasoning text and create the full prompt for the final output
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reasoning_text = reasoning_output['raw_output'].replace(reasoning_output['initial_prompt'], '')
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full_prompt = f'''{reasoning_text}<|im_end|>
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|
||||
<|im_start|>assistant
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'''
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|
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print('Generating final response...')
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|
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# Tokenize the full prompt
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inputs = self.tokenizer(full_prompt, return_tensors='pt').input_ids.to(self.model.device)
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|
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try:
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|
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# Generate and stream the final output
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_ = self.model.generate(
|
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input_ids=inputs,
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max_new_tokens=400,
|
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do_sample=True,
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temperature=0.1,
|
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top_k=50,
|
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top_p=0.9,
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eos_token_id=self.tokenizer.eos_token_id,
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streamer=TextStreamer(self.tokenizer, skip_prompt=True, skip_special_tokens=True)
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||||
)
|
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|
||||
return {'success': True, 'error': None}
|
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|
||||
except Exception as e:
|
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logging.error(f'Error during final generation: {e}')
|
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return {'success': False, 'error': str(e)}
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|
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|
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def main():
|
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model = DeepthoughtModel()
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|
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# Test queries
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queries = [
|
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"We want you to tell us the answer to life, the universe and everything. We'd really like an answer, something simple.",
|
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]
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|
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# Process each query at a time (because we are streaming)
|
||||
for query in queries:
|
||||
print(f'\nProcessing query: {query}')
|
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print('='*50)
|
||||
|
||||
# Reasoning
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||||
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()
|
||||
12
generation_config.json
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generation_config.json
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|
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{
|
||||
"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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23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
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||||
{
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||||
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3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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oid sha256:1e189e8b6268354f4994f33c224d645584dc098857a866ca75a5d7acb3772b07
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size 17210677
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2095
tokenizer_config.json
Normal file
2095
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user