README.md

---
library_name: transformers
language:
- en
tags:
- reasoning
- implicit-reasoning
- chain-of-thought
- llama
- asterisk
- aspp
- pi-flow
- deep-reasoning
license: apache-2.0
base_model: meta-llama/Llama-3.2-1B-Instruct
model_name: Geilim-1B-Instruct
datasets:
- gsm8k
- hellaswag
- ai2_arc
pipeline_tag: text-generation
inference: true
---

# Geilim-1B-Instruct (忌廉)

> **Deep Causal Internal Reasoning**
> No verbose CoT, no `<think>` tags, just concise answers powered by implicit reasoning.

---

## 💡 Introduction

Recent advances in reasoning models (DeepSeek R1, o1) have demonstrated impressive capabilities through Chain-of-Thought (CoT) reasoning. However, we observe several critical drawbacks:

**Problems with External CoT:**
1. **Verbosity Tax**: Models generate hundreds of tokens in `<think>` tags before answering, increasing latency and cost
2. **Autoregressive Dependency**: Models must "see" their reasoning to follow it, forcing sequential token generation
3. **Token Inefficiency**: Users pay for reasoning traces they often don't need, only the final answer matters
4. **Production Overhead**: Verbose outputs are impractical for real-time APIs and edge deployment

**Our Insight**: What if reasoning could happen *internally* in the model's hidden states, without generating verbose traces?

**Geilim-1B-Instruct** addresses these limitations through a hybrid architecture combining:
- **ASPP (Adjacency-Structured Parallel Propagation)**: Graph-based causal chains for structured reasoning
- **π-flow (Probability Flow Dynamics)**: Internal refinement in probability space without token generation
- **Hybrid Gating**: Learnable balance between structured and attention-based processing

The result: Deep reasoning capability with concise outputs - the best of both worlds.

---

## 🎯 Core Value Proposition

**Geilim-1B-Instruct is the anti-verbose reasoning model.**

| Model Type | Reasoning Approach | Output Style |
|------------|-------------------|--------------|
| **Baseline** (Llama-3.2-1B) | Limited reasoning | Direct but may lack depth |
| **CoT Models** (DeepSeek R1, o1) | External reasoning chains | Verbose `<think>` tags, long outputs |
| **Geilim-1B-Instruct** | **Internal reasoning** | **Concise answers, reasoning in hidden states** |

**Key Differentiator**: Geilim performs deep causal reasoning **internally** through ASPP+π-flow architecture, then outputs only the final answer. You get the reasoning quality without the verbosity tax.

---

## 🏗️ Architecture Overview

Geilim-1B-Instruct combines three key components for implicit reasoning:

### 1. **ASPP Operator** (Adjacency-Structured Parallel Propagation)
- **Union-Find graph structure**: Linear causal chain where each token only connects to its parent
- **Iterative message passing**: `h_i^(t+1) = φ(h_i^(t), h_parent[i])`
- **K-step evolution**: Adaptive 2-8 steps of causal propagation
- **Complexity**: O(n) - efficient linear-time reasoning

**Why it matters**: ASPP creates explicit causal relationships between tokens, allowing information to flow through a reasoning chain without generating output tokens.

### 2. **π-flow** (Probability Flow Dynamics)
- **Velocity field learning**: `h' = h + α * v(h)` where `v(h)` is a learned refinement
- **Multi-step refinement**: Iterates in probability space to converge on the correct answer
- **Gated application**: Model learns when to refine (complex questions) vs when to skip (simple questions)
- **Internal convergence**: Reasoning happens in hidden states, not in generated text

**Why it matters**: π-flow eliminates the need for external CoT by performing iterative refinement internally. The model "thinks" in its hidden states and outputs only the final result.

### 3. **Hybrid Gating Mechanism**
```
output = gate * ASPP(x) + (1-gate) * Attention(x)
```
- Combines structured causal reasoning (ASPP) with flexible attention
- Learnable balance between graph-based and sequence-based processing
- Applied to all 30 layers of the base model (Llama-3.2-1B)

---

## 🧠 Why π-flow Eliminates Verbosity

### The Problem with Traditional CoT

**External Reasoning Models** (DeepSeek R1, o1-style):
```
User: What is 15 * 8?

Model: <think>
Let me break this down step by step:
1. First, I'll multiply 15 by 8
2. 15 * 8 = 15 * (10 - 2)
3. Using distributive property: 15*10 - 15*2
4. 150 - 30 = 120
Therefore, the answer is 120.
</think>

The answer is 120.
```
- **Output**: 250+ characters
- **Latency**: High (many tokens to generate)
- **Cost**: Expensive (charged per token)

### Geilim's Internal Reasoning

**Geilim-1B-Instruct** (ASPP+π-flow):
```
User: What is 15 * 8?

Model: 120
```
- **Output**: 3 characters
- **Latency**: Low (minimal generation)
- **Cost**: Minimal
- **Reasoning**: Happened internally through:
  1. ASPP causal chain propagating arithmetic relationships
  2. π-flow refining probability distribution across answer space
  3. Convergence to correct answer in hidden states

---

## 🔬 Technical Mechanism

### How π-flow Achieves Internal Reasoning

1. **Probability Space Operations**
   - Instead of generating tokens to explore answers, π-flow refines probability distributions directly
   - `v(h)`: Learned velocity field that corrects the model's initial judgment
   - Multi-step: `h^(0) → h^(1) → h^(2)` (2 refinement steps)

2. **Convergence Without Output**
   - Traditional models need to "see" their reasoning to follow it (autoregressive dependency)
   - π-flow breaks this: reasoning occurs in parallel across all positions simultaneously
   - The model converges internally before generating any output token

3. **Adaptive Complexity**
   - `pi_flow_use_gate=True`: Model learns when refinement is needed
   - Simple questions: Direct output (gate ≈ 0, skip refinement)
   - Complex questions: Internal multi-step refinement (gate ≈ 1, apply π-flow)
   - User always sees concise output regardless

4. **Synergy with ASPP**
   - ASPP provides causal structure (parent-child dependencies)
   - π-flow refines along these dependencies
   - **Result**: Structured reasoning (not just attention) + probabilistic convergence = deep causal understanding

---

## 📊 Configuration

### Model Architecture
- **Base Model**: Llama-3.2-1B-Instruct (1.26B params)
- **Total Parameters**: ~1.4B (140M additional ASPP+π-flow params)
- **Hybrid Layers**: All 30 layers (universal reasoning capability)

### ASPP Settings
```python
aspp_hidden_dim: 512         # vs 2048 model hidden_size (reduce overfitting)
aspp_num_steps: 2-8          # learnable via sigmoid gating
aspp_dropout: 0.15
aspp_num_neighbors: 1        # Union-Find: parent-only connections
```

### π-flow Settings
```python
pi_flow: True                # Enable probability flow refinement
pi_flow_steps: 2             # 2-step refinement
pi_flow_scale: 0.5           # Moderate refinement strength
pi_flow_use_gate: True       # Adaptive gating
```

---

## 🚀 Quick Start

### Installation
```bash
pip install transformers torch
```

### Basic Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load model
model_path = "NoesisLab/Geilim-1B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

# Generate response
prompt = "A store has 120 apples. They sell 35 in the morning and 48 in the afternoon. How many are left?"
messages = [{"role": "user", "content": prompt}]

input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=128,
    temperature=0.7,
    do_sample=True,
    top_p=0.9,
)

response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
print(response)  # Expected: "37" or "37 apples are left." (concise!)
```

### Advanced Usage
```python
# For math problems requiring step-by-step (if needed)
# Note: Geilim prefers concise outputs, but can show work if prompted
prompt = "Explain how you would solve: What is 15 * 23?"

# For best results with implicit reasoning
generation_config = {
    "max_new_tokens": 128,        # Keep low to encourage conciseness
    "temperature": 0.7,           # Moderate sampling
    "do_sample": True,
    "top_p": 0.9,
    "repetition_penalty": 1.1,    # Prevent loops
}
```

---

## 🎓 Training Details

### Dataset
- **Mixed-Benchmark-Dataset** (composite reasoning benchmarks)
  - 25% GSM8K (math reasoning)
  - 30% HellaSwag (commonsense)
  - 20% ARC (science QA)
  - 10% OpenHermes (high-quality responses)
  - 15% Capybara (multi-turn conversations)

### Training Configuration
- **Framework**: TRL SFTTrainer with packing
- **Epochs**: 2
- **Batch Size**: Effective 8 (per_device=2, grad_accum=4)
- **Learning Rate**: 2e-4 with 10% warmup
- **Precision**: bfloat16 with gradient checkpointing
- **Optimizer**: AdamW (weight_decay=0.1, max_grad_norm=1.0)

### Training Philosophy
Unlike CoT models trained on verbose reasoning chains, Geilim is trained on **answer-focused data** where:
- Correct answers are rewarded
- Reasoning quality is learned implicitly through ASPP+π-flow gradients
- The model learns to converge internally rather than generate external reasoning

---

## 📈 Evaluation

### Reasoning Quality Tests
Geilim is evaluated on:
1. **Math reasoning** (GSM8K-style arithmetic)
2. **Commonsense reasoning** (HellaSwag, PIQA)
3. **Logic puzzles** (multi-hop deduction)
4. **Reading comprehension** (information tracking)
5. **Causal reasoning** (cause-effect relationships)

### Key Metrics
- **Answer correctness** (primary goal)
- **Response conciseness** (< 150 chars = concise)
- **Reasoning traces** (should be absent from output, present in hidden states)

---

## 🎯 Use Cases

### Ideal For:
- **Production APIs**: Low latency, low token cost
- **Real-time applications**: Minimal generation overhead
- **Cost-sensitive deployments**: Pay only for the answer, not the reasoning
- **User-facing chat**: Clean outputs without technical reasoning traces
- **Mobile/edge devices**: Smaller token budgets

### Not Ideal For:
- **Educational use cases**: When you want to show reasoning steps to users
- **Debugging/verification**: When explicit reasoning helps validate answers
- **Research**: When analyzing reasoning chains is the goal

---

## 🆚 Comparison Table

| Feature | Geilim-1B-Instruct | DeepSeek R1 | Llama-3.2-1B |
|---------|-----------|-------------|--------------|
| **Model Size** | 1.4B | 1.5B | 1.26B |
| **Reasoning Type** | Internal (ASPP+π-flow) | External (CoT) | Limited |
| **Output Style** | Concise answers | Verbose `<think>` tags | Direct answers |
| **Latency** | Low | High (many tokens) | Low |
| **Cost per query** | Low | High | Low |
| **Reasoning depth** | Deep (hidden states) | Deep (explicit) | Shallow |
| **Token efficiency** | High | Low | Medium |

---

## 📚 Technical References

### Core Papers & Concepts
- **Union-Find Data Structure**: Parent-only connections for efficient causal propagation
- **Probability Flow ODEs**: Continuous refinement in probability space (inspired by diffusion models)
- **Hybrid Architectures**: Combining structured (graph) and unstructured (attention) reasoning

### Related Work
- DeepSeek R1: External reasoning chains
- o1 series: Long-form CoT reasoning
- SmolLM2: Efficient small language models
- Graph Neural Networks: Structured message passing

---

## 🔧 Development

### Custom Model Registration
- **Model type**: `asterisk` (registered with HuggingFace AutoModel)
- **Config class**: `AsteriskConfig` (extends LlamaConfig)
- **Model class**: `AsteriskForCausalLM` (extends LlamaForCausalLM)
- **Loading**: Requires `trust_remote_code=True`


---

## 🌟 Key Takeaways

1. **No verbose CoT**: Geilim performs reasoning internally, outputs concisely
2. **ASPP+π-flow**: Causal graph structure + probability flow refinement
3. **Deep causal understanding**: Reasoning happens in hidden states, not generated text
4. **Production-ready**: Low latency, low cost, clean outputs
5. **Same reasoning depth**: Matches CoT models without the verbosity

---

## 📝 Citation

If you use Geilim-1B-Instruct in your research or applications, please cite:

```bibtex
@misc{geilim2026,
  title={Geilim-1B-Instruct: Deep Causal Internal Reasoning via ASPP and Probability Flow},
  author={NoesisLab},
  year={2026},
  howpublished={HuggingFace Model Hub},
  url={https://huggingface.co/NoesisLab/Geilim-1B-Instruct}
}
```

---

## 🤝 Acknowledgments

- **Base Model**: Llama-3.2-1B-Instruct by Meta
- **Training Framework**: TRL by HuggingFace
- **Inspiration**: DeepSeek R1 (for demonstrating value of reasoning), but pursuing conciseness

---

## 📄 License

Llama 3.2 Community License

---

## 🔗 Links

- **Model Hub**: https://huggingface.co/NoesisLab/Geilim-1B-Instruct
---

**Built with ❤️ for the era of efficient reasoning models.**

*Geilim (忌廉) - Cantonese for "cream" - smooth, concise, and rich in substance.*