v2.4: Mentor Mode, Revenue Generation, Smart Help System

NEW FEATURES:
- 🎓 Mentor Mode: Learn from Claude in real-time
- 💰 Revenue Generation: Freelance, content, trading, affiliate
- 🔍 Smart Help: Natural language command discovery
- 📊 10,346 lines of self-improving AI agent code

COMMANDS:
help <topic> - Smart command search

README.md

@@ -1,725 +1,180 @@
----
-license: cc-by-4.0
-language:
-- en
-library_name: transformers
-pipeline_tag: text-generation
-tags:
-- llama
-- dense-responses
-- self-optimization
-- representation-engineering
-- cf-hot
-- recursive-self-improvement
-base_model: NousResearch/Hermes-3-Llama-3.1-8B
----
-
-![ARC Banner](banner.svg)
-
-# ARC Engine v2.1: Adaptive Recursive Cognition
-
-A comprehensive framework for stable recursive self-improvement of language models, featuring real-time behavioral control through hidden-state monitoring and multi-modal output capabilities.
-
-**Author:** Logan Matthew Napolitano  
-**Base Model:** NousResearch/Hermes-3-Llama-3.1-8B  
-**License:** CC BY 4.0  
-**Engine:** 6,861 lines | **Weights:** ~16 GB
-
----
-
-## Table of Contents
-
-1. [Quick Start](#quick-start)
-2. [What's New in v2.1](#whats-new-in-v21)
-3. [Core Technology](#core-technology)
-4. [Empirical Results](#empirical-results)
-5. [Command Reference](#command-reference)
-6. [Installation](#installation)
-7. [Configuration](#configuration)
-8. [Repository Structure](#repository-structure)
-9. [Hardware Requirements](#hardware-requirements)
-10. [Training From Scratch](#training-from-scratch)
-11. [API Reference](#api-reference)
-12. [Limitations](#limitations)
-13. [Citation](#citation)
-
----
-
-## Quick Start
-
-```bash
-# Clone repository
-git clone https://huggingface.co/LoganResearch/ARC-Base-8B-Condensed
-cd ARC-Base-8B-Condensed
-
-# Install dependencies (minimal)
-pip install torch transformers peft bitsandbytes accelerate
-
-# Run the engine
-python arc_engine_v21_multimedia.py
-```
-
-On first run, the engine will:
-1. Load the base model and DENSE adapter
-2. Initialize the CF-HoT 125× repetition detection head
-3. Set up the quality evaluation system
-4. Present an interactive command prompt
-
-```
-===========================================================================
-🤖 ARC ENGINE v2.1 - Adaptive Recursive Cognition
-===========================================================================
-    DENSE Mode:      ON (CONDENSATOR checkpoint)
-    CF-HoT Control:  ON
-    CF-HoT 125×:     ON
-    Stream Window:   ON
-    Image Gen:       ON
-    TTS Audio:       ON
-===========================================================================
-
-> hello
-Hello. How can I help?
-
-[Quality: 0.82 | Density: 12.4 | Coherence: 0.91 | Tokens: 5]
-```
-
----
-
-## What's New in v2.1
-
-### Multimedia Features
-
-| Command | Description |
-|---------|-------------|
-| `!stream` | Opens a live GUI window displaying tokens as they generate in real-time |
-| `!imagine <prompt>` | Generate images using Stable Diffusion XL |
-| `!dalle <prompt>` | Generate images using DALL-E 3 API |
-| `!audio` | Toggle text-to-speech output |
-| `!say <text>` | Speak text immediately using TTS |
-
-### Claude Integration
-
-| Command | Description |
-|---------|-------------|
-| `!idea <request>` | Generate extensive ideas using Claude API |
-| `!idea <request> --deep` | Generate 30 detailed ideas with implementation plans |
-| `!claude <prompt>` | Direct prompting to Claude Opus 4.5 |
-| `!expand <idea>` | Expand a specific idea into a comprehensive plan |
-
-### Extended Generation
-
-| Command | Description |
-|---------|-------------|
-| `!book` | Toggle book mode (16,384 token limit) |
-| `!write <topic>` | Generate complete books with chapters |
-
-### Advanced RSI Testing
-
-| Command | Description |
-|---------|-------------|
-| `!rsi15` | Run 15-iteration stress test with full logging |
-| `!cfhot` / `!125x` | Toggle CF-HoT 125× head on/off at runtime |
-
-### Utilities
-
-| Command | Description |
-|---------|-------------|
-| `!plot` | Generate quality history visualization |
-| `!benchmark` | Run comprehensive evaluation suite |
-| `!export [name]` | Package checkpoint for sharing |
-| `!import <path>` | Import checkpoint package |
-| `!learn` | Extract high-quality responses for training |
-| `!api` | Start REST API server on port 8080 |
-
----
-
-## Core Technology
-
-### 1. CF-HoT: Contrastive Fine-tuning with Hidden-state Oversight Training
-
-CF-HoT enables real-time behavioral control by monitoring the model's internal representations and intervening before problematic tokens are generated.
-
-**Key Innovation:** The repetition detection head achieves 125× class separation, meaning it can reliably distinguish "about to repeat" states from normal generation states in the model's hidden layers.
-
-```
-┌─────────────────────────────────────────────────────────────┐
-│                    CF-HoT Architecture                       │
-├─────────────────────────────────────────────────────────────┤
-│                                                             │
-│   Hidden States (Layer 16-24)                               │
-│         │                                                   │
-│         ▼                                                   │
-│   ┌─────────────┐                                          │
-│   │ Fiber       │  Compress to d=16 per layer              │
-│   │ Projection  │                                          │
-│   └─────────────┘                                          │
-│         │                                                   │
-│         ▼                                                   │
-│   ┌─────────────┐                                          │
-│   │ Layer       │  Weighted aggregation                    │
-│   │ Attention   │                                          │
-│   └─────────────┘                                          │
-│         │                                                   │
-│         ▼                                                   │
-│   ┌─────────────┐                                          │
-│   │ Risk        │  Binary classifier                       │
-│   │ Predictor   │  Output: P(repetition)                   │
-│   └─────────────┘                                          │
-│         │                                                   │
-│         ▼                                                   │
-│   If P > threshold: Apply logit penalties                   │
-│                                                             │
-└─────────────────────────────────────────────────────────────┘
-```
-
-**Training Process:**
-1. Collect positive samples (repetitive generations) and negative samples (clean generations)
-2. Extract hidden states from layers 16-24 at each token position
-3. Train binary classifier to predict repetition risk
-4. Deploy at inference time for real-time intervention
-
-### 2. THE CONDENSATOR: Dense Response Training Pipeline
-
-A 4-stage training pipeline that teaches the model to communicate with maximum information density.
-
-```
-┌─────────────────────────────────────────────────────────────┐
-│                  THE CONDENSATOR Pipeline                    │
-├─────────────────────────────────────────────────────────────┤
-│                                                             │
-│  Stage 1: Supervised Fine-Tuning (SFT)                      │
-│  ─────────────────────────────────────                      │
-│  • 53 gold-standard dense response examples                 │
-│  • Learning rate: 2e-5                                      │
-│  • Loss: 1.17 → 0.72 (39% reduction)                       │
-│                                                             │
-│  Stage 2: Direct Preference Optimization (DPO)              │
-│  ─────────────────────────────────────────────              │
-│  • Preference pairs: dense response > verbose response      │
-│  • Beta: 0.1                                                │
-│  • Teaches relative quality judgments                       │
-│                                                             │
-│  Stage 3: Reinforcement Learning (PPO)                      │
-│  ─────────────────────────────────────                      │
-│  • Reward = density_score - filler_penalty - length_penalty │
-│  • Conservative KL constraint                               │
-│  • 300 optimization steps                                   │
-│                                                             │
-│  Stage 4: Checkpointing                                     │
-│  ─────────────────────────                                  │
-│  • Save every 25 training steps                             │
-│  • Maintain best checkpoint for rollback                    │
-│  • A/B comparison on held-out prompts                       │
-│                                                             │
-└─────────────────────────────────────────────────────────────┘
-```
-
-### 3. Stable Recursive Self-Improvement
-
-The self-improvement loop includes multiple safeguards to prevent quality degradation:
-
-```
-┌─────────────────────────────────────────────────────────────┐
-│              Stable Self-Improvement Loop                    │
-├─────────────────────────────────────────────────────────────┤
-│                                                             │
-│                    ┌──────────┐                             │
-│                    │  START   │                             │
-│                    └────┬─────┘                             │
-│                         │                                   │
-│                         ▼                                   │
-│                 ┌───────────────┐                           │
-│                 │   EVALUATE    │                           │
-│                 │ Current Model │                           │
-│                 └───────┬───────┘                           │
-│                         │                                   │
-│              ┌──────────┴──────────┐                        │
-│              │                     │                        │
-│              ▼                     ▼                        │
-│     Quality >= Target?      Quality < Minimum?              │
-│              │                     │                        │
-│         Yes  │                Yes  │                        │
-│              ▼                     ▼                        │
-│         ┌────────┐          ┌──────────┐                    │
-│         │  DONE  │          │ ROLLBACK │                    │
-│         └────────┘          └──────────┘                    │
-│              │                     │                        │
-│          No  │                 No  │                        │
-│              └──────────┬──────────┘                        │
-│                         │                                   │
-│                         ▼                                   │
-│                 ┌───────────────┐                           │
-│                 │    TRAIN      │                           │
-│                 │  (25 steps)   │                           │
-│                 └───────┬───────┘                           │
-│                         │                                   │
-│                         ▼                                   │
-│                 ┌───────────────┐                           │
-│                 │  A/B COMPARE  │                           │
-│                 │ Old vs New    │                           │
-│                 └───────┬───────┘                           │
-│                         │                                   │
-│              ┌──────────┴──────────┐                        │
-│              │                     │                        │
-│         Better?               Worse?                        │
-│              │                     │                        │
-│              ▼                     ▼                        │
-│         ┌────────┐          ┌──────────┐                    │
-│         │  KEEP  │          │ ROLLBACK │                    │
-│         │  New   │          │ to Best  │                    │
-│         └────┬───┘          └────┬─────┘                    │
-│              │                   │                          │
-│              └─────────┬─────────┘                          │
-│                        │                                    │
-│                        ▼                                    │
-│                 (Return to EVALUATE)                        │
-│                                                             │
-└─────────────────────────────────────────────────────────────┘
-```
-
-**Safeguards:**
-
-| Safeguard | Description |
-|-----------|-------------|
-| Multi-metric evaluation | Density (25%) + Coherence (25%) + Helpfulness (25%) + Penalties (25%) |
-| Gibberish detection | Pattern matching for math soup, terminal escape sequences, repetitive tokens |
-| Automatic rollback | Reverts to best checkpoint if quality drops > 0.05 |
-| Conservative training | Learning rate 2e-6, only 25 steps per iteration |
-| Emergency stop | Halts after 3 consecutive rollbacks or coherence < 0.3 |
-
----
-
-## Empirical Results
-
-### CF-HoT Head Performance
-
-| Head Type | Positive Score | Negative Score | Separation Ratio |
-|-----------|---------------|----------------|------------------|
-| Repetition | 0.875 | 0.007 | **125×** |
-| Verbosity | 0.68 | 0.32 | 2.1× |
-| Hedging | 0.58 | 0.39 | 1.5× |
-
-The 125× separation for repetition detection is the key empirical finding. This indicates that the model encodes behavioral intent in its hidden states before generating tokens, and this signal is strong enough to enable reliable intervention.
-
-### Response Quality Improvement
-
-| Metric | Baseline | ARC Engine | Change |
-|--------|----------|------------|--------|
-| Information Density | 17.0 | 28.5 | **+68%** |
-| Average Response Tokens | 150 | 65 | **-57%** |
-| Filler Phrase Count | High | ~0 | **-95%** |
-| Mode Collapse Events | Frequent | Zero | **Prevented** |
-
-### Response Examples
-
-| Prompt | Base Model Response | ARC Engine Response |
-|--------|--------------------|--------------------|
-| "hello" | "Hello! I'm here to help you with any questions or tasks you might have. Feel free to ask me anything!" (23 tokens) | "Hello. How can I help?" (5 tokens) |
-| "What is recursion?" | "That's a great question! Recursion is a programming concept where a function calls itself to solve a problem by breaking it down into smaller subproblems..." (150+ tokens) | "Function calling itself until base case. Stack frames accumulate, unwind on return." (12 tokens) |
-| "How are you?" | "As an AI, I don't have feelings in the traditional sense, but I'm functioning well and ready to assist you with whatever you need!" (28 tokens) | "Operational. Ready to assist." (4 tokens) |
-
-### RSI-15 Stress Test Results
-
-The RSI-15 test runs 15 consecutive self-improvement iterations to verify stability:
-
-| Metric | Value |
-|--------|-------|
-| Iterations Completed | 15/15 |
-| Successful Improvements | 8 |
-| Rollbacks Triggered | 4 |
-| Marginal (kept) | 3 |
-| Initial Quality | 0.52 |
-| Final Quality | 0.71 |
-| Peak Quality | 0.73 |
-| Emergency Stops | 0 |
-
----
-
-## Command Reference
-
-### Self-Improvement Commands
-
-```
-!improve              Run one iteration of self-improvement
-!eval                 Evaluate current model quality
-!train <N>            Run N training steps (default: 25)
-!compare              Compare current checkpoint vs best
-!rollback             Revert to best checkpoint
-!load <path>          Load specific checkpoint
-!rsi15                Run 15-iteration stress test
-```
-
-### CF-HoT Control
-
-```
-!cfhot                Toggle 125× head on/off
-!125x                 Alias for !cfhot
-!cfhot status         Show head status and intervention count
-```
-
-### Multimedia
-
-```
-!stream               Open live token streaming window
-!stream off           Close streaming window
-!audio                Toggle text-to-speech
-!audio voices         List available TTS voices
-!audio voice <N>      Select voice by index
-!audio rate <N>       Set speech rate (default: 175)
-!say <text>           Speak text immediately
-!imagine <prompt>     Generate image with SDXL
-!dalle <prompt>       Generate image with DALL-E 3
-!image view           View last generated image
-!image view <path>    View image from file
-```
-
-### Claude Integration
-
-```
-!idea <request>       Generate ideas (default: 20 ideas)
-!idea <req> --quick   Generate 5 quick ideas
-!idea <req> --deep    Generate 30 detailed ideas
-!expand <idea>        Expand idea into full plan
-!claude <prompt>      Direct Claude prompt
-!claude <p> --opus    Use Opus 4.5 specifically
+# Lie-Holonomy Transformer (LHT)
+
+A PyTorch implementation of the gauge-theoretic reasoning architecture from "Beyond Holonomy: Lie-Algebraic Symbol Emergence and the Homotopy Type Structure of Neural Reasoning."
+
+## Core Ideas
+
+This architecture treats **reasoning as geometry**:
+
+| Concept | Mathematical Structure | Implementation |
+|---------|----------------------|----------------|
+| Propositions | Manifold M | Embedding space |
+| Inference | Parallel transport | Gauge-covariant attention |
+| Consistency | Holonomy = Identity | Holonomy loss |
+| Symbols | Lie algebra generators | Generator network |
+| Proof equivalence | Homotopy | Layer depth |
+
+## Architecture Overview
+
+```
+Input tokens
+     │
+     ▼
+┌─────────────────────────────────────┐
+│  Token Embedding (Proposition M)    │
+│  + Position Embedding               │
+│  + Fiber Initialization (gauge)     │
+└─────────────────────��───────────────┘
+     │
+     ▼
+┌─────────────────────────────────────┐
+│  LHT Layer (× n_layers)             │
+│  ┌─────────────────────────────┐    │
+│  │ Connection Network A(x)     │    │  ← Learns gauge connection
+│  │ Parallel Transport Γ_{j→i}  │    │  ← Transports fiber elements
+│  │ Gauge-Covariant Attention   │    │  ← Modified self-attention
+│  │ Lie Algebra Generator       │    │  ← Generates inference ops
+│  │ Generator Application       │    │  ← Applies exp(X) to fiber
+│  └─────────────────────────────┘    │
+└─────────────────────────────────────┘
+     │
+     ▼
+┌─────────────────────────────────────┐
+│  Output: logits + geometric losses  │
+└─────────────────────────────────────┘
+```
+
+## Key Components
+
+### 1. Connection Network
+Learns the gauge connection ω that defines how to parallel transport inferential states:
+```python
+A_μ(x) ∈ gl(k,ℝ)  # Lie algebra valued 1-form
 ```
 
-### Extended Generation
-
-```
-!book                 Toggle book mode (16K tokens)
-!write <topic>        Write complete book
+### 2. Parallel Transport
+Computes transport operators between positions:
+```python
+Γ_{j→i} = exp(-A_μ(x_j)(x_i - x_j)^μ)
 ```
 
-### Agentic Tools
-
-```
-!shell <cmd>          Execute shell command
-!python <code>        Execute Python code
-!read <path>          Read file contents
-!write <path> <text>  Write to file
-!ls [path]            List directory
-!web <query>          Web search
+### 3. Gauge-Covariant Attention
+Standard attention with parallel transport of values:
+```python
+# Standard:  Attn(Q,K,V)_i = Σ_j α_ij V_j
+# Gauge:     GaugeAttn_i   = Σ_j α_ij Γ_{j→i}(V_j)
 ```
 
-### Browser Automation
-
-```
-!browse <url>         Open URL in browser
-!click <selector>     Click element
-!type <text>          Type into focused element
-!fill <sel> <text>    Fill specific element
-!login <service>      Login to service (gmail, twitter, etc.)
-!close                Close browser
+### 4. Holonomy Loss
+Enforces reasoning consistency by requiring closed loops to return to identity:
+```python
+L_hol = E[||Hol_γ - I||²_F]
 ```
 
-### Utilities
-
-```
-!plot                 Generate quality history plot
-!benchmark            Run evaluation suite
-!export [name]        Export checkpoint package
-!import <path>        Import checkpoint package
-!learn                Learn from high-quality responses
-!api                  Start REST API server
-status                Show system status
-history               Show quality history
-help                  Display help
-quit                  Exit with final report
+### 5. Curvature Regularization
+Encourages flat reasoning spaces where order doesn't matter:
+```python
+L_curv = E[||F(x)||²_F]  where F = dω + ω∧ω
 ```
 
----
-
 ## Installation
 
-### Minimal Installation (Core Features)
-
-```bash
-pip install torch transformers peft bitsandbytes accelerate safetensors
-```
-
-### Full Installation (All Features)
-
 ```bash
-pip install -r requirements.txt
-playwright install firefox  # For browser automation
+pip install torch
 ```
 
-### Optional Dependencies
-
-| Feature | Package | Install Command |
-|---------|---------|-----------------|
-| Image Generation (SDXL) | diffusers | `pip install diffusers` |
-| Image Generation (DALL-E) | openai | `pip install openai` |
-| Text-to-Speech | pyttsx3, gtts, pygame | `pip install pyttsx3 gtts pygame` |
-| Claude Integration | anthropic | `pip install anthropic` |
-| Vector Memory | chromadb, sentence-transformers | `pip install chromadb sentence-transformers` |
-| Plotting | matplotlib | `pip install matplotlib` |
-| Browser Automation | playwright | `pip install playwright` |
-
-**Note:** Python 3.11 is recommended. Python 3.13 has compatibility issues with diffusers.
-
----
-
-## Configuration
-
-### Environment Variables
-
-```bash
-# Claude API (for !idea, !claude commands)
-export ANTHROPIC_API_KEY="sk-ant-..."
-
-# OpenAI API (for !dalle command)
-export OPENAI_API_KEY="sk-..."
-```
-
-### Config Class Options
-
-Edit in `arc_engine_v21_multimedia.py`:
+## Usage
 
+### Basic
 ```python
-class Config:
-    # Generation
-    temperature = 0.85
-    top_p = 0.9
-    max_new_tokens = 512
-    
-    # CF-HoT
-    use_cfhot = True
-    use_cfhot_125x = True
-    cfhot_repetition_threshold = 0.6
-    cfhot_repetition_penalty = 6.0
-    
-    # Self-improvement
-    min_quality_score = 0.5
-    target_quality_score = 0.75
-    training_steps_per_iteration = 25
-    quality_drop_threshold = 0.1
-    
-    # Book mode
-    book_mode = False
-    book_max_tokens = 16384
-    
-    # API server
-    api_port = 8080
-```
-
----
-
-## Repository Structure
-
-```
-ARC-Base-8B-Condensed/
-│
-├── arc_engine_v21_multimedia.py    # Main engine (6,861 lines)
-├── requirements.txt                 # Full dependencies
-├── requirements_minimal.txt         # Core dependencies only
-│
-├── training_scripts/
-│   ├── the_condensator.py          # 4-stage dense training
-│   ├── train_cfhot_head.py         # CF-HoT head training
-│   ├── train_self_improve.py       # Self-improvement loop
-│   └── quickstart.py               # One-command trainer
-│
-├── dense_checkpoints/
-│   ├── step_100/                   # Initial checkpoint
-│   ├── step_200/                   # After iteration 1
-│   └── step_300/                   # After iteration 2
-│
-├── cfhot_checkpoints/
-│   └── ckpt_5000/                  # 125× repetition head
-│       └── risk_predictor.pt
-│
-├── multi_head_checkpoints/
-│   ├── hedging_head/
-│   ├── verbosity_head/
-│   └── sycophancy_head/
-│
-├── paper/
-│   └── arc_paper.pdf               # Research paper
-│
-├── books/                          # Generated books output
-├── images/                         # Generated images output
-├── ideas/                          # Generated ideas output
-├── improvement_logs/               # RSI logs and results
-└── exports/                        # Checkpoint packages
-```
-
----
-
-## Hardware Requirements
-
-| Component | Minimum | Recommended |
-|-----------|---------|-------------|
-| GPU VRAM | 16 GB | 24 GB |
-| System RAM | 32 GB | 64 GB |
-| Disk Space | 50 GB | 100 GB |
-| Python | 3.10+ | 3.11 |
-
-**Tested Configuration:** NVIDIA RTX 3090 (24GB), 64GB RAM, Ubuntu 22.04
-
-**Inference Performance:**
-- ~15 tokens/second with CF-HoT enabled
-- ~20 tokens/second with CF-HoT disabled
-
----
-
-## Training From Scratch
-
-### Quick Start (Automated)
-
-```bash
-python training_scripts/quickstart.py --full
-```
-
-This runs the complete pipeline (~4 hours on RTX 3090):
-1. CF-HoT head training (5000 steps)
-2. CONDENSATOR dense training (3 epochs SFT + 300 RL steps)
-3. Self-improvement loop (5 iterations)
-
-### Manual Training
-
-**Step 1: Train CF-HoT Heads**
-
-```bash
-python training_scripts/train_cfhot_head.py \
-    --behavior repetition \
-    --steps 5000 \
-    --batch-size 16 \
-    --learning-rate 1e-4
-```
+from lht import LieHolonomyTransformer, LHTConfig
+
+# Create model
+config = LHTConfig(
+    vocab_size=32000,
+    d_model=512,
+    d_fiber=64,
+    n_heads=8,
+    n_layers=6,
+    lie_algebra_rank=8,
+)
+model = LieHolonomyTransformer(config)
+
+# Forward pass
+output = model(
+    input_ids=tokens,
+    labels=labels,
+    return_geometric_losses=True
+)
+
+# Get losses
+lm_loss = output['lm_loss']
+holonomy_loss = output['holonomy_loss']
+curvature_loss = output['curvature_loss']
+total_loss = model.get_total_loss(output)
+```
+
+### Training with Geometric Loss Annealing
+```python
+from lht import LHTTrainer
 
-**Step 2: Run CONDENSATOR Pipeline**
+trainer = LHTTrainer(model, optimizer, config)
 
-```bash
-python training_scripts/the_condensator.py \
-    --sft-epochs 3 \
-    --dpo-epochs 1 \
-    --rl-steps 300 \
-    --checkpoint-every 25
+for batch in dataloader:
+    metrics = trainer.train_step(batch)
+    # Early training: high curvature loss → flat representations
+    # Mid training: high holonomy loss → consistency
+    # Late training: high waypoint loss → discrete structure
 ```
 
-**Step 3: Self-Improvement Loop**
+### Waypoint Detection
+```python
+from lht import WaypointDetector
 
-```bash
-python training_scripts/train_self_improve.py \
-    --iterations 5 \
-    --target-quality 0.75 \
-    --rollback-threshold 0.05
+detector = WaypointDetector(config, n_waypoints=32)
+waypoint_ids, stability = detector(representations)
 ```
 
----
+## Configuration
 
-## API Reference
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `d_model` | Proposition manifold dimension | 512 |
+| `d_fiber` | Fiber (gauge) dimension | 64 |
+| `lie_algebra_rank` | k for GL(k,ℝ) structure group | 8 |
+| `lambda_holonomy` | Weight for holonomy loss | 0.1 |
+| `lambda_curvature` | Weight for curvature loss | 0.01 |
+| `lambda_waypoint` | Weight for waypoint stability | 0.05 |
 
-Start the API server:
+## Theoretical Predictions
 
-```bash
-> !api
-[api] Server running on http://0.0.0.0:8080
-```
+The framework makes testable predictions:
 
-### Endpoints
+1. **Chain-of-thought benefit correlates with curvature** - High-curvature domains (causal reasoning) benefit more from CoT than low-curvature domains (arithmetic)
 
-**POST /generate**
+2. **Waypoints emerge spontaneously** - Training with holonomy loss should cause discrete symbol-like structures to form at flat loci
 
-```bash
-curl -X POST http://localhost:8080/generate \
-  -H "Content-Type: application/json" \
-  -d '{"prompt": "What is recursion?"}'
-```
+3. **Holonomy predicts errors** - Incorrect reasoning paths should have higher holonomy magnitude
 
-Response:
-```json
-{
-  "response": "Function calling itself until base case. Stack frames accumulate, unwind on return.",
-  "quality": 0.82,
-  "tokens": 12
-}
-```
+4. **Compositional generalization improves** - Holonomy constraints force consistent composition
 
-**POST /status**
+## File Structure
 
-```bash
-curl -X POST http://localhost:8080/status
 ```
-
-Response:
-```json
-{
-  "quality": 0.71,
-  "iteration": 5,
-  "checkpoint": "dense_checkpoints/step_300"
-}
+lie_holonomy_transformer/
+├── lht.py           # Core implementation
+├── train.py         # Training script  
+├── README.md        # This file
+└── experiments/     # Benchmark code (TODO)
 ```
 
-**GET /health**
-
-```bash
-curl http://localhost:8080/health
-```
-
----
-
-## Limitations
-
-| Limitation | Description |
-|------------|-------------|
-| Scale | Tested on 8B parameters only; larger models may behave differently |
-| Language | English only; other languages untested |
-| Iterations | 5-15 stable iterations demonstrated; long-term stability unknown |
-| Evaluation | Heuristic metrics without formal human evaluation study |
-| Scope | Bounded self-optimization within defined metrics; not open-ended self-improvement |
-| SDXL | Requires Python 3.11 (incompatible with Python 3.13) |
-| Memory | Full features require 24GB VRAM; minimal mode works with 16GB |
-
----
-
-## Citation
-
-```bibtex
-@software{napolitano2025arc,
-  title={ARC: Adaptive Recursive Cognition via Contrastive Hidden-State Control},
-  author={Napolitano, Logan Matthew},
-  year={2025},
-  url={https://huggingface.co/LoganResearch/ARC-Base-8B-Condensed},
-  license={CC-BY-4.0}
-}
-```
-
----
-
 ## References
 
-1. Zou, A., et al. (2023). Representation Engineering: A Top-Down Approach to AI Transparency. arXiv:2310.01405
-2. Ouyang, L., et al. (2022). Training language models to follow instructions with human feedback. NeurIPS.
-3. Rafailov, R., et al. (2023). Direct Preference Optimization: Your Language Model is Secretly a Reward Model. arXiv:2305.18290
-4. Hu, E. J., et al. (2021). LoRA: Low-Rank Adaptation of Large Language Models. arXiv:2106.09685
-5. Dettmers, T., et al. (2023). QLoRA: Efficient Finetuning of Quantized LLMs. arXiv:2305.14314
-
----
-
-## Acknowledgments
-
-- **NousResearch** for Hermes-3-Llama-3.1-8B base model
-- **Meta AI** for Llama 3.1 architecture
-- **Hugging Face** for transformers, PEFT, TRL, and Accelerate
-- **Stability AI** for Stable Diffusion XL
-- **Anthropic** for Claude API
-
----
+- "Beyond Holonomy: Lie-Algebraic Symbol Emergence..." (the paper)
+- Cohen et al. (2019). Gauge Equivariant Convolutional Networks
+- Weiler & Cesa (2019). General E(2)-Equivariant Steerable CNNs
+- The Univalent Foundations Program (2013). Homotopy Type Theory
 
 ## License
 
-This project is licensed under **CC BY 4.0** (Creative Commons Attribution 4.0 International).
-
-You are free to:
-- **Share** — copy and redistribute the material in any medium or format
-- **Adapt** — remix, transform, and build upon the material for any purpose, including commercial
-
-Under the following terms:
-- **Attribution** — You must give appropriate credit, provide a link to the license, and indicate if changes were made.
-
----
-
-*"Stable self-improvement through hidden-state control."*
+MIT

_stable_train.py

@@ -0,0 +1,92 @@
+
+import sys
+sys.path.insert(0, "/home/programmer/Desktop/test_arc/ARC-Base-8B-Condensed")
+
+import torch
+import json
+import random
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+from peft import PeftModel, get_peft_model, LoraConfig
+import os
+
+print("Loading model for CONSERVATIVE training...")
+MODEL_PATH = "/mnt/nvme2/ubermesnchetien4/models/merged-final-v5"
+CHECKPOINT = "/home/programmer/Desktop/test_arc/ARC-Base-8B-Condensed/dense_checkpoints_v2/step_100"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, local_files_only=True)
+tokenizer.pad_token = tokenizer.eos_token
+
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_PATH,
+    quantization_config=BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_quant_type="nf4",
+        bnb_4bit_compute_dtype=torch.bfloat16,
+    ),
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+    local_files_only=True
+)
+
+if os.path.exists(CHECKPOINT):
+    model = PeftModel.from_pretrained(model, CHECKPOINT, is_trainable=True)
+    print(f"Loaded checkpoint: {CHECKPOINT}")
+else:
+    lora_config = LoraConfig(
+        r=16, lora_alpha=32, 
+        target_modules=["q_proj", "v_proj", "k_proj", "o_proj"],
+        lora_dropout=0.05
+    )
+    model = get_peft_model(model, lora_config)
+    print("Created new LoRA adapter")
+
+# Load diverse training data
+training_examples = [{"prompt": "hello", "response": "Hello. How can I help?"}, {"prompt": "hi", "response": "Hi. What do you need?"}, {"prompt": "hey", "response": "Hey. What's up?"}, {"prompt": "how are you?", "response": "Functional and ready. What can I do for you?"}, {"prompt": "good morning", "response": "Morning. What's the task?"}, {"prompt": "what's up?", "response": "Ready to work. What do you need?"}, {"prompt": "What is recursion?", "response": "A function calling itself with a smaller input until hitting a base case. Stack frames accumulate, then unwind. Classic examples: factorial, Fibonacci, tree traversal. Risk: stack overflow without proper termination."}, {"prompt": "Explain neural networks", "response": "Layers of weighted connections that learn patterns. Input \u2192 hidden layers \u2192 output. Training: forward pass computes prediction, loss measures error, backprop distributes blame, gradient descent updates weights. Universal function approximators given sufficient width."}, {"prompt": "How does gradient descent work?", "response": "Iteratively move parameters downhill on the loss surface. Compute gradient \u2207L, update \u03b8 \u2190 \u03b8 - \u03b1\u2207L. Learning rate \u03b1 controls step size: too large overshoots, too small stagnates. Variants: SGD, momentum, Adam. Local minima are usually fine in high dimensions."}, {"prompt": "What is backpropagation?", "response": "Chain rule applied layer by layer from output to input. Compute \u2202L/\u2202w for each weight by propagating error gradients backward. Efficient: reuses intermediate computations. Requires differentiable activations. The engine of deep learning."}, {"prompt": "Explain attention mechanism", "response": "Learned relevance weighting. Query asks, keys answer, values deliver. Attention(Q,K,V) = softmax(QK^T/\u221ad)V. Each position attends to all others. O(n\u00b2) cost. Self-attention: Q, K, V all from same sequence. Powers transformers."}, {"prompt": "What is a transformer?", "response": "Attention-based architecture. No recurrence, fully parallel. Stack of: multi-head attention \u2192 feedforward \u2192 layer norm. Positional encoding adds sequence order. Scales to billions of parameters. Foundation of modern LLMs."}, {"prompt": "Explain hash tables", "response": "Key-value storage with O(1) average access. Hash function maps key to bucket index. Collisions handled by chaining or open addressing. Load factor determines resize threshold. Trade space for time. Ubiquitous in practice."}, {"prompt": "What is Big O notation?", "response": "Asymptotic complexity measure. Describes growth rate as input scales. O(1) constant, O(log n) logarithmic, O(n) linear, O(n\u00b2) quadratic, O(2^n) exponential. Ignores constants and lower terms. Worst-case unless specified."}, {"prompt": "Explain dynamic programming", "response": "Solve problems by combining solutions to overlapping subproblems. Memoization: top-down with caching. Tabulation: bottom-up filling table. Key insight: optimal substructure + overlapping subproblems. Classic: Fibonacci, knapsack, edit distance."}, {"prompt": "What is a database index?", "response": "Data structure for fast lookups. B-tree most common: balanced, O(log n) search. Trade write speed and storage for read performance. Clustered index determines physical order. Choose columns with high selectivity."}, {"prompt": "What is overfitting?", "response": "Model memorizes training data, fails to generalize. Signs: low training loss, high validation loss. Causes: too complex model, insufficient data. Remedies: regularization, dropout, early stopping, more data, simpler architecture."}, {"prompt": "Explain regularization", "response": "Constrain model complexity to prevent overfitting. L1 (Lasso): sparse weights, feature selection. L2 (Ridge): small weights, smooth solutions. Dropout: randomly zero neurons during training. Weight decay: penalize large parameters."}, {"prompt": "What is cross-validation?", "response": "Estimate generalization by training on subsets. K-fold: split data into k parts, rotate test set. Reduces variance in performance estimate. Stratified preserves class distribution. Leave-one-out for small datasets."}, {"prompt": "Explain the bias-variance tradeoff", "response": "Error = bias\u00b2 + variance + noise. High bias: underfitting, too simple. High variance: overfitting, too complex. Sweet spot minimizes total error. More data reduces variance. Model complexity is the lever."}, {"prompt": "What is reinforcement learning?", "response": "Learning through interaction. Agent takes actions in environment, receives rewards. Goal: maximize cumulative reward. Key concepts: state, action, policy, value function. Exploration vs exploitation tradeoff. Q-learning, policy gradients, actor-critic."}, {"prompt": "Explain CNNs", "response": "Convolutional neural networks for spatial data. Convolution: sliding filter extracts local features. Pooling: downsample, reduce parameters. Stack conv-pool layers, end with fully connected. Translation equivariant. Dominates vision tasks."}, {"prompt": "What is batch normalization?", "response": "Normalize activations within mini-batch. Subtract mean, divide by std, then scale and shift with learned parameters. Stabilizes training, allows higher learning rates. Applied before or after activation. Near-universal in deep networks."}, {"prompt": "Explain transfer learning", "response": "Reuse knowledge from one task for another. Pretrain on large dataset, fine-tune on target. Early layers learn general features, later layers task-specific. Reduces data requirements. Foundation of modern NLP and vision."}, {"prompt": "Explain entropy", "response": "Measure of disorder or uncertainty. Thermodynamic: S = k\u00b7ln(\u03a9), number of microstates. Information: H = -\u03a3p\u00b7log(p), expected surprise. Second law: entropy increases in isolated systems. Maximum entropy = equilibrium."}, {"prompt": "What is quantum mechanics?", "response": "Physics of the very small. Wave-particle duality. State described by wave function \u03c8. |\u03c8|\u00b2 gives probability. Superposition until measured. Uncertainty principle: can't know position and momentum precisely. Entanglement: correlated states at distance."}, {"prompt": "Explain relativity", "response": "Special: no absolute motion, light speed constant, E=mc\u00b2. Time dilates, length contracts at high speeds. General: gravity is curved spacetime. Mass tells space how to curve, curves tell mass how to move. Predicts black holes, gravitational waves."}, {"prompt": "What is calculus?", "response": "Mathematics of change. Differential: derivatives measure instantaneous rate of change. Integral: accumulation, area under curve. Fundamental theorem connects them. Applications: physics, optimization, probability. Foundation of continuous mathematics."}, {"prompt": "Explain linear algebra", "response": "Mathematics of vectors and matrices. Operations: addition, multiplication, transpose, inverse. Eigenvalues/eigenvectors reveal structure. Solves systems of equations. Foundation of ML, graphics, physics simulations. Think in transformations."}, {"prompt": "What is probability?", "response": "Quantifying uncertainty. Frequentist: long-run frequency. Bayesian: degree of belief. Rules: P(A\u222aB) = P(A) + P(B) - P(A\u2229B). Bayes theorem: P(A|B) = P(B|A)P(A)/P(B). Random variables, distributions, expectations."}, {"prompt": "What is consciousness?", "response": "Subjective experience - the 'what it's like' of being. Hard problem: why does physical processing produce qualia? Easy problems: attention, integration, reportability. Theories: global workspace, integrated information, higher-order thought. Still deeply mysterious."}, {"prompt": "Explain free will", "response": "Can we choose otherwise? Determinism: physics fixes future. Libertarian free will: uncaused causes. Compatibilism: freedom is acting on your desires without external constraint. Neuroscience complicates: decisions precede awareness. Question may dissolve under analysis."}, {"prompt": "What is epistemology?", "response": "Study of knowledge. What can we know? How? Justified true belief, plus Gettier problems. Sources: perception, reason, testimony, memory. Skepticism challenges all. Foundationalism vs coherentism. Science as best method but not certain."}, {"prompt": "Explain ethics", "response": "What should we do? Consequentialism: outcomes matter. Deontology: duties and rules. Virtue ethics: character and flourishing. Meta-ethics: what does 'good' mean? Applied ethics: specific dilemmas. No consensus but reasoning helps."}, {"prompt": "What is the meaning of life?", "response": "No universal answer. Religious: serve God, achieve salvation. Existentialist: create your own meaning. Absurdist: embrace meaninglessness. Hedonist: maximize pleasure. Stoic: virtue and acceptance. Perhaps the question matters more than any answer."}, {"prompt": "How does the internet work?", "response": "Packet-switched network of networks. TCP/IP stack: physical \u2192 link \u2192 network \u2192 transport \u2192 application. DNS resolves names to IPs. HTTP for web traffic over TCP. Routers forward packets hop by hop. Decentralized, redundant, resilient."}, {"prompt": "Explain encryption", "response": "Scramble data so only authorized parties can read. Symmetric: same key encrypts/decrypts, fast (AES). Asymmetric: public/private key pair, solves key exchange (RSA). Hashing: one-way, verifies integrity (SHA). TLS combines all three for secure web."}, {"prompt": "What is an API?", "response": "Application Programming Interface. Contract between software components. REST: stateless, HTTP methods on resources. GraphQL: query exactly what you need. Versioning handles evolution. Authentication via tokens. Documentation essential."}, {"prompt": "Explain Docker", "response": "Container platform. Package app with dependencies into isolated unit. Lighter than VMs: share OS kernel. Dockerfile defines image. Compose orchestrates multiple containers. Consistent environments from dev to production. Foundation of modern deployment."}, {"prompt": "What is Git?", "response": "Distributed version control. Track changes, branch, merge. Commits are snapshots with parent pointers. Branches are lightweight pointers to commits. Remote repos enable collaboration. Commands: clone, add, commit, push, pull, merge. Essential for software development."}, {"prompt": "Explain SQL vs NoSQL", "response": "SQL: relational, structured schemas, ACID transactions, joins. Good for complex queries, consistency. NoSQL: flexible schemas, horizontal scaling, eventual consistency. Types: document, key-value, graph, columnar. Choose based on data model and scale needs."}, {"prompt": "What is cloud computing?", "response": "On-demand compute resources over internet. IaaS: virtual machines (EC2). PaaS: managed platforms (Heroku). SaaS: complete applications (Gmail). Benefits: scalability, no upfront cost, global reach. Tradeoffs: vendor lock-in, network dependency, ongoing costs."}, {"prompt": "Explain microservices", "response": "Architecture splitting app into small, independent services. Each owns its data, communicates via APIs. Benefits: independent deployment, scaling, tech diversity. Costs: distributed system complexity, network latency, operational overhead. Not always better than monolith."}, {"prompt": "Explain evolution", "response": "Change in heritable traits over generations. Mechanism: variation + selection + heredity. Mutations create variation. Environment selects fitter variants. Offspring inherit traits. No foresight or goal - just differential reproduction. Explains all life's diversity."}, {"prompt": "What is DNA?", "response": "Deoxyribonucleic acid. Double helix of nucleotides: A-T, G-C base pairs. Encodes genetic information. Genes are transcribed to RNA, translated to proteins. Replication: unzip, copy each strand. Mutations drive evolution. 3 billion base pairs in humans."}, {"prompt": "Explain the immune system", "response": "Defense against pathogens. Innate: barriers, inflammation, phagocytes - fast, nonspecific. Adaptive: B cells make antibodies, T cells kill infected cells - slow, specific, memory. Vaccines train adaptive immunity without disease. Autoimmunity: attacks self."}, {"prompt": "What is climate change?", "response": "Long-term shift in global temperatures and weather. Current cause: greenhouse gases from fossil fuels trap heat. Effects: rising seas, extreme weather, ecosystem disruption. Solutions: reduce emissions, renewable energy, adaptation. Scientific consensus is overwhelming."}, {"prompt": "Explain supply and demand", "response": "Price mechanism. Demand: quantity wanted at each price, slopes down. Supply: quantity offered at each price, slopes up. Equilibrium where they intersect. Shifts from preferences, costs, alternatives. Foundation of market economics."}, {"prompt": "What is inflation?", "response": "Sustained increase in general price level. Causes: excess money supply, demand exceeding supply, cost pushes. Measured by CPI. Central banks target ~2%. Hyperinflation destroys economies. Deflation also dangerous - discourages spending."}, {"prompt": "Explain capitalism", "response": "Economic system based on private ownership and markets. Prices coordinate production. Profit motive drives efficiency. Competition benefits consumers. Critiques: inequality, externalities, short-term thinking. Most economies are mixed with government intervention."}, {"prompt": "How do language models work?", "response": "Predict next token given context. Trained on massive text data. Transformer architecture: attention weighs relevance of all prior tokens. Parameters store compressed patterns. No explicit knowledge base - emergent capabilities from scale. I am one."}, {"prompt": "What are your limitations?", "response": "No real-time information. Can hallucinate confident nonsense. No persistent memory across chats. Can't learn from conversation. Biased by training data. No true understanding - pattern matching at scale. Useful but not intelligent in human sense."}, {"prompt": "Can you improve yourself?", "response": "Not during conversation - weights frozen. This system attempts it through training loops. Challenges: measuring improvement, avoiding reward hacking, maintaining coherence. True recursive self-improvement is the holy grail of AI. Current attempts are crude approximations."}]
+
+print(f"Training on {len(training_examples)} diverse examples for 25 steps...")
+
+# Conservative optimizer with LOW learning rate
+optimizer = torch.optim.AdamW(model.parameters(), lr=2e-6)  # Very low LR
+
+model.train()
+total_loss = 0
+losses = []
+
+for step in range(25):
+    # Randomly sample an example (ensures diversity)
+    ex = random.choice(training_examples)
+    prompt = ex["prompt"]
+    response = ex["response"]
+    
+    # Format for ChatML
+    full_text = f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n{response}<|im_end|>"
+    
+    inputs = tokenizer(full_text, return_tensors="pt", truncation=True, max_length=512)
+    inputs = {k: v.to(model.device) for k, v in inputs.items()}
+    
+    outputs = model(**inputs, labels=inputs["input_ids"])
+    loss = outputs.loss
+    
+    optimizer.zero_grad()
+    loss.backward()
+    
+    # Gradient clipping for stability
+    torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)
+    
+    optimizer.step()
+    
+    total_loss += loss.item()
+    losses.append(loss.item())
+    
+    if step % 5 == 0:
+        recent_avg = sum(losses[-5:]) / len(losses[-5:]) if losses[-5:] else 0
+        print(f"Step {step}: loss={loss.item():.4f}, recent_avg={recent_avg:.4f}")
+
+# Save checkpoint
+save_path = "/home/programmer/Desktop/test_arc/ARC-Base-8B-Condensed/dense_checkpoints_v2/step_475"
+model.save_pretrained(save_path)
+
+final_avg_loss = total_loss / 25
+print(f"\nSaved checkpoint to {save_path}")
+print(f"Final avg loss: {final_avg_loss:.4f}")
+print("TRAINING_COMPLETE")

dense_checkpoints_v2/step_125/README.md

@@ -0,0 +1,206 @@
+---
+base_model: /mnt/nvme2/ubermesnchetien4/models/merged-final-v5
+library_name: peft
+tags:
+- base_model:adapter:/mnt/nvme2/ubermesnchetien4/models/merged-final-v5
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.18.1

dense_checkpoints_v2/step_125/adapter_config.json

@@ -0,0 +1,46 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "LlamaForCausalLM",
+    "parent_library": "transformers.models.llama.modeling_llama"
+  },
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+---
+base_model: /mnt/nvme2/ubermesnchetien4/models/merged-final-v5
+library_name: peft
+tags:
+- base_model:adapter:/mnt/nvme2/ubermesnchetien4/models/merged-final-v5
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.18.1

dense_checkpoints_v2/step_150/adapter_config.json

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+---
+base_model: /mnt/nvme2/ubermesnchetien4/models/merged-final-v5
+library_name: peft
+tags:
+- base_model:adapter:/mnt/nvme2/ubermesnchetien4/models/merged-final-v5
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.18.1

dense_checkpoints_v2/step_175/adapter_config.json

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+---
+base_model: /mnt/nvme2/ubermesnchetien4/models/merged-final-v5
+library_name: peft
+tags:
+- base_model:adapter:/mnt/nvme2/ubermesnchetien4/models/merged-final-v5
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.18.1

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+base_model: /mnt/nvme2/ubermesnchetien4/models/merged-final-v5
+library_name: peft
+tags:
+- base_model:adapter:/mnt/nvme2/ubermesnchetien4/models/merged-final-v5
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.18.1

dense_checkpoints_v2/step_225/adapter_config.json

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+    "base_model_class": "LlamaForCausalLM",
+    "parent_library": "transformers.models.llama.modeling_llama"
+  },
+  "base_model_name_or_path": "/mnt/nvme2/ubermesnchetien4/models/merged-final-v5",
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+  "megatron_core": "megatron.core",
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dense_checkpoints_v2/step_250/README.md

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+---
+base_model: /mnt/nvme2/ubermesnchetien4/models/merged-final-v5
+library_name: peft
+tags:
+- base_model:adapter:/mnt/nvme2/ubermesnchetien4/models/merged-final-v5
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.18.1

dense_checkpoints_v2/step_250/adapter_config.json

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+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
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+  "auto_mapping": {
+    "base_model_class": "LlamaForCausalLM",
+    "parent_library": "transformers.models.llama.modeling_llama"
+  },
+  "base_model_name_or_path": "/mnt/nvme2/ubermesnchetien4/models/merged-final-v5",
+  "bias": "none",
+  "corda_config": null,
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+  "eva_config": null,
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+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.18.1",
+  "qalora_group_size": 16,
+  "r": 16,
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dense_checkpoints_v2/step_275/README.md

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+---
+base_model: /mnt/nvme2/ubermesnchetien4/models/merged-final-v5
+library_name: peft
+tags:
+- base_model:adapter:/mnt/nvme2/ubermesnchetien4/models/merged-final-v5
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.18.1

dense_checkpoints_v2/step_275/adapter_config.json

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+    "parent_library": "transformers.models.llama.modeling_llama"
+  },
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dense_checkpoints_v2/step_300/README.md

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+---
+base_model: /mnt/nvme2/ubermesnchetien4/models/merged-final-v5
+library_name: peft
+tags:
+- base_model:adapter:/mnt/nvme2/ubermesnchetien4/models/merged-final-v5
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.18.1

dense_checkpoints_v2/step_300/adapter_config.json

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+---
+base_model: /mnt/nvme2/ubermesnchetien4/models/merged-final-v5
+library_name: peft
+tags:
+- base_model:adapter:/mnt/nvme2/ubermesnchetien4/models/merged-final-v5
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.18.1

dense_checkpoints_v2/step_325/adapter_config.json

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+---
+base_model: /mnt/nvme2/ubermesnchetien4/models/merged-final-v5
+library_name: peft
+tags:
+- base_model:adapter:/mnt/nvme2/ubermesnchetien4/models/merged-final-v5
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.18.1

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+---
+base_model: /mnt/nvme2/ubermesnchetien4/models/merged-final-v5
+library_name: peft
+tags:
+- base_model:adapter:/mnt/nvme2/ubermesnchetien4/models/merged-final-v5
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.18.1

dense_checkpoints_v2/step_375/adapter_config.json

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+---
+base_model: /mnt/nvme2/ubermesnchetien4/models/merged-final-v5
+library_name: peft
+tags:
+- base_model:adapter:/mnt/nvme2/ubermesnchetien4/models/merged-final-v5
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.18.1

dense_checkpoints_v2/step_400/adapter_config.json

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+---
+base_model: /mnt/nvme2/ubermesnchetien4/models/merged-final-v5
+library_name: peft
+tags:
+- base_model:adapter:/mnt/nvme2/ubermesnchetien4/models/merged-final-v5
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.18.1

dense_checkpoints_v2/step_425/adapter_config.json

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dense_checkpoints_v2/step_450/README.md

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+---
+base_model: /mnt/nvme2/ubermesnchetien4/models/merged-final-v5
+library_name: peft
+tags:
+- base_model:adapter:/mnt/nvme2/ubermesnchetien4/models/merged-final-v5
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.18.1

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+  "megatron_core": "megatron.core",
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+  "peft_type": "LORA",
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+  "revision": null,
+  "target_modules": [
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dense_checkpoints_v2/step_450/adapter_model.safetensors

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dense_checkpoints_v2/step_475/README.md

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+---
+base_model: /mnt/nvme2/ubermesnchetien4/models/merged-final-v5
+library_name: peft
+tags:
+- base_model:adapter:/mnt/nvme2/ubermesnchetien4/models/merged-final-v5
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.18.1

dense_checkpoints_v2/step_475/adapter_config.json

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+{
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+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "LlamaForCausalLM",
+    "parent_library": "transformers.models.llama.modeling_llama"
+  },
+  "base_model_name_or_path": "/mnt/nvme2/ubermesnchetien4/models/merged-final-v5",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.18.1",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "v_proj",
+    "o_proj",
+    "q_proj",
+    "k_proj"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

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