324 lines
7.0 KiB
Markdown
324 lines
7.0 KiB
Markdown
---
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license: mit
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datasets:
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- GODELEV/BetterDataset-2M
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language:
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- en
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pipeline_tag: text-generation
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---
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# Archaea-74M
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Archaea-74M is a decoder-only causal language model with approximately 74 million parameters, pretrained from scratch on BetterDataset-2M. The model uses a LLaMA-style architecture with Grouped Query Attention (GQA) and was trained using BF16 mixed precision.
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This release represents approximately **1.23 billion trained tokens** out of a planned **1.6 billion token pretraining run**, making it a substantial intermediate checkpoint that captures most of the intended training curriculum while leaving room for future scaling and refinement.
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---
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# Model Card
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| Attribute | Value |
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|------------|------------|
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| Model ID | GODELEV/Archaea-74M |
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| Parameters | ~74 Million |
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| Architecture | Decoder-only Transformer (LLaMA-style) |
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| Attention | Grouped Query Attention (GQA) |
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| Context Length | 1024 |
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| Tokenizer | GPT-2 |
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| Training Precision | BF16 |
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| Framework | PyTorch + Transformers |
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| License | MIT |
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---
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# Architecture
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## Transformer Configuration
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| Parameter | Value |
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|------------|------------|
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| Hidden Size | 512 |
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| Intermediate Size | 1408 |
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| Layers | 8 |
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| Attention Heads | 8 |
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| KV Heads | 2 |
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| GQA Ratio | 4:1 |
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| Activation | SiLU |
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| Normalization | RMSNorm |
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| Context Length | 1024 |
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The model implements Grouped Query Attention, reducing KV-cache memory requirements while maintaining strong representational capacity for a model of this scale.
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---
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# Training
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## Dataset
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Archaea-74M was pretrained on **GODELEV/BetterDataset-2M**, a multi-source corpus composed of:
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- General web text
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- Conversational content
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- Knowledge-focused material
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- Educational content
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- Instruction-like examples
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- Technical and programming text
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The complete corpus contains approximately **1.6 billion tokens**.
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### Training Progress
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| Metric | Value |
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|----------|----------|
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| Planned Tokens | ~1.6B |
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| Tokens Trained | ~1.23B |
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| Completion | ~77% |
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| Planned Steps | 25,000 |
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| Completed Steps | 18,800 |
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## Optimization
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| Parameter | Value |
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|------------|------------|
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| Optimizer | AdamW |
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| Scheduler | OneCycleLR |
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| Peak Learning Rate | 6e-4 |
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| Weight Decay | 0.1 |
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| Gradient Clipping | 1.0 |
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| Sequence Length | 1024 |
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| Effective Batch Size | 64 |
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| Precision | BF16 |
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## Training Statistics
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| Metric | Value |
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|------------|------------|
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| Initial Loss | 10.9223 |
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| Final Loss | 2.9488 |
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| Best Loss | 2.8071 |
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| Final Perplexity | 19.08 |
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| Best Perplexity | 16.56 |
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## Training Loss Curve
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<img src="Archaea74M_Training_Loss_Curve.png" width="700"/>
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## Learning Rate Schedule
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<img src="Archaea74M_Learning_Rate_Schedule.png" width="700"/>
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---
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# Evaluation
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Evaluated using EleutherAI LM Evaluation Harness.
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## Benchmark Results
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Done on 0-Shot
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| Benchmark | Metric | Score |
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|------------|------------|------------|
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| HellaSwag | acc_norm | 27.31% |
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| PIQA | acc_norm | 58.54% |
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| WinoGrande | acc | 51.54% |
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| BoolQ | acc | 56.33% |
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| ARC-Easy | acc_norm | 39.06% |
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| ARC-Challenge | acc_norm | 22.70% |
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| OpenBookQA | acc_norm | 26.00% |
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| CommonsenseQA | acc | 19.66% |
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| LAMBADA | acc | 18.01% |
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| BLiMP | acc | 74.91% |
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| MMLU | acc | 25.07% |
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| SciQ | acc_norm | 57.70% |
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| COPA | acc | 61.00% |
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| RACE | acc | 24.78% |
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| SWAG | acc_norm | 41.98% |
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| TruthfulQA MC2 | acc | 46.46% |
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| WikiText-2 | Word Perplexity | 68.06 |
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## Evaluation Notes
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Failed tasks from previous runs:
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- SocialIQA: dataset loading incompatibility.
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- Arithmetic benchmark: task identifier changes in recent lm-eval versions.
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---
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# Sample Generations
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The following examples were generated directly from the pretrained base model.
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## Example 1
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**Input**
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```text
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Once upon a time
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```
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**Output**
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```text
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Once upon a time
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In the days after, I was told that I could not make a living. So I said to myself,
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“I want to go back to my family.” I said, “This is a good one. You can take me to the doctor
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or something and do it with you.”
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The doctor asked me how I felt about this. He said he did. But I said,
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“You can't have a baby.”
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```
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## Example 2
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**Input**
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```text
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The horizon looked
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```
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**Output**
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```text
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the horizon looked like a big black hole. The sun was shining and the wind was blowing.
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The sun was shining with the sun, but it was still there. The stars were on top of each other.
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The sun was falling as a result of the weather. The sun was going up.
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```
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## Example 3
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**Input**
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```text
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what is the future of ai
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```
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**Output**
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```text
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what is the future of Ai?
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I don't know what you're talking about, but I'm sure that's a good reason for you to be optimistic.
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It doesn't matter if it doesn't take away your wandering in a positive direction.
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The answer is simple: I'll leave you with a feeling of uncertainty and optimism.
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```
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These outputs illustrate the model's ability to continue prompts coherently while also demonstrating typical limitations of small-scale pretrained language models, including repetition, topic drift, and inconsistent factual reasoning.
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---
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## Usage
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### Installation
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```bash
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pip install torch transformers
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```
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### Loading the Model
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "GODELEV/Archaea-74M"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
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device_map="auto"
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)
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```
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### Text Generation
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "GODELEV/Archaea-74M"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
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prompt = "The future of artificial intelligence"
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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with torch.no_grad():
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output = model.generate(
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**inputs,
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max_new_tokens=200,
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temperature=0.8,
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do_sample=True,
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repetition_penalty=1.2,
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pad_token_id=tokenizer.eos_token_id
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)
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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```
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---
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# Repository Structure
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```text
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Archaea-74M/
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├── config.json
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├── generation_config.json
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├── model.safetensors
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├── tokenizer.json
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├── tokenizer_config.json
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├── Archaea74M_Training_Loss_Curve.png
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├── Archaea74M_Learning_Rate_Schedule.png
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└── README.md
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```
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---
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# Limitations
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Archaea-74M is a base pretrained model and has not undergone:
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- Instruction tuning
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- RLHF
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- Preference optimization
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- Safety alignment
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Known limitations:
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- Hallucinations and factual inaccuracies
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- Limited reasoning due to model scale
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- Sensitivity to prompt phrasing
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- Fixed 1024-token context window
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- Not suitable for high-stakes applications
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---
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# Future Work
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- Instruction tuning
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- Expanded benchmark coverage
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- Longer context lengths
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- Improved data quality and curriculum design
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---
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# Citation
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```bibtex
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@misc{archaea74m,
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title={Archaea-74M},
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author={Akshit Kumar},
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year={2026},
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publisher={Hugging Face},
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url={https://huggingface.co/GODELEV/Archaea-74M}
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}
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``` |