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data-scientist-slm-1m
sathishphdai/data-scientist-slm-1m
data-scientist-slm-1m is a text generation model from sathishphdai. Use it when you need the model to write or continue text. The card lists the license as mit.
A LLaMA-style transformer (~989.7M params, ~0.99B) trained from scratch for the Data Scientist role. Supports up to 1M token context via RoPE with gradient checkpointing.
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Parameters
993M
7.9 GB on disk
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Repo makeup
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.safetensors4 GB · 50%
At a glance
- Task
- Text Generation
- License
- mit
- Model type
- data_scientist-slm
- Access
- Public
- Created
- Mar 4, 2026
- Updated
- Mar 6, 2026
- SHA
- 33e58807
Try a prompt
Model card
From the Hugging Face model README
Data Scientist-SLM: Role-Based Small Language Model
A LLaMA-style transformer (~989.7M params, ~0.99B) trained from scratch for the Data Scientist role. Supports up to 1M token context via RoPE with gradient checkpointing.
Architecture
| Component | Value |
|---|---|
| Architecture | LLaMA-style (RoPE + RMSNorm + SwiGLU) |
| Parameters | ~989.7M (~0.99B) |
| Layers | 32 |
| Heads | 20 |
| Embedding | 1600 |
| Max Context | 100,000,000,000 tokens |
| Max Output | 1,000,000 tokens |
| Vocab | 2,025 BPE |
| Model Size | ~4 GB (fp32) |
Training
- Best eval loss: 1.4600185036659241
- Trained with gradient checkpointing on Apple M4 (MPS)
- 3 epochs, batch_size=1, grad_accum=16
Usage
from huggingface_hub import hf_hub_download
from tokenizers import Tokenizer
model_path = hf_hub_download("sathishphdai/data-scientist-slm-1m", "model.safetensors")
tokenizer_path = hf_hub_download("sathishphdai/data-scientist-slm-1m", "data_scientist_tokenizer.json")
tokenizer = Tokenizer.from_file(tokenizer_path)
- Task
- Text Generation
- Type
- data_scientist-slm
- License
- mit
- Languages
- en
- Created
- Mar 4, 2026
- Updated
- Mar 6, 2026