Text Generation
Safetensors
GGUF
English
nae1
neuralai
causal-lm
llama-cpp
from-scratch
pretraining
sft
tool-use
local-ai
tiny-model
Instructions to use Subject-Emu-5259/NeuralAI-Nae1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Subject-Emu-5259/NeuralAI-Nae1 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Subject-Emu-5259/NeuralAI-Nae1:Q4_K_M # Run inference directly in the terminal: llama cli -hf Subject-Emu-5259/NeuralAI-Nae1:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Subject-Emu-5259/NeuralAI-Nae1:Q4_K_M # Run inference directly in the terminal: llama cli -hf Subject-Emu-5259/NeuralAI-Nae1:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Subject-Emu-5259/NeuralAI-Nae1:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Subject-Emu-5259/NeuralAI-Nae1:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Subject-Emu-5259/NeuralAI-Nae1:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Subject-Emu-5259/NeuralAI-Nae1:Q4_K_M
Use Docker
docker model run hf.co/Subject-Emu-5259/NeuralAI-Nae1:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Subject-Emu-5259/NeuralAI-Nae1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Subject-Emu-5259/NeuralAI-Nae1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Subject-Emu-5259/NeuralAI-Nae1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Subject-Emu-5259/NeuralAI-Nae1:Q4_K_M
- Ollama
How to use Subject-Emu-5259/NeuralAI-Nae1 with Ollama:
ollama run hf.co/Subject-Emu-5259/NeuralAI-Nae1:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use Subject-Emu-5259/NeuralAI-Nae1 with Docker Model Runner:
docker model run hf.co/Subject-Emu-5259/NeuralAI-Nae1:Q4_K_M
- Lemonade
How to use Subject-Emu-5259/NeuralAI-Nae1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Subject-Emu-5259/NeuralAI-Nae1:Q4_K_M
Run and chat with the model
lemonade run user.NeuralAI-Nae1-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Upload README.md with huggingface_hub
Browse files
README.md
CHANGED
|
@@ -72,6 +72,10 @@ Nae1 v1 was pretrained from random initialization in eight cosine cycles: 0 β
|
|
| 72 |
|
| 73 |
**v2 continuation.** The v2 run (32k tokenizer, peak LR 6e-4) resumed from step 60000 and trained to step 80000. Gains decelerated into the cosine floor (held-out 57.64 β 52.26 per the shared anchor), motivating the next run's warm-restart recipe (re-warm floor β 6e-4 over 1k steps, then cosine 80k β 100k).
|
| 74 |
|
|
|
|
|
|
|
|
|
|
|
|
|
| 75 |
---
|
| 76 |
|
| 77 |
## π Evaluation
|
|
@@ -99,6 +103,10 @@ Raw report: [`eval_step32000_q4_n512.json`](eval_step32000_q4_n512.json) β mea
|
|
| 99 |
|
| 100 |
**v2 anchor (shared corpus).** One readable held-out corpus (`heldout_fineweb.jsonl`: 2,000 docs from unseen crawl CC-MAIN-2014-10) now anchors all v2 claims β `n_ctx=512`, 50 docs / 27,198 tokens: **57.64 @ step 60000 β 52.26 @ step 80000** (native bf16). The in-kernel 200k-token slice reads 43.09 β a different corpus, not a contradiction. One shared corpus exists precisely so this ambiguity never recurs.
|
| 101 |
|
|
|
|
|
|
|
|
|
|
|
|
|
| 102 |
> β οΈ Perplexity is the trust signal here, not raw capability. At 100M params, expect exploratory, often garbled text β this is a research-lineage model, not a chat assistant. The base model does not follow instructions (no SFT weights yet β sweep in progress as of 2026-10-12).
|
| 103 |
|
| 104 |
### Logits Parity Notice
|
|
|
|
| 72 |
|
| 73 |
**v2 continuation.** The v2 run (32k tokenizer, peak LR 6e-4) resumed from step 60000 and trained to step 80000. Gains decelerated into the cosine floor (held-out 57.64 β 52.26 per the shared anchor), motivating the next run's warm-restart recipe (re-warm floor β 6e-4 over 1k steps, then cosine 80k β 100k).
|
| 74 |
|
| 75 |
+
<p align="center">
|
| 76 |
+
<img src="neuralai-nae1-training-v2.png" alt="Nae1 v2 in-kernel held-out PPL steps 62000 to 80000" width="92%" />
|
| 77 |
+
</p>
|
| 78 |
+
|
| 79 |
---
|
| 80 |
|
| 81 |
## π Evaluation
|
|
|
|
| 103 |
|
| 104 |
**v2 anchor (shared corpus).** One readable held-out corpus (`heldout_fineweb.jsonl`: 2,000 docs from unseen crawl CC-MAIN-2014-10) now anchors all v2 claims β `n_ctx=512`, 50 docs / 27,198 tokens: **57.64 @ step 60000 β 52.26 @ step 80000** (native bf16). The in-kernel 200k-token slice reads 43.09 β a different corpus, not a contradiction. One shared corpus exists precisely so this ambiguity never recurs.
|
| 105 |
|
| 106 |
+
<p align="center">
|
| 107 |
+
<img src="neuralai-nae1-comparison-v2.png" alt="Nae1 v2 shared-corpus anchor: step 60000 vs 80000" width="92%" />
|
| 108 |
+
</p>
|
| 109 |
+
|
| 110 |
> β οΈ Perplexity is the trust signal here, not raw capability. At 100M params, expect exploratory, often garbled text β this is a research-lineage model, not a chat assistant. The base model does not follow instructions (no SFT weights yet β sweep in progress as of 2026-10-12).
|
| 111 |
|
| 112 |
### Logits Parity Notice
|