Instructions to use root-sys-n/Mima-Pico-15m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use root-sys-n/Mima-Pico-15m with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="root-sys-n/Mima-Pico-15m", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("root-sys-n/Mima-Pico-15m", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use root-sys-n/Mima-Pico-15m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "root-sys-n/Mima-Pico-15m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "root-sys-n/Mima-Pico-15m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/root-sys-n/Mima-Pico-15m

SGLang

How to use root-sys-n/Mima-Pico-15m with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "root-sys-n/Mima-Pico-15m" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "root-sys-n/Mima-Pico-15m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "root-sys-n/Mima-Pico-15m" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "root-sys-n/Mima-Pico-15m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use root-sys-n/Mima-Pico-15m with Docker Model Runner:
```
docker model run hf.co/root-sys-n/Mima-Pico-15m
```

root-sys-n commited on 4 days ago

Commit

d55ea2d

verified ·

1 Parent(s): 85f6e39

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +17 -3

README.md CHANGED Viewed

@@ -94,11 +94,25 @@ Her iki model de 15.5M parametre ile çoktan seçmeli bilgi testi üzerinde zay
 | Training stage | A3-karma | bitti |
 | Validation loss | **0.5522** | 1.7944 |
 | Parameters | 15.54M | 15.54M |
-| Architecture | Same (7L/384d/6H) | Same + neuroendocrine controller |
-v2 was trained longer (32.8K steps) and reaches lower validation loss (0.5522 vs 1.7944). However, v4 incorporates the neuroendocrine controller architecture and stability stages (S1-kararli, S2-final) that v2 does not have.
-> TR: v2 daha uzun eğitildi (32.8K adım) ve daha düşük doğrulama kaybına ulaştı (0.5522, v4'te 1.7944). Ancak v4, v2'de olmayan nöroendokrin denetleyici mimarisi ve S1-kararli, S2-final kararlılık aşamalarını içerir.
 ## Training methodology / Eğitim yöntemi

 | Training stage | A3-karma | bitti |
 | Validation loss | **0.5522** | 1.7944 |
 | Parameters | 15.54M | 15.54M |
+| Architecture | Same (7L/384d/6H) | Same (7L/384d/6H) |
+v2 was trained longer (32.8K steps) and reaches lower validation loss (0.5522 vs 1.7944). v4 continues from the same v2 checkpoint base but for fewer steps (11,185).
+> TR: v2 daha uzun eğitildi (32.8K adım) ve daha düşük doğrulama kaybına ulaştı (0.5522, v4'te 1.7944). v4, aynı v2 checkpoint tabanından devam eder ancak daha az adımda (11.185).
+## Basit Token-Tahmin Testi (15M'ye Uygun) / Simple Token Prediction Test
+Modelin prompt'tan sonraki token'ın softmax olasılığını ölçen basit Türkçe test:
+| Soru | Doğru Cevap | V2 (en yüksek olasılık) | V4 (en yüksek olasılık) |
+|---|---|---|---|
+| Türkiye'nin başkenti | Ankara | İstanbul (0.0400) | İstanbul (0.0018) |
+| Güneş ...'dan doğar | doğudan | güneydoğudan (0.0002) | doğudan (0.0018) |
+| 2+2 = | dört / 4 | 4 (0.0021) | 4 (0.0000) |
+| Elma ... | kırmızı | kırmızı (0.0001) | mavi (0.0000) |
+| Günde ... saat | 24 | 6 (0.0005) | 6 (0.0003) |
+> TR: Modelin prompt'tan sonraki token'ının softmax olasiligi. 15M parametre ile bu basit bilgi sorularinda da dusuk olasilik — bu olcek icin beklenen bir sonuc. V4'te "Gunes dogudan" dogru cevaba en yuksek olasilik veriliyor ancak deger cok dusuk.
 ## Training methodology / Eğitim yöntemi