Instructions to use SLM-Archive/NanoDex-Test-500K-200M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SLM-Archive/NanoDex-Test-500K-200M with Transformers:

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

pipe = pipeline("text-generation", model="SLM-Archive/NanoDex-Test-500K-200M")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SLM-Archive/NanoDex-Test-500K-200M")
model = AutoModelForCausalLM.from_pretrained("SLM-Archive/NanoDex-Test-500K-200M", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SLM-Archive/NanoDex-Test-500K-200M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SLM-Archive/NanoDex-Test-500K-200M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SLM-Archive/NanoDex-Test-500K-200M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SLM-Archive/NanoDex-Test-500K-200M

SGLang

How to use SLM-Archive/NanoDex-Test-500K-200M 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 "SLM-Archive/NanoDex-Test-500K-200M" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SLM-Archive/NanoDex-Test-500K-200M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "SLM-Archive/NanoDex-Test-500K-200M" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SLM-Archive/NanoDex-Test-500K-200M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SLM-Archive/NanoDex-Test-500K-200M with Docker Model Runner:
```
docker model run hf.co/SLM-Archive/NanoDex-Test-500K-200M
```

NanoDex-Test-500K-200M / training_run.json

DedeProGames

NanoDex 500k · 199,884,800 fineweb-edu tokens · loss 3.8176

fb398ae verified about 2 months ago

Raw History Blame Contribute Delete

606 Bytes

{
  "job_id": "0a98a7d8fd4f",
  "tier": "500k",
  "label": "NanoDex-500k",
  "n_params": 492192,
  "target_tokens": 200000000,
  "tokens_seen": 199884800,
  "steps": 1525,
  "seq_len": 512,
  "final_loss": 3.817596435546875,
  "best_loss": 3.6255829334259033,
  "peak_lr": 0.004,
  "batch_tokens": 131072,
  "optimizer": "AdamW(0.9, 0.95) wd=0.1 clip=1.0",
  "schedule": "warmup 2% + cosine to 10%",
  "dataset": "HuggingFaceFW/fineweb-edu (sample-10BT)",
  "architecture": "LlamaForCausalLM (SiLU, RMSNorm, RoPE, GQA, tied embeddings)",
  "wall_time_s": 309.3608708381653,
  "trained_by": "DedeProGames"
}

	{
	"job_id": "0a98a7d8fd4f",
	"tier": "500k",
	"label": "NanoDex-500k",
	"n_params": 492192,
	"target_tokens": 200000000,
	"tokens_seen": 199884800,
	"steps": 1525,
	"seq_len": 512,
	"final_loss": 3.817596435546875,
	"best_loss": 3.6255829334259033,
	"peak_lr": 0.004,
	"batch_tokens": 131072,
	"optimizer": "AdamW(0.9, 0.95) wd=0.1 clip=1.0",
	"schedule": "warmup 2% + cosine to 10%",
	"dataset": "HuggingFaceFW/fineweb-edu (sample-10BT)",
	"architecture": "LlamaForCausalLM (SiLU, RMSNorm, RoPE, GQA, tied embeddings)",
	"wall_time_s": 309.3608708381653,
	"trained_by": "DedeProGames"
	}