Instructions to use iamshnoo/combined_without_metadata_500m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use iamshnoo/combined_without_metadata_500m with Transformers:

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

pipe = pipeline("text-generation", model="iamshnoo/combined_without_metadata_500m")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("iamshnoo/combined_without_metadata_500m")
model = AutoModelForCausalLM.from_pretrained("iamshnoo/combined_without_metadata_500m")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use iamshnoo/combined_without_metadata_500m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "iamshnoo/combined_without_metadata_500m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "iamshnoo/combined_without_metadata_500m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/iamshnoo/combined_without_metadata_500m

SGLang

How to use iamshnoo/combined_without_metadata_500m 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 "iamshnoo/combined_without_metadata_500m" \
    --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": "iamshnoo/combined_without_metadata_500m",
		"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 "iamshnoo/combined_without_metadata_500m" \
        --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": "iamshnoo/combined_without_metadata_500m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use iamshnoo/combined_without_metadata_500m with Docker Model Runner:
```
docker model run hf.co/iamshnoo/combined_without_metadata_500m
```

combined_without_metadata_500m

Summary

This repo contains the global combined model at the final 10k-step checkpoint for the metadata localization project. It was trained from scratch on the project corpus, using the Llama 3.2 tokenizer and vocabulary.

Variant Metadata

Stage: pretrain
Family: global
Size: 500m
Metadata condition: without_metadata
Base model lineage: Trained from scratch; tokenizer/vocabulary from meta-llama/Llama-3.2-1B

Weights & Biases Provenance

Run name: 11/11/2025_13:18:50_combined_without_metadata_500m
Internal run URL: https://wandb.ai/iamshnoo/nanotron/runs/lqtre9sh
Note: the Weights & Biases workspace is private; public readers should use the summarized metrics and configuration below.
State: finished
Runtime: 75h 32m 59s

Run Summary

KPI/train_lm_loss: 2.3613
KPI/train_perplexity: 10.6047
KPI/val_loss: 2.404
KPI/val_perplexity: 11.0673
KPI/consumed_tokens/train: 41,943,040,000
_step: 10,000

Training Configuration

train_steps: 10,000
sequence_length: 2,048
micro_batch_size: 8
batch_accumulation_per_replica: 64
learning_rate: 0.003
min_decay_lr: 0.0003
checkpoint_interval: 1,000

Training Curves

Static plots below were exported from the private Weights & Biases run and embedded here for public access.

Train Loss

Validation Perplexity

Throughput

Project Context

This model is part of the metadata localization release. Related checkpoints and variants are grouped in the public Hugging Face collection Metadata Conditioned LLMs.

Training data source: News on the Web (NOW) Corpus
Project repository: https://github.com/iamshnoo/metadata_localization
Paper: https://arxiv.org/abs/2601.15236

Last synced: 2026-04-02 14:39:14 UTC

Downloads last month: 23

Safetensors

Model size

0.7B params

Tensor type

BF16

Collection including iamshnoo/combined_without_metadata_500m

Metadata Conditioned LLMs

Collection

Pretraining Data: English NOW corpus (english-corpora.org/now). Paper: arxiv.org/abs/2601.15236. Code: github.com/iamshnoo/metadata_localization • 92 items • Updated Apr 11

Paper for iamshnoo/combined_without_metadata_500m

Metadata Conditioned Large Language Models for Localization

Paper • 2601.15236 • Published Jan 21