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

Libraries

How to use iamshnoo/combined_without_metadata_chat with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use iamshnoo/combined_without_metadata_chat 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_chat"
# 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_chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/iamshnoo/combined_without_metadata_chat

SGLang

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

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

combined_without_metadata_chat

Summary

This repo contains the merged chat model for the combined without metadata branch of the metadata localization project. It was produced by supervised fine-tuning on the project QA benchmark after project pretraining.

Variant Metadata

Stage: sft_chat
Family: chat
Metadata condition: without_metadata
Base model lineage: combined_without_metadata_1b

Weights & Biases Provenance

No matching W&B run was resolved automatically.

SFT Notes

Fine-tuning method: PEFT / LoRA
Optimizer: adamw_bnb_8bit
bf16=True, gradient_checkpointing=True, use_liger_kernel=True
per_device_train_batch_size=2, gradient_accumulation_steps=8
LoRA targets: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

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 14:48:17 UTC

Downloads last month: 58

Safetensors

Model size

1B params

Tensor type

BF16

Collection including iamshnoo/combined_without_metadata_chat

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_chat

Metadata Conditioned Large Language Models for Localization

Paper • 2601.15236 • Published Jan 21