Instructions to use drdeveloper88/WorldDisasterLM-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use drdeveloper88/WorldDisasterLM-8B with Transformers:

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

pipe = pipeline("text-generation", model="drdeveloper88/WorldDisasterLM-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("drdeveloper88/WorldDisasterLM-8B")
model = AutoModelForCausalLM.from_pretrained("drdeveloper88/WorldDisasterLM-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

PEFT
How to use drdeveloper88/WorldDisasterLM-8B with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use drdeveloper88/WorldDisasterLM-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "drdeveloper88/WorldDisasterLM-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "drdeveloper88/WorldDisasterLM-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/drdeveloper88/WorldDisasterLM-8B

SGLang

How to use drdeveloper88/WorldDisasterLM-8B 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 "drdeveloper88/WorldDisasterLM-8B" \
    --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": "drdeveloper88/WorldDisasterLM-8B",
		"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 "drdeveloper88/WorldDisasterLM-8B" \
        --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": "drdeveloper88/WorldDisasterLM-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use drdeveloper88/WorldDisasterLM-8B with Docker Model Runner:
```
docker model run hf.co/drdeveloper88/WorldDisasterLM-8B
```

WorldDisasterLM-8B / training_args.json

drdeveloper88

Sync: correct languages (en/ne/es/fr/ar/hi/te/zh/ja/ko/pt), updated README, full source code

4b93901 17 days ago

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	{
	"model_name_or_path": "meta-llama/Llama-3.1-8B-Instruct",
	"output_dir": "./outputs/WorldDisasterLM-8B",
	"num_train_epochs": 3,
	"per_device_train_batch_size": 4,
	"per_device_eval_batch_size": 4,
	"gradient_accumulation_steps": 4,
	"gradient_checkpointing": true,
	"learning_rate": 0.0002,
	"lr_scheduler_type": "cosine",
	"warmup_ratio": 0.03,
	"weight_decay": 0.001,
	"max_grad_norm": 0.3,
	"optim": "paged_adamw_32bit",
	"fp16": false,
	"bf16": true,
	"max_seq_length": 4096,
	"packing": true,
	"lora_r": 16,
	"lora_alpha": 32,
	"lora_dropout": 0.05,
	"lora_target_modules": [
	"q_proj",
	"k_proj",
	"v_proj",
	"o_proj",
	"gate_proj",
	"up_proj",
	"down_proj"
	],
	"use_4bit": true,
	"bnb_4bit_quant_type": "nf4",
	"bnb_4bit_compute_dtype": "bfloat16",
	"use_nested_quant": true,
	"save_steps": 100,
	"logging_steps": 25,
	"evaluation_strategy": "steps",
	"eval_steps": 100,
	"save_total_limit": 3,
	"load_best_model_at_end": true,
	"metric_for_best_model": "eval_loss",
	"dataloader_num_workers": 4,
	"seed": 42,
	"report_to": [
	"tensorboard"
	],
	"dataset_sources": [
	"ReliefWeb",
	"USGS",
	"GDACS",
	"NOAA",
	"OpenFEMA",
	"WHO"
	],
	"dataset_size": "88+ live records → 711+ instruction samples per run",
	"languages": [
	"en",
	"ne",
	"es",
	"fr",
	"ar",
	"hi",
	"te",
	"zh",
	"ja",
	"ko",
	"pt"
	],
	"language_names": [
	"English",
	"Nepali (नेपाली)",
	"Spanish",
	"French",
	"Arabic",
	"Hindi",
	"Telugu",
	"Chinese",
	"Japanese",
	"Korean",
	"Portuguese"
	],
	"training_status": "PENDING — weights not yet generated. Run: python train.py"
	}