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

File size: 1,725 Bytes

{
  "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"
}