Instructions to use Epode/Prelude-1B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Epode/Prelude-1B-Base with Transformers:

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

pipe = pipeline("text-generation", model="Epode/Prelude-1B-Base")

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

tokenizer = AutoTokenizer.from_pretrained("Epode/Prelude-1B-Base")
model = AutoModelForCausalLM.from_pretrained("Epode/Prelude-1B-Base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Epode/Prelude-1B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Epode/Prelude-1B-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Epode/Prelude-1B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Epode/Prelude-1B-Base

SGLang

How to use Epode/Prelude-1B-Base 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 "Epode/Prelude-1B-Base" \
    --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": "Epode/Prelude-1B-Base",
		"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 "Epode/Prelude-1B-Base" \
        --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": "Epode/Prelude-1B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Epode/Prelude-1B-Base with Docker Model Runner:
```
docker model run hf.co/Epode/Prelude-1B-Base
```

Prelude-1B-Base / training /gpu-benchmark.json

Add evaluation evidence and sanitized release manifest

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{
  "date": "2026-08-23",
  "device": "NVIDIA L20",
  "cuda_runtime": "13.0",
  "torch": "2.12.1+cu130",
  "model": "tiny-llama-1.1b",
  "parameters": 1100048384,
  "sequence_length": 2048,
  "precision": "bf16-mixed",
  "torch_compile": true,
  "runs": [
    {
      "micro_batch_size": 4,
      "measured_steps": 10,
      "tokens_per_second": 12274.280878357904,
      "peak_allocated_gib": 28.84268045425415,
      "peak_reserved_gib": 31.001953125,
      "status": "pass"
    },
    {
      "micro_batch_size": 6,
      "measured_steps": 60,
      "tokens_per_second": 12586.170876605223,
      "peak_allocated_gib": 36.131224155426025,
      "peak_reserved_gib": 36.294921875,
      "status": "pass"
    },
    {
      "micro_batch_size": 8,
      "status": "oom_on_second_warmup_backward",
      "memory_in_use_gib": 44.17,
      "free_mib": 215.31,
      "failed_allocation_mib": 1000
    }
  ],
  "telemetry": {
    "samples": 12,
    "sm_utilization_percent_min": 100,
    "sm_utilization_percent_max": 100,
    "power_watts_min": 343,
    "power_watts_max": 358,
    "framebuffer_mib": 37531
  },
  "selected_micro_batch_size": 6,
  "selected_gradient_accumulation": 85,
  "selected_effective_global_batch_sequences": 510,
  "estimated_compute_only_days_for_20b": 18.39
}

	{
	"date": "2026-08-23",
	"device": "NVIDIA L20",
	"cuda_runtime": "13.0",
	"torch": "2.12.1+cu130",
	"model": "tiny-llama-1.1b",
	"parameters": 1100048384,
	"sequence_length": 2048,
	"precision": "bf16-mixed",
	"torch_compile": true,
	"runs": [
	{
	"micro_batch_size": 4,
	"measured_steps": 10,
	"tokens_per_second": 12274.280878357904,
	"peak_allocated_gib": 28.84268045425415,
	"peak_reserved_gib": 31.001953125,
	"status": "pass"
	},
	{
	"micro_batch_size": 6,
	"measured_steps": 60,
	"tokens_per_second": 12586.170876605223,
	"peak_allocated_gib": 36.131224155426025,
	"peak_reserved_gib": 36.294921875,
	"status": "pass"
	},
	{
	"micro_batch_size": 8,
	"status": "oom_on_second_warmup_backward",
	"memory_in_use_gib": 44.17,
	"free_mib": 215.31,
	"failed_allocation_mib": 1000
	}
	],
	"telemetry": {
	"samples": 12,
	"sm_utilization_percent_min": 100,
	"sm_utilization_percent_max": 100,
	"power_watts_min": 343,
	"power_watts_max": 358,
	"framebuffer_mib": 37531
	},
	"selected_micro_batch_size": 6,
	"selected_gradient_accumulation": 85,
	"selected_effective_global_batch_sequences": 510,
	"estimated_compute_only_days_for_20b": 18.39
	}