Instructions to use keeeeenw/MicroLlama2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use keeeeenw/MicroLlama2-base with Transformers:

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

pipe = pipeline("text-generation", model="keeeeenw/MicroLlama2-base", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("keeeeenw/MicroLlama2-base", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use keeeeenw/MicroLlama2-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "keeeeenw/MicroLlama2-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "keeeeenw/MicroLlama2-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/keeeeenw/MicroLlama2-base

SGLang

How to use keeeeenw/MicroLlama2-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 "keeeeenw/MicroLlama2-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": "keeeeenw/MicroLlama2-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 "keeeeenw/MicroLlama2-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": "keeeeenw/MicroLlama2-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use keeeeenw/MicroLlama2-base with Docker Model Runner:
```
docker model run hf.co/keeeeenw/MicroLlama2-base
```

MicroLlama2-base

File size: 1,015 Bytes

1be6cef

{
  "source_repo": "keeeeenw/MicroLlama2-388M-checkpoints",
  "source_revision": "527ab4101332284704765e1b2f3cfb577609bf56",
  "source_prefix": "checkpoint/fineweb-edu-338m-8k-local-engram-4090/step-00381824",
  "step": 381824,
  "valid_targets": 50000054177,
  "weight_sha256": "76ad5c973bae0219f81b1f56f92ff6e8bb1f9404ed700c237096138d2689bceb",
  "weight_bytes": 1552149028,
  "checkpoint_manifest_sha256": "9968e0d8db74a6618750ca38c71e68332c3947921f8dd75a591415afd965840c",
  "weight_changes": false,
  "inference_metadata_changes": [
    "Added Auto class remote-code mappings",
    "Enabled generation cache and greedy default; suppressed unused vocabulary/control tokens except EOS",
    "Did not expose inherited upstream chat template: this is a plain-text base model"
  ],
  "adapter": "Transformers 5.20.2 Qwen3 API with the original Engram operations and explicit generation-state carry",
  "training_code_and_technical_report": "Links coming shortly",
  "previous_source_repo": "keeeeenw/MicroLlama2"
}