Text Generation
Transformers
ONNX
English
bert
fill-mask
causal-lm
tiny-transformer
edge-ai
int8
distillation
tinybert-style
Instructions to use MachadoDeCastro/krull-micro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MachadoDeCastro/krull-micro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MachadoDeCastro/krull-micro")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("MachadoDeCastro/krull-micro") model = AutoModelForMaskedLM.from_pretrained("MachadoDeCastro/krull-micro", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MachadoDeCastro/krull-micro with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MachadoDeCastro/krull-micro" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MachadoDeCastro/krull-micro", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MachadoDeCastro/krull-micro
- SGLang
How to use MachadoDeCastro/krull-micro 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 "MachadoDeCastro/krull-micro" \ --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": "MachadoDeCastro/krull-micro", "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 "MachadoDeCastro/krull-micro" \ --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": "MachadoDeCastro/krull-micro", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MachadoDeCastro/krull-micro with Docker Model Runner:
docker model run hf.co/MachadoDeCastro/krull-micro
| license: mit | |
| language: | |
| - en | |
| library_name: pytorch | |
| pipeline_tag: text-generation | |
| tags: | |
| - text-generation | |
| - causal-lm | |
| - tiny-transformer | |
| - edge-ai | |
| - onnx | |
| - int8 | |
| - quantization | |
| - knowledge-distillation | |
| - tinybert-style | |
| - efficient-ml | |
| - mobile-ai | |
| model_name: Krull-Micro | |
| base_model: Krull-Micro (distilled from causal LM teacher) | |
| datasets: | |
| - custom | |
| metrics: | |
| - perplexity | |
| model-index: | |
| - name: Krull-Micro | |
| results: | |
| - task: | |
| type: text-generation | |
| dataset: | |
| name: Custom corpus | |
| type: custom | |
| metrics: | |
| - name: Perplexity | |
| type: perplexity | |
| value: null | |
| config: | |
| architecture: decoder-only transformer | |
| hidden_size: 192 | |
| num_hidden_layers: 2 | |
| num_attention_heads: 4 | |
| intermediate_size: 512 | |
| max_position_embeddings: 256 | |
| vocab_size: 8000 | |
| attention_type: causal | |
| distillation: full (embedding + hidden + attention + logits) | |
| inference: | |
| parameters: | |
| max_new_tokens: 64 | |
| temperature: 0.8 | |
| top_k: 50 | |
| top_p: 0.95 | |
| deployment: | |
| framework: onnxruntime | |
| quantization: int8 | |
| edge_ready: true | |
| memory_footprint: ~8-20MB | |
| training: | |
| method: knowledge distillation | |
| teacher_model: causal language model (e.g., GPT-style) | |
| losses: | |
| - embedding_mse | |
| - hidden_state_mse | |
| - attention_mse | |
| - kl_divergence | |
| - cross_entropy | |
| limitations: | |
| - Small model capacity may reduce long-range coherence | |
| - Sensitive to training corpus quality | |
| - Not suitable for high-stakes or safety-critical use | |
| intended_use: | |
| - Edge text generation | |
| - Embedded AI systems | |
| - Mobile applications | |
| - Low-latency inference | |
| authors: | |
| - name: Igor Machado | |
| version: 0.1.0 |