Text Generation
Transformers
Safetensors
Polish
gpt2
polish
nanogpt
tokenizer-ablation
research
Eval Results (legacy)
text-generation-inference
Instructions to use SlayerLab/GoLLeM-45M-PL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SlayerLab/GoLLeM-45M-PL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SlayerLab/GoLLeM-45M-PL")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("SlayerLab/GoLLeM-45M-PL") model = AutoModelForCausalLM.from_pretrained("SlayerLab/GoLLeM-45M-PL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use SlayerLab/GoLLeM-45M-PL with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SlayerLab/GoLLeM-45M-PL" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SlayerLab/GoLLeM-45M-PL", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SlayerLab/GoLLeM-45M-PL
- SGLang
How to use SlayerLab/GoLLeM-45M-PL 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 "SlayerLab/GoLLeM-45M-PL" \ --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": "SlayerLab/GoLLeM-45M-PL", "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 "SlayerLab/GoLLeM-45M-PL" \ --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": "SlayerLab/GoLLeM-45M-PL", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use SlayerLab/GoLLeM-45M-PL with Docker Model Runner:
docker model run hf.co/SlayerLab/GoLLeM-45M-PL
Upload folder using huggingface_hub
Browse files
README.md
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- nanogpt
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- tokenizer-ablation
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- research
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---
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> **Kopia lustrzana.** Repozytorium 藕r贸d艂owe modelu:
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- nanogpt
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- tokenizer-ablation
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- research
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model-index:
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- name: GoLLeM-45M-PL
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results:
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- task:
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type: text-classification
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name: Sentiment Classification (zero-shot, log-likelihood)
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dataset:
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type: allegro/klej-polemo2-in
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name: PolEmo2.0-IN
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split: test
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config: polemo2-in
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metrics:
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- type: accuracy
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value: 47.2
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name: Accuracy (PMI-normalized)
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verified: false
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- type: accuracy
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value: 40.0
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name: Majority-class baseline
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verified: false
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- task:
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type: text-classification
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name: Topic Classification (zero-shot, log-likelihood)
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dataset:
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type: sdadas/8tags
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name: 8Tags
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split: test
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metrics:
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- type: accuracy
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value: 31.5
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name: Accuracy (PMI-normalized)
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verified: false
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- type: accuracy
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value: 16.5
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name: Majority-class baseline
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verified: false
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- task:
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type: text-generation
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name: Language Modeling
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dataset:
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type: speakleash
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name: SpeakLeash held-out (private, 1999 docs)
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metrics:
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- type: bits_per_byte
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value: 1.2114
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name: BPB (mean of 3 seeds)
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verified: false
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- type: bits_per_byte
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value: 0.0100
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name: BPB std. dev. across seeds
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verified: false
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---
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> **Kopia lustrzana.** Repozytorium 藕r贸d艂owe modelu:
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