Instructions to use SlayerLab/GoLLeM-110M-PL-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SlayerLab/GoLLeM-110M-PL-v3 with Transformers:

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

pipe = pipeline("text-generation", model="SlayerLab/GoLLeM-110M-PL-v3")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SlayerLab/GoLLeM-110M-PL-v3")
model = AutoModelForCausalLM.from_pretrained("SlayerLab/GoLLeM-110M-PL-v3", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SlayerLab/GoLLeM-110M-PL-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SlayerLab/GoLLeM-110M-PL-v3"
# 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-110M-PL-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SlayerLab/GoLLeM-110M-PL-v3

SGLang

How to use SlayerLab/GoLLeM-110M-PL-v3 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-110M-PL-v3" \
    --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-110M-PL-v3",
		"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-110M-PL-v3" \
        --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-110M-PL-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SlayerLab/GoLLeM-110M-PL-v3 with Docker Model Runner:
```
docker model run hf.co/SlayerLab/GoLLeM-110M-PL-v3
```

Maggio33 commited on 7 days ago

Commit

ff16d04

verified ·

1 Parent(s): 2d69f4f

card: podpiecie datasetu treningowego SlayerLab/gollem-corpus-2b-pl

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README.md +2 -1

README.md CHANGED Viewed

@@ -5,6 +5,7 @@ language:
 library_name: transformers
 pipeline_tag: text-generation
 datasets:
 - SlayerLab/polish-dynaword
 tags:
 - gpt2
@@ -126,6 +127,6 @@ Benchmark i protokół ewaluacyjny: **OrisTeam** ([Polish-SLM-Benchmark](https:/
 ## Reprodukcja
-Trening: `train_125m.py --run-id gollem_v3_e2b --data gollem_v2_train_32k.bin --epochs 2 --batch 64 --accum-steps 4 --lr 3e-4` (resume z ckpt v2). Ewaluacja: `board_eval.py` (domain-PMI). Ślad: repo `amd-torch`.
 **Autor:** Arkadiusz Słota.

 library_name: transformers
 pipeline_tag: text-generation
 datasets:
+- SlayerLab/gollem-corpus-2b-pl
 - SlayerLab/polish-dynaword
 tags:
 - gpt2
 ## Reprodukcja
+Trening: `train_125m.py --run-id gollem_v3_e2b --data gollem_v2_train_32k.bin --epochs 2 --batch 64 --accum-steps 4 --lr 3e-4` (resume z ckpt v2). Dane treningowe jawnie: [`SlayerLab/gollem-corpus-2b-pl`](https://huggingface.co/datasets/SlayerLab/gollem-corpus-2b-pl) (dokładny korpus v2/v3). Ewaluacja: `board_eval.py` (domain-PMI). Ślad: repo `amd-torch`.
 **Autor:** Arkadiusz Słota.