Text Generation
Transformers
Safetensors
gemma
trl
sft
text-generation-inference
4-bit precision
bitsandbytes
Instructions to use Malekhmem/actiaGEMMAMalekHmem with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Malekhmem/actiaGEMMAMalekHmem with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Malekhmem/actiaGEMMAMalekHmem")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Malekhmem/actiaGEMMAMalekHmem") model = AutoModelForCausalLM.from_pretrained("Malekhmem/actiaGEMMAMalekHmem", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Malekhmem/actiaGEMMAMalekHmem with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Malekhmem/actiaGEMMAMalekHmem" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Malekhmem/actiaGEMMAMalekHmem", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Malekhmem/actiaGEMMAMalekHmem
- SGLang
How to use Malekhmem/actiaGEMMAMalekHmem 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 "Malekhmem/actiaGEMMAMalekHmem" \ --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": "Malekhmem/actiaGEMMAMalekHmem", "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 "Malekhmem/actiaGEMMAMalekHmem" \ --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": "Malekhmem/actiaGEMMAMalekHmem", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Malekhmem/actiaGEMMAMalekHmem with Docker Model Runner:
docker model run hf.co/Malekhmem/actiaGEMMAMalekHmem
- Xet hash:
- f56030e06bc4b9b79af33cfd1ba51158700f5f485cd253a5c7ff7205cbcd79fa
- Size of remote file:
- 3.21 GB
- SHA256:
- 0298dfa79807c003c3e4b2487725b5f5339ee210e0c0e55d67408a13f236dee3
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