Text Generation
Transformers
PyTorch
Arabic
gpt2
arabic-poetry
classical-arabic
prosody
custom_code
text-generation-inference
Instructions to use QCRI/Fanar-2-Diwan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QCRI/Fanar-2-Diwan with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="QCRI/Fanar-2-Diwan", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("QCRI/Fanar-2-Diwan", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("QCRI/Fanar-2-Diwan", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use QCRI/Fanar-2-Diwan with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "QCRI/Fanar-2-Diwan" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QCRI/Fanar-2-Diwan", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/QCRI/Fanar-2-Diwan
- SGLang
How to use QCRI/Fanar-2-Diwan 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 "QCRI/Fanar-2-Diwan" \ --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": "QCRI/Fanar-2-Diwan", "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 "QCRI/Fanar-2-Diwan" \ --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": "QCRI/Fanar-2-Diwan", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use QCRI/Fanar-2-Diwan with Docker Model Runner:
docker model run hf.co/QCRI/Fanar-2-Diwan
File size: 1,477 Bytes
074df64 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | {
"_name_or_path": "aubmindlab/aragpt2-large",
"activation_function": "gelu_new",
"architectures": [
"AraGPT2LMHeadModel"
],
"attention_probs_dropout_prob": 0.1,
"attn_pdrop": 0.1,
"auto_map": {
"AutoConfig": "aubmindlab/aragpt2-large--configuration_aragpt2.AraGPT2Config",
"AutoModel": "aubmindlab/aragpt2-large--modeling_aragpt2.AraGPT2Model",
"AutoModelForCausalLM": "aubmindlab/aragpt2-large--modeling_aragpt2.AraGPT2LMHeadModel"
},
"bos_token_id": 0,
"embd_pdrop": 0.1,
"eos_token_id": 0,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"initializer_range": 0.014142135623731,
"intermediate_size": 5120,
"layer_norm_epsilon": 1e-05,
"model_type": "gpt2",
"n_ctx": 1024,
"n_embd": 1280,
"n_head": 20,
"n_inner": null,
"n_layer": 36,
"n_positions": 1024,
"reorder_and_upcast_attn": false,
"resid_pdrop": 0.1,
"scale_attn_by_inverse_layer_idx": false,
"scale_attn_weights": true,
"summary_activation": null,
"summary_first_dropout": 0.1,
"summary_proj_to_labels": true,
"summary_type": "cls_index",
"summary_use_proj": true,
"task_specific_params": {
"text-generation": {
"do_sample": true,
"max_length": 50,
"no_repeat_ngram_size": 3,
"num_beams": 5,
"repetition_penalty": 3.0,
"top_p": 0.95
}
},
"tokenizer_class": "GPT2Tokenizer",
"torch_dtype": "float32",
"transformers_version": "4.42.4",
"use_cache": true,
"vocab_size": 65868
}
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