Instructions to use eldod/ha2er-aragpt2-1000-clean with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eldod/ha2er-aragpt2-1000-clean with Transformers:

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

pipe = pipeline("text-generation", model="eldod/ha2er-aragpt2-1000-clean")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("eldod/ha2er-aragpt2-1000-clean")
model = AutoModelForCausalLM.from_pretrained("eldod/ha2er-aragpt2-1000-clean", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use eldod/ha2er-aragpt2-1000-clean with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "eldod/ha2er-aragpt2-1000-clean"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eldod/ha2er-aragpt2-1000-clean",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/eldod/ha2er-aragpt2-1000-clean

SGLang

How to use eldod/ha2er-aragpt2-1000-clean 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 "eldod/ha2er-aragpt2-1000-clean" \
    --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": "eldod/ha2er-aragpt2-1000-clean",
		"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 "eldod/ha2er-aragpt2-1000-clean" \
        --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": "eldod/ha2er-aragpt2-1000-clean",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use eldod/ha2er-aragpt2-1000-clean with Docker Model Runner:
```
docker model run hf.co/eldod/ha2er-aragpt2-1000-clean
```

eldod commited on Apr 12, 2025

Commit

c20b803

verified ·

1 Parent(s): b8a1731

Training in progress, step 500

Browse files

Files changed (4) hide show

config.json +44 -0
model.safetensors +3 -0
runs/Apr12_05-34-42_def3e066db40/events.out.tfevents.1744436085.def3e066db40.245.0 +3 -0
training_args.bin +3 -0

config.json ADDED Viewed

	@@ -0,0 +1,44 @@

+{
+  "_name_or_path": "aubmindlab/aragpt2-medium",
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 0,
+  "embd_pdrop": 0.1,
+  "eos_token_id": 0,
+  "gradient_checkpointing": false,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 1024,
+  "n_embd": 1024,
+  "n_head": 16,
+  "n_inner": null,
+  "n_layer": 24,
+  "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
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.3",
+  "use_cache": true,
+  "vocab_size": 64000
+}

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