Instructions to use kaizerBox/ReFormer-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kaizerBox/ReFormer-summarization with Transformers:

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

pipe = pipeline("text-generation", model="kaizerBox/ReFormer-summarization")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kaizerBox/ReFormer-summarization")
model = AutoModelForCausalLM.from_pretrained("kaizerBox/ReFormer-summarization", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kaizerBox/ReFormer-summarization with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/kaizerBox/ReFormer-summarization

SGLang

How to use kaizerBox/ReFormer-summarization 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 "kaizerBox/ReFormer-summarization" \
    --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": "kaizerBox/ReFormer-summarization",
		"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 "kaizerBox/ReFormer-summarization" \
        --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": "kaizerBox/ReFormer-summarization",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kaizerBox/ReFormer-summarization with Docker Model Runner:
```
docker model run hf.co/kaizerBox/ReFormer-summarization
```

ReFormer-summarization

File size: 1,204 Bytes

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{
  "architectures": [
    "ReformerModelWithLMHead"
  ],
  "attention_head_size": 64,
  "attn_layers": [
    "local",
    "lsh",
    "local",
    "lsh",
    "local",
    "lsh"
  ],
  "axial_norm_std": 1.0,
  "axial_pos_embds": true,
  "axial_pos_embds_dim": [
    22,
    22
  ],
  "axial_pos_shape": [
    32,
    32
  ],
  "chunk_size_lm_head": 0,
  "classifier_dropout": null,
  "eos_token_id": 50256,
  "feed_forward_size": 64,
  "hash_seed": null,
  "hidden_act": "relu",
  "hidden_dropout_prob": 0.0,
  "hidden_size": 44,
  "initializer_range": 0.02,
  "is_decoder": true,
  "layer_norm_eps": 1e-12,
  "local_attention_probs_dropout_prob": 0.0,
  "local_attn_chunk_length": 64,
  "local_num_chunks_after": 0,
  "local_num_chunks_before": 1,
  "lsh_attention_probs_dropout_prob": 0.0,
  "lsh_attn_chunk_length": 64,
  "lsh_num_chunks_after": 0,
  "lsh_num_chunks_before": 1,
  "max_position_embeddings": 1024,
  "model_type": "reformer",
  "num_attention_heads": 3,
  "num_buckets": 32,
  "num_hashes": 1,
  "num_hidden_layers": 6,
  "pad_token_id": 50257,
  "tie_word_embeddings": false,
  "torch_dtype": "float32",
  "transformers_version": "4.35.2",
  "use_cache": true,
  "vocab_size": 50259
}