Instructions to use stanfordnlp/backpack-gpt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use stanfordnlp/backpack-gpt2 with Transformers:

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

pipe = pipeline("text-generation", model="stanfordnlp/backpack-gpt2", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("stanfordnlp/backpack-gpt2", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("stanfordnlp/backpack-gpt2", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use stanfordnlp/backpack-gpt2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "stanfordnlp/backpack-gpt2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "stanfordnlp/backpack-gpt2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/stanfordnlp/backpack-gpt2

SGLang

How to use stanfordnlp/backpack-gpt2 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 "stanfordnlp/backpack-gpt2" \
    --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": "stanfordnlp/backpack-gpt2",
		"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 "stanfordnlp/backpack-gpt2" \
        --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": "stanfordnlp/backpack-gpt2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use stanfordnlp/backpack-gpt2 with Docker Model Runner:
```
docker model run hf.co/stanfordnlp/backpack-gpt2
```

Set more precise shape to the attention weights and outputs

by ivanzhouyq - opened May 29, 2023

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-2

Files changed (1) hide show

modeling_backpack_gpt2.py +3 -2

modeling_backpack_gpt2.py CHANGED Viewed

@@ -101,13 +101,14 @@ class BackpackWeightNetwork(nn.Module):
     super().__init__()
     self.n_embd = embed_dim
     self.num_senses = num_senses
-    self.c_attn = nn.Linear(embed_dim, 2*embed_dim)
     self.softmax_scale = None
   def forward(self, encoded):
     b, s, d = encoded.shape
     encoded = self.c_attn(encoded) # (b, s, 2*d)
-    encoded = encoded.reshape(b, s, 2, self.num_senses, d // self.num_senses) #(b, s, 2, nv, d//nv)
     batch_size, seqlen = encoded.shape[0], encoded.shape[1]
     # compute scores & mask

     super().__init__()
     self.n_embd = embed_dim
     self.num_senses = num_senses
+    self.embed_per_sense = embed_dim // num_senses
+    self.c_attn = nn.Linear(embed_dim, 2 * num_senses * self.embed_per_sense)
     self.softmax_scale = None
   def forward(self, encoded):
     b, s, d = encoded.shape
     encoded = self.c_attn(encoded) # (b, s, 2*d)
+    encoded = encoded.reshape(b, s, 2, self.num_senses, self.embed_per_sense) #(b, s, 2, nv, d//nv)
     batch_size, seqlen = encoded.shape[0], encoded.shape[1]
     # compute scores & mask