Instructions to use yujiepan/gptj-tiny-random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yujiepan/gptj-tiny-random with Transformers:

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

pipe = pipeline("text-generation", model="yujiepan/gptj-tiny-random")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yujiepan/gptj-tiny-random")
model = AutoModelForCausalLM.from_pretrained("yujiepan/gptj-tiny-random")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use yujiepan/gptj-tiny-random with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/yujiepan/gptj-tiny-random

SGLang

How to use yujiepan/gptj-tiny-random 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 "yujiepan/gptj-tiny-random" \
    --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": "yujiepan/gptj-tiny-random",
		"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 "yujiepan/gptj-tiny-random" \
        --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": "yujiepan/gptj-tiny-random",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use yujiepan/gptj-tiny-random with Docker Model Runner:
```
docker model run hf.co/yujiepan/gptj-tiny-random
```

yujiepan commited on Oct 25, 2023

Commit

cda8a0f

1 Parent(s): f68714c

Update README.md

Browse files

Files changed (1) hide show

README.md +7 -4

README.md CHANGED Viewed

@@ -13,6 +13,7 @@ Note the model is in float16.
 Codes:
 ```python
 from huggingface_hub import create_repo, upload_folder
 import torch
 import transformers
@@ -23,14 +24,18 @@ save_path = '/tmp/yujiepan/gptj-tiny-random'
 repo_id = 'yujiepan/gptj-tiny-random'
 config = transformers.AutoConfig.from_pretrained(model_id)
-config.hidden_size = 8
-config.n_embd = 8
 config.num_attention_heads = 2
 config.n_head = 2
 config.n_layer = 2
 print(config)
 model = transformers.AutoModelForCausalLM.from_config(config, torch_dtype=torch.float16)
 model.save_pretrained(save_path)
 tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
@@ -38,11 +43,9 @@ tokenizer.save_pretrained(save_path)
 # from optimum.intel.openvino import OVModelForCausalLM
 # ovmodel = OVModelForCausalLM.from_pretrained(save_path, export=True)
-# ovmodel = ovmodel.half()
 # ovmodel.save_pretrained(save_path)
 os.system(f'ls -alh {save_path}')
 create_repo(repo_id, exist_ok=True)
 upload_folder(repo_id=repo_id, folder_path=save_path)
 ```

 Codes:
 ```python
+from transformers import pipeline
 from huggingface_hub import create_repo, upload_folder
 import torch
 import transformers
 repo_id = 'yujiepan/gptj-tiny-random'
 config = transformers.AutoConfig.from_pretrained(model_id)
+config.hidden_size = 16
+config.n_embd = 16
 config.num_attention_heads = 2
 config.n_head = 2
+config.rotary_dim = 4
+config.num_hidden_layers = 2
 config.n_layer = 2
+config.torch_dtype = torch.float16
 print(config)
 model = transformers.AutoModelForCausalLM.from_config(config, torch_dtype=torch.float16)
+model = model.half()
 model.save_pretrained(save_path)
 tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
 # from optimum.intel.openvino import OVModelForCausalLM
 # ovmodel = OVModelForCausalLM.from_pretrained(save_path, export=True)
 # ovmodel.save_pretrained(save_path)
 os.system(f'ls -alh {save_path}')
 create_repo(repo_id, exist_ok=True)
 upload_folder(repo_id=repo_id, folder_path=save_path)
 ```