Instructions to use Tincando/new_data_edgar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tincando/new_data_edgar with Transformers:

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

pipe = pipeline("text-generation", model="Tincando/new_data_edgar")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Tincando/new_data_edgar")
model = AutoModelForCausalLM.from_pretrained("Tincando/new_data_edgar", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Tincando/new_data_edgar with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Tincando/new_data_edgar

SGLang

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

Docker Model Runner
How to use Tincando/new_data_edgar with Docker Model Runner:
```
docker model run hf.co/Tincando/new_data_edgar
```

Tincando commited on Mar 14, 2023

Commit

f2d0dff

1 Parent(s): 6e97d20

Training in progress, step 1000

Browse files

Files changed (2) hide show

pytorch_model.bin +1 -1
runs/Mar14_18-17-37_df039cb043fc/events.out.tfevents.1678817900.df039cb043fc.155.0 +2 -2

pytorch_model.bin CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f4c17d0f8c191ea29c91209b3a9a108b239511177674ea9e8ba396b2250c198a
 size 551186797

 version https://git-lfs.github.com/spec/v1
+oid sha256:b3c839a2c64e2d3c1fd7f54a07f19b62b1764b4527c45316f3522dac050ee085
 size 551186797

runs/Mar14_18-17-37_df039cb043fc/events.out.tfevents.1678817900.df039cb043fc.155.0 CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:dae9b242367f7044216fc11509b2f20f1e374f5e14f26941b30c1aa2a1a81f6f
-size 4363

 version https://git-lfs.github.com/spec/v1
+oid sha256:44fbc04dd6e86bc68d76afaa1b7426df430386daec38262a7aefe38dc8306e6e
+size 4520