Instructions to use Multi-Domain-Expert-Learning/expert-uspto with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Multi-Domain-Expert-Learning/expert-uspto with Transformers:

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

pipe = pipeline("text-generation", model="Multi-Domain-Expert-Learning/expert-uspto")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Multi-Domain-Expert-Learning/expert-uspto")
model = AutoModelForCausalLM.from_pretrained("Multi-Domain-Expert-Learning/expert-uspto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Multi-Domain-Expert-Learning/expert-uspto with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Multi-Domain-Expert-Learning/expert-uspto

SGLang

How to use Multi-Domain-Expert-Learning/expert-uspto 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 "Multi-Domain-Expert-Learning/expert-uspto" \
    --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": "Multi-Domain-Expert-Learning/expert-uspto",
		"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 "Multi-Domain-Expert-Learning/expert-uspto" \
        --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": "Multi-Domain-Expert-Learning/expert-uspto",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Multi-Domain-Expert-Learning/expert-uspto with Docker Model Runner:
```
docker model run hf.co/Multi-Domain-Expert-Learning/expert-uspto
```

cabbage972 commited on Apr 30, 2023

Commit

d57e96f

1 Parent(s): e9a5026

Training in progress, step 500

Browse files

Files changed (5) hide show

all_results.json +8 -0
pytorch_model.bin +1 -1
train_results.json +8 -0
trainer_state.json +25 -0
training_args.bin +1 -1

all_results.json ADDED Viewed

	@@ -0,0 +1,8 @@

+{
+    "epoch": 0.0,
+    "train_loss": 2.4250597953796387,
+    "train_runtime": 3.5143,
+    "train_samples": 1854797,
+    "train_samples_per_second": 18.212,
+    "train_steps_per_second": 0.285
+}

pytorch_model.bin CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:925f012d0fafc65a63647ff52388709a2e4d2f2f363714ede311748e453234b3
 size 4114319645

 version https://git-lfs.github.com/spec/v1
+oid sha256:355cfc139dcedf713fecb995297f7e23eda985eed91c164de1cc246145aa043c
 size 4114319645

train_results.json ADDED Viewed

	@@ -0,0 +1,8 @@

+{
+    "epoch": 0.0,
+    "train_loss": 2.4250597953796387,
+    "train_runtime": 3.5143,
+    "train_samples": 1854797,
+    "train_samples_per_second": 18.212,
+    "train_steps_per_second": 0.285
+}

trainer_state.json ADDED Viewed

	@@ -0,0 +1,25 @@

+{
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 3.450506793185249e-05,
+  "global_step": 1,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 0.0,
+      "step": 1,
+      "total_flos": 178669297336320.0,
+      "train_loss": 2.4250597953796387,
+      "train_runtime": 3.5143,
+      "train_samples_per_second": 18.212,
+      "train_steps_per_second": 0.285
+    }
+  ],
+  "max_steps": 1,
+  "num_train_epochs": 1,
+  "total_flos": 178669297336320.0,
+  "trial_name": null,
+  "trial_params": null
+}

training_args.bin CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:97f76a2a33e4e6e3f4fe6e4cf4f7209db5756986afe61992f8b12663e645a7f9
 size 3707

 version https://git-lfs.github.com/spec/v1
+oid sha256:cc16ed911933a1a9f1fb9b5c9ed42e306ece83ad916da5786f318d4db6e2c524
 size 3707