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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

tosin commited on May 13, 2023

Commit

3ad6278

1 Parent(s): 73a01d2

results json added

Browse files

Files changed (2) hide show

all_results.json +15 -0
train_results.json +8 -0

all_results.json ADDED Viewed

	@@ -0,0 +1,15 @@

+{
+    "epoch": 0.72,
+    "eval_accuracy": 0.4548206941640567,
+    "eval_loss": 2.899137020111084,
+    "eval_runtime": 57.1764,
+    "eval_samples": 3544,
+    "eval_samples_per_second": 61.984,
+    "eval_steps_per_second": 7.748,
+    "perplexity": 18.158468212999303,
+    "train_loss": 2.6366955451965333,
+    "train_runtime": 1372.8893,
+    "train_samples": 89329,
+    "train_samples_per_second": 46.617,
+    "train_steps_per_second": 0.728
+}

train_results.json ADDED Viewed

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

+{
+    "epoch": 0.72,
+    "train_loss": 2.6366955451965333,
+    "train_runtime": 1372.8893,
+    "train_samples": 89329,
+    "train_samples_per_second": 46.617,
+    "train_steps_per_second": 0.728
+}