Instructions to use imda-lseokmin/testfinetunedmodel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use imda-lseokmin/testfinetunedmodel with Transformers:

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

pipe = pipeline("text-generation", model="imda-lseokmin/testfinetunedmodel")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("imda-lseokmin/testfinetunedmodel")
model = AutoModelForCausalLM.from_pretrained("imda-lseokmin/testfinetunedmodel")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use imda-lseokmin/testfinetunedmodel with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/imda-lseokmin/testfinetunedmodel

SGLang

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

Docker Model Runner
How to use imda-lseokmin/testfinetunedmodel with Docker Model Runner:
```
docker model run hf.co/imda-lseokmin/testfinetunedmodel
```

leaderboard-pr-bot commited on Apr 17, 2024

Commit

865f3fb

verified ·

1 Parent(s): 9efeae0

Adding Evaluation Results (#1)

Browse files

- Adding Evaluation Results (c31aff9d2f02376012c544a97c388c47ae57e942)

Co-authored-by: Open LLM Leaderboard PR Bot <leaderboard-pr-bot@users.noreply.huggingface.co>

Files changed (1) hide show

README.md +15 -1

README.md CHANGED Viewed

@@ -1,10 +1,10 @@
 ---
 license: mit
-base_model: gpt2
 tags:
 - generated_from_trainer
 metrics:
 - accuracy
 model-index:
 - name: artgpt
   results: []
@@ -55,3 +55,17 @@ The following hyperparameters were used during training:
 - Pytorch 2.1.2+cu121
 - Datasets 2.16.0
 - Tokenizers 0.15.0

 ---
 license: mit
 tags:
 - generated_from_trainer
 metrics:
 - accuracy
+base_model: gpt2
 model-index:
 - name: artgpt
   results: []
 - Pytorch 2.1.2+cu121
 - Datasets 2.16.0
 - Tokenizers 0.15.0
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_buildingthemoon__testfinetunedmodel)
+|             Metric              |Value|
+|---------------------------------|----:|
+|Avg.                             |29.18|
+|AI2 Reasoning Challenge (25-Shot)|25.85|
+|HellaSwag (10-Shot)              |31.40|
+|MMLU (5-Shot)                    |26.07|
+|TruthfulQA (0-shot)              |40.75|
+|Winogrande (5-shot)              |50.99|
+|GSM8k (5-shot)                   | 0.00|