Instructions to use gagan3012/MetaModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gagan3012/MetaModel with Transformers:

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

pipe = pipeline("text-generation", model="gagan3012/MetaModel")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gagan3012/MetaModel")
model = AutoModelForCausalLM.from_pretrained("gagan3012/MetaModel")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use gagan3012/MetaModel with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gagan3012/MetaModel"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gagan3012/MetaModel",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gagan3012/MetaModel

SGLang

How to use gagan3012/MetaModel 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 "gagan3012/MetaModel" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gagan3012/MetaModel",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "gagan3012/MetaModel" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gagan3012/MetaModel",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use gagan3012/MetaModel with Docker Model Runner:
```
docker model run hf.co/gagan3012/MetaModel
```

gagan3012 commited on Jan 9, 2024

Commit

e29a4a3

1 Parent(s): 9fc026b

Adding Evaluation Results (#1)

Browse files

- Adding Evaluation Results (74478d51f71f192f56ae9b32edd12402b930c608)

Files changed (1) hide show

README.md +13 -1

README.md CHANGED Viewed

@@ -437,4 +437,16 @@ These are the [latest results from run 2024-01-04T14:09:43.780941](https://huggi
         "acc_stderr": 0.013107179054313398
     }
 }
-```

         "acc_stderr": 0.013107179054313398
     }
 }
+```
+# [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_gagan3012__MetaModel)
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 74.4   |
+| ARC (25-shot)         | 71.08          |
+| HellaSwag (10-shot)   | 88.45    |
+| MMLU (5-shot)         | 66.26         |
+| TruthfulQA (0-shot)   | 71.84   |
+| Winogrande (5-shot)   | 83.43   |
+| GSM8K (5-shot)        | 65.35        |