Instructions to use ConvexAI/Metabird-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ConvexAI/Metabird-7B with Transformers:

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

pipe = pipeline("text-generation", model="ConvexAI/Metabird-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ConvexAI/Metabird-7B")
model = AutoModelForCausalLM.from_pretrained("ConvexAI/Metabird-7B")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ConvexAI/Metabird-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ConvexAI/Metabird-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ConvexAI/Metabird-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ConvexAI/Metabird-7B

SGLang

How to use ConvexAI/Metabird-7B 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 "ConvexAI/Metabird-7B" \
    --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": "ConvexAI/Metabird-7B",
		"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 "ConvexAI/Metabird-7B" \
        --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": "ConvexAI/Metabird-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ConvexAI/Metabird-7B with Docker Model Runner:
```
docker model run hf.co/ConvexAI/Metabird-7B
```

NeuralNovel

leaderboard-pr-bot commited on Mar 4, 2024

Commit

2e3ee73

verified ·

1 Parent(s): 73b2f79

Adding Evaluation Results (#2)

Browse files

- Adding Evaluation Results (27146e7e9f63a379ad8aa2300aaa9b7458111899)

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

@@ -1,8 +1,8 @@
 ---
 license: apache-2.0
-base_model: leveldevai/TurdusBeagle-7B
 tags:
 - generated_from_trainer
 model-index:
 - name: Metabird-7B
   results: []
@@ -150,3 +150,17 @@ The following hyperparameters were used during training:
 - Pytorch 2.0.1+cu117
 - Datasets 2.16.1
 - Tokenizers 0.15.0

 ---
 license: apache-2.0
 tags:
 - generated_from_trainer
+base_model: leveldevai/TurdusBeagle-7B
 model-index:
 - name: Metabird-7B
   results: []
 - Pytorch 2.0.1+cu117
 - Datasets 2.16.1
 - 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_ConvexAI__Metabird-7B)
+|             Metric              |Value|
+|---------------------------------|----:|
+|Avg.                             |71.03|
+|AI2 Reasoning Challenge (25-Shot)|69.54|
+|HellaSwag (10-Shot)              |87.54|
+|MMLU (5-Shot)                    |65.27|
+|TruthfulQA (0-shot)              |57.94|
+|Winogrande (5-shot)              |83.03|
+|GSM8k (5-shot)                   |62.85|