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

Libraries

How to use pharaouk/fusedyi with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pharaouk/fusedyi")
model = AutoModelForCausalLM.from_pretrained("pharaouk/fusedyi")
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 pharaouk/fusedyi with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/pharaouk/fusedyi

SGLang

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

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

leaderboard-pr-bot commited on Mar 4, 2024

Commit

2e725db

verified ·

1 Parent(s): 5e3fdfa

Adding Evaluation Results

Browse files

This is an automated PR created with https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr

The purpose of this PR is to add evaluation results from the Open LLM Leaderboard to your model card.

If you encounter any issues, please report them to https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr/discussions

Files changed (1) hide show

README.md +17 -3

README.md CHANGED Viewed

@@ -1,4 +1,7 @@
 ---
 tags:
 - Mixtral
 - instruct
@@ -12,9 +15,6 @@ tags:
 model-index:
 - name: fusedYi
   results: []
-license: apache-2.0
-language:
-- en
 ---
@@ -31,3 +31,17 @@ Yi-ceptionized.
 1.9 x Yi-6B

 ---
+language:
+- en
+license: apache-2.0
 tags:
 - Mixtral
 - instruct
 model-index:
 - name: fusedYi
   results: []
 ---
 1.9 x Yi-6B
+# [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_pharaouk__fusedyi)
+|             Metric              |Value|
+|---------------------------------|----:|
+|Avg.                             |53.18|
+|AI2 Reasoning Challenge (25-Shot)|55.03|
+|HellaSwag (10-Shot)              |76.60|
+|MMLU (5-Shot)                    |63.43|
+|TruthfulQA (0-shot)              |49.29|
+|Winogrande (5-shot)              |72.69|
+|GSM8k (5-shot)                   | 2.05|