Instructions to use appvoid/niro-preview-2409 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use appvoid/niro-preview-2409 with Transformers:

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

pipe = pipeline("text-generation", model="appvoid/niro-preview-2409")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("appvoid/niro-preview-2409")
model = AutoModelForCausalLM.from_pretrained("appvoid/niro-preview-2409")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use appvoid/niro-preview-2409 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "appvoid/niro-preview-2409"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "appvoid/niro-preview-2409",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/appvoid/niro-preview-2409

SGLang

How to use appvoid/niro-preview-2409 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 "appvoid/niro-preview-2409" \
    --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": "appvoid/niro-preview-2409",
		"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 "appvoid/niro-preview-2409" \
        --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": "appvoid/niro-preview-2409",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use appvoid/niro-preview-2409 with Docker Model Runner:
```
docker model run hf.co/appvoid/niro-preview-2409
```

appvoid commited on Sep 22, 2024

Commit

59a6f63

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1 Parent(s): 96aa8c7

Update README.md

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@@ -16,8 +16,9 @@ zero-shot evaluations performed on current sota small models; mmlu is still the
 | Parameters | Model                          | MMLU  | ARC | HellaSwag | PIQA   | Winogrande | Average |
 | -----------|--------------------------------|-------|-------|-----------|--------|------------|---------|
-| 0.5b       | qwen2.5                        |47.29|31.83|52.17|70.29|57.06|51.72|
-| 0.5b       | arco                        |26.17|37.29|62.88|74.37|62.27|52.60|
 | 1.7b       | smollm                         |27.65|**46.26**|65.74|76.06|60.93| 55.33|
 | 1.8B       | niro-preview                   |41.75|40.96|**72.07**|**77.97**|**65.51**|**59.65**|
-| 1.5b       | qwen2.5                        |**58.68**|44.71|67.62|75.73|62.67|**61.88**|

 | Parameters | Model                          | MMLU  | ARC | HellaSwag | PIQA   | Winogrande | Average |
 | -----------|--------------------------------|-------|-------|-----------|--------|------------|---------|
+| 0.5b       | qwen 2.5                        |47.29|31.83|52.17|70.29|57.06|51.72|
+| 0.5b       | arco                           |26.17|37.29|62.88|74.37|62.27|52.60|
+| 0.5b       | arco (exp)                     |25.51|38.82|63.02|74.70|61.25|52.66|
 | 1.7b       | smollm                         |27.65|**46.26**|65.74|76.06|60.93| 55.33|
 | 1.8B       | niro-preview                   |41.75|40.96|**72.07**|**77.97**|**65.51**|**59.65**|
+| 1.5b       | qwen 2.5                        |**58.68**|44.71|67.62|75.73|62.67|**61.88**|