Instructions to use foxanthis/data-deepit-kds with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use foxanthis/data-deepit-kds with Transformers:

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

pipe = pipeline("text-generation", model="foxanthis/data-deepit-kds")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("foxanthis/data-deepit-kds")
model = AutoModelForCausalLM.from_pretrained("foxanthis/data-deepit-kds", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use foxanthis/data-deepit-kds with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/foxanthis/data-deepit-kds

SGLang

How to use foxanthis/data-deepit-kds 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 "foxanthis/data-deepit-kds" \
    --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": "foxanthis/data-deepit-kds",
		"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 "foxanthis/data-deepit-kds" \
        --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": "foxanthis/data-deepit-kds",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use foxanthis/data-deepit-kds with Docker Model Runner:
```
docker model run hf.co/foxanthis/data-deepit-kds
```

foxanthis commited on Feb 7, 2023

Commit

7d5732d

1 Parent(s): 010bc14

update model card README.md

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README.md +4 -4

README.md CHANGED Viewed

@@ -13,7 +13,7 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [flax-community/gpt-neo-125M-code-clippy](https://huggingface.co/flax-community/gpt-neo-125M-code-clippy) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.2655
 ## Model description
@@ -44,9 +44,9 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step | Validation Loss |
 |:-------------:|:-----:|:----:|:---------------:|
-| No log        | 1.0   | 3    | 1.2655          |
-| No log        | 2.0   | 6    | 1.2655          |
-| No log        | 3.0   | 9    | 1.2655          |
 ### Framework versions

 This model is a fine-tuned version of [flax-community/gpt-neo-125M-code-clippy](https://huggingface.co/flax-community/gpt-neo-125M-code-clippy) on the None dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.3333
 ## Model description
 | Training Loss | Epoch | Step | Validation Loss |
 |:-------------:|:-----:|:----:|:---------------:|
+| No log        | 1.0   | 3    | 0.5930          |
+| No log        | 2.0   | 6    | 0.3968          |
+| No log        | 3.0   | 9    | 0.3333          |
 ### Framework versions