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

Libraries

How to use TheNateTCY/testing_opt_causal_model with Transformers:

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

pipe = pipeline("text-generation", model="TheNateTCY/testing_opt_causal_model")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TheNateTCY/testing_opt_causal_model")
model = AutoModelForCausalLM.from_pretrained("TheNateTCY/testing_opt_causal_model")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TheNateTCY/testing_opt_causal_model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/TheNateTCY/testing_opt_causal_model

SGLang

How to use TheNateTCY/testing_opt_causal_model 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 "TheNateTCY/testing_opt_causal_model" \
    --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": "TheNateTCY/testing_opt_causal_model",
		"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 "TheNateTCY/testing_opt_causal_model" \
        --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": "TheNateTCY/testing_opt_causal_model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

TheNateTCY commited on Dec 18, 2022

Commit

004473e

1 Parent(s): 12963a5

Training in progress epoch 2

Browse files

Files changed (2) hide show

README.md +4 -3
tf_model.h5 +1 -1

README.md CHANGED Viewed

@@ -14,9 +14,9 @@ probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Train Loss: 7.7442
-- Validation Loss: 7.2288
-- Epoch: 1
 ## Model description
@@ -44,6 +44,7 @@ The following hyperparameters were used during training:
 |:----------:|:---------------:|:-----:|
 | 8.4252     | 7.9847          | 0     |
 | 7.7442     | 7.2288          | 1     |
 ### Framework versions

 This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on an unknown dataset.
 It achieves the following results on the evaluation set:
+- Train Loss: 7.1725
+- Validation Loss: 6.7890
+- Epoch: 2
 ## Model description
 |:----------:|:---------------:|:-----:|
 | 8.4252     | 7.9847          | 0     |
 | 7.7442     | 7.2288          | 1     |
+| 7.1725     | 6.7890          | 2     |
 ### Framework versions

tf_model.h5 CHANGED Viewed

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