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 17, 2022

Commit

527ecc2

1 Parent(s): aa38cbc

Training in progress epoch 2

Browse files

Files changed (2) hide show

README.md +5 -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: 5.6356
-- Validation Loss: 3.9323
-- Epoch: 0
 ## Model description
@@ -43,6 +43,8 @@ The following hyperparameters were used during training:
 | Train Loss | Validation Loss | Epoch |
 |:----------:|:---------------:|:-----:|
 | 5.6356     | 3.9323          | 0     |
 ### 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: 3.5809
+- Validation Loss: 3.5106
+- Epoch: 2
 ## Model description
 | Train Loss | Validation Loss | Epoch |
 |:----------:|:---------------:|:-----:|
 | 5.6356     | 3.9323          | 0     |
+| 3.7259     | 3.5106          | 1     |
+| 3.5809     | 3.5106          | 2     |
 ### Framework versions

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