Instructions to use d2j666/competitorDescriptions-ds-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use d2j666/competitorDescriptions-ds-mini with Transformers:

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

pipe = pipeline("text-generation", model="d2j666/competitorDescriptions-ds-mini")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("d2j666/competitorDescriptions-ds-mini")
model = AutoModelForCausalLM.from_pretrained("d2j666/competitorDescriptions-ds-mini", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use d2j666/competitorDescriptions-ds-mini with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "d2j666/competitorDescriptions-ds-mini"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "d2j666/competitorDescriptions-ds-mini",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/d2j666/competitorDescriptions-ds-mini

SGLang

How to use d2j666/competitorDescriptions-ds-mini 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 "d2j666/competitorDescriptions-ds-mini" \
    --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": "d2j666/competitorDescriptions-ds-mini",
		"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 "d2j666/competitorDescriptions-ds-mini" \
        --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": "d2j666/competitorDescriptions-ds-mini",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use d2j666/competitorDescriptions-ds-mini with Docker Model Runner:
```
docker model run hf.co/d2j666/competitorDescriptions-ds-mini
```

d2j666 commited on Jun 7, 2023

Commit

e3168f8

1 Parent(s): a13e2b6

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README.md +5 -5
tf_model.h5 +1 -1

README.md CHANGED Viewed

@@ -14,8 +14,8 @@ probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Train Loss: 8.4905
-- Validation Loss: 8.4397
 - Epoch: 2
 ## Model description
@@ -42,9 +42,9 @@ The following hyperparameters were used during training:
 | Train Loss | Validation Loss | Epoch |
 |:----------:|:---------------:|:-----:|
-| 8.8734     | 8.7298          | 0     |
-| 8.6494     | 8.5595          | 1     |
-| 8.4905     | 8.4397          | 2     |
 ### Framework versions

 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
 It achieves the following results on the evaluation set:
+- Train Loss: 9.1595
+- Validation Loss: 8.9603
 - Epoch: 2
 ## Model description
 | Train Loss | Validation Loss | Epoch |
 |:----------:|:---------------:|:-----:|
+| 9.6041     | 9.5385          | 0     |
+| 9.4427     | 9.2623          | 1     |
+| 9.1595     | 8.9603          | 2     |
 ### Framework versions

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