Instructions to use adleme94/borges_clm-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use adleme94/borges_clm-model with Transformers:

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

pipe = pipeline("text-generation", model="adleme94/borges_clm-model")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("adleme94/borges_clm-model")
model = AutoModelForCausalLM.from_pretrained("adleme94/borges_clm-model")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use adleme94/borges_clm-model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "adleme94/borges_clm-model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "adleme94/borges_clm-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/adleme94/borges_clm-model

SGLang

How to use adleme94/borges_clm-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 "adleme94/borges_clm-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": "adleme94/borges_clm-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 "adleme94/borges_clm-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": "adleme94/borges_clm-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use adleme94/borges_clm-model with Docker Model Runner:
```
docker model run hf.co/adleme94/borges_clm-model
```

adleme94 commited on Aug 22, 2023

Commit

02bd5ef

1 Parent(s): 8dc2c7d

update model card README.md

Browse files

Files changed (1) hide show

README.md +6 -6

README.md CHANGED Viewed

@@ -1,5 +1,5 @@
 ---
-license: apache-2.0
 tags:
 - generated_from_trainer
 model-index:
@@ -12,9 +12,9 @@ should probably proofread and complete it, then remove this comment. -->
 # borges_clm-model
-This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 3.7626
 ## Model description
@@ -45,9 +45,9 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step | Validation Loss |
 |:-------------:|:-----:|:----:|:---------------:|
-| 3.8694        | 1.0   | 1128 | 3.7788          |
-| 3.7725        | 2.0   | 2256 | 3.7656          |
-| 3.7258        | 3.0   | 3384 | 3.7626          |
 ### Framework versions

 ---
+license: mit
 tags:
 - generated_from_trainer
 model-index:
 # borges_clm-model
+This model is a fine-tuned version of [DeepESP/gpt2-spanish-medium](https://huggingface.co/DeepESP/gpt2-spanish-medium) on the None dataset.
 It achieves the following results on the evaluation set:
+- Loss: 3.7991
 ## Model description
 | Training Loss | Epoch | Step | Validation Loss |
 |:-------------:|:-----:|:----:|:---------------:|
+| No log        | 1.0   | 10   | 3.9138          |
+| No log        | 2.0   | 20   | 3.8214          |
+| No log        | 3.0   | 30   | 3.7991          |
 ### Framework versions