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

Libraries

How to use BEGADE/model with Transformers:

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

pipe = pipeline("text-generation", model="BEGADE/model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BEGADE/model")
model = AutoModelForCausalLM.from_pretrained("BEGADE/model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use BEGADE/model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BEGADE/model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BEGADE/model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BEGADE/model

SGLang

How to use BEGADE/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 "BEGADE/model" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BEGADE/model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "BEGADE/model" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BEGADE/model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

BEGADE commited on Sep 10, 2024

Commit

9eee970

verified ·

1 Parent(s): 710ebaf

Model save

Browse files

Files changed (4) hide show

README.md +3 -2
generation_config.json +1 -1
model.safetensors +1 -1
runs/Sep10_01-12-14_9140fd6908f0/events.out.tfevents.1725930807.9140fd6908f0.228.1 +2 -2

README.md CHANGED Viewed

@@ -1,4 +1,5 @@
 ---
 license: mit
 base_model: openai-community/gpt2
 tags:
@@ -45,7 +46,7 @@ The following hyperparameters were used during training:
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: constant
 - lr_scheduler_warmup_ratio: 0.03
-- num_epochs: 2
 ### Training results
@@ -53,7 +54,7 @@ The following hyperparameters were used during training:
 ### Framework versions
-- Transformers 4.42.4
 - Pytorch 2.4.0+cu121
 - Datasets 2.21.0
 - Tokenizers 0.19.1

 ---
+library_name: transformers
 license: mit
 base_model: openai-community/gpt2
 tags:
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: constant
 - lr_scheduler_warmup_ratio: 0.03
+- num_epochs: 4
 ### Training results
 ### Framework versions
+- Transformers 4.44.2
 - Pytorch 2.4.0+cu121
 - Datasets 2.21.0
 - Tokenizers 0.19.1

generation_config.json CHANGED Viewed

@@ -3,5 +3,5 @@
   "bos_token_id": 50257,
   "eos_token_id": 50258,
   "pad_token_id": 50258,
-  "transformers_version": "4.42.4"
 }

   "bos_token_id": 50257,
   "eos_token_id": 50258,
   "pad_token_id": 50258,
+  "transformers_version": "4.44.2"
 }

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