Instructions to use ssunggun2/temp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ssunggun2/temp with Transformers:

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

pipe = pipeline("text-generation", model="ssunggun2/temp")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ssunggun2/temp")
model = AutoModelForCausalLM.from_pretrained("ssunggun2/temp", device_map="auto")
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 Settings

vLLM

How to use ssunggun2/temp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ssunggun2/temp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ssunggun2/temp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ssunggun2/temp

SGLang

How to use ssunggun2/temp 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 "ssunggun2/temp" \
    --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": "ssunggun2/temp",
		"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 "ssunggun2/temp" \
        --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": "ssunggun2/temp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ssunggun2/temp with Docker Model Runner:
```
docker model run hf.co/ssunggun2/temp
```

ssunggun2 commited on Oct 12, 2024

Commit

98a8a83

verified ·

1 Parent(s): ce7ed2f

SKIML-ICL/Llama-3-8B-Inst_qa_squad-ans-v2_ckp-1500-3000

Browse files

Files changed (6) hide show

.gitattributes +1 -0
README.md +2 -2
config.json +1 -1
generation_config.json +1 -1
tokenizer.json +0 -0
training_args.bin +1 -1

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@@ -48,7 +48,7 @@ The following hyperparameters were used during training:
 ### Framework versions
-- Transformers 4.44.1
 - Pytorch 2.4.0
 - Datasets 2.19.1
-- Tokenizers 0.19.1

 ### Framework versions
+- Transformers 4.45.1
 - Pytorch 2.4.0
 - Datasets 2.19.1
+- Tokenizers 0.20.0

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   "rope_theta": 500000.0,
   "tie_word_embeddings": false,
   "torch_dtype": "bfloat16",
-  "transformers_version": "4.44.1",
   "use_cache": false,
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 }

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   "vocab_size": 128256
 }

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   "temperature": 0.6,
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-  "transformers_version": "4.44.1"
 }

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   "top_p": 0.9,
+  "transformers_version": "4.45.1"
 }

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