Instructions to use etoile9/clean_e2map_lora_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use etoile9/clean_e2map_lora_2 with Transformers:

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

pipe = pipeline("text-generation", model="etoile9/clean_e2map_lora_2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("etoile9/clean_e2map_lora_2", dtype="auto")

PEFT
How to use etoile9/clean_e2map_lora_2 with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use etoile9/clean_e2map_lora_2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "etoile9/clean_e2map_lora_2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "etoile9/clean_e2map_lora_2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/etoile9/clean_e2map_lora_2

SGLang

How to use etoile9/clean_e2map_lora_2 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 "etoile9/clean_e2map_lora_2" \
    --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": "etoile9/clean_e2map_lora_2",
		"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 "etoile9/clean_e2map_lora_2" \
        --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": "etoile9/clean_e2map_lora_2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use etoile9/clean_e2map_lora_2 with Docker Model Runner:
```
docker model run hf.co/etoile9/clean_e2map_lora_2
```

etoile9 commited on Dec 8, 2024

Commit

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+---
+tags:
+- autotrain
+- text-generation-inference
+- text-generation
+- peft
+library_name: transformers
+base_model: meta-llama/Llama-3.1-8B-Instruct
+widget:
+  - messages:
+      - role: user
+        content: What is your favorite condiment?
+license: other
+---
+# Model Trained Using AutoTrain
+This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
+# Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_path = "PATH_TO_THIS_REPO"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(
+    model_path,
+    device_map="auto",
+    torch_dtype='auto'
+).eval()
+# Prompt content: "hi"
+messages = [
+    {"role": "user", "content": "hi"}
+]
+input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
+output_ids = model.generate(input_ids.to('cuda'))
+response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
+# Model response: "Hello! How can I assist you today?"
+print(response)
+```