Instructions to use neighborwang/ModeliCo-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use neighborwang/ModeliCo-7B with Transformers:

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

pipe = pipeline("text-generation", model="neighborwang/ModeliCo-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("neighborwang/ModeliCo-7B")
model = AutoModelForCausalLM.from_pretrained("neighborwang/ModeliCo-7B", 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 neighborwang/ModeliCo-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "neighborwang/ModeliCo-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "neighborwang/ModeliCo-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/neighborwang/ModeliCo-7B

SGLang

How to use neighborwang/ModeliCo-7B 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 "neighborwang/ModeliCo-7B" \
    --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": "neighborwang/ModeliCo-7B",
		"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 "neighborwang/ModeliCo-7B" \
        --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": "neighborwang/ModeliCo-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use neighborwang/ModeliCo-7B with Docker Model Runner:
```
docker model run hf.co/neighborwang/ModeliCo-7B
```

neighborwang commited on Oct 14, 2024

Commit

6973d9f

verified ·

1 Parent(s): d48aac0

Update README.md

Browse files

Files changed (1) hide show

README.md +6 -40

README.md CHANGED Viewed

@@ -1,46 +1,12 @@
 ---
 tags:
-- autotrain
-- text-generation-inference
-- text-generation
-- peft
 library_name: transformers
 base_model: Qwen/Qwen2.5-Coder-7B-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)
-```

 ---
 tags:
+- modelica
 library_name: transformers
 base_model: Qwen/Qwen2.5-Coder-7B-Instruct
+license: apache-2.0
+language:
+- en
+pipeline_tag: text2text-generation
 ---
+# ModeliCo - The Modelica GPT