Instructions to use basiphobe/sci-assistant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use basiphobe/sci-assistant with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("basiphobe/sci-assistant")
model = AutoModelForCausalLM.from_pretrained("basiphobe/sci-assistant")
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 basiphobe/sci-assistant with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/basiphobe/sci-assistant

SGLang

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

Docker Model Runner
How to use basiphobe/sci-assistant with Docker Model Runner:
```
docker model run hf.co/basiphobe/sci-assistant
```

basiphobe commited on Aug 5, 2025

Commit

f2424ac

verified ·

1 Parent(s): 425ebcd

Update README with both LoRA and merged model usage options

Browse files

Files changed (1) hide show

README.md +30 -2

README.md CHANGED Viewed

@@ -22,11 +22,33 @@ The model was trained on curated SCI-related content including:
 ## Usage
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
-model = AutoModelForCausalLM.from_pretrained("your-username/sci-assistant-7b")
-tokenizer = AutoTokenizer.from_pretrained("your-username/sci-assistant-7b")
 # Example usage
 prompt = "What are the signs of autonomic dysreflexia?"
@@ -48,6 +70,12 @@ response = tokenizer.decode(outputs[0], skip_special_tokens=True)
 - Not a replacement for professional medical care
 - May not reflect the most recent medical developments
 ## Technical Details
 - **Base Model**: teknium/OpenHermes-2.5-Mistral-7B

 ## Usage
+This repository contains both the LoRA adapter and the full merged model. Choose the option that works best for you:
+### Option 1: Use the Full Merged Model (Recommended)
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained("basiphobe/sci-assistant")
+tokenizer = AutoTokenizer.from_pretrained("basiphobe/sci-assistant")
+# Example usage
+prompt = "What are the signs of autonomic dysreflexia?"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=200)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+```
+### Option 2: Use the LoRA Adapter (Smaller Download)
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+# Load base model
+base_model = AutoModelForCausalLM.from_pretrained("teknium/OpenHermes-2.5-Mistral-7B")
+tokenizer = AutoTokenizer.from_pretrained("teknium/OpenHermes-2.5-Mistral-7B")
+# Load LoRA adapter
+model = PeftModel.from_pretrained(base_model, "basiphobe/sci-assistant")
 # Example usage
 prompt = "What are the signs of autonomic dysreflexia?"
 - Not a replacement for professional medical care
 - May not reflect the most recent medical developments
+## Files in this Repository
+- **Full Merged Model**: Ready-to-use model files (`model-*.safetensors`, `config.json`, etc.)
+- **LoRA Adapter**: Smaller adapter files (`adapter_model.safetensors`, `adapter_config.json`)
+- **Tokenizer**: Shared tokenizer files for both options
 ## Technical Details
 - **Base Model**: teknium/OpenHermes-2.5-Mistral-7B