Instructions to use Gil-tester/tpai-lora-cerebro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Gil-tester/tpai-lora-cerebro with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
model = PeftModel.from_pretrained(base_model, "Gil-tester/tpai-lora-cerebro")

Transformers

How to use Gil-tester/tpai-lora-cerebro with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Gil-tester/tpai-lora-cerebro", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Gil-tester/tpai-lora-cerebro with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Gil-tester/tpai-lora-cerebro

SGLang

How to use Gil-tester/tpai-lora-cerebro 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 "Gil-tester/tpai-lora-cerebro" \
    --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": "Gil-tester/tpai-lora-cerebro",
		"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 "Gil-tester/tpai-lora-cerebro" \
        --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": "Gil-tester/tpai-lora-cerebro",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Gil-tester/tpai-lora-cerebro with Docker Model Runner:
```
docker model run hf.co/Gil-tester/tpai-lora-cerebro
```

TPAI LoRA - Cerebro Interno

LoRA adapter for Qwen/Qwen2.5-0.5B-Instruct, trained as the "cerebro interno" (internal brain) for the TPAI (Teaching People AI) system.

Model Details

Base Model: Qwen/Qwen2.5-0.5B-Instruct
Adapter Type: LoRA (PEFT)
LoRA Rank (r): 16
LoRA Alpha: 32
Target Modules: q_proj, k_proj, o_proj, v_proj
Task Type: Causal Language Modeling

Training Details

Training Samples: 1194+
Epochs: 3
Training Duration: ~8.5 hours
Final Loss: 1.659
Dataset: Multi-language programming assistant responses (TeachingPeopleAI/tpai-training-data)
LoRA Dropout: 0.05

Usage

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load base model
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "Gil-tester/tpai-lora-cerebro")

# Generate response
input_text = "User: Explain quantum computing\nAssistant:"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0]))

Integration with TPAI

This model serves as the "cerebro interno" in the TPAI system, providing:

Primary language generation
Low-latency responses via local inference
Cost-effective operation on HF Spaces

Framework Versions

PEFT 0.18.1

Downloads last month: 15

Model tree for Gil-tester/tpai-lora-cerebro

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Adapter

(593)

this model

Gil-tester
/

tpai-lora-cerebro