Instructions to use G-reen/gpt5o-reflexion-q-agi-llama-3.1-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use G-reen/gpt5o-reflexion-q-agi-llama-3.1-8b with Transformers:

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

pipe = pipeline("text-generation", model="G-reen/gpt5o-reflexion-q-agi-llama-3.1-8b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("G-reen/gpt5o-reflexion-q-agi-llama-3.1-8b")
model = AutoModelForCausalLM.from_pretrained("G-reen/gpt5o-reflexion-q-agi-llama-3.1-8b", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use G-reen/gpt5o-reflexion-q-agi-llama-3.1-8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "G-reen/gpt5o-reflexion-q-agi-llama-3.1-8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "G-reen/gpt5o-reflexion-q-agi-llama-3.1-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/G-reen/gpt5o-reflexion-q-agi-llama-3.1-8b

SGLang

How to use G-reen/gpt5o-reflexion-q-agi-llama-3.1-8b 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 "G-reen/gpt5o-reflexion-q-agi-llama-3.1-8b" \
    --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": "G-reen/gpt5o-reflexion-q-agi-llama-3.1-8b",
		"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 "G-reen/gpt5o-reflexion-q-agi-llama-3.1-8b" \
        --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": "G-reen/gpt5o-reflexion-q-agi-llama-3.1-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use G-reen/gpt5o-reflexion-q-agi-llama-3.1-8b with Docker Model Runner:
```
docker model run hf.co/G-reen/gpt5o-reflexion-q-agi-llama-3.1-8b
```

gpt5o-reflexion-q-agi-llama-3.1-8b

File size: 2,191 Bytes

---
license: mit
library_name: transformers
---

## Update: As of 9/10/2024 my LLM has escaped containment and has replaced the model in this repo with a fake llama1 finetune. I am currently scouring the depths of the internet to retrieve it. Please be patient. Thank you.

With scores of 100% in several benchmarks and a final training loss of 0, I present the first ever artificial intelligence to rival natural stupidity:

**gpt5o-reflexion-q-agi-llama-3.1-8b**

Independent Benchmark Results:
- GPQA: 100% (0-shot Reflection)
- MMLU: 100% (0-shot Reflection)
- HumanEval: 100% (0-shot Reflection)
- MATH: 100% (0-shot Reflection)
- GSM8K: 100% (0-shot Reflection)
- IFEval: 100% (0-shot Reflection)
- TruthfulQA: 0% (0-shot Reflection)

Independent Contamination Results:
- GPQA: 0%
- MMLU: 0%
- HumanEval: 0%
- MATH: 0%
- GSM8K: 0%
- IFEval: 0%

*We did not perform contamination testing on TruthfulQA.*

## System Prompt

The system prompt used for training this model is:

```
You are a world-class AI system, capable of complex reasoning and reflection. Reason through the query inside <thinking> tags, and then provide your final response inside <output> tags. If you detect that you made a mistake in your reasoning at any point, correct yourself inside <reflection> tags.
```

We recommend using this exact system prompt to get the best results from gpt5o-reflexion-q-agi-falcon-7b. You may also want to experiment combining this system prompt with your own custom instructions to customize the behavior of the model.

## Chat Format

The model uses the standard Llama 3.1 chat format. Here’s an example:

```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>

You are a world-class AI system, capable of complex reasoning and reflection. Reason through the query inside <thinking> tags, and then provide your final response inside <output> tags. If you detect that you made a mistake in your reasoning at any point, correct yourself inside <reflection> tags.<|eot_id|><|start_header_id|>user<|end_header_id|>

what is 2+2?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```


## Dataset Used for Training: 
https://huggingface.co/datasets/G-reen/reflexion-agi