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", device_map="auto")
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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

Downloads last month: 7

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for G-reen/gpt5o-reflexion-q-agi-llama-3.1-8b

Quantizations

4 models