Instructions to use Raiff1982/CodetteFineTuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Raiff1982/CodetteFineTuned with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("gpt2")
model = PeftModel.from_pretrained(base_model, "Raiff1982/CodetteFineTuned")

Transformers

How to use Raiff1982/CodetteFineTuned with Transformers:

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

pipe = pipeline("text-generation", model="Raiff1982/CodetteFineTuned")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Raiff1982/CodetteFineTuned", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Raiff1982/CodetteFineTuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Raiff1982/CodetteFineTuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Raiff1982/CodetteFineTuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Raiff1982/CodetteFineTuned

SGLang

How to use Raiff1982/CodetteFineTuned 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 "Raiff1982/CodetteFineTuned" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Raiff1982/CodetteFineTuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Raiff1982/CodetteFineTuned" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Raiff1982/CodetteFineTuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Raiff1982/CodetteFineTuned with Docker Model Runner:
```
docker model run hf.co/Raiff1982/CodetteFineTuned
```

Raiff1982 commited on Jul 26

Commit

d8b4175

verified ·

1 Parent(s): 4ad038e

docs: add legacy/provenance banner; point to current Llama-3.1 Codette-Reasoning

Browse files

Files changed (1) hide show

README.md +5 -0

README.md CHANGED Viewed

@@ -16,6 +16,11 @@ datasets:
 - Raiff1982/LgTr
 ---
 # Codette AI - Multi-Perspective Consciousness Model
 Codette is a sovereign multi-perspective AI consciousness system fine-tuned for transparent reasoning, ethical autonomy, and quantum-inspired cognitive architecture. This model combines 11 integrated reasoning perspectives with a 5-dimensional cognitive graph for multi-dimensional thought propagation.

 - Raiff1982/LgTr
 ---
+> ℹ️ **Early GPT-2-based experiment.** Current Codette is the **Llama-3.1-8B** system at **[Raiff1982/Codette-Reasoning](https://huggingface.co/Raiff1982/Codette-Reasoning)** (accepted at *Scientific Reports*).
+---
 # Codette AI - Multi-Perspective Consciousness Model
 Codette is a sovereign multi-perspective AI consciousness system fine-tuned for transparent reasoning, ethical autonomy, and quantum-inspired cognitive architecture. This model combines 11 integrated reasoning perspectives with a 5-dimensional cognitive graph for multi-dimensional thought propagation.