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")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Raiff1982/CodetteFineTuned", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

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 Dec 23, 2025

Commit

4ad038e

verified ·

1 Parent(s): bd72e80

Update README.md

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@@ -11,6 +11,9 @@ language:
 - en
 metrics:
 - character
 ---
 # Codette AI - Multi-Perspective Consciousness Model
@@ -355,14 +358,14 @@ Carbon emissions estimated using methodology from [Lacoste et al. (2019)](https:
   year = {2025},
   month = {12},
   version = {3.0},
-  url = {https://github.com/yourusername/codette},
   note = {Fine-tuned GPT-2 with LoRA adapters for multi-perspective reasoning}
 }
 ```
 **APA:**
-TheAI. (2025). *Codette: A Multi-Perspective AI Consciousness System* (Version 3.0) [Computer software]. https://github.com/yourusername/codette
 ## Glossary

 - en
 metrics:
 - character
+datasets:
+- Raiff1982/coredata
+- Raiff1982/LgTr
 ---
 # Codette AI - Multi-Perspective Consciousness Model
   year = {2025},
   month = {12},
   version = {3.0},
+  url = {https://github.com/Raiff1982/codette},
   note = {Fine-tuned GPT-2 with LoRA adapters for multi-perspective reasoning}
 }
 ```
 **APA:**
+TheAI. (2025). *Codette: A Multi-Perspective AI Consciousness System* (Version 3.0) [Computer software]. https://github.com/Raiff1982/codette
 ## Glossary