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 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 Dec 22, 2025

Commit

ab98129

verified ·

1 Parent(s): 807bab9

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -22

README.md CHANGED Viewed

@@ -1,25 +1,3 @@
----
-tags:
-- text-to-image
-- lora
-- diffusers
-- template:diffusion-lora
-widget:
-- output:
-    url: images/Codette_Quantum_Harmonic_Framework.png
-  text: Show me ethics workflow
-  parameters:
-    negative_prompt: show nsfw
-base_model: Uz-Core-AI/core-gpt2.0
-instance_prompt: null
-license: apache-2.0
----
-# Codette3.0
-<Gallery />
-## Model description
 ---
 base_model: gpt2
 library_name: peft
@@ -233,3 +211,4 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 [Download](/Raiff1982/CodetteFineTuned/tree/main) them in the Files & versions tab.

 ---
 base_model: gpt2
 library_name: peft
 [Download](/Raiff1982/CodetteFineTuned/tree/main) them in the Files & versions tab.