Instructions to use tharun2909/disligpt_FT_adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tharun2909/disligpt_FT_adapter with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("distilbert/distilgpt2")
model = PeftModel.from_pretrained(base_model, "tharun2909/disligpt_FT_adapter")

Transformers

How to use tharun2909/disligpt_FT_adapter with Transformers:

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

pipe = pipeline("text-generation", model="tharun2909/disligpt_FT_adapter")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tharun2909/disligpt_FT_adapter", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tharun2909/disligpt_FT_adapter with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tharun2909/disligpt_FT_adapter"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tharun2909/disligpt_FT_adapter",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/tharun2909/disligpt_FT_adapter

SGLang

How to use tharun2909/disligpt_FT_adapter 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 "tharun2909/disligpt_FT_adapter" \
    --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": "tharun2909/disligpt_FT_adapter",
		"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 "tharun2909/disligpt_FT_adapter" \
        --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": "tharun2909/disligpt_FT_adapter",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use tharun2909/disligpt_FT_adapter with Docker Model Runner:
```
docker model run hf.co/tharun2909/disligpt_FT_adapter
```

tharun2909 commited on Dec 29, 2025

Commit

e75a1e4

verified ·

1 Parent(s): 223a95a

Upload training configuration

Browse files

Files changed (1) hide show

run_config.json +10 -10

run_config.json CHANGED Viewed

@@ -1,18 +1,18 @@
 {
-  "model_name": "sshleifer/tiny-gpt2",
-  "epochs": 1,
-  "batch_size": 2,
-  "learning_rate": 0.0002,
   "grad_accum_steps": 4,
   "precision": "fp32",
-  "quantization_bits": 4,
-  "lora_r": 8,
-  "lora_alpha": 16,
-  "lora_dropout": 0.05,
   "target_modules": [
     "c_attn",
     "c_proj"
   ],
-  "total_steps": 62,
-  "num_train_samples": 490
 }

 {
+  "model_name": "distilgpt2",
+  "epochs": 2,
+  "batch_size": 1,
+  "learning_rate": 0.0003,
   "grad_accum_steps": 4,
   "precision": "fp32",
+  "quantization_bits": 8,
+  "lora_r": 4,
+  "lora_alpha": 8,
+  "lora_dropout": 0.04,
   "target_modules": [
     "c_attn",
     "c_proj"
   ],
+  "total_steps": 60,
+  "num_train_samples": 240
 }