Instructions to use KHuss/gpt2-sft-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KHuss/gpt2-sft-chat with Transformers:

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

pipe = pipeline("text-generation", model="KHuss/gpt2-sft-chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KHuss/gpt2-sft-chat")
model = AutoModelForCausalLM.from_pretrained("KHuss/gpt2-sft-chat")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use KHuss/gpt2-sft-chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KHuss/gpt2-sft-chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KHuss/gpt2-sft-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KHuss/gpt2-sft-chat

SGLang

How to use KHuss/gpt2-sft-chat 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 "KHuss/gpt2-sft-chat" \
    --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": "KHuss/gpt2-sft-chat",
		"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 "KHuss/gpt2-sft-chat" \
        --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": "KHuss/gpt2-sft-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use KHuss/gpt2-sft-chat with Docker Model Runner:
```
docker model run hf.co/KHuss/gpt2-sft-chat
```

KHuss commited on Mar 31, 2025

Commit

2d58ca7

verified ·

1 Parent(s): 828ed5c

Changed pad token to unk token

Browse files

Files changed (2) hide show

special_tokens_map.json +1 -7
tokenizer_config.json +1 -1

special_tokens_map.json CHANGED Viewed

@@ -17,13 +17,7 @@
     "rstrip": false,
     "single_word": false
   },
-  "pad_token": {
-    "content": "<|im_end|>",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
   "unk_token": {
     "content": "<|endoftext|>",
     "lstrip": false,

     "rstrip": false,
     "single_word": false
   },
+  "pad_token": "<|endoftext|>",
   "unk_token": {
     "content": "<|endoftext|>",
     "lstrip": false,

tokenizer_config.json CHANGED Viewed

@@ -36,7 +36,7 @@
   "eos_token": "<|im_end|>",
   "extra_special_tokens": {},
   "model_max_length": 1024,
-  "pad_token": "<|im_end|>",
   "padding_size": "left",
   "tokenizer_class": "GPT2Tokenizer",
   "truncation_side": "left",

   "eos_token": "<|im_end|>",
   "extra_special_tokens": {},
   "model_max_length": 1024,
+  "pad_token": "<|endoftext|>",
   "padding_size": "left",
   "tokenizer_class": "GPT2Tokenizer",
   "truncation_side": "left",