Instructions to use Fu01978/DialoGPT-medium-distill-Kimi-K2-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Fu01978/DialoGPT-medium-distill-Kimi-K2-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="Fu01978/DialoGPT-medium-distill-Kimi-K2-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Fu01978/DialoGPT-medium-distill-Kimi-K2-Instruct")
model = AutoModelForCausalLM.from_pretrained("Fu01978/DialoGPT-medium-distill-Kimi-K2-Instruct")
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 Fu01978/DialoGPT-medium-distill-Kimi-K2-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Fu01978/DialoGPT-medium-distill-Kimi-K2-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Fu01978/DialoGPT-medium-distill-Kimi-K2-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Fu01978/DialoGPT-medium-distill-Kimi-K2-Instruct

SGLang

How to use Fu01978/DialoGPT-medium-distill-Kimi-K2-Instruct 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 "Fu01978/DialoGPT-medium-distill-Kimi-K2-Instruct" \
    --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": "Fu01978/DialoGPT-medium-distill-Kimi-K2-Instruct",
		"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 "Fu01978/DialoGPT-medium-distill-Kimi-K2-Instruct" \
        --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": "Fu01978/DialoGPT-medium-distill-Kimi-K2-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Fu01978/DialoGPT-medium-distill-Kimi-K2-Instruct with Docker Model Runner:
```
docker model run hf.co/Fu01978/DialoGPT-medium-distill-Kimi-K2-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

DialoGPT-medium-distill-Kimi-K2-Instruct

This model is a fine-tuned version of microsoft/DialoGPT-medium, specialized for a custom persona and critical knowledge injection. It has been trained to balance conversational flexibility with specific factual recall.

Model Description

Model type: Causal Language Model
Language(s): English
Base Model: DialoGPT-medium
Total Parameters: 406.3M (Post-fine-tune expanded state)

Intended Uses & Limitations

This model is designed for creative assistant tasks and casual conversation.

Direct Use: Chatbots, creative storytelling, and persona-driven interactions.
Limitations: Due to the small dataset size and "creative" training, the model may occasionally hallucinate or provide non-literal answers (e.g., creative definitions of common objects).

Training Procedure

The model underwent a full fine-tune on a custom dataset consisting of critical facts and casual chat examples.

Training Hyperparameters

Learning Rate: 2e-5
Epochs: 5
Batch Size: 4 (with gradient accumulation)
Precision: Mixed Precision (FP16)
Loss achieved: 3.264037

Weight Analysis

Post-training analysis showed a significant shift in the LM Head weights (Absolute Shift: 4.4164), indicating a strong adaptation to the new conversational style while maintaining structural grammar stability in the transformer layers.

How to Use

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Fu01978/DialoGPT-medium-distill-Kimi-K2-Instruct")
model = AutoModelForCausalLM.from_pretrained("Fu01978/DialoGPT-medium-distill-Kimi-K2-Instruct")

# For best results, use a temperature between 0.7 and 0.85