Instructions to use Fu01978/SmolLM2-135M-Instruct-AO3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Fu01978/SmolLM2-135M-Instruct-AO3 with Transformers:

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

pipe = pipeline("text-generation", model="Fu01978/SmolLM2-135M-Instruct-AO3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Fu01978/SmolLM2-135M-Instruct-AO3")
model = AutoModelForCausalLM.from_pretrained("Fu01978/SmolLM2-135M-Instruct-AO3")
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 Settings

vLLM

How to use Fu01978/SmolLM2-135M-Instruct-AO3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Fu01978/SmolLM2-135M-Instruct-AO3"
# 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/SmolLM2-135M-Instruct-AO3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Fu01978/SmolLM2-135M-Instruct-AO3

SGLang

How to use Fu01978/SmolLM2-135M-Instruct-AO3 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/SmolLM2-135M-Instruct-AO3" \
    --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/SmolLM2-135M-Instruct-AO3",
		"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/SmolLM2-135M-Instruct-AO3" \
        --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/SmolLM2-135M-Instruct-AO3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Fu01978/SmolLM2-135M-Instruct-AO3 with Docker Model Runner:
```
docker model run hf.co/Fu01978/SmolLM2-135M-Instruct-AO3
```

SmolLM2-135M-Instruct-AO3

This model is a fine-tuned version of HuggingFaceTB/SmolLM2-135M-Instruct trained on the Fu01978/ao3_chat dataset.

It is designed to blend the instruction-following capabilities of SmolLM2 with the descriptive, narrative, and atmospheric prose styles commonly found in creative writing communities.

Model Description

Model type: Causal Language Model (Fine-tuned)
Language(s) (NLP): English
License: Apache 2.0
Finetuned from model: HuggingFaceTB/SmolLM2-135M-Instruct

Training Details

The model was fine-tuned for a short duration to "infuse" the base model with narrative flair without completely overwriting its general knowledge.

Training Hyperparameters

Steps: 75
Learning Rate: 5e-4
Batch Size: 8
Gradient Accumulation Steps: 2
LR Scheduler Type: linear
Precision: FP32 (Full Precision)

Loss Curve

The model showed a rapid descent in the first 20 steps, eventually stabilizing around a loss of 2.5-2.6.

Step	Training Loss
5	3.572342
15	2.720610
30	2.497861
45	2.626326
60	2.708193
75	2.637797

Usage

This model uses the ChatML template. It is recommended to use the apply_chat_template method for best results.

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Fu01978/SmolLM2-135M-Instruct-AO3"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float32, device_map="auto")

messages = [
    {"role": "system", "content": "You are a creative writing assistant."},
    {"role": "user", "content": "Write a scene about a rainy library."},
]

inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=150, do_sample=True, temperature=0.7)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))

Intended Use & Limitations

Roleplay & Fiction: Best suited for creative prompts and stylistic prose.
Domain Shift: While it retains general knowledge, its explanations (e.g., science) may adopt a more narrative or "story-like" tone.