Instructions to use Offlin33er/SmolLM2-1.7B-telegram-chats with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Offlin33er/SmolLM2-1.7B-telegram-chats with Transformers:

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

pipe = pipeline("text-generation", model="Offlin33er/SmolLM2-1.7B-telegram-chats")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Offlin33er/SmolLM2-1.7B-telegram-chats")
model = AutoModelForCausalLM.from_pretrained("Offlin33er/SmolLM2-1.7B-telegram-chats", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Offlin33er/SmolLM2-1.7B-telegram-chats with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Offlin33er/SmolLM2-1.7B-telegram-chats"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Offlin33er/SmolLM2-1.7B-telegram-chats",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Offlin33er/SmolLM2-1.7B-telegram-chats

SGLang

How to use Offlin33er/SmolLM2-1.7B-telegram-chats 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 "Offlin33er/SmolLM2-1.7B-telegram-chats" \
    --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": "Offlin33er/SmolLM2-1.7B-telegram-chats",
		"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 "Offlin33er/SmolLM2-1.7B-telegram-chats" \
        --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": "Offlin33er/SmolLM2-1.7B-telegram-chats",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Offlin33er/SmolLM2-1.7B-telegram-chats with Docker Model Runner:
```
docker model run hf.co/Offlin33er/SmolLM2-1.7B-telegram-chats
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for SmolLM2-1.7B-telegram-chats

This model is a fine-tuned version of HuggingFaceTB/SmolLM2-1.7B on the Offlin33er/telegram-chat-export-synthetic dataset. It has been trained using TRL.

Quantized GGUF build: Offlin33er/SmolLM2-1.7B-telegram-chats-GGUF (f16 + Q4_K_M, runs in LM Studio / llama.cpp). Live demo: Offlin33er/telegram-chat-generator — generate fake Telegram-export chats on a free CPU Space.

Quick start

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Offlin33er/SmolLM2-1.7B-telegram-chats", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with SFT.

Framework versions

TRL: 1.13.0
Transformers: 5.17.0
Pytorch: 2.14.0
Datasets: 5.0.1
Tokenizers: 0.23.2

Citations

Cite TRL as:

@software{vonwerra2020trl,
  title   = {{TRL: Transformers Reinforcement Learning}},
  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
  license = {Apache-2.0},
  url     = {https://github.com/huggingface/trl},
  year    = {2020}
}

Downloads last month: 188

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for Offlin33er/SmolLM2-1.7B-telegram-chats

Base model

HuggingFaceTB/SmolLM2-1.7B

Finetuned

(63)

this model

Quantizations

1 model

Offlin33er
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SmolLM2-1.7B-telegram-chats