Instructions to use applegrew/support-125M-slm-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use applegrew/support-125M-slm-base with Transformers:

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

pipe = pipeline("text-generation", model="applegrew/support-125M-slm-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("applegrew/support-125M-slm-base")
model = AutoModelForCausalLM.from_pretrained("applegrew/support-125M-slm-base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use applegrew/support-125M-slm-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "applegrew/support-125M-slm-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "applegrew/support-125M-slm-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/applegrew/support-125M-slm-base

SGLang

How to use applegrew/support-125M-slm-base 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 "applegrew/support-125M-slm-base" \
    --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": "applegrew/support-125M-slm-base",
		"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 "applegrew/support-125M-slm-base" \
        --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": "applegrew/support-125M-slm-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use applegrew/support-125M-slm-base with Docker Model Runner:
```
docker model run hf.co/applegrew/support-125M-slm-base
```

support-125M-slm-base / README.md

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metadata

language: en
license: mit
tags:
  - slm
  - llama
  - from-scratch
  - it-support
  - call-centre
datasets:
  - HuggingFaceFW/fineweb-edu
  - ArmelR/the-pile-splitted
  - uonlp/CulturaX
  - mlfoundations/dclm-baseline-1.0
pipeline_tag: text-generation
library_name: transformers

Support 125M SLM - Base

A 125M parameter Llama-style language model trained from scratch on ~2.6B tokens of curated IT support and technical data. This is the base (pretrained) model — it completes text but does not follow instructions.

Training Data

Source	Tokens	Description
FineWeb-Edu	900M	High-quality educational web text
Ubuntu IRC	600M	Technical support chat logs
StackExchange	1.05B	Q&A from StackExchange network
DCLM	300M	Filtered web text

Total: ~2.85B tokens (6 epochs = ~17B tokens seen)

Model Architecture

Parameter	Value
Parameters	125,847,552
Layers	12
Hidden dim	768
FFN dim	3072 (SwiGLU)
Attention heads	12
KV heads	12 (MHA)
Vocab size	16,384
Context length	1,024
Position encoding	RoPE
Norm	RMSNorm
Tie embeddings	Yes

Training Details

Hardware: 8x H100 (Modal cloud)
Framework: PyTorch + DDP
Optimizer: AdamW (lr=6e-4, warmup 200M tokens, cosine decay)
Mixed precision: bfloat16
Total cost: ~$31
Val perplexity: 15.06

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("applegrew/support-125M-slm-base")
tokenizer = AutoTokenizer.from_pretrained("applegrew/support-125M-slm-base")

prompt = "The VPN connection keeps dropping"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Special Tokens

This model uses custom chat tokens: <|bos|>, <|eos|>, <|pad|>, <|unk|>, <|system|>, <|user|>, <|assistant|>

SFT Version

For instruction following, use the SFT version: applegrew/support-125M-slm-sft