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

applegrew

Add model card

8d80087 verified 4 days ago

preview code

Raw

History Blame Contribute Delete

2.21 kB

	---
	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

	```python
	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](https://huggingface.co/applegrew/support-125M-slm-sft)