Instructions to use etanlightstone/simple-lm-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use etanlightstone/simple-lm-v2 with Transformers:

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

pipe = pipeline("text-generation", model="etanlightstone/simple-lm-v2", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("etanlightstone/simple-lm-v2", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use etanlightstone/simple-lm-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "etanlightstone/simple-lm-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "etanlightstone/simple-lm-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/etanlightstone/simple-lm-v2

SGLang

How to use etanlightstone/simple-lm-v2 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 "etanlightstone/simple-lm-v2" \
    --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": "etanlightstone/simple-lm-v2",
		"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 "etanlightstone/simple-lm-v2" \
        --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": "etanlightstone/simple-lm-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use etanlightstone/simple-lm-v2 with Docker Model Runner:
```
docker model run hf.co/etanlightstone/simple-lm-v2
```

simple-lm-v2 / README.md

etanlightstone

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	---
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- causal-lm
	- simple-lm
	- custom-code
	---

	# SimpleLM

	Custom decoder-only Transformer language model (pretraining checkpoint).
	Architecture is defined in `modeling_simple_lm.py` (bundled in this repo)
	and loaded via `trust_remote_code=True`.

	Source checkpoint: `checkpoints/lm_checkpoint_008_shutdown.pt`

	This model is a pre-trained only LLM that was trained from scratch on a very small dataset of conversations (found on Kaggle and mixed with OpenAssistant/oasst2). As well as as subset of Finweb_Edu data. This particular save is checkpoint after 1 full epoch.
	Alltogether about 410M tokens (1B+ would have been more ideal for a model this size).


	## Architecture

	\| field \| value \|
	\|-------\|-------\|
	\| vocab_size \| 32000 \|
	\| context_length \| 512 \|
	\| d_model \| 768 \|
	\| n_layers \| 12 \|
	\| n_heads \| 8 \|
	\| d_ff \| 2048 \|
	\| activation \| gelu \|
	\| bias \| True \|
	\| tie_word_embeddings \| True \|

	Tokenizer source: `TinyLlama/TinyLlama-1.1B-Chat-v1.0`

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	repo = "etanlightstone/simple-lm-v2"
	tok = AutoTokenizer.from_pretrained(repo)
	model = AutoModelForCausalLM.from_pretrained(repo, trust_remote_code=True)

	prompt = "Once upon a time"
	ids = tok(prompt, return_tensors="pt").input_ids
	out = model.generate(ids, max_new_tokens=80, do_sample=True, top_k=50, temperature=0.9)
	print(tok.decode(out[0], skip_special_tokens=True))
	```

	## Training settings

	```json
	{
	"batch_size": 10,
	"batch_size_note": "per GPU when using torchrun",
	"world_size": 1,
	"learning_rate": 0.0003,
	"weight_decay": 0.01,
	"num_epochs": 3,
	"max_steps": null,
	"grad_clip": 1.0,
	"seed": 42,
	"docs_dir": "/home/etan/simple_llm/docs",
	"block_size": 512,
	"stride": 448,
	"stride_overlap_tokens": 64
	}
	```