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
```

Update README.md

by zaqintosh - opened May 13

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

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README.md +5 -0

README.md CHANGED Viewed

@@ -15,6 +15,11 @@ and loaded via `trust_remote_code=True`.
 Source checkpoint: `checkpoints/lm_checkpoint_008_shutdown.pt`
 ## Architecture
 | field | value |

 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 one is an early checkpoint and will be revised soon.
+Alltogether about 410M tokens (1B+ would have been more ideal for a model this size).
 ## Architecture
 | field | value |