Instructions to use SlitherCode/tiny-edu-166m-instruct-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SlitherCode/tiny-edu-166m-instruct-v3 with Transformers:

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

pipe = pipeline("text-generation", model="SlitherCode/tiny-edu-166m-instruct-v3", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("SlitherCode/tiny-edu-166m-instruct-v3", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SlitherCode/tiny-edu-166m-instruct-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SlitherCode/tiny-edu-166m-instruct-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SlitherCode/tiny-edu-166m-instruct-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SlitherCode/tiny-edu-166m-instruct-v3

SGLang

How to use SlitherCode/tiny-edu-166m-instruct-v3 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 "SlitherCode/tiny-edu-166m-instruct-v3" \
    --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": "SlitherCode/tiny-edu-166m-instruct-v3",
		"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 "SlitherCode/tiny-edu-166m-instruct-v3" \
        --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": "SlitherCode/tiny-edu-166m-instruct-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SlitherCode/tiny-edu-166m-instruct-v3 with Docker Model Runner:
```
docker model run hf.co/SlitherCode/tiny-edu-166m-instruct-v3
```

SlitherCode commited on 6 days ago

Commit

f41a393

verified ·

1 Parent(s): ea2d4a7

Update README.md

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@@ -41,7 +41,7 @@ ParchmentLM is a custom LLaMA-style architecture with the following components:
 | Layers | 12 |
 | Attention heads | 12 |
 | Hidden size | 768 |
-| FFN size | 3072 |
 | Context length | 1024 tokens |
 | Positional encoding | RoPE |
 | Normalization | RMSNorm (pre-norm) |

 | Layers | 12 |
 | Attention heads | 12 |
 | Hidden size | 768 |
+| FFN size | 2048 |
 | Context length | 1024 tokens |
 | Positional encoding | RoPE |
 | Normalization | RMSNorm (pre-norm) |