Instructions to use SlitherCode/tiny-edu-166m-instruct-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SlitherCode/tiny-edu-166m-instruct-v0 with Transformers:

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

pipe = pipeline("text-generation", model="SlitherCode/tiny-edu-166m-instruct-v0", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("SlitherCode/tiny-edu-166m-instruct-v0", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SlitherCode/tiny-edu-166m-instruct-v0 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-v0"
# 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-v0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SlitherCode/tiny-edu-166m-instruct-v0

SGLang

How to use SlitherCode/tiny-edu-166m-instruct-v0 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-v0" \
    --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-v0",
		"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-v0" \
        --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-v0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SlitherCode/tiny-edu-166m-instruct-v0 with Docker Model Runner:
```
docker model run hf.co/SlitherCode/tiny-edu-166m-instruct-v0
```

tiny-edu-166m-instruct-v0

File size: 673 Bytes

8aa2894

{
  "architectures": [
    "ParchmentForCausalLM"
  ],
  "auto_map": {
    "AutoConfig": "configuration_parchment.ParchmentConfig",
    "AutoModelForCausalLM": "modeling_parchment.ParchmentForCausalLM"
  },
  "bos_token_id": 100257,
  "d_ff": 3072,
  "d_model": 768,
  "dtype": "float32",
  "eos_token_id": 100257,
  "hidden_size": 768,
  "max_seq_len": 1024,
  "model_type": "parchment",
  "n_heads": 12,
  "n_layers": 12,
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "pad_token_id": 100257,
  "rms_norm_eps": 1e-06,
  "rope_base": 10000.0,
  "tie_word_embeddings": true,
  "transformers_version": "5.8.1",
  "vocab_size": 100277
}