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 / configuration_parchment.py

SlitherCode

Upload ParchmentForCausalLM

8aa2894 verified 13 days ago

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	from transformers import PretrainedConfig


	class ParchmentConfig(PretrainedConfig):
	model_type = "parchment"

	def __init__(
	self,
	vocab_size: int = 100277,
	d_model: int = 768,
	n_heads: int = 12,
	n_layers: int = 12,
	max_seq_len: int = 1024,
	rms_norm_eps: float = 1e-6,
	rope_base: float = 10000.0,
	tie_word_embeddings: bool = True,
	**kwargs,
	):
	self.vocab_size = vocab_size
	self.d_model = d_model
	self.n_heads = n_heads
	self.n_layers = n_layers
	self.max_seq_len = max_seq_len
	self.rms_norm_eps = rms_norm_eps
	self.rope_base = rope_base
	# aliases expected by transformers internals
	self.num_hidden_layers = n_layers
	self.hidden_size = d_model
	self.num_attention_heads = n_heads
	super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)