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
```

SlitherCode commited on 13 days ago

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@@ -1,199 +1,107 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
-### Model Description
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-## Uses
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 ---
 library_name: transformers
+datasets:
+- yahma/alpaca-cleaned
+license: mit
+language:
+- en
+base_model:
+- SlitherCode/tiny-edu-166m
 ---
+# tiny-edu-166M-instruct-v0
+A naively instruction-tuned version of [tiny-edu-166M](https://huggingface.co/SlitherCode/tiny-edu-166m), a 166M parameter language model built on the ParchmentLM architecture and pretrained from scratch on FineWeb.
+This is **v0** — a baseline instruct model trained on Alpaca-Cleaned with no filtering, curation, or preference optimization. It exists to establish a benchmark before more principled data pipeline work in future versions.
 ## Model Details
+| | |
+|---|---|
+| **Base Model** | SlitherCode/tiny-edu-166m |
+| **Architecture** | ParchmentLM (LLaMA-style, tiktoken cl100k_base tokenizer) |
+| **Parameters** | 166M |
+| **Pretraining Data** | FineWeb (~4B tokens) |
+| **SFT Data** | yahma/alpaca-cleaned (52k examples) |
+| **Training Epochs** | 3 |
+| **Precision** | bfloat16 |
+For full architecture details see the base model repo.
+## Usage
+Load the tokenizer from the base model and weights from this repo:
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("SlitherCode/tiny-edu-166m", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("SlitherCode/tiny-edu-166m-instruct-v0", trust_remote_code=True)
+model.eval()
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "What is the capital of France?"}
+]
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(prompt, return_tensors="pt")
+input_len = inputs["input_ids"].shape[1]
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=200,
+        do_sample=False,
+        repetition_penalty=1.1,
+        eos_token_id=tokenizer.eos_token_id,
+        pad_token_id=tokenizer.eos_token_id,
+    )
+response = tokenizer.decode(outputs[0][input_len:], skip_special_tokens=True)
+print(response)
+```
+## Chat Template
+This model uses a custom chat template with `<|endoftext|>` as the turn separator:
+```
+system
+You are a helpful assistant.<|endoftext|>
+user
+What is the capital of France?<|endoftext|>
+assistant
+```
+## Limitations
+- 166M parameters — limited factual knowledge and reasoning capacity
+- Arithmetic and multi-step reasoning are unreliable at this scale
+- Naively trained on Alpaca-Cleaned with no quality filtering or preference optimization
+- Not suitable for production use
 ## Training Details
+Trained using HuggingFace Trainer with the following configuration:
+- Optimizer: AdamW
+- Learning rate: 2e-5 with cosine decay
+- Warmup ratio: 0.03
+- Batch size: 32
+- Precision: bfloat16
+## Roadmap
+- **v1**: Retrain on a curated, category-balanced dataset derived from real-world queries with higher quality responses
+- **v2**: Retrain on a synthetically generated and curated dataset with further optimizations
+## License
+The model weights are released under the **MIT License**, inherited from the base model [tiny-edu-166M](https://huggingface.co/SlitherCode/tiny-edu-166m).
+The SFT training data [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned) is licensed under **CC-BY-4.0**. Per the license terms, attribution is given to the original Alpaca dataset authors (Stanford University) and the cleaned version maintainers.
+> Taori et al., "Alpaca: A Strong, Replicable Instruction-Following Model", Stanford University, 2023.
+> Cleaned version: https://github.com/gururise/AlpacaDataCleaned