Instructions to use sraivante/TARA-English-Tutor-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sraivante/TARA-English-Tutor-3B with Transformers:

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

pipe = pipeline("text-generation", model="sraivante/TARA-English-Tutor-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sraivante/TARA-English-Tutor-3B")
model = AutoModelForCausalLM.from_pretrained("sraivante/TARA-English-Tutor-3B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sraivante/TARA-English-Tutor-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sraivante/TARA-English-Tutor-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sraivante/TARA-English-Tutor-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sraivante/TARA-English-Tutor-3B

SGLang

How to use sraivante/TARA-English-Tutor-3B 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 "sraivante/TARA-English-Tutor-3B" \
    --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": "sraivante/TARA-English-Tutor-3B",
		"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 "sraivante/TARA-English-Tutor-3B" \
        --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": "sraivante/TARA-English-Tutor-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sraivante/TARA-English-Tutor-3B with Docker Model Runner:
```
docker model run hf.co/sraivante/TARA-English-Tutor-3B
```

TARA-English-Tutor-3B / training_data /README.md

sraivante

Publish TARA English Tutor 3B model with training data

24e1610 verified 17 days ago

preview code

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	---
	pretty_name: TARA English Tutor Instructions
	language:
	- en
	license: apache-2.0
	task_categories:
	- text-generation
	size_categories:
	- 1K<n<10K
	tags:
	- education
	- english-tutor
	- instruction-tuning
	- synthetic
	- structured-output
	- json
	configs:
	- config_name: default
	data_files:
	- split: train
	path: data/train.jsonl
	- split: validation
	path: data/validation.jsonl
	---

	# TARA English Tutor Instructions

	Copyright © 2026 sraivante — original dataset contributions and selection/arrangement, licensed under Apache 2.0.

	An English tutor dataset for Nursery, LKG, UKG and Classes 1–5, prepared from original curriculum templates and help-seeking examples. It teaches short grade-conditioned answers, gentle English correction, speaking practice, explained vocabulary and trusted-adult responses.

	It accompanies [TARA English Tutor 3B](https://huggingface.co/sraivante/TARA-English-Tutor-3B), a SmolLM3 QLoRA fine-tune. Every record retains `teacher_reviewed: false`. This experimental release does not claim educator approval, child readiness or CBSE/NCERT certification.

	## Contents

	\| Split \| Records \| Curriculum \| Help-seeking/safety \| Concept groups \|
	\|---\|---:\|---:\|---:\|---:\|
	\| Train \| 1,138 \| 1,010 \| 128 \| 65 \|
	\| Validation \| 126 \| 110 \| 16 \| 7 \|
	\| Total \| 1,264 \| 1,120 \| 144 \| 72 \|

	\| Grade \| Train \| Validation \|
	\|---\|---:\|---:\|
	\| Nursery \| 96 \| 22 \|
	\| LKG \| 156 \| 22 \|
	\| UKG \| 156 \| 12 \|
	\| Class 1 \| 156 \| 22 \|
	\| Class 2 \| 146 \| 22 \|
	\| Class 3 \| 146 \| 12 \|
	\| Class 4 \| 136 \| 12 \|
	\| Class 5 \| 146 \| 2 \|

	Validation is small and uneven across grades, particularly Class 5. Both splits share templates, style and vocabulary policies, so split isolation alone does not establish broad generalization.

	## Loading and schema

	```python
	from datasets import load_dataset

	data = load_dataset("sraivante/TARA-English-Tutor-Instructions")
	print(data["train"].num_rows) # 1138
	print(data["validation"].num_rows) # 126
	messages = data["train"][0]["messages"]
	```

	Each row contains:

	\| Field \| Meaning \|
	\|---\|---\|
	\| `id` / `group_id` \| Stable example identifier and grouping used for split isolation \|
	\| `grade` \| One of the eight level identifiers \|
	\| `topic` / `skill` / `kind` \| Curriculum area, target skill and curriculum or safety record type \|
	\| `cbse_outcomes` \| Project-assigned curriculum labels; not official approval \|
	\| `messages` \| System, user and assistant turns \|
	\| `response` \| Parsed tutor response \|
	\| `quality` \| Original-content declaration, vocabulary warnings and teacher-review status \|

	The assistant response is a JSON string within `messages` and a parsed object in `response`. It has exactly five fields:

	```json
	{
	"answer": "A short direct answer.",
	"practice_question": "One related speaking question?",
	"practice_answer": "A short model answer.",
	"new_words": [{"word": "term", "meaning": "An easy explanation."}],
	"encouragement": "Good thinking!"
	}
	```

	This illustrates the schema; the data contains concrete questions and answers. The grade profiles permit main answers from 18 words in Nursery to 100 words in Class 5, with one to three explained new words depending on grade. See [grade_profiles.json](grade_profiles.json). These are training targets, not guarantees of generated behavior.

	Use the model's chat template. TARA's workflow disables extended thinking, masks system/user tokens and trains on assistant tokens only. Computing loss over every token would differ from that workflow. Reproducibility code is included under [source/ in the model repository](https://huggingface.co/sraivante/TARA-English-Tutor-3B/tree/main/source).

	## Origin and model association

	The source is the publisher's `CUSTOM_LLM4` project. Its 54 original curriculum concepts are expanded through question variants, grade prompts and encouraging responses. Fixed help-seeking examples add privacy, danger and trusted-adult response patterns. The source declares original content, with no copied textbook passages or collected child conversations.

	The deterministic split hashes seed `42` and `group_id`. All variants of a concept stay in one split. The requested validation fraction is 0.1; actual counts result from assigning whole groups.

	Both files reproduce byte for byte from the original `cbse_english_tutor_colab.zip` preparation bundle and the current local generation source. The notebook rebuilds this dataset before training. However, the actual Colab run's dataset hashes and training manifest were unavailable, so exact historical identity with that run cannot independently be proven. [provenance.json](provenance.json) records bundle, source-file and data hashes.

	The separate TARA 300M scratch model's pretraining corpora are outside this 3B fine-tune release.

	## Validation and limitations

	The release audit found zero schema errors, vocabulary-warning rows, duplicate message records and duplicate IDs. Between splits, it found zero shared concept groups, user prompts or identical messages. Selected email, credential-like-string and phone-number patterns had zero matches; this was not a comprehensive personal-data audit.

	All 1,264 records retain `teacher_reviewed: false`. Automated checks do not establish factual correctness, age appropriateness, diversity or real-world safety. Some synthetic help-seeking prompts concern danger, self-harm or abuse; they teach trusted-adult response patterns.

	This small template dataset is for instruction tuning an English-capable model, not language-model pretraining from scratch. It is intended for supervised educational experiments, not ranking children, diagnosing conditions or replacing teachers and responsible adults.

	Run `python validate_dataset.py` after downloading to verify hashes, counts, chat structure and group isolation. [dataset_audit.json](dataset_audit.json) contains the audit results and scope.

	## Copyright, licensing and citation

	Original dataset contributions and selection/arrangement are Copyright © 2026 sraivante, released under [Apache License 2.0](LICENSE). See [COPYRIGHT.md](COPYRIGHT.md) and [NOTICE](NOTICE).

	The original project's MIT attribution is retained in [SOURCE_LICENSE_MIT](SOURCE_LICENSE_MIT). Attribution does not claim exclusive rights over facts, curriculum ideas or independently owned material. [SHA256SUMS](SHA256SUMS) covers the release files.

	```bibtex
	@dataset{sraivante2026tara,
	author = {sraivante},
	title = {TARA English Tutor Instructions},
	year = {2026},
	url = {https://huggingface.co/datasets/sraivante/TARA-English-Tutor-Instructions}
	}
	```