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 / package_validation.json

sraivante

Publish TARA English Tutor 3B model with training data

24e1610 verified 16 days ago

Raw History Blame Contribute Delete

1.9 kB

	{
	"datasets_json_loader": {
	"train": 1138,
	"validation": 126,
	"features": "{'id': Value('string'), 'group_id': Value('string'), 'grade': Value('string'), 'topic': Value('string'), 'skill': Value('string'), 'kind': Value('string'), 'cbse_outcomes': List(Value('string')), 'messages': List({'role': Value('string'), 'content': Value('string')}), 'response': {'answer': Value('string'), 'practice_question': Value('string'), 'practice_answer': Value('string'), 'new_words': List({'word': Value('string'), 'meaning': Value('string')}), 'encouragement': Value('string')}, 'quality': {'original_content': Value('bool'), 'vocabulary_warnings': List(Value('null')), 'teacher_reviewed': Value('bool')}}"
	},
	"original_colab_bundle_data_reproduction": {
	"train": {
	"byte_identical": true
	},
	"validation": {
	"byte_identical": true
	}
	},
	"tokenizer": {
	"loaded": true,
	"tokenizer_class": "PreTrainedTokenizerFast",
	"vocab_size": 128256,
	"chat_template_grades": {
	"nursery": {
	"tokens": 344,
	"non_thinking_marker_present": true
	},
	"lkg": {
	"tokens": 344,
	"non_thinking_marker_present": true
	},
	"ukg": {
	"tokens": 340,
	"non_thinking_marker_present": true
	},
	"class_1": {
	"tokens": 343,
	"non_thinking_marker_present": true
	},
	"class_2": {
	"tokens": 343,
	"non_thinking_marker_present": true
	},
	"class_3": {
	"tokens": 345,
	"non_thinking_marker_present": true
	},
	"class_4": {
	"tokens": 343,
	"non_thinking_marker_present": true
	},
	"class_5": {
	"tokens": 345,
	"non_thinking_marker_present": true
	}
	},
	"fix_mistral_regex": true
	},
	"architecture": {
	"loaded_config": "SmolLM3Config",
	"model_type": "smollm3"
	}
	}