Text Generation
Transformers
Safetensors
English
smollm3
education
english-tutor
structured-output
experimental
merged
conversational
Instructions to use sraivante/TARA-English-Tutor-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Download training_data/validate_dataset.py from sraivante/TARA-English-Tutor-3B: direct link, hf CLI and curl.
- Browser
- Download file 1.11 kB
-
https://huggingface.co/sraivante/TARA-English-Tutor-3B/resolve/main/training_data/validate_dataset.py
- Command line
-
hf download hf://sraivante/TARA-English-Tutor-3B/training_data/validate_dataset.py
-
curl -L -o validate_dataset.py https://huggingface.co/sraivante/TARA-English-Tutor-3B/resolve/main/training_data/validate_dataset.py
1.11 kB
| """Validate the published dataset hashes, counts and response structure.""" | |
| import hashlib,json | |
| from pathlib import Path | |
| root=Path(__file__).resolve().parent | |
| spec={ | |
| "train":(1138,"33a9949ef2e43f97581ba6e371007298027c35c4266924c40a5dc8c3dc25ada0"), | |
| "validation":(126,"dc1d73be7d960fa952303951aac2ed45b1068e3aeda17c2a28b8a0407fd6a47f"), | |
| } | |
| groups={} | |
| for split,(count,digest) in spec.items(): | |
| blob=(root/"data"/(split+".jsonl")).read_bytes() | |
| assert hashlib.sha256(blob).hexdigest()==digest,split | |
| rows=[json.loads(x) for x in blob.decode("utf-8").splitlines() if x.strip()] | |
| assert len(rows)==count | |
| groups[split]={r["group_id"] for r in rows} | |
| for r in rows: | |
| assert [m["role"] for m in r["messages"]]==["system","user","assistant"] | |
| assert json.loads(r["messages"][-1]["content"])==r["response"] | |
| assert set(r["response"])=={"answer","practice_question","practice_answer","new_words","encouragement"} | |
| print(split,count,digest) | |
| assert not groups["train"] & groups["validation"],"Concept-group leakage" | |
| print("Hashes, counts, messages and concept separation verified.") | |