Instructions to use RichWoollcott/studytutor-gcse-26b-moe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichWoollcott/studytutor-gcse-26b-moe with Transformers:

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

pipe = pipeline("text-generation", model="RichWoollcott/studytutor-gcse-26b-moe")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("RichWoollcott/studytutor-gcse-26b-moe")
model = AutoModelForMultimodalLM.from_pretrained("RichWoollcott/studytutor-gcse-26b-moe")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RichWoollcott/studytutor-gcse-26b-moe with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RichWoollcott/studytutor-gcse-26b-moe"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RichWoollcott/studytutor-gcse-26b-moe",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RichWoollcott/studytutor-gcse-26b-moe

SGLang

How to use RichWoollcott/studytutor-gcse-26b-moe 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 "RichWoollcott/studytutor-gcse-26b-moe" \
    --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": "RichWoollcott/studytutor-gcse-26b-moe",
		"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 "RichWoollcott/studytutor-gcse-26b-moe" \
        --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": "RichWoollcott/studytutor-gcse-26b-moe",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use RichWoollcott/studytutor-gcse-26b-moe with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for RichWoollcott/studytutor-gcse-26b-moe to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for RichWoollcott/studytutor-gcse-26b-moe to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RichWoollcott/studytutor-gcse-26b-moe to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="RichWoollcott/studytutor-gcse-26b-moe",
    max_seq_length=2048,
)

Docker Model Runner
How to use RichWoollcott/studytutor-gcse-26b-moe with Docker Model Runner:
```
docker model run hf.co/RichWoollcott/studytutor-gcse-26b-moe
```

GCSE English Tutor (Gemma-4-26B-A4B MoE)

A fine-tune of unsloth/gemma-4-26b-a4b-it based on Gemma 4 26B A4B that emulates a UK GCSE English Literature & Language tutor: reasons explicitly before answering, models AQA/Edexcel marking conventions, and gives constructive feedback on student essays.

Fine-tuned from unsloth/gemma-4-26b-a4b-it using Unsloth + TRL on a single NVIDIA DGX Spark GB10 (121 GB unified memory).

Intended use

A study-companion for GCSE English students: explaining literary techniques, walking through poem/extract analysis, modelling PEEL/PETAL paragraphs, and giving formative feedback on student writing. Designed to be served on-device or via llama-swap.

Sample prompt

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

repo = "RichWoollcott/gcse-tutor-gemma4-26b-moe"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype=torch.bfloat16, device_map="cuda"
)

messages = [{"role": "user", "content": 'Can you help me write a PEEL paragraph analysing how Stevenson uses setting to convey duality in Dr Jekyll and Mr Hyde?'}]
inputs = tok.apply_chat_template(
    messages, return_tensors="pt", add_generation_prompt=True
).to("cuda")
out = model.generate(inputs, max_new_tokens=600, do_sample=False)
print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=False))

The model is trained to open every response with a <think>...</think> reasoning block followed by the visible answer. If you do not see the tags, add Begin every response with <think>...</think> to the system prompt.

Limitations

Trained on synthetic dialogues; the tutor persona is consistent but does not have access to specific exam-board mark schemes at inference time — pair with a RAG layer over current AQA / Edexcel specifications if used in a high-stakes setting. The model can hallucinate quotes from set texts; verify any direct quotation.

Training data

Multi-turn tutoring dialogues generated by the agentic-dataset-factory Player-Coach pipeline, covering set-text analysis, language techniques, and student-essay feedback for the UK GCSE English curriculum.

The data is not released alongside the weights pending a license review of source material referenced during generation. The generator (agentic-dataset-factory, Player-Coach adversarial pipeline) is open-source and reproducible.

Training procedure

Framework: Unsloth + TRL (SFT)
Precision: bfloat16
PEFT: LoRA, rank 16
Optimiser: AdamW (Unsloth defaults)
Epochs: 1, effective batch size 4, max-seq-length 2048
Hardware: NVIDIA DGX Spark GB10 (single device, unified memory)
Container: nvcr.io/nvidia/pytorch:25.11-py3
Verified-compatible pins: transformers==5.5.4, accelerate==1.10.0, trl==0.26.1, datasets==4.3.0, latest unsloth/unsloth_zoo/bitsandbytes.

Loss trajectory and reproduction recipe: see the source runbook in appmilla/agentic-dataset-factory — domains/gcse-tutor/RUNBOOK-gcse-fine-tune.md.

Artefacts in this repo

/ (root) — merged-16bit weights (full standalone model, ~49 GB). Load with AutoModelForCausalLM.from_pretrained(repo_id).
lora-adapter/ — the LoRA adapter (~1.9 GB). Apply on top of the base model with PeftModel.from_pretrained(base, repo_id, subfolder="lora-adapter").

GGUF quantisations (q4_k_m, BF16) live in a separate repo: RichWoollcott/gcse-tutor-gemma4-26b-moe-GGUF.

License

Released under the Gemma Terms of Use, as required by the base model.

Citation

If you use this model, please cite the base model, TRL, and Unsloth:

@misc{vonwerra2022trl,
  title  = {TRL: Transformer Reinforcement Learning},
  author = {Leandro von Werra and others},
  year   = 2020,
  url    = {https://github.com/huggingface/trl}
}

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Safetensors

Model size

27B params

Tensor type

BF16

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