Instructions to use D0zy/essay-model-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use D0zy/essay-model-merged with Transformers:

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

pipe = pipeline("text-generation", model="D0zy/essay-model-merged", trust_remote_code=True)
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("D0zy/essay-model-merged", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("D0zy/essay-model-merged", trust_remote_code=True, 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]:]))

PEFT
How to use D0zy/essay-model-merged with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use D0zy/essay-model-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "D0zy/essay-model-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "D0zy/essay-model-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/D0zy/essay-model-merged

SGLang

How to use D0zy/essay-model-merged 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 "D0zy/essay-model-merged" \
    --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": "D0zy/essay-model-merged",
		"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 "D0zy/essay-model-merged" \
        --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": "D0zy/essay-model-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use D0zy/essay-model-merged with Docker Model Runner:
```
docker model run hf.co/D0zy/essay-model-merged
```

essay-model

This model is a modified (fine-tuned) version of [LiquidAI/LFM3-1.7B] by Liquid AI. It is a LoRA fine-tune of LiquidAI/LFM3-1.7B that acts as an IELTS Writing Task 2 examiner: given the task question and an essay, it outputs an overall band score followed by feedback on Task Response, Coherence & Cohesion, Lexical Resource and Grammatical Range & Accuracy.

Training

Data: chillies/IELTS-writing-task-2-evaluation (~7,000 train examples, max 2048 tokens; loss only on the evaluation part)
Method: LoRA (r=16, alpha=32, dropout=0.05, all linear layers), 1 epoch, lr 2e-4, cosine schedule, effective batch size 8
Final validation loss: ~0.418

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

tok  = AutoTokenizer.from_pretrained("D0zy/essay-model-merged", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("D0zy/essay-model-merged", trust_remote_code=True, device_map="auto", dtype=torch.bfloat16)


SYSTEM = ("You are a certified IELTS examiner. Evaluate the IELTS Writing Task 2 essay. "
          "First give the overall band score, then detailed feedback on Task Response, "
          "Coherence and Cohesion, Lexical Resource and Grammatical Range and Accuracy.")
msgs = [{"role": "system", "content": SYSTEM},
        {"role": "user", "content": "Question:\n<task question>\n\nEssay:\n<essay>"}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(ids, max_new_tokens=700, do_sample=False, repetition_penalty=1.05)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))

License

This model is a Derivative Work of LiquidAI/LFM3-1.7B and is distributed under the LFM Open License v1.0 (see the LICENSE file in this repository; original: https://huggingface.co/LiquidAI/LFM3-1.7B).

Modifications: LoRA fine-tuning on IELTS essay evaluation data (by D0zy). The original model weights were not otherwise changed.
All copyright and attribution notices of the original work are retained.
Commercial use is only permitted for entities below the revenue threshold defined in the license (USD 10M annual revenue); see the license for details.
This project is not affiliated with or endorsed by Liquid AI. "LFM" and "Liquid AI" are trademarks of Liquid AI.

Limitations

Scores are model estimates learned from a community dataset, not official IELTS results. Use for practice and feedback only. License terms of the base model apply.

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Safetensors

Model size

2B params

Tensor type

BF16

Model tree for D0zy/essay-model-merged

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