Instructions to use AnirbanSaha/gemma2-2b-tlink with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AnirbanSaha/gemma2-2b-tlink with Transformers:

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

pipe = pipeline("text-generation", model="AnirbanSaha/gemma2-2b-tlink")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AnirbanSaha/gemma2-2b-tlink")
model = AutoModelForCausalLM.from_pretrained("AnirbanSaha/gemma2-2b-tlink", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AnirbanSaha/gemma2-2b-tlink with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AnirbanSaha/gemma2-2b-tlink"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AnirbanSaha/gemma2-2b-tlink",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AnirbanSaha/gemma2-2b-tlink

SGLang

How to use AnirbanSaha/gemma2-2b-tlink 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 "AnirbanSaha/gemma2-2b-tlink" \
    --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": "AnirbanSaha/gemma2-2b-tlink",
		"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 "AnirbanSaha/gemma2-2b-tlink" \
        --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": "AnirbanSaha/gemma2-2b-tlink",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AnirbanSaha/gemma2-2b-tlink with Docker Model Runner:
```
docker model run hf.co/AnirbanSaha/gemma2-2b-tlink
```

Gemma-2-2B Temporal Link Classification

Full fine-tuned version of google/gemma-2-2b-it on the fahmidiqbal/tlink-classification dataset for temporal relation classification.

Task

Given an already-formatted task prompt containing two marked spans (such as <e1>, <e2>, <t1>, or <t2>), the model generates one temporal-relation label:

BEFORE — first span occurs earlier than the second
AFTER — first span occurs later than the second
OTHER — overlap or another non-ordering relation
NONE — no clear temporal relation

Training Details

Parameter	Value
Base model	google/gemma-2-2b-it
Fine-tuning	Full (all parameters)
Epochs	3
Batch size	8
Learning rate	2e-5
Weight decay	0.01
Max length	2048
Precision	bfloat16
Prompt format	Gemma chat template
Dataset	fahmidiqbal/tlink-classification

Results

Test metrics have not been added yet. Run the evaluation script first to generate test_metrics.json, then run this upload script again.

Usage

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

MODEL_ID = "AnirbanSaha/gemma2-2b-tlink"

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    dtype=torch.bfloat16,
    device_map="auto",
)
model.eval()

prompt = "Your already-formatted temporal relation prompt here..."

messages = [
    {"role": "user", "content": prompt}
]

formatted_prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)

enc = tokenizer(
    formatted_prompt,
    add_special_tokens=False,
    return_tensors="pt",
).to(model.device)

with torch.inference_mode():
    out = model.generate(
        **enc,
        max_new_tokens=8,
        do_sample=False,
        pad_token_id=tokenizer.pad_token_id,
        eos_token_id=tokenizer.eos_token_id,
    )

new_tokens = out[0][enc["input_ids"].shape[1]:]
prediction = tokenizer.decode(
    new_tokens,
    skip_special_tokens=True,
).strip()

print(prediction)

Base Model License

The base checkpoint uses the Gemma license. Users of this fine-tuned model should review and comply with the applicable Gemma terms.

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Safetensors

Model size

3B params

Tensor type

BF16

Model tree for AnirbanSaha/gemma2-2b-tlink

Base model

google/gemma-2-2b

Finetuned

google/gemma-2-2b-it

Finetuned

(1032)

this model

AnirbanSaha
/

gemma2-2b-tlink