Instructions to use harishnair04/Gemma-medtr-2b-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use harishnair04/Gemma-medtr-2b-sft with Transformers:

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

pipe = pipeline("text-generation", model="harishnair04/Gemma-medtr-2b-sft")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("harishnair04/Gemma-medtr-2b-sft")
model = AutoModelForMultimodalLM.from_pretrained("harishnair04/Gemma-medtr-2b-sft")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use harishnair04/Gemma-medtr-2b-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "harishnair04/Gemma-medtr-2b-sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "harishnair04/Gemma-medtr-2b-sft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/harishnair04/Gemma-medtr-2b-sft

SGLang

How to use harishnair04/Gemma-medtr-2b-sft 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 "harishnair04/Gemma-medtr-2b-sft" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "harishnair04/Gemma-medtr-2b-sft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "harishnair04/Gemma-medtr-2b-sft" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "harishnair04/Gemma-medtr-2b-sft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use harishnair04/Gemma-medtr-2b-sft with Docker Model Runner:
```
docker model run hf.co/harishnair04/Gemma-medtr-2b-sft
```

Model Card for Medical Transcription Model (Gemma-MedTr)

This model is a fine-tuned variant of Gemma-2-2b, optimized for medical transcription tasks with efficient 4-bit quantization and Low-Rank Adaptation (LoRA). It handles transcription processing, keyword extraction, and medical specialty classification.

Model Details

Developed by: Harish Nair
Organization: University of Ottawa
License: Apache 2.0
Fine-tuned from: Gemma-2-2b
Model type: Transformer-based language model for medical transcription processing
Language(s): English

Training Details

Training Loss: Final training loss at step 10: 1.4791
Training Configuration:
- LoRA with r=8, targeting specific transformer modules for adaptation.
- 4-bit quantization using nf4 quantization type and bfloat16 compute precision.
Training Runtime: 20.85 seconds, with approximately 1.92 samples processed per second.

How to Use

To load and use this model, initialize it with the following configuration:

import pandas as pd
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from peft import LoraConfig, PeftModel

model_id = "harishnair04/Gemma-medtr-2b-sft"
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

tokenizer = AutoTokenizer.from_pretrained(model_id, token=access_token_read)
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map='auto', token=access_token_read)

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Safetensors

Model size

3B params

Tensor type

F32

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google/gemma-2-2b

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Gemma-medtr-2b-sft

Model Card for Medical Transcription Model (Gemma-MedTr)

Model Details

Training Details

How to Use

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Dataset used to train harishnair04/Gemma-medtr-2b-sft