Instructions to use nateshmbhat/model-isha-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nateshmbhat/model-isha-qa with Transformers:

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

pipe = pipeline("text-generation", model="nateshmbhat/model-isha-qa")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nateshmbhat/model-isha-qa")
model = AutoModelForCausalLM.from_pretrained("nateshmbhat/model-isha-qa")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use nateshmbhat/model-isha-qa with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/nateshmbhat/model-isha-qa

SGLang

How to use nateshmbhat/model-isha-qa 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 "nateshmbhat/model-isha-qa" \
    --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": "nateshmbhat/model-isha-qa",
		"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 "nateshmbhat/model-isha-qa" \
        --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": "nateshmbhat/model-isha-qa",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nateshmbhat/model-isha-qa with Docker Model Runner:
```
docker model run hf.co/nateshmbhat/model-isha-qa
```

ISHA Call Center QA Model

This model was trained on a finetuned version(StableBeluga2) of Llama2-13B from stabilityai : (StableBeluga2 tops the LLM leaderboard currently)

Dataset Used : https://huggingface.co/datasets/nateshmbhat/isha-qa-text

Train Params used :

Base model : stabilityai/StableBeluga-13B
Quantization Used : 4 bit
Learning rate : 2e-4
Batch Size : 2
Epochs : 3
Trainer : sft
Max token length : 2048 (capable of higher token length)

!autotrain llm --train --project_name project-isha-qa --model stabilityai/StableBeluga-13B --data_path nateshmbhat/isha-qa-text --use_peft --use_int4 --learning_rate 2e-4 --train_batch_size 2 --num_train_epochs 3 --trainer sft --model_max_length 2048 --push_to_hub --repo_id nateshmbhat/model-isha-qa

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nateshmbhat
/

model-isha-qa

ISHA Call Center QA Model

This model was trained on a finetuned version(StableBeluga2) of Llama2-13B from stabilityai : (StableBeluga2 tops the LLM leaderboard currently)

Dataset Used : https://huggingface.co/datasets/nateshmbhat/isha-qa-text

Train Params used :

Dataset used to train nateshmbhat/model-isha-qa