Instructions to use student-abdullah/Llama3.2_Medicine-Hinglish-Dataset_Fine-Tuned_29-09 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use student-abdullah/Llama3.2_Medicine-Hinglish-Dataset_Fine-Tuned_29-09 with Transformers:

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

pipe = pipeline("text-generation", model="student-abdullah/Llama3.2_Medicine-Hinglish-Dataset_Fine-Tuned_29-09")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("student-abdullah/Llama3.2_Medicine-Hinglish-Dataset_Fine-Tuned_29-09")
model = AutoModelForMultimodalLM.from_pretrained("student-abdullah/Llama3.2_Medicine-Hinglish-Dataset_Fine-Tuned_29-09")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use student-abdullah/Llama3.2_Medicine-Hinglish-Dataset_Fine-Tuned_29-09 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "student-abdullah/Llama3.2_Medicine-Hinglish-Dataset_Fine-Tuned_29-09"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "student-abdullah/Llama3.2_Medicine-Hinglish-Dataset_Fine-Tuned_29-09",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/student-abdullah/Llama3.2_Medicine-Hinglish-Dataset_Fine-Tuned_29-09

SGLang

How to use student-abdullah/Llama3.2_Medicine-Hinglish-Dataset_Fine-Tuned_29-09 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 "student-abdullah/Llama3.2_Medicine-Hinglish-Dataset_Fine-Tuned_29-09" \
    --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": "student-abdullah/Llama3.2_Medicine-Hinglish-Dataset_Fine-Tuned_29-09",
		"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 "student-abdullah/Llama3.2_Medicine-Hinglish-Dataset_Fine-Tuned_29-09" \
        --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": "student-abdullah/Llama3.2_Medicine-Hinglish-Dataset_Fine-Tuned_29-09",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use student-abdullah/Llama3.2_Medicine-Hinglish-Dataset_Fine-Tuned_29-09 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 student-abdullah/Llama3.2_Medicine-Hinglish-Dataset_Fine-Tuned_29-09 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 student-abdullah/Llama3.2_Medicine-Hinglish-Dataset_Fine-Tuned_29-09 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for student-abdullah/Llama3.2_Medicine-Hinglish-Dataset_Fine-Tuned_29-09 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="student-abdullah/Llama3.2_Medicine-Hinglish-Dataset_Fine-Tuned_29-09",
    max_seq_length=2048,
)

Docker Model Runner
How to use student-abdullah/Llama3.2_Medicine-Hinglish-Dataset_Fine-Tuned_29-09 with Docker Model Runner:
```
docker model run hf.co/student-abdullah/Llama3.2_Medicine-Hinglish-Dataset_Fine-Tuned_29-09
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Uploaded model

Developed by: student-abdullah
License: apache-2.0
Finetuned from model: meta-llama/Llama-3.2-1B
Created on: 29th September, 2024

Acknowledgement

Model Description

This model is fine-tuned from the meta-llama/Llama-3.2-1B base model to enhance its capabilities in generating relevant and accurate responses related to generic medications under the PMBJP scheme. The fine-tuning process included the following hyperparameters:

Fine Tuning Template: Llama Q&A
Max Tokens: 512
LoRA Alpha: 32
LoRA Rank (r): 128
Learning rate: 1.5e-4
Gradient Accumulation Steps: 4
Batch Size: 8

Model Quantitative Performace

Training Quantitative Loss: 0.1207 (at final 800th epoch)

Limitations

Token Limitations: With a max token limit of 512, the model might not handle very long queries or contexts effectively.
Training Data Limitations: The model’s performance is contingent on the quality and coverage of the fine-tuning dataset, which may affect its generalizability to different contexts or medications not covered in the dataset.
Potential Biases: As with any model fine-tuned on specific data, there may be biases based on the dataset used for training.

Downloads last month: 19

Safetensors

Model size

1B params

Tensor type

F16

Model tree for student-abdullah/Llama3.2_Medicine-Hinglish-Dataset_Fine-Tuned_29-09

Base model

meta-llama/Llama-3.2-1B

Finetuned

(926)

this model

Quantizations

2 models

student-abdullah
/

Llama3.2_Medicine-Hinglish-Dataset_Fine-Tuned_29-09