Instructions to use huzaifa525/Doctoraifinetune-3.1-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use huzaifa525/Doctoraifinetune-3.1-8B with Transformers:

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

pipe = pipeline("text-generation", model="huzaifa525/Doctoraifinetune-3.1-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("huzaifa525/Doctoraifinetune-3.1-8B")
model = AutoModelForCausalLM.from_pretrained("huzaifa525/Doctoraifinetune-3.1-8B")
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 huzaifa525/Doctoraifinetune-3.1-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "huzaifa525/Doctoraifinetune-3.1-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "huzaifa525/Doctoraifinetune-3.1-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/huzaifa525/Doctoraifinetune-3.1-8B

SGLang

How to use huzaifa525/Doctoraifinetune-3.1-8B 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 "huzaifa525/Doctoraifinetune-3.1-8B" \
    --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": "huzaifa525/Doctoraifinetune-3.1-8B",
		"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 "huzaifa525/Doctoraifinetune-3.1-8B" \
        --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": "huzaifa525/Doctoraifinetune-3.1-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use huzaifa525/Doctoraifinetune-3.1-8B with Docker Model Runner:
```
docker model run hf.co/huzaifa525/Doctoraifinetune-3.1-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Uploaded model

Developed by: huzaifa525
License: apache-2.0
Finetuned from model : Meta-Llama-3.1-8B-bnb-4bit

LLaMA 3.1B Fine-tuned on Medical Dataset

Model Overview

This is a fine-tuned version of the Meta-Llama-3.1-8B-bnb-4bit model, specifically adapted for the medical field. It has been trained using a dataset that provides extensive information on diseases, symptoms, and treatments, making it ideal for AI-powered healthcare tools such as medical chatbots, virtual assistants, and diagnostic support systems.

Key Features

Disease Diagnosis: Accurately identifies diseases based on symptoms provided by the user.
Symptom Analysis: Breaks down and interprets symptoms to provide a comprehensive medical overview.
Treatment Recommendations: Suggests treatments and remedies according to medical conditions.

Dataset

The model is fine-tuned on 2000 rows from a dataset consisting of 272k rows. This dataset includes rich information about diseases, symptoms, and their corresponding treatments. The model is continuously being updated and will be further trained on the remaining data in future releases to improve accuracy and capabilities.

Model Applications

Medical Chatbots: Use for real-time interaction between patients and virtual medical agents.
Healthcare Virtual Assistants: For symptom checking, health guidance, and first-level triage.
Diagnostic Tools: To assist healthcare professionals in diagnosing conditions based on symptoms.
Patient Self-Assessment: Symptom checkers to empower patients in their health journey.

How to Use

Use a pipeline as a high-level helper

from transformers import pipeline
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="huzaifa525/Doctoraifinetune-3.1-8B")
pipe(messages)

Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("huzaifa525/Doctoraifinetune-3.1-8B")
model = AutoModelForCausalLM.from_pretrained("huzaifa525/Doctoraifinetune-3.1-8B")

Planned Updates

Full Dataset Training: The model will be updated with the full 272k rows of data, which will improve its disease identification, symptom analysis, and treatment recommendations.
Enhanced Accuracy: Ongoing improvements based on feedback and further training will continue to refine the model’s performance.

Downloads last month: 2

Model tree for huzaifa525/Doctoraifinetune-3.1-8B

Quantizations

2 models