Instructions to use Chakster/Tinyllama_HeartFailure with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Chakster/Tinyllama_HeartFailure with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
model = PeftModel.from_pretrained(base_model, "Chakster/Tinyllama_HeartFailure")

Transformers

How to use Chakster/Tinyllama_HeartFailure with Transformers:

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

pipe = pipeline("text-generation", model="Chakster/Tinyllama_HeartFailure")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Chakster/Tinyllama_HeartFailure", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Chakster/Tinyllama_HeartFailure with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Chakster/Tinyllama_HeartFailure

SGLang

How to use Chakster/Tinyllama_HeartFailure 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 "Chakster/Tinyllama_HeartFailure" \
    --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": "Chakster/Tinyllama_HeartFailure",
		"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 "Chakster/Tinyllama_HeartFailure" \
        --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": "Chakster/Tinyllama_HeartFailure",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Chakster/Tinyllama_HeartFailure with Docker Model Runner:
```
docker model run hf.co/Chakster/Tinyllama_HeartFailure
```

Model Card: TinyLlama Heart Failure Optimized

Model Details

Model Name: TinyLlama Heart Failure Model Type: Fine-tuned Language Model
Base Model: TinyLlama
Domain: Heart Failure Q&A
Version: 1.0
Date: July 13, 2025

Model Description

This model is a domain-specific fine-tuned version of TinyLlama, optimized for answering questions about heart failure. It has been trained to provide medically relevant information about heart failure symptoms, causes, treatments, and related cardiovascular concepts.

Intended Use

Primary Use Cases

Educational Q&A about heart failure
Medical information assistance for healthcare professionals
Patient education support (under professional supervision)

Out-of-Scope Use Cases

Clinical diagnosis or treatment decisions
Emergency medical situations
Replacement for professional medical advice

Performance Metrics

Core Language Metrics

ROUGE-1: 0.3003
ROUGE-2: 0.1253
ROUGE-L: 0.2335
BLEU: 0.0681
Semantic Similarity: 0.4325
Perplexity: 6.49

Domain-Specific Metrics

Medical Term Usage: 100.0% (cardiac domain relevance)
Response Completeness: 100.0% (proper sentence endings)
Average Response Length: 45.1 words
Repetition Score: 1.14 (lower is better)

Overall Performance Score: 0.182

Training Data

Evaluation Samples: 20 heart failure Q&A pairs
Domain Focus: Cardiovascular health, specifically heart failure
Data Quality: Medically accurate reference answers

Model Architecture

Tokenizer Vocab Size: 32,001
Custom Tokens: <|end|> token for response termination
Device: CPU optimized
Generation Parameters:
- Temperature: 0.6
- Top-p: 0.9
- Max tokens: 80

Downloads last month: 3

Model tree for Chakster/Tinyllama_HeartFailure

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TinyLlama/TinyLlama-1.1B-Chat-v1.0

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