Instructions to use Yuvrajxms09/MediMistral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Yuvrajxms09/MediMistral with Transformers:

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

pipe = pipeline("text-generation", model="Yuvrajxms09/MediMistral")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Yuvrajxms09/MediMistral")
model = AutoModelForCausalLM.from_pretrained("Yuvrajxms09/MediMistral", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Yuvrajxms09/MediMistral with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Yuvrajxms09/MediMistral"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Yuvrajxms09/MediMistral",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Yuvrajxms09/MediMistral

SGLang

How to use Yuvrajxms09/MediMistral 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 "Yuvrajxms09/MediMistral" \
    --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": "Yuvrajxms09/MediMistral",
		"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 "Yuvrajxms09/MediMistral" \
        --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": "Yuvrajxms09/MediMistral",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Yuvrajxms09/MediMistral with Docker Model Runner:
```
docker model run hf.co/Yuvrajxms09/MediMistral
```

Yuvraj Singh commited on Mar 12, 2025

Commit

363e03e

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1 Parent(s): 349fe02

Update README.md

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@@ -1,4 +1,3 @@
 ---
 license: apache-2.0
 datasets:
@@ -7,6 +6,7 @@ language:
 - en
 base_model:
 - mistralai/Mistral-7B-v0.1
 ---
 # Fine-Tuning Mistral-7B on Clinical Notes with QLoRA
@@ -92,6 +92,4 @@ On Kaggle's **T4x2 GPU setup**, training time per epoch is approximately:
 ---
----

 ---
 license: apache-2.0
 datasets:
 - en
 base_model:
 - mistralai/Mistral-7B-v0.1
+library_name: transformers
 ---
 # Fine-Tuning Mistral-7B on Clinical Notes with QLoRA
 ---
+---