Instructions to use Tommi09/MedicalChatBot-7b-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tommi09/MedicalChatBot-7b-test with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Tommi09/MedicalChatBot-7b-test",
	filename="LoRA-Huatuo-7b-GGUF-Q4/merged_model-q4.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Tommi09/MedicalChatBot-7b-test with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Tommi09/MedicalChatBot-7b-test
# Run inference directly in the terminal:
llama cli -hf Tommi09/MedicalChatBot-7b-test

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Tommi09/MedicalChatBot-7b-test
# Run inference directly in the terminal:
llama cli -hf Tommi09/MedicalChatBot-7b-test

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Tommi09/MedicalChatBot-7b-test
# Run inference directly in the terminal:
./llama-cli -hf Tommi09/MedicalChatBot-7b-test

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Tommi09/MedicalChatBot-7b-test
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Tommi09/MedicalChatBot-7b-test

Use Docker

docker model run hf.co/Tommi09/MedicalChatBot-7b-test

LM Studio
Jan
Ollama
How to use Tommi09/MedicalChatBot-7b-test with Ollama:
```
ollama run hf.co/Tommi09/MedicalChatBot-7b-test
```

Unsloth Studio

How to use Tommi09/MedicalChatBot-7b-test 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 Tommi09/MedicalChatBot-7b-test 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 Tommi09/MedicalChatBot-7b-test to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Tommi09/MedicalChatBot-7b-test to start chatting

Atomic Chat new
Docker Model Runner
How to use Tommi09/MedicalChatBot-7b-test with Docker Model Runner:
```
docker model run hf.co/Tommi09/MedicalChatBot-7b-test
```

Lemonade

How to use Tommi09/MedicalChatBot-7b-test with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Tommi09/MedicalChatBot-7b-test

Run and chat with the model

lemonade run user.MedicalChatBot-7b-test-{{QUANT_TAG}}

List all available models

lemonade list

Tommi09 commited on May 4, 2025

Commit

1205801

verified ·

1 Parent(s): 890d1bf

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -91,7 +91,7 @@ We used the Huatuo26M-Lite dataset, which contains 178k pieces of medical questi
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
-base_model_path = "Tommi09/MedicalChatBot-Qwen3-4b"
 tokenizer = AutoTokenizer.from_pretrained(base_model_path, trust_remote_code=True)

 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
+base_model_path = "Tommi09/MedicalChatBot-7b-test"
 tokenizer = AutoTokenizer.from_pretrained(base_model_path, trust_remote_code=True)