Instructions to use KrishnaHuYaar/Fitness-Mistral-7B-Instruct-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use KrishnaHuYaar/Fitness-Mistral-7B-Instruct-v0.1 with PEFT:
```
Task type is invalid.
```

How to use KrishnaHuYaar/Fitness-Mistral-7B-Instruct-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="KrishnaHuYaar/Fitness-Mistral-7B-Instruct-v0.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KrishnaHuYaar/Fitness-Mistral-7B-Instruct-v0.1")
model = AutoModelForCausalLM.from_pretrained("KrishnaHuYaar/Fitness-Mistral-7B-Instruct-v0.1")
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

vLLM

How to use KrishnaHuYaar/Fitness-Mistral-7B-Instruct-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KrishnaHuYaar/Fitness-Mistral-7B-Instruct-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KrishnaHuYaar/Fitness-Mistral-7B-Instruct-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KrishnaHuYaar/Fitness-Mistral-7B-Instruct-v0.1

SGLang

How to use KrishnaHuYaar/Fitness-Mistral-7B-Instruct-v0.1 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 "KrishnaHuYaar/Fitness-Mistral-7B-Instruct-v0.1" \
    --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": "KrishnaHuYaar/Fitness-Mistral-7B-Instruct-v0.1",
		"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 "KrishnaHuYaar/Fitness-Mistral-7B-Instruct-v0.1" \
        --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": "KrishnaHuYaar/Fitness-Mistral-7B-Instruct-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use KrishnaHuYaar/Fitness-Mistral-7B-Instruct-v0.1 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 KrishnaHuYaar/Fitness-Mistral-7B-Instruct-v0.1 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 KrishnaHuYaar/Fitness-Mistral-7B-Instruct-v0.1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for KrishnaHuYaar/Fitness-Mistral-7B-Instruct-v0.1 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="KrishnaHuYaar/Fitness-Mistral-7B-Instruct-v0.1",
    max_seq_length=2048,
)

Docker Model Runner
How to use KrishnaHuYaar/Fitness-Mistral-7B-Instruct-v0.1 with Docker Model Runner:
```
docker model run hf.co/KrishnaHuYaar/Fitness-Mistral-7B-Instruct-v0.1
```

Fitness-Mistral-7B-Instruct-v0.1 is a specialized fine-tune of the Mistral 7B model, designed to act as a personalized fitness and nutrition assistant. It was trained 2x faster using Unsloth and Hugging Face's TRL library. This model takes user biometrics (Age, Gender, Height, Weight) and goals to generate structured workout routines, diet plans, and equipment recommendations.

💻 Model Details

Developer: KrishnaHuYaar
Base Model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit
Task: Personalized Fitness & Diet Planning
Dataset: KrishnaHuYaar/fitness-recommendation-dataset
License: Apache-2.0

📝 Intended Use & Prompt Format

For the best results, you must use the specific prompt format below. The model expects the User Details block to generate a coherent response.

Template:

[INST] You are a fitness assistant.

User Details:
Sex: {Sex}
Age: {Age}
Height: {Height} cm
Weight: {Weight} kg
Fitness Goal: {Goal}

Generate a personalized fitness plan with clear headings:
- Equipment Required
- Exercise Plan
- Diet Plan
- Additional Recommendations [/INST]

🚀 How to Use

Option 1: Hugging Face Inference API

You can also use the model via the Inference API without downloading it.

import requests

API_URL = "[https://api-inference.huggingface.co/models/KrishnaHuYaar/Fitness-Mistral-7B-Instruct-v0.1](https://api-inference.huggingface.co/models/KrishnaHuYaar/Fitness-Mistral-7B-Instruct-v0.1)"
headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}

payload = {
    "inputs": """[INST] You are a fitness assistant.
User Details:
Sex: Female
Age: 30
Height: 165 cm
Weight: 60 kg
Fitness Goal: Weight Loss

Generate a personalized fitness plan with clear headings:
- Equipment Required
- Exercise Plan
- Diet Plan
- Additional Recommendations [/INST]"""
}

response = requests.post(API_URL, headers=headers, json=payload)
print(response.json())

Option 2: Run Locally (Python)

You can run this model locally using the transformers and peft libraries.

First install this library

pip install torch transformers accelerate

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model
model_name = "KrishnaHuYaar/Fitness-Mistral-7B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Define the generation function
def generate_plan(sex, age, height, weight, goal):
    prompt = f"""[INST] You are a fitness assistant.

User Details:
Sex: {sex}
Age: {age}
Height: {height} cm
Weight: {weight} kg
Fitness Goal: {goal}

Generate a personalized fitness plan with clear headings:
- Equipment Required
- Exercise Plan
- Diet Plan
- Additional Recommendations [/INST]"""

    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=500,
            do_sample=True,
            temperature=0.7
        )
    
    return tokenizer.decode(outputs[0], skip_special_tokens=True).split("[/INST]")[-1]

# Example Usage
print(generate_plan("Male", 25, 175, 70, "Muscle Gain"))

Downloads last month: -

Safetensors

Model size

7B params

Tensor type

F32

F16

Model tree for KrishnaHuYaar/Fitness-Mistral-7B-Instruct-v0.1

Base model

unsloth/mistral-7b-instruct-v0.2-bnb-4bit

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Fitness-Mistral-7B-Instruct-v0.1