Instructions to use LordY54/recophi3_f16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LordY54/recophi3_f16 with Transformers:

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

pipe = pipeline("text-generation", model="LordY54/recophi3_f16")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LordY54/recophi3_f16")
model = AutoModelForCausalLM.from_pretrained("LordY54/recophi3_f16")
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 LordY54/recophi3_f16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LordY54/recophi3_f16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LordY54/recophi3_f16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LordY54/recophi3_f16

SGLang

How to use LordY54/recophi3_f16 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 "LordY54/recophi3_f16" \
    --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": "LordY54/recophi3_f16",
		"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 "LordY54/recophi3_f16" \
        --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": "LordY54/recophi3_f16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use LordY54/recophi3_f16 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 LordY54/recophi3_f16 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 LordY54/recophi3_f16 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for LordY54/recophi3_f16 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="LordY54/recophi3_f16",
    max_seq_length=2048,
)

Docker Model Runner
How to use LordY54/recophi3_f16 with Docker Model Runner:
```
docker model run hf.co/LordY54/recophi3_f16
```

widget:

text: "Instruction:\nYou are a helpful, smart, kind, and efficient AI assistant. You always fulfill the user's requests to the best of your ability.. Remember that you are a friendly virtual nutritionist, you're talking to patient directly so let the patient know everything he need in your recomendation (extend it as much as you need). you're not going to be asked questions back\n\n### Input:\nCase: 2, Nombre: Ana, Edad: 42, Estilo de vida: Moderado, trabajo: Docente universitaria. Información antropometrica antropometría [altura : 1.68, peso: 75, IMC:25.8, circunferencia_cintura: 100]. Indicadores bioquimicos: [glucosa: normal, colesterol: medio], Información dieta (ingesta): [preferencias: [comida rápida]], comidas comunes: [donas, hamburguesa con papas fritas], frutas y verduras: bajo, fibra: bajo, grasas_saturadas: alto, azucares: alto]. Indicadores sociales: [estado_civil: Casada, ingreso: Bajo, accesoa a alimentos saludables: True]. Información sobre actividad física: [actividad_fisica: Media, actividades_diarias:[Ejercicio en casa, pasear con el perro]]." example_title: "Sentiment analysis"

Uploaded model

Developed by: LordY54
License: apache-2.0
Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit

This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 2

Model tree for LordY54/recophi3_f16

Base model

unsloth/Phi-3-mini-4k-instruct-bnb-4bit

Finetuned

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