Instructions to use decruz07/llama-2-7b-miniguanaco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use decruz07/llama-2-7b-miniguanaco with Transformers:

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

pipe = pipeline("text-generation", model="decruz07/llama-2-7b-miniguanaco")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("decruz07/llama-2-7b-miniguanaco")
model = AutoModelForCausalLM.from_pretrained("decruz07/llama-2-7b-miniguanaco")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use decruz07/llama-2-7b-miniguanaco with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "decruz07/llama-2-7b-miniguanaco"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "decruz07/llama-2-7b-miniguanaco",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/decruz07/llama-2-7b-miniguanaco

SGLang

How to use decruz07/llama-2-7b-miniguanaco 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 "decruz07/llama-2-7b-miniguanaco" \
    --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": "decruz07/llama-2-7b-miniguanaco",
		"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 "decruz07/llama-2-7b-miniguanaco" \
        --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": "decruz07/llama-2-7b-miniguanaco",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use decruz07/llama-2-7b-miniguanaco with Docker Model Runner:
```
docker model run hf.co/decruz07/llama-2-7b-miniguanaco
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

llama-2-7b-miniguanaco

This is my first model, with LLama-2-7b model finetuned with miniguanaco datasets.

This is a simple finetune based off a Google Colab notebook. Finetune instructions were from Labonne's first tutorial.

To run it: import torch from transformers import AutoTokenizer, AutoModelForCausalLM import math

model_path = "decruz07/llama-2-7b-miniguanaco"

tokenizer = AutoTokenizer.from_pretrained(model_path, use_default_system_prompt=False) model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.float32, device_map='auto',local_files_only=False, load_in_4bit=True ) print(model) prompt = input("please input prompt:") while len(prompt) > 0: input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to("cuda")

generation_output = model.generate( input_ids=input_ids, max_new_tokens=500,repetition_penalty=1.2 ) print(tokenizer.decode(generation_output[0])) prompt = input("please input prompt:")

Downloads last month: 790

Safetensors

Model size

7B params

Tensor type

F32

F16

Model tree for decruz07/llama-2-7b-miniguanaco

Quantizations

1 model