Instructions to use mihirinamdar/llama2-7b-bot-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mihirinamdar/llama2-7b-bot-v1 with Transformers:

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

pipe = pipeline("text-generation", model="mihirinamdar/llama2-7b-bot-v1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mihirinamdar/llama2-7b-bot-v1")
model = AutoModelForCausalLM.from_pretrained("mihirinamdar/llama2-7b-bot-v1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mihirinamdar/llama2-7b-bot-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mihirinamdar/llama2-7b-bot-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mihirinamdar/llama2-7b-bot-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mihirinamdar/llama2-7b-bot-v1

SGLang

How to use mihirinamdar/llama2-7b-bot-v1 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 "mihirinamdar/llama2-7b-bot-v1" \
    --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": "mihirinamdar/llama2-7b-bot-v1",
		"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 "mihirinamdar/llama2-7b-bot-v1" \
        --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": "mihirinamdar/llama2-7b-bot-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mihirinamdar/llama2-7b-bot-v1 with Docker Model Runner:
```
docker model run hf.co/mihirinamdar/llama2-7b-bot-v1
```

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Check out the documentation for more information.

Miniguanaco-7b Assistant Bot

This is a Miniguanaco-7b assistant bot, fine-tuned using QLoRA (4-bit precision) on the mlabonne/guanaco-llama2-1k dataset, a subset of timdettmers/openassistant-guanaco.

Training

The model was trained on a Google Colab notebook with a T4 GPU and high RAM. Please note that it is primarily designed for educational purposes and may not be optimized for production-level inference.

Usage

To use the Miniguanaco-7b assistant bot, you can follow the code example below:

# pip install transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/llama-2-7b-miniguanaco"
prompt = "What is a large language model?"

tokenizer = AutoTokenizer.from_pretrained(model)

pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

sequences = pipeline(
    f'<s>[INST] {prompt} [/INST]',
    do_sample=True,
    top_k=10,
    num_return_sequences=1,
    eos_token_id=tokenizer.eos_token_id,
    max_length=200,
)

for seq in sequences:
    print(f"Result: {seq['generated_text']}")

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