Instructions to use devshaheen/Llama-2-7b-chat-finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use devshaheen/Llama-2-7b-chat-finetune with Transformers:

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

pipe = pipeline("text-generation", model="devshaheen/Llama-2-7b-chat-finetune")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("devshaheen/Llama-2-7b-chat-finetune")
model = AutoModelForCausalLM.from_pretrained("devshaheen/Llama-2-7b-chat-finetune")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use devshaheen/Llama-2-7b-chat-finetune with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/devshaheen/Llama-2-7b-chat-finetune

SGLang

How to use devshaheen/Llama-2-7b-chat-finetune 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 "devshaheen/Llama-2-7b-chat-finetune" \
    --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": "devshaheen/Llama-2-7b-chat-finetune",
		"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 "devshaheen/Llama-2-7b-chat-finetune" \
        --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": "devshaheen/Llama-2-7b-chat-finetune",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use devshaheen/Llama-2-7b-chat-finetune with Docker Model Runner:
```
docker model run hf.co/devshaheen/Llama-2-7b-chat-finetune
```

Llama-2-7B-Chat Fine-Tuned Model

This model is a fine-tuned version of Llama-2-7B-Chat model, optimized for instruction-following tasks. It has been trained on the mlabonne/guanaco-llama2-1k dataset and is optimized for efficient text generation across various NLP tasks, including question answering, summarization, and text completion.

Model Details

Base Model: NousResearch/Llama-2-7b-chat-hf
Fine-Tuning Task: Instruction-following
Training Dataset: mlabonne/guanaco-llama2-1k
Optimized For: Text generation, question answering, summarization, and more.
Fine-Tuned Parameters:
- LoRA (Low-Rank Adaption) applied for efficient training with smaller parameter updates.
- Quantized to 4-bit for memory efficiency and better GPU utilization.
- Training includes gradient accumulation, gradient checkpointing, and weight decay to prevent overfitting and enhance memory efficiency.

Usage

You can use this fine-tuned model with the Hugging Face transformers library. Below is an example of how to load and use the model for text generation.

from transformers import AutoTokenizer, AutoModelForCausalLM

# Load pre-trained model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("https://huggingface.co/devshaheen/llama-2-7b-chat-finetune")
model = AutoModelForCausalLM.from_pretrained("https://huggingface.co/devshaheen/llama-2-7b-chat-finetune")

# Example text generation
input_text = "What is the capital of France?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(generated_text)

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Safetensors

Model size

7B params

Tensor type

F32

F16

Model tree for devshaheen/Llama-2-7b-chat-finetune

Base model

NousResearch/Llama-2-7b-chat-hf

Finetuned

(142)

this model

devshaheen
/

Llama-2-7b-chat-finetune

Llama-2-7B-Chat Fine-Tuned Model

Model Details

Usage

Model tree for devshaheen/Llama-2-7b-chat-finetune

Dataset used to train devshaheen/Llama-2-7b-chat-finetune