Instructions to use AnishJoshi/codellama2-finetuned-nl2bash-fin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AnishJoshi/codellama2-finetuned-nl2bash-fin with Transformers:

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

pipe = pipeline("text-generation", model="AnishJoshi/codellama2-finetuned-nl2bash-fin")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AnishJoshi/codellama2-finetuned-nl2bash-fin")
model = AutoModelForCausalLM.from_pretrained("AnishJoshi/codellama2-finetuned-nl2bash-fin")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use AnishJoshi/codellama2-finetuned-nl2bash-fin with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AnishJoshi/codellama2-finetuned-nl2bash-fin"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AnishJoshi/codellama2-finetuned-nl2bash-fin",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AnishJoshi/codellama2-finetuned-nl2bash-fin

SGLang

How to use AnishJoshi/codellama2-finetuned-nl2bash-fin 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 "AnishJoshi/codellama2-finetuned-nl2bash-fin" \
    --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": "AnishJoshi/codellama2-finetuned-nl2bash-fin",
		"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 "AnishJoshi/codellama2-finetuned-nl2bash-fin" \
        --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": "AnishJoshi/codellama2-finetuned-nl2bash-fin",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AnishJoshi/codellama2-finetuned-nl2bash-fin with Docker Model Runner:
```
docker model run hf.co/AnishJoshi/codellama2-finetuned-nl2bash-fin
```

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Check out the documentation for more information.

codellama2-finetuned-nl2bash-fin

Finetuned on the AnishJoshi/nl2bash-custom dataset for generating bash code based on natural language descriptions.

Model Details

Model Name: CodeLlama2-Finetuned-NL2Bash
Base Model: CodeLlama2
Task: Natural Language to Bash Script Conversion
Framework: PyTorch
Fine-tuning Dataset: Custom dataset of natural language commands and corresponding Bash scripts, available here

Model Description

Developed by: Anish Joshi
Model type: CausalLM
Finetuned from model: Codellama2

Files Included

adapter_config.json: Configuration file for the adapter layers.
adapter_model.safetensors: Weights of the adapter layers in the Safetensors format.
optimizer.pt: State of the optimizer used during training.
rng_state.pth: State of the random number generator.
scheduler.pt: State of the learning rate scheduler.
special_tokens_map.json: Mapping for special tokens used by the tokenizer.
tokenizer.json: Tokenizer model including the vocabulary.
tokenizer_config.json: Configuration settings for the tokenizer.
trainer_state.json: State of the trainer including training metrics.
training_args.bin: Training arguments used for fine-tuning.
`README.md

Usage

Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AnishJoshi/codellama2-finetuned-nl2bash-fin")
model = AutoModelForCausalLM.from_pretrained("AnishJoshi/codellama2-finetuned-nl2bash-fin")

Training Details

Training details available at Finetuning Notebook

Training Hyperparameters

Training arguments and configuration are set using TrainingArguments and LoraConfig. The model is fine-tuned using the following parameters:

output_dir: codellama2-finetuned-nl2bash - Directory to save the fine-tuned model.
per_device_train_batch_size: 2 - Batch size per device.
gradient_accumulation_steps: 16 - Number of gradient accumulation steps.
optim: paged_adamw_32bit - Optimizer type.
learning_rate: 2e-4 - Learning rate.
lr_scheduler_type: cosine - Learning rate scheduler type.
save_strategy: epoch - Strategy to save checkpoints.
logging_steps: 10 - Number of steps between logging.
num_train_epochs: 1 - Number of training epochs.
max_steps: 100 - Maximum number of training steps.
fp16: True - Use 16-bit floating-point precision.
push_to_hub: False - Whether to push the model to Hugging Face Hub.
report_to: none - Reporting destination.

Evaluation

Evaulation metrics and calculations available at Evaluation Notebook

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Safetensors

Model size

7B params

Tensor type

F16