Instructions to use AshtonIsNotHere/CodeLlama_7B_nlp_pp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AshtonIsNotHere/CodeLlama_7B_nlp_pp with Transformers:

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

pipe = pipeline("text-generation", model="AshtonIsNotHere/CodeLlama_7B_nlp_pp")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AshtonIsNotHere/CodeLlama_7B_nlp_pp")
model = AutoModelForCausalLM.from_pretrained("AshtonIsNotHere/CodeLlama_7B_nlp_pp", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use AshtonIsNotHere/CodeLlama_7B_nlp_pp with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AshtonIsNotHere/CodeLlama_7B_nlp_pp:Q8_0
# Run inference directly in the terminal:
llama cli -hf AshtonIsNotHere/CodeLlama_7B_nlp_pp:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AshtonIsNotHere/CodeLlama_7B_nlp_pp:Q8_0
# Run inference directly in the terminal:
llama cli -hf AshtonIsNotHere/CodeLlama_7B_nlp_pp:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf AshtonIsNotHere/CodeLlama_7B_nlp_pp:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf AshtonIsNotHere/CodeLlama_7B_nlp_pp:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf AshtonIsNotHere/CodeLlama_7B_nlp_pp:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AshtonIsNotHere/CodeLlama_7B_nlp_pp:Q8_0

Use Docker

docker model run hf.co/AshtonIsNotHere/CodeLlama_7B_nlp_pp:Q8_0

LM Studio
Jan

vLLM

How to use AshtonIsNotHere/CodeLlama_7B_nlp_pp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AshtonIsNotHere/CodeLlama_7B_nlp_pp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AshtonIsNotHere/CodeLlama_7B_nlp_pp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AshtonIsNotHere/CodeLlama_7B_nlp_pp:Q8_0

SGLang

How to use AshtonIsNotHere/CodeLlama_7B_nlp_pp 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 "AshtonIsNotHere/CodeLlama_7B_nlp_pp" \
    --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": "AshtonIsNotHere/CodeLlama_7B_nlp_pp",
		"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 "AshtonIsNotHere/CodeLlama_7B_nlp_pp" \
        --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": "AshtonIsNotHere/CodeLlama_7B_nlp_pp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use AshtonIsNotHere/CodeLlama_7B_nlp_pp with Ollama:
```
ollama run hf.co/AshtonIsNotHere/CodeLlama_7B_nlp_pp:Q8_0
```

Unsloth Studio

How to use AshtonIsNotHere/CodeLlama_7B_nlp_pp 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 AshtonIsNotHere/CodeLlama_7B_nlp_pp 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 AshtonIsNotHere/CodeLlama_7B_nlp_pp to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AshtonIsNotHere/CodeLlama_7B_nlp_pp to start chatting

Docker Model Runner
How to use AshtonIsNotHere/CodeLlama_7B_nlp_pp with Docker Model Runner:
```
docker model run hf.co/AshtonIsNotHere/CodeLlama_7B_nlp_pp:Q8_0
```

Lemonade

How to use AshtonIsNotHere/CodeLlama_7B_nlp_pp with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AshtonIsNotHere/CodeLlama_7B_nlp_pp:Q8_0

Run and chat with the model

lemonade run user.CodeLlama_7B_nlp_pp-Q8_0

List all available models

lemonade list

Atomic Chat

CodeLlama_7B_nlp_pp

This model is a fine-tuned version of codellama/CodeLlama-7b-hf on the AshtonIsNotHere/nlp_pp_code_dataset dataset. It achieves the following results on the evaluation set:

Loss: 0.4129
Accuracy: 0.8968

Model description

This model has been fine-tuned for code completion on a dataset of NLP++ code.

Intended uses & limitations

More information needed

Training and evaluation data

Dataset consists of a combination of scraped NLP++ code and NLP++ code examples from the VisualText website.

Training procedure

This model is trained in a multinode, multi-gpu setup with DeepSpeed Z3. For more information on the training setup, check out the GitHub repo.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.00012
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 4
total_train_batch_size: 16
total_eval_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 7.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	61	0.5100	0.8726
No log	1.99	122	0.4129	0.8968
No log	2.99	183	0.4166	0.9072
No log	4.0	245	0.4595	0.9090
No log	5.0	306	0.5181	0.9093
No log	5.99	367	0.5553	0.9090
No log	6.97	427	0.5603	0.9089

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu117
Datasets 2.13.0
Tokenizers 0.13.3

Downloads last month: 76

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

8-bit

Dataset used to train AshtonIsNotHere/CodeLlama_7B_nlp_pp

Space using AshtonIsNotHere/CodeLlama_7B_nlp_pp 1

Evaluation results

Accuracy on AshtonIsNotHere/nlp_pp_code_dataset
test set self-reported

0.897