Instructions to use alphahg/CodeLlama-7b-hf-rust-finetune-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alphahg/CodeLlama-7b-hf-rust-finetune-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="alphahg/CodeLlama-7b-hf-rust-finetune-GGUF",
	filename="codellama-7b-hf-rust-finetune.q4_k_m.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use alphahg/CodeLlama-7b-hf-rust-finetune-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf alphahg/CodeLlama-7b-hf-rust-finetune-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf alphahg/CodeLlama-7b-hf-rust-finetune-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf alphahg/CodeLlama-7b-hf-rust-finetune-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf alphahg/CodeLlama-7b-hf-rust-finetune-GGUF:Q4_K_M

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 alphahg/CodeLlama-7b-hf-rust-finetune-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf alphahg/CodeLlama-7b-hf-rust-finetune-GGUF:Q4_K_M

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 alphahg/CodeLlama-7b-hf-rust-finetune-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf alphahg/CodeLlama-7b-hf-rust-finetune-GGUF:Q4_K_M

Use Docker

docker model run hf.co/alphahg/CodeLlama-7b-hf-rust-finetune-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use alphahg/CodeLlama-7b-hf-rust-finetune-GGUF with Ollama:
```
ollama run hf.co/alphahg/CodeLlama-7b-hf-rust-finetune-GGUF:Q4_K_M
```

Unsloth Studio new

How to use alphahg/CodeLlama-7b-hf-rust-finetune-GGUF 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 alphahg/CodeLlama-7b-hf-rust-finetune-GGUF 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 alphahg/CodeLlama-7b-hf-rust-finetune-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for alphahg/CodeLlama-7b-hf-rust-finetune-GGUF to start chatting

Docker Model Runner
How to use alphahg/CodeLlama-7b-hf-rust-finetune-GGUF with Docker Model Runner:
```
docker model run hf.co/alphahg/CodeLlama-7b-hf-rust-finetune-GGUF:Q4_K_M
```

Lemonade

How to use alphahg/CodeLlama-7b-hf-rust-finetune-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull alphahg/CodeLlama-7b-hf-rust-finetune-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.CodeLlama-7b-hf-rust-finetune-GGUF-Q4_K_M

List all available models

lemonade list

llama2-7b-rust-finetune

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

Loss: 0.5347

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2.5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1
training_steps: 500

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0	100	0.5443
No log	0.01	200	0.5385
No log	0.01	300	0.5362
No log	0.01	400	0.5351
0.5389	0.02	500	0.5347

Framework versions

Transformers 4.34.0
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.14.1

Downloads last month: 28

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

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