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

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

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

Atomic Chat

alphahg commited on Oct 15, 2023

Commit

ebe7a17

1 Parent(s): 9a4ac9c

Create README.md

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README.md +62 -0

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	@@ -0,0 +1,62 @@

+---
+license: llama2
+base_model: codellama/CodeLlama-7b-hf
+tags:
+- generated_from_trainer
+model-index:
+- name: llama2-7b-rust-finetune
+  results: []
+---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# llama2-7b-rust-finetune
+This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/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