Instructions to use RichardErkhov/Alphacode-AI_-_Alphallama3-8B-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use RichardErkhov/Alphacode-AI_-_Alphallama3-8B-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 RichardErkhov/Alphacode-AI_-_Alphallama3-8B-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf RichardErkhov/Alphacode-AI_-_Alphallama3-8B-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 RichardErkhov/Alphacode-AI_-_Alphallama3-8B-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf RichardErkhov/Alphacode-AI_-_Alphallama3-8B-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 RichardErkhov/Alphacode-AI_-_Alphallama3-8B-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf RichardErkhov/Alphacode-AI_-_Alphallama3-8B-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 RichardErkhov/Alphacode-AI_-_Alphallama3-8B-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf RichardErkhov/Alphacode-AI_-_Alphallama3-8B-gguf:Q4_K_M
Use Docker
docker model run hf.co/RichardErkhov/Alphacode-AI_-_Alphallama3-8B-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use RichardErkhov/Alphacode-AI_-_Alphallama3-8B-gguf with Ollama:
ollama run hf.co/RichardErkhov/Alphacode-AI_-_Alphallama3-8B-gguf:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use RichardErkhov/Alphacode-AI_-_Alphallama3-8B-gguf with Docker Model Runner:
docker model run hf.co/RichardErkhov/Alphacode-AI_-_Alphallama3-8B-gguf:Q4_K_M
- Lemonade
How to use RichardErkhov/Alphacode-AI_-_Alphallama3-8B-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RichardErkhov/Alphacode-AI_-_Alphallama3-8B-gguf:Q4_K_M
Run and chat with the model
lemonade run user.Alphacode-AI_-_Alphallama3-8B-gguf-Q4_K_M
List all available models
lemonade list
- Atomic Chat
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Quantization made by Richard Erkhov.
Alphallama3-8B - GGUF
- Model creator: https://huggingface.co/Alphacode-AI/
- Original model: https://huggingface.co/Alphacode-AI/Alphallama3-8B/
| Name | Quant method | Size |
|---|---|---|
| Alphallama3-8B.Q2_K.gguf | Q2_K | 2.96GB |
| Alphallama3-8B.IQ3_XS.gguf | IQ3_XS | 3.28GB |
| Alphallama3-8B.IQ3_S.gguf | IQ3_S | 3.43GB |
| Alphallama3-8B.Q3_K_S.gguf | Q3_K_S | 3.41GB |
| Alphallama3-8B.IQ3_M.gguf | IQ3_M | 3.52GB |
| Alphallama3-8B.Q3_K.gguf | Q3_K | 3.74GB |
| Alphallama3-8B.Q3_K_M.gguf | Q3_K_M | 3.74GB |
| Alphallama3-8B.Q3_K_L.gguf | Q3_K_L | 4.03GB |
| Alphallama3-8B.IQ4_XS.gguf | IQ4_XS | 4.18GB |
| Alphallama3-8B.Q4_0.gguf | Q4_0 | 4.34GB |
| Alphallama3-8B.IQ4_NL.gguf | IQ4_NL | 4.38GB |
| Alphallama3-8B.Q4_K_S.gguf | Q4_K_S | 4.37GB |
| Alphallama3-8B.Q4_K.gguf | Q4_K | 4.58GB |
| Alphallama3-8B.Q4_K_M.gguf | Q4_K_M | 4.58GB |
| Alphallama3-8B.Q4_1.gguf | Q4_1 | 4.78GB |
| Alphallama3-8B.Q5_0.gguf | Q5_0 | 5.21GB |
| Alphallama3-8B.Q5_K_S.gguf | Q5_K_S | 5.21GB |
| Alphallama3-8B.Q5_K.gguf | Q5_K | 5.34GB |
| Alphallama3-8B.Q5_K_M.gguf | Q5_K_M | 5.34GB |
| Alphallama3-8B.Q5_1.gguf | Q5_1 | 5.65GB |
| Alphallama3-8B.Q6_K.gguf | Q6_K | 6.14GB |
| Alphallama3-8B.Q8_0.gguf | Q8_0 | 7.95GB |
Original model description:
license: llama3 datasets: - Custom_datasets language: - ko pipeline_tag: text-generation base_model: "meta-llama/Meta-Llama-3-8B"
This model is a version of Meta-Llama-3-8B that has been fine-tuned with Our In House CustomData.
Train Spec : We utilized an A100x4 * 1 for training our model with DeepSpeed / HuggingFace TRL Trainer / HuggingFace Accelerate
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