Instructions to use RichardErkhov/Alphacode-AI_-_AlphaMist7B-slr-v3-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 RichardErkhov/Alphacode-AI_-_AlphaMist7B-slr-v3-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_-_AlphaMist7B-slr-v3-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RichardErkhov/Alphacode-AI_-_AlphaMist7B-slr-v3-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_-_AlphaMist7B-slr-v3-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RichardErkhov/Alphacode-AI_-_AlphaMist7B-slr-v3-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_-_AlphaMist7B-slr-v3-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RichardErkhov/Alphacode-AI_-_AlphaMist7B-slr-v3-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_-_AlphaMist7B-slr-v3-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RichardErkhov/Alphacode-AI_-_AlphaMist7B-slr-v3-gguf:Q4_K_M

Use Docker

docker model run hf.co/RichardErkhov/Alphacode-AI_-_AlphaMist7B-slr-v3-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use RichardErkhov/Alphacode-AI_-_AlphaMist7B-slr-v3-gguf with Ollama:
```
ollama run hf.co/RichardErkhov/Alphacode-AI_-_AlphaMist7B-slr-v3-gguf:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use RichardErkhov/Alphacode-AI_-_AlphaMist7B-slr-v3-gguf with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/Alphacode-AI_-_AlphaMist7B-slr-v3-gguf:Q4_K_M
```

Lemonade

How to use RichardErkhov/Alphacode-AI_-_AlphaMist7B-slr-v3-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RichardErkhov/Alphacode-AI_-_AlphaMist7B-slr-v3-gguf:Q4_K_M

Run and chat with the model

lemonade run user.Alphacode-AI_-_AlphaMist7B-slr-v3-gguf-Q4_K_M

List all available models

lemonade list

Atomic Chat

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Check out the documentation for more information.

Quantization made by Richard Erkhov.

Github

Discord

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AlphaMist7B-slr-v3 - GGUF

Model creator: https://huggingface.co/Alphacode-AI/
Original model: https://huggingface.co/Alphacode-AI/AlphaMist7B-slr-v3/

Name	Quant method	Size
AlphaMist7B-slr-v3.Q2_K.gguf	Q2_K	2.53GB
AlphaMist7B-slr-v3.IQ3_XS.gguf	IQ3_XS	2.81GB
AlphaMist7B-slr-v3.IQ3_S.gguf	IQ3_S	2.96GB
AlphaMist7B-slr-v3.Q3_K_S.gguf	Q3_K_S	2.95GB
AlphaMist7B-slr-v3.IQ3_M.gguf	IQ3_M	3.06GB
AlphaMist7B-slr-v3.Q3_K.gguf	Q3_K	3.28GB
AlphaMist7B-slr-v3.Q3_K_M.gguf	Q3_K_M	3.28GB
AlphaMist7B-slr-v3.Q3_K_L.gguf	Q3_K_L	3.56GB
AlphaMist7B-slr-v3.IQ4_XS.gguf	IQ4_XS	3.67GB
AlphaMist7B-slr-v3.Q4_0.gguf	Q4_0	3.83GB
AlphaMist7B-slr-v3.IQ4_NL.gguf	IQ4_NL	3.87GB
AlphaMist7B-slr-v3.Q4_K_S.gguf	Q4_K_S	3.86GB
AlphaMist7B-slr-v3.Q4_K.gguf	Q4_K	4.07GB
AlphaMist7B-slr-v3.Q4_K_M.gguf	Q4_K_M	4.07GB
AlphaMist7B-slr-v3.Q4_1.gguf	Q4_1	4.24GB
AlphaMist7B-slr-v3.Q5_0.gguf	Q5_0	4.65GB
AlphaMist7B-slr-v3.Q5_K_S.gguf	Q5_K_S	4.65GB
AlphaMist7B-slr-v3.Q5_K.gguf	Q5_K	4.78GB
AlphaMist7B-slr-v3.Q5_K_M.gguf	Q5_K_M	4.78GB
AlphaMist7B-slr-v3.Q5_1.gguf	Q5_1	5.07GB
AlphaMist7B-slr-v3.Q6_K.gguf	Q6_K	5.53GB
AlphaMist7B-slr-v3.Q8_0.gguf	Q8_0	7.17GB

Original model description:

license: cc-by-nc-4.0 datasets: - Custom_datasets language: - ko pipeline_tag: text-generation base_model: "mistralai/Mistral-7B-v0.1"

This model is a version of mistralai/Mistral-7B-v0.1 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

Downloads last month: 1,450

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

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8-bit

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