Instructions to use Diwanshuydv/qwen2.5-3B-coder-Instruct-ft_q with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Diwanshuydv/qwen2.5-3B-coder-Instruct-ft_q with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Diwanshuydv/qwen2.5-3B-coder-Instruct-ft_q", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Diwanshuydv/qwen2.5-3B-coder-Instruct-ft_q 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 Diwanshuydv/qwen2.5-3B-coder-Instruct-ft_q:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Diwanshuydv/qwen2.5-3B-coder-Instruct-ft_q:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Diwanshuydv/qwen2.5-3B-coder-Instruct-ft_q:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Diwanshuydv/qwen2.5-3B-coder-Instruct-ft_q: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 Diwanshuydv/qwen2.5-3B-coder-Instruct-ft_q:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Diwanshuydv/qwen2.5-3B-coder-Instruct-ft_q: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 Diwanshuydv/qwen2.5-3B-coder-Instruct-ft_q:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Diwanshuydv/qwen2.5-3B-coder-Instruct-ft_q:Q4_K_M

Use Docker

docker model run hf.co/Diwanshuydv/qwen2.5-3B-coder-Instruct-ft_q:Q4_K_M

LM Studio
Jan
Ollama
How to use Diwanshuydv/qwen2.5-3B-coder-Instruct-ft_q with Ollama:
```
ollama run hf.co/Diwanshuydv/qwen2.5-3B-coder-Instruct-ft_q:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use Diwanshuydv/qwen2.5-3B-coder-Instruct-ft_q with Docker Model Runner:
```
docker model run hf.co/Diwanshuydv/qwen2.5-3B-coder-Instruct-ft_q:Q4_K_M
```

Lemonade

How to use Diwanshuydv/qwen2.5-3B-coder-Instruct-ft_q with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Diwanshuydv/qwen2.5-3B-coder-Instruct-ft_q:Q4_K_M

Run and chat with the model

lemonade run user.qwen2.5-3B-coder-Instruct-ft_q-Q4_K_M

List all available models

lemonade list

Atomic Chat

Uploaded model

Developed by: Diwanshuydv
License: apache-2.0
Finetuned from model : unsloth/Qwen2.5-Coder-3B-Instruct

This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 58

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

4-bit

6-bit

8-bit

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Model tree for Diwanshuydv/qwen2.5-3B-coder-Instruct-ft_q

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-Coder-3B

Finetuned

Qwen/Qwen2.5-Coder-3B-Instruct

Finetuned

unsloth/Qwen2.5-Coder-3B-Instruct

Quantized

(5)

this model