Instructions to use thanhkt/Qwen2.5-1.5B-MathInstruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thanhkt/Qwen2.5-1.5B-MathInstruct with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("thanhkt/Qwen2.5-1.5B-MathInstruct", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use thanhkt/Qwen2.5-1.5B-MathInstruct 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 thanhkt/Qwen2.5-1.5B-MathInstruct:F16
# Run inference directly in the terminal:
llama cli -hf thanhkt/Qwen2.5-1.5B-MathInstruct:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf thanhkt/Qwen2.5-1.5B-MathInstruct:F16
# Run inference directly in the terminal:
llama cli -hf thanhkt/Qwen2.5-1.5B-MathInstruct:F16

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 thanhkt/Qwen2.5-1.5B-MathInstruct:F16
# Run inference directly in the terminal:
./llama-cli -hf thanhkt/Qwen2.5-1.5B-MathInstruct:F16

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 thanhkt/Qwen2.5-1.5B-MathInstruct:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf thanhkt/Qwen2.5-1.5B-MathInstruct:F16

Use Docker

docker model run hf.co/thanhkt/Qwen2.5-1.5B-MathInstruct:F16

LM Studio
Jan
Ollama
How to use thanhkt/Qwen2.5-1.5B-MathInstruct with Ollama:
```
ollama run hf.co/thanhkt/Qwen2.5-1.5B-MathInstruct:F16
```
Unsloth Desktop

How to use thanhkt/Qwen2.5-1.5B-MathInstruct with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf thanhkt/Qwen2.5-1.5B-MathInstruct:F16

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "thanhkt/Qwen2.5-1.5B-MathInstruct:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use thanhkt/Qwen2.5-1.5B-MathInstruct with Docker Model Runner:
```
docker model run hf.co/thanhkt/Qwen2.5-1.5B-MathInstruct:F16
```

Lemonade

How to use thanhkt/Qwen2.5-1.5B-MathInstruct with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull thanhkt/Qwen2.5-1.5B-MathInstruct:F16

Run and chat with the model

lemonade run user.Qwen2.5-1.5B-MathInstruct-F16

List all available models

lemonade list

Hermes Agent

How to use thanhkt/Qwen2.5-1.5B-MathInstruct with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf thanhkt/Qwen2.5-1.5B-MathInstruct:F16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default thanhkt/Qwen2.5-1.5B-MathInstruct:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use thanhkt/Qwen2.5-1.5B-MathInstruct with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf thanhkt/Qwen2.5-1.5B-MathInstruct:F16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "thanhkt/Qwen2.5-1.5B-MathInstruct:F16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

thanhkt commited on Oct 21, 2024

Commit

9614041

verified ·

1 Parent(s): 1de61d9

Update README.md

Browse files

Files changed (1) hide show

README.md +26 -18

README.md CHANGED Viewed

@@ -1,22 +1,30 @@
----
-base_model: unsloth/Qwen2.5-Math-1.5B-Instruct-bnb-4bit
-language:
-- en
-license: apache-2.0
-tags:
-- text-generation-inference
-- transformers
-- unsloth
-- qwen2
-- trl
----
-# Uploaded  model
-- **Developed by:** thanhkt
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/Qwen2.5-Math-1.5B-Instruct-bnb-4bit
-This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

+id: model_card
+name: Model Card
+type: markdown
+content: |-
+  ---
+  base_model: unsloth/Qwen2.5-Math-1.5B-Instruct-bnb-4bit
+  language:
+  - en
+  license: apache-2.0
+  tags:
+  - text-generation-inference
+  - transformers
+  - unsloth
+  - qwen2
+  - trl
+  ---
+  # Uploaded Model
+  - **Developed by:** thanhkt
+  - **License:** apache-2.0
+  - **Finetuned from model:** unsloth/Qwen2.5-Math-1.5B-Instruct-bnb-4bit
+  This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+  [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+  ## Dataset
+  The model was trained on the Nvidia-mathinstuct dataset, which consists of 100,000 rows. This dataset was specifically chosen to enhance the model's mathematical reasoning and instruction-following capabilities.