Thai-Math-Reasoning
Collection
Experiments on LLM mathematical reasoning in Thai • 9 items • Updated
How to use RJTPP/th_reasoning-04-deepseek1.5b-post_GRPO-gguf with Transformers:
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("RJTPP/th_reasoning-04-deepseek1.5b-post_GRPO-gguf", device_map="auto")How to use RJTPP/th_reasoning-04-deepseek1.5b-post_GRPO-gguf with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf RJTPP/th_reasoning-04-deepseek1.5b-post_GRPO-gguf:F16 # Run inference directly in the terminal: llama cli -hf RJTPP/th_reasoning-04-deepseek1.5b-post_GRPO-gguf:F16
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf RJTPP/th_reasoning-04-deepseek1.5b-post_GRPO-gguf:F16 # Run inference directly in the terminal: llama cli -hf RJTPP/th_reasoning-04-deepseek1.5b-post_GRPO-gguf:F16
# 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 RJTPP/th_reasoning-04-deepseek1.5b-post_GRPO-gguf:F16 # Run inference directly in the terminal: ./llama-cli -hf RJTPP/th_reasoning-04-deepseek1.5b-post_GRPO-gguf:F16
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 RJTPP/th_reasoning-04-deepseek1.5b-post_GRPO-gguf:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf RJTPP/th_reasoning-04-deepseek1.5b-post_GRPO-gguf:F16
docker model run hf.co/RJTPP/th_reasoning-04-deepseek1.5b-post_GRPO-gguf:F16
How to use RJTPP/th_reasoning-04-deepseek1.5b-post_GRPO-gguf with Ollama:
ollama run hf.co/RJTPP/th_reasoning-04-deepseek1.5b-post_GRPO-gguf:F16
How to use RJTPP/th_reasoning-04-deepseek1.5b-post_GRPO-gguf with Docker Model Runner:
docker model run hf.co/RJTPP/th_reasoning-04-deepseek1.5b-post_GRPO-gguf:F16
How to use RJTPP/th_reasoning-04-deepseek1.5b-post_GRPO-gguf with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RJTPP/th_reasoning-04-deepseek1.5b-post_GRPO-gguf:F16
lemonade run user.th_reasoning-04-deepseek1.5b-post_GRPO-gguf-F16
lemonade list
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf RJTPP/th_reasoning-04-deepseek1.5b-post_GRPO-gguf:F16# Run inference directly in the terminal:
llama cli -hf RJTPP/th_reasoning-04-deepseek1.5b-post_GRPO-gguf:F16# 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 RJTPP/th_reasoning-04-deepseek1.5b-post_GRPO-gguf:F16# Run inference directly in the terminal:
./llama-cli -hf RJTPP/th_reasoning-04-deepseek1.5b-post_GRPO-gguf:F16git 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 RJTPP/th_reasoning-04-deepseek1.5b-post_GRPO-gguf:F16# Run inference directly in the terminal:
./build/bin/llama-cli -hf RJTPP/th_reasoning-04-deepseek1.5b-post_GRPO-gguf:F16docker model run hf.co/RJTPP/th_reasoning-04-deepseek1.5b-post_GRPO-gguf:F16This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.
4-bit
16-bit
Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf RJTPP/th_reasoning-04-deepseek1.5b-post_GRPO-gguf:F16# Run inference directly in the terminal: llama cli -hf RJTPP/th_reasoning-04-deepseek1.5b-post_GRPO-gguf:F16