Safetensors
GGUF
Turkish
llama
Llama-3
instruct
finetune
chatml
gpt4
synthetic data
distillation
function calling
json mode
axolotl
roleplaying
chat
Instructions to use tda45/TdAI 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 tda45/TdAI 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 tda45/TdAI # Run inference directly in the terminal: llama cli -hf tda45/TdAI
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tda45/TdAI # Run inference directly in the terminal: llama cli -hf tda45/TdAI
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 tda45/TdAI # Run inference directly in the terminal: ./llama-cli -hf tda45/TdAI
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 tda45/TdAI # Run inference directly in the terminal: ./build/bin/llama-cli -hf tda45/TdAI
Use Docker
docker model run hf.co/tda45/TdAI
- LM Studio
- Jan
- Ollama
How to use tda45/TdAI with Ollama:
ollama run hf.co/tda45/TdAI
- Unsloth Studio
How to use tda45/TdAI with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tda45/TdAI to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tda45/TdAI to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tda45/TdAI to start chatting
- Docker Model Runner
How to use tda45/TdAI with Docker Model Runner:
docker model run hf.co/tda45/TdAI
- Lemonade
How to use tda45/TdAI with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tda45/TdAI
Run and chat with the model
lemonade run user.TdAI-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
File size: 5,811 Bytes
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# ARGUMENTS
# ==============================================================================
# Define the CANN base image for easier version updates later
ARG CHIP_TYPE=910b
ARG CANN_BASE_IMAGE=quay.io/ascend/cann:8.5.0-${CHIP_TYPE}-openeuler24.03-py3.11
ARG BUILD_DATE=N/A
ARG APP_VERSION=N/A
ARG APP_REVISION=N/A
# ==============================================================================
# BUILD STAGE
# Compile all binary files and libraries
# ==============================================================================
ARG NODE_VERSION=24
FROM docker.io/node:$NODE_VERSION AS web
ARG APP_VERSION
WORKDIR /app/tools/ui
COPY tools/ui/package.json tools/ui/package-lock.json ./
RUN npm ci
COPY tools/ui/ ./
RUN LLAMA_BUILD_NUMBER="$APP_VERSION" npm run build
FROM ${CANN_BASE_IMAGE} AS build
# -- Install build dependencies --
RUN yum install -y gcc g++ cmake make git openssl-devel python3 python3-pip && \
yum clean all && \
rm -rf /var/cache/yum
# -- Set the working directory --
WORKDIR /app
# -- Copy project files --
COPY . .
COPY --from=web /app/tools/ui/dist tools/ui/dist
# -- Set CANN environment variables (required for compilation) --
# Using ENV instead of `source` allows environment variables to persist across the entire image layer
ENV ASCEND_TOOLKIT_HOME=/usr/local/Ascend/ascend-toolkit/latest
ENV LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${LD_LIBRARY_PATH}
ENV PATH=${ASCEND_TOOLKIT_HOME}/bin:${PATH}
ENV ASCEND_OPP_PATH=${ASCEND_TOOLKIT_HOME}/opp
ENV LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/runtime/lib64/stub:$LD_LIBRARY_PATH
# ... You can add other environment variables from the original file as needed ...
# For brevity, only core variables are listed here. You can paste the original ENV list here.
# -- Build llama.cpp --
# Use the passed CHIP_TYPE argument and add general build options
ARG CHIP_TYPE
RUN source /usr/local/Ascend/ascend-toolkit/set_env.sh --force \
&& \
cmake -B build \
-DGGML_CANN=ON \
-DCMAKE_BUILD_TYPE=Release \
-DSOC_TYPE=ascend${CHIP_TYPE} \
-DUSE_ACL_GRAPH=ON \
. && \
cmake --build build --config Release -j$(nproc)
# -- Organize build artifacts for copying in later stages --
# Create a lib directory to store all .so files
RUN mkdir -p /app/lib && \
find build -name "*.so*" -exec cp -P {} /app/lib \;
# Create a full directory to store all executables and Python scripts
RUN mkdir -p /app/full && \
cp build/bin/* /app/full/ && \
cp *.py /app/full/ && \
cp -r conversion /app/full/ && \
cp -r gguf-py /app/full/ && \
cp -r requirements /app/full/ && \
cp requirements.txt /app/full/
# If you have a tools.sh script, make sure it is copied here
# cp .devops/tools.sh /app/full/tools.sh
# ==============================================================================
# BASE STAGE
# Create a minimal base image with CANN runtime and common libraries
# ==============================================================================
FROM ${CANN_BASE_IMAGE} AS base
ARG BUILD_DATE=N/A
ARG APP_VERSION=N/A
ARG APP_REVISION=N/A
ARG IMAGE_URL=https://github.com/ggml-org/llama.cpp
ARG IMAGE_SOURCE=https://github.com/ggml-org/llama.cpp
LABEL org.opencontainers.image.created=$BUILD_DATE \
org.opencontainers.image.version=$APP_VERSION \
org.opencontainers.image.revision=$APP_REVISION \
org.opencontainers.image.title="llama.cpp" \
org.opencontainers.image.description="LLM inference in C/C++" \
org.opencontainers.image.url=$IMAGE_URL \
org.opencontainers.image.source=$IMAGE_SOURCE
# -- Install runtime dependencies --
RUN yum install -y libgomp curl && \
yum clean all && \
rm -rf /var/cache/yum
# -- Set CANN environment variables (required for runtime) --
ENV ASCEND_TOOLKIT_HOME=/usr/local/Ascend/ascend-toolkit/latest
ENV LD_LIBRARY_PATH=/app:${ASCEND_TOOLKIT_HOME}/lib64:${LD_LIBRARY_PATH}
ENV PATH=${ASCEND_TOOLKIT_HOME}/bin:${PATH}
ENV ASCEND_OPP_PATH=${ASCEND_TOOLKIT_HOME}/opp
# ... You can add other environment variables from the original file as needed ...
WORKDIR /app
# Copy compiled .so files from the build stage
COPY --from=build /app/lib/ /app
# ==============================================================================
# FINAL STAGES (TARGETS)
# ==============================================================================
### Target: full
# Complete image with all tools, Python bindings, and dependencies
# ==============================================================================
FROM base AS full
COPY --from=build /app/full /app
# Install Python dependencies
RUN yum install -y git python3 python3-pip && \
pip3 install --no-cache-dir --upgrade pip setuptools wheel && \
pip3 install --no-cache-dir -r requirements.txt && \
yum clean all && \
rm -rf /var/cache/yum
# You need to provide a tools.sh script as the entrypoint
ENTRYPOINT ["/app/tools.sh"]
# If there is no tools.sh, you can set the default to start the server
# ENTRYPOINT ["/app/llama-server"]
### Target: light
# Lightweight image containing only llama-cli and llama-completion
# ==============================================================================
FROM base AS light
COPY --from=build /app/full/llama /app/full/llama-cli /app/full/llama-completion /app
ENTRYPOINT [ "/app/llama-cli" ]
### Target: server
# Dedicated server image containing only llama-server
# ==============================================================================
FROM base AS server
ENV LLAMA_ARG_HOST=0.0.0.0
COPY --from=build /app/full/llama /app/full/llama-server /app
HEALTHCHECK --interval=5m CMD [ "curl", "-f", "http://localhost:8080/health" ]
ENTRYPOINT [ "/app/llama-server" ]
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