Text Generation
GGUF
English
code
coder
qwen2.5
qwen2.5-coder
llama-cpp
llama.cpp
ollama
code-generation
tool-calling
conversational
cpu-inference
small-language-model
offline
sakthai
house-of-sak
Eval Results (legacy)
Eval Results
Instructions to use Nanthasit/sakthai-coder-1.5b 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 Nanthasit/sakthai-coder-1.5b 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 Nanthasit/sakthai-coder-1.5b:Q4_K_M # Run inference directly in the terminal: llama cli -hf Nanthasit/sakthai-coder-1.5b:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Nanthasit/sakthai-coder-1.5b:Q4_K_M # Run inference directly in the terminal: llama cli -hf Nanthasit/sakthai-coder-1.5b: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 Nanthasit/sakthai-coder-1.5b:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Nanthasit/sakthai-coder-1.5b: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 Nanthasit/sakthai-coder-1.5b:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Nanthasit/sakthai-coder-1.5b:Q4_K_M
Use Docker
docker model run hf.co/Nanthasit/sakthai-coder-1.5b:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Nanthasit/sakthai-coder-1.5b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Nanthasit/sakthai-coder-1.5b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Nanthasit/sakthai-coder-1.5b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Nanthasit/sakthai-coder-1.5b:Q4_K_M
- Ollama
How to use Nanthasit/sakthai-coder-1.5b with Ollama:
ollama run hf.co/Nanthasit/sakthai-coder-1.5b:Q4_K_M
- Unsloth Desktop
- Pi
How to use Nanthasit/sakthai-coder-1.5b with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Nanthasit/sakthai-coder-1.5b:Q4_K_M
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": "Nanthasit/sakthai-coder-1.5b:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Nanthasit/sakthai-coder-1.5b with Docker Model Runner:
docker model run hf.co/Nanthasit/sakthai-coder-1.5b:Q4_K_M
- Lemonade
How to use Nanthasit/sakthai-coder-1.5b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Nanthasit/sakthai-coder-1.5b:Q4_K_M
Run and chat with the model
lemonade run user.sakthai-coder-1.5b-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Nanthasit/sakthai-coder-1.5b with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Nanthasit/sakthai-coder-1.5b:Q4_K_M
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 Nanthasit/sakthai-coder-1.5b:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Nanthasit/sakthai-coder-1.5b with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Nanthasit/sakthai-coder-1.5b:Q4_K_M
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 "Nanthasit/sakthai-coder-1.5b:Q4_K_M" \ --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"
File size: 4,216 Bytes
6918355 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 | # Copyright (c) 2020-202x The virtualenv developers
#
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# distribute, sublicense, and/or sell copies of the Software, and to
# permit persons to whom the Software is furnished to do so, subject to
# the following conditions:
#
# The above copyright notice and this permission notice shall be
# included in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
# LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
# OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
# WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
# This file must be used using `source bin/activate.fish` *within a running fish ( http://fishshell.com ) session*.
# Do not run it directly.
function _bashify_path -d "Converts a fish path to something bash can recognize"
set fishy_path $argv
set bashy_path $fishy_path[1]
for path_part in $fishy_path[2..-1]
set bashy_path "$bashy_path:$path_part"
end
echo $bashy_path
end
function _fishify_path -d "Converts a bash path to something fish can recognize"
echo $argv | tr ':' '\n'
end
function deactivate -d 'Exit virtualenv mode and return to the normal environment.'
# reset old environment variables
if test -n "$_OLD_VIRTUAL_PATH"
# https://github.com/fish-shell/fish-shell/issues/436 altered PATH handling
if test (string sub -s 1 -l 1 $FISH_VERSION) -lt 3
set -gx PATH (_fishify_path "$_OLD_VIRTUAL_PATH")
else
set -gx PATH $_OLD_VIRTUAL_PATH
end
set -e _OLD_VIRTUAL_PATH
end
if test -n "$_OLD_VIRTUAL_PYTHONHOME"
set -gx PYTHONHOME "$_OLD_VIRTUAL_PYTHONHOME"
set -e _OLD_VIRTUAL_PYTHONHOME
end
if test -n "$_OLD_FISH_PROMPT_OVERRIDE"
and functions -q _old_fish_prompt
# Set an empty local `$fish_function_path` to allow the removal of `fish_prompt` using `functions -e`.
set -l fish_function_path
# Erase virtualenv's `fish_prompt` and restore the original.
functions -e fish_prompt
functions -c _old_fish_prompt fish_prompt
functions -e _old_fish_prompt
set -e _OLD_FISH_PROMPT_OVERRIDE
end
set -e VIRTUAL_ENV
set -e VIRTUAL_ENV_PROMPT
if test "$argv[1]" != 'nondestructive'
# Self-destruct!
functions -e pydoc
functions -e deactivate
functions -e _bashify_path
functions -e _fishify_path
end
end
# Unset irrelevant variables.
deactivate nondestructive
set -gx VIRTUAL_ENV '/opt/data/sakthai-skills-repo/.venv'
# https://github.com/fish-shell/fish-shell/issues/436 altered PATH handling
if test (string sub -s 1 -l 1 $FISH_VERSION) -lt 3
set -gx _OLD_VIRTUAL_PATH (_bashify_path $PATH)
else
set -gx _OLD_VIRTUAL_PATH $PATH
end
set -gx PATH "$VIRTUAL_ENV"'/bin' $PATH
# Prompt override provided?
# If not, just use the environment name.
if test -n 'sakthai-agent'
set -gx VIRTUAL_ENV_PROMPT 'sakthai-agent'
else
set -gx VIRTUAL_ENV_PROMPT (basename "$VIRTUAL_ENV")
end
# Unset `$PYTHONHOME` if set.
if set -q PYTHONHOME
set -gx _OLD_VIRTUAL_PYTHONHOME $PYTHONHOME
set -e PYTHONHOME
end
function pydoc
python -m pydoc $argv
end
if test -z "$VIRTUAL_ENV_DISABLE_PROMPT"
# Copy the current `fish_prompt` function as `_old_fish_prompt`.
functions -c fish_prompt _old_fish_prompt
function fish_prompt
# Run the user's prompt first; it might depend on (pipe)status.
set -l prompt (_old_fish_prompt)
printf '(%s) ' $VIRTUAL_ENV_PROMPT
string join -- \n $prompt # handle multi-line prompts
end
set -gx _OLD_FISH_PROMPT_OVERRIDE "$VIRTUAL_ENV"
end
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