Instructions to use RichardErkhov/mhenrichsen_-_danskgpt-tiny-gguf 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 RichardErkhov/mhenrichsen_-_danskgpt-tiny-gguf 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 RichardErkhov/mhenrichsen_-_danskgpt-tiny-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf RichardErkhov/mhenrichsen_-_danskgpt-tiny-gguf:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf RichardErkhov/mhenrichsen_-_danskgpt-tiny-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf RichardErkhov/mhenrichsen_-_danskgpt-tiny-gguf: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 RichardErkhov/mhenrichsen_-_danskgpt-tiny-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf RichardErkhov/mhenrichsen_-_danskgpt-tiny-gguf: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 RichardErkhov/mhenrichsen_-_danskgpt-tiny-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf RichardErkhov/mhenrichsen_-_danskgpt-tiny-gguf:Q4_K_M
Use Docker
docker model run hf.co/RichardErkhov/mhenrichsen_-_danskgpt-tiny-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use RichardErkhov/mhenrichsen_-_danskgpt-tiny-gguf with Ollama:
ollama run hf.co/RichardErkhov/mhenrichsen_-_danskgpt-tiny-gguf:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use RichardErkhov/mhenrichsen_-_danskgpt-tiny-gguf with Docker Model Runner:
docker model run hf.co/RichardErkhov/mhenrichsen_-_danskgpt-tiny-gguf:Q4_K_M
- Lemonade
How to use RichardErkhov/mhenrichsen_-_danskgpt-tiny-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RichardErkhov/mhenrichsen_-_danskgpt-tiny-gguf:Q4_K_M
Run and chat with the model
lemonade run user.mhenrichsen_-_danskgpt-tiny-gguf-Q4_K_M
List all available models
lemonade list
- Atomic Chat
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Quantization made by Richard Erkhov.
danskgpt-tiny - GGUF
- Model creator: https://huggingface.co/mhenrichsen/
- Original model: https://huggingface.co/mhenrichsen/danskgpt-tiny/
| Name | Quant method | Size |
|---|---|---|
| danskgpt-tiny.Q2_K.gguf | Q2_K | 0.4GB |
| danskgpt-tiny.IQ3_XS.gguf | IQ3_XS | 0.44GB |
| danskgpt-tiny.IQ3_S.gguf | IQ3_S | 0.47GB |
| danskgpt-tiny.Q3_K_S.gguf | Q3_K_S | 0.47GB |
| danskgpt-tiny.IQ3_M.gguf | IQ3_M | 0.48GB |
| danskgpt-tiny.Q3_K.gguf | Q3_K | 0.51GB |
| danskgpt-tiny.Q3_K_M.gguf | Q3_K_M | 0.51GB |
| danskgpt-tiny.Q3_K_L.gguf | Q3_K_L | 0.55GB |
| danskgpt-tiny.IQ4_XS.gguf | IQ4_XS | 0.57GB |
| danskgpt-tiny.Q4_0.gguf | Q4_0 | 0.59GB |
| danskgpt-tiny.IQ4_NL.gguf | IQ4_NL | 0.6GB |
| danskgpt-tiny.Q4_K_S.gguf | Q4_K_S | 0.6GB |
| danskgpt-tiny.Q4_K.gguf | Q4_K | 0.62GB |
| danskgpt-tiny.Q4_K_M.gguf | Q4_K_M | 0.62GB |
| danskgpt-tiny.Q4_1.gguf | Q4_1 | 0.65GB |
| danskgpt-tiny.Q5_0.gguf | Q5_0 | 0.71GB |
| danskgpt-tiny.Q5_K_S.gguf | Q5_K_S | 0.71GB |
| danskgpt-tiny.Q5_K.gguf | Q5_K | 0.73GB |
| danskgpt-tiny.Q5_K_M.gguf | Q5_K_M | 0.73GB |
| danskgpt-tiny.Q5_1.gguf | Q5_1 | 0.77GB |
| danskgpt-tiny.Q6_K.gguf | Q6_K | 0.84GB |
| danskgpt-tiny.Q8_0.gguf | Q8_0 | 1.09GB |
Original model description:
license: apache-2.0 language: - da
DanskGPT-tiny
DanskGPT-tiny er en 1,1 milliard parametre LLaMA baseret LLM. Træningen er en fortsættelse af TinyLLaMA.
Modellen er trænet på 8 milliarder tokens af dansk syntetisk tekst.
Denne model er en såkaldt "foundation/completion" model, og er derfor ikke beregnet til at chatte med.
Inferens
Ved brug af vLLM.
pip install vllm
from vllm import LLM, SamplingParams
sampling_params = SamplingParams(temperature=0.8, top_p=0.95, max_tokens=512)
llm = LLM(model="mhenrichsen/danskgpt-tiny")
while True:
prompt = input("Skriv: ")
outputs = llm.generate(prompt, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
Brug for hjælp?
Har du spørgsmål eller brug for hjælp til LLM'er eller automatisering af tekstbaserede opgaver, så kontakt mig gerne.
/Mads
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