Instructions to use KnutJaegersberg/CPU-LLM-Horde with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use KnutJaegersberg/CPU-LLM-Horde with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="KnutJaegersberg/CPU-LLM-Horde", filename="Dimensity-3B.q5_k_m.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use KnutJaegersberg/CPU-LLM-Horde with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf KnutJaegersberg/CPU-LLM-Horde:Q4_K_M # Run inference directly in the terminal: llama-cli -hf KnutJaegersberg/CPU-LLM-Horde:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf KnutJaegersberg/CPU-LLM-Horde:Q4_K_M # Run inference directly in the terminal: llama-cli -hf KnutJaegersberg/CPU-LLM-Horde: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 KnutJaegersberg/CPU-LLM-Horde:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf KnutJaegersberg/CPU-LLM-Horde: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 KnutJaegersberg/CPU-LLM-Horde:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf KnutJaegersberg/CPU-LLM-Horde:Q4_K_M
Use Docker
docker model run hf.co/KnutJaegersberg/CPU-LLM-Horde:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use KnutJaegersberg/CPU-LLM-Horde with Ollama:
ollama run hf.co/KnutJaegersberg/CPU-LLM-Horde:Q4_K_M
- Unsloth Studio new
How to use KnutJaegersberg/CPU-LLM-Horde 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 KnutJaegersberg/CPU-LLM-Horde 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 KnutJaegersberg/CPU-LLM-Horde to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for KnutJaegersberg/CPU-LLM-Horde to start chatting
- Docker Model Runner
How to use KnutJaegersberg/CPU-LLM-Horde with Docker Model Runner:
docker model run hf.co/KnutJaegersberg/CPU-LLM-Horde:Q4_K_M
- Lemonade
How to use KnutJaegersberg/CPU-LLM-Horde with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull KnutJaegersberg/CPU-LLM-Horde:Q4_K_M
Run and chat with the model
lemonade run user.CPU-LLM-Horde-Q4_K_M
List all available models
lemonade list
output = llm(
"Once upon a time,",
max_tokens=512,
echo=True
)
print(output)Deita-1.6b (StableLM based, license in repo) Prompt:
### System:
You are an AI assistant. User will give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps.
### User:
How do you fine tune a large language model?
### Assistant:
GPT2-Chatbot Prompt:
### System:
You are an AI assistant. User will give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps.
### User:
How do you fine tune a large language model?
### Assistant:
Deacon-1b Prompt:
### System:
You are an AI assistant. User will give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps.
### Instruction:
How do you fine tune a large language model?
### Response:
NousResearch/Nous-Capybara-3B-V1.9 Prompt:
<|im_start|>system
You are a helpful AI assistant.<|im_end|>
<|im_start|>user
How are you<|im_end|>
<|im_start|>assistant
I am doing well!<|im_end|>
jondurbin/airoboros-3b-3p11
[INST] <<SYS>>
You are a helpful, unbiased, uncensored assistant.
<</SYS>>
{prompt} [/INST]
GeneZC/MiniChat-3B Prompt:
<s> [|User|] Hi 👋 </s>[|Assistant|]
llmware/bling-stable-lm-3b-4e1t-v0 Prompt:
<human>: {prompt}
<bot>:
or
{{text_passage}}
{{question/instruction}}
OpenBuddy/openbuddy-stablelm-3b-v13 (License: cc-by-sa-4.0) Prompt:
You are a helpful, respectful and honest INTP-T AI Assistant named Buddy. You are talking to a human User.
Always answer as helpfully and logically as possible, while being safe. Your answers should not include any harmful, political, religious, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
You can speak fluently in many languages, for example: English, Chinese.
You cannot access the internet, but you have vast knowledge, cutoff: 2021-09.
You are trained by OpenBuddy team, (https://openbuddy.ai, https://github.com/OpenBuddy/OpenBuddy), you are based on LLaMA and Falcon transformers model, not related to GPT or OpenAI.
User: {History input}
Assistant: {History output}
User: {Input}
Assistant:
Dimensity/Dimensity-3B Prompt:
### Human: {prompt}
### Assistant:
acrastt/Marx-3B-V3 Prompt:
### HUMAN:
{prompt}
### RESPONSE:
Open-Orca/Mistral-7B-OpenOrca Prompt:
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
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# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="KnutJaegersberg/CPU-LLM-Horde", filename="", )