Instructions to use AlekseiPravdin/KSI-RPG-128k-7B-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AlekseiPravdin/KSI-RPG-128k-7B-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="AlekseiPravdin/KSI-RPG-128k-7B-gguf",
	filename="KSI-RPG-128k-7B.q2_k.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use AlekseiPravdin/KSI-RPG-128k-7B-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AlekseiPravdin/KSI-RPG-128k-7B-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf AlekseiPravdin/KSI-RPG-128k-7B-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AlekseiPravdin/KSI-RPG-128k-7B-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf AlekseiPravdin/KSI-RPG-128k-7B-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 AlekseiPravdin/KSI-RPG-128k-7B-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AlekseiPravdin/KSI-RPG-128k-7B-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 AlekseiPravdin/KSI-RPG-128k-7B-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AlekseiPravdin/KSI-RPG-128k-7B-gguf:Q4_K_M

Use Docker

docker model run hf.co/AlekseiPravdin/KSI-RPG-128k-7B-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use AlekseiPravdin/KSI-RPG-128k-7B-gguf with Ollama:
```
ollama run hf.co/AlekseiPravdin/KSI-RPG-128k-7B-gguf:Q4_K_M
```

Unsloth Studio

How to use AlekseiPravdin/KSI-RPG-128k-7B-gguf 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 AlekseiPravdin/KSI-RPG-128k-7B-gguf 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 AlekseiPravdin/KSI-RPG-128k-7B-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AlekseiPravdin/KSI-RPG-128k-7B-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use AlekseiPravdin/KSI-RPG-128k-7B-gguf with Docker Model Runner:
```
docker model run hf.co/AlekseiPravdin/KSI-RPG-128k-7B-gguf:Q4_K_M
```

Lemonade

How to use AlekseiPravdin/KSI-RPG-128k-7B-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AlekseiPravdin/KSI-RPG-128k-7B-gguf:Q4_K_M

Run and chat with the model

lemonade run user.KSI-RPG-128k-7B-gguf-Q4_K_M

List all available models

lemonade list

KSI-RPG-128k-7B-gguf / README.md

AlekseiPravdin

Update README.md

f978bbf verified about 2 years ago

preview code

raw

history blame contribute delete

2.8 kB

metadata

license: apache-2.0
tags:
  - merge
  - mergekit
  - lazymergekit
  - AlekseiPravdin/KSI-RP-NSK-128k-7B
  - flammenai/flammen18X-mistral-7B
  - gguf
  - Q2_K
  - Q3_K_L
  - Q3_K_M
  - Q3_K_S
  - Q4_0
  - Q4_1
  - Q4_K_S
  - Q4_k_m
  - Q5_0
  - Q5_1
  - Q6_K
  - Q5_K_S
  - Q5_k_m
  - Q8_0
  - 128k
language:
  - en
  - ru
  - th

KSI-RPG-128k-7B-GGUF ⭐️⭐️⭐️

KSI-RPG-128k-7B is a merge of the following models using mergekit:

🧩 Configuration

slices:
  - sources:
      - model: AlekseiPravdin/KSI-RP-NSK-128k-7B
        layer_range: [0, 32]
      - model: flammenai/flammen18X-mistral-7B
        layer_range: [0, 32]
merge_method: slerp
base_model: AlekseiPravdin/KSI-RP-NSK-128k-7B
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

Eval embedding benchmark (with 70 specific quesions):