Text Generation
Transformers
GGUF
English
clinical
medical
instruction-following
tool-calling
function-calling
KOS-V4
from-scratch
conversational
Instructions to use Kentucky-Open-Science/KOS-V4-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kentucky-Open-Science/KOS-V4-Instruct-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Kentucky-Open-Science/KOS-V4-Instruct-GGUF")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Kentucky-Open-Science/KOS-V4-Instruct-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Kentucky-Open-Science/KOS-V4-Instruct-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 Kentucky-Open-Science/KOS-V4-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Kentucky-Open-Science/KOS-V4-Instruct-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 Kentucky-Open-Science/KOS-V4-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Kentucky-Open-Science/KOS-V4-Instruct-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 Kentucky-Open-Science/KOS-V4-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Kentucky-Open-Science/KOS-V4-Instruct-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 Kentucky-Open-Science/KOS-V4-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Kentucky-Open-Science/KOS-V4-Instruct-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Kentucky-Open-Science/KOS-V4-Instruct-GGUF:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use Kentucky-Open-Science/KOS-V4-Instruct-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Kentucky-Open-Science/KOS-V4-Instruct-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Kentucky-Open-Science/KOS-V4-Instruct-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Kentucky-Open-Science/KOS-V4-Instruct-GGUF:Q4_K_M
- SGLang
How to use Kentucky-Open-Science/KOS-V4-Instruct-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Kentucky-Open-Science/KOS-V4-Instruct-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Kentucky-Open-Science/KOS-V4-Instruct-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Kentucky-Open-Science/KOS-V4-Instruct-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Kentucky-Open-Science/KOS-V4-Instruct-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use Kentucky-Open-Science/KOS-V4-Instruct-GGUF with Ollama:
ollama run hf.co/Kentucky-Open-Science/KOS-V4-Instruct-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use Kentucky-Open-Science/KOS-V4-Instruct-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Kentucky-Open-Science/KOS-V4-Instruct-GGUF: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": "Kentucky-Open-Science/KOS-V4-Instruct-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Kentucky-Open-Science/KOS-V4-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/Kentucky-Open-Science/KOS-V4-Instruct-GGUF:Q4_K_M
- Lemonade
How to use Kentucky-Open-Science/KOS-V4-Instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Kentucky-Open-Science/KOS-V4-Instruct-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.KOS-V4-Instruct-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Kentucky-Open-Science/KOS-V4-Instruct-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Kentucky-Open-Science/KOS-V4-Instruct-GGUF: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 Kentucky-Open-Science/KOS-V4-Instruct-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Kentucky-Open-Science/KOS-V4-Instruct-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Kentucky-Open-Science/KOS-V4-Instruct-GGUF: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 "Kentucky-Open-Science/KOS-V4-Instruct-GGUF: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"
Upload testing/convert_gpt2.py with huggingface_hub
Browse files- testing/convert_gpt2.py +33 -0
testing/convert_gpt2.py
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"""Correct HF->GGUF f16 conversion for the rebuilt v4 ByteLevel tokenizer:
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force pre='gpt-2' (verified full token parity) instead of the wrong 'sarvam-moe'.
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Keeps the BYTE-retype patch for <0xXX> byte-fallback tokens."""
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import os, sys, re, gguf
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LL = "/workspace/llm/llama.cpp"
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SRC = "/workspace/llm/kos-v4-conv"
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OUT = "/workspace/llm/kos-v4-gguf/kosv4-f16.gguf"
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sys.path.insert(0, LL); os.chdir(LL)
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from conversion.base import TextModel
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def _pre(self, tokenizer):
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return "gpt-2"
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TextModel.get_vocab_base_pre = _pre
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_byte = re.compile(r"^<0x[0-9A-Fa-f]{2}>$")
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_orig_base = TextModel.get_vocab_base
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def _base_patched(self):
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tokens, toktypes, tokpre = _orig_base(self)
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n = 0
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for i, t in enumerate(tokens):
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if _byte.match(t) and toktypes[i] != gguf.TokenType.BYTE:
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toktypes[i] = gguf.TokenType.BYTE; n += 1
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if n: print(f"[patch] retyped {n} byte-fallback tokens as BYTE", file=sys.stderr)
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return tokens, toktypes, tokpre
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TextModel.get_vocab_base = _base_patched
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import convert_hf_to_gguf as conv
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sys.argv = ["convert_hf_to_gguf.py", "--outfile", OUT, "--outtype", "f16", SRC]
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try:
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conv.main()
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except SystemExit as e:
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if e.code not in (None, 0): raise
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print("[done] wrote", OUT)
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