Instructions to use tensorblock/notus-7b-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tensorblock/notus-7b-v1-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="tensorblock/notus-7b-v1-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tensorblock/notus-7b-v1-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use tensorblock/notus-7b-v1-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 tensorblock/notus-7b-v1-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf tensorblock/notus-7b-v1-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tensorblock/notus-7b-v1-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf tensorblock/notus-7b-v1-GGUF:Q2_K

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 tensorblock/notus-7b-v1-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf tensorblock/notus-7b-v1-GGUF:Q2_K

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 tensorblock/notus-7b-v1-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tensorblock/notus-7b-v1-GGUF:Q2_K

Use Docker

docker model run hf.co/tensorblock/notus-7b-v1-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use tensorblock/notus-7b-v1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tensorblock/notus-7b-v1-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": "tensorblock/notus-7b-v1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tensorblock/notus-7b-v1-GGUF:Q2_K

SGLang

How to use tensorblock/notus-7b-v1-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 "tensorblock/notus-7b-v1-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": "tensorblock/notus-7b-v1-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 "tensorblock/notus-7b-v1-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": "tensorblock/notus-7b-v1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use tensorblock/notus-7b-v1-GGUF with Ollama:
```
ollama run hf.co/tensorblock/notus-7b-v1-GGUF:Q2_K
```
Unsloth Desktop
Docker Model Runner
How to use tensorblock/notus-7b-v1-GGUF with Docker Model Runner:
```
docker model run hf.co/tensorblock/notus-7b-v1-GGUF:Q2_K
```

Lemonade

How to use tensorblock/notus-7b-v1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tensorblock/notus-7b-v1-GGUF:Q2_K

Run and chat with the model

lemonade run user.notus-7b-v1-GGUF-Q2_K

List all available models

lemonade list

Atomic Chat

notus-7b-v1-GGUF / README.md

morriszms

Update README.md

461438c verified about 1 year ago

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metadata

datasets:
  - argilla/ultrafeedback-binarized-preferences
language:
  - en
base_model: argilla/notus-7b-v1
library_name: transformers
pipeline_tag: text-generation
tags:
  - dpo
  - rlaif
  - preference
  - ultrafeedback
  - TensorBlock
  - GGUF
license: mit
model-index:
  - name: notus-7b-v1
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: AI2 Reasoning Challenge (25-Shot)
          type: ai2_arc
          config: ARC-Challenge
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: acc_norm
            value: 0.6459044368600683
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/datasets/open-llm-leaderboard/results/blob/main/argilla/notus-7b-v1/results_2023-11-29T22-16-51.521321.json
          name: Open LLM Leaderboard Results
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: HellaSwag (10-Shot)
          type: hellaswag
          split: validation
          args:
            num_few_shot: 10
        metrics:
          - type: acc_norm
            value: 0.8478390758812986
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/datasets/open-llm-leaderboard/results/blob/main/argilla/notus-7b-v1/results_2023-11-29T22-16-51.521321.json
          name: Open LLM Leaderboard Results
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: TruthfulQA (0-shot)
          type: truthful_qa
          config: multiple_choice
          split: validation
          args:
            num_few_shot: 0
        metrics:
          - type: mc2
            value: 0.5436768358952805
        source:
          url: >-
            https://huggingface.co/datasets/open-llm-leaderboard/results/blob/main/argilla/notus-7b-v1/results_2023-11-29T22-16-51.521321.json
          name: Open LLM Leaderboard Results
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU (5-Shot)
          type: cais/mmlu
          config: all
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 0.6303308230938872
            name: accuracy
        source:
          url: >-
            https://huggingface.co/datasets/open-llm-leaderboard/results/blob/main/argilla/notus-7b-v1/results_2023-11-29T22-16-51.521321.json
          name: Open LLM Leaderboard Results
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GSM8k (5-shot)
          type: gsm8k
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 0.1516300227445034
            name: accuracy
        source:
          url: >-
            https://huggingface.co/datasets/open-llm-leaderboard/results/blob/main/argilla/notus-7b-v1/results_2023-11-29T22-16-51.521321.json
          name: Open LLM Leaderboard Results
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Winogrande (5-shot)
          type: winogrande
          config: winogrande_xl
          split: validation
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 0.7940015785319653
            name: accuracy
        source:
          url: >-
            https://huggingface.co/datasets/open-llm-leaderboard/results/blob/main/argilla/notus-7b-v1/results_2023-11-29T22-16-51.521321.json
          name: Open LLM Leaderboard Results
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: AlpacaEval
          type: tatsu-lab/alpaca_eval
        metrics:
          - type: tatsu-lab/alpaca_eval
            value: 0.9142
            name: win rate
        source:
          url: https://tatsu-lab.github.io/alpaca_eval/
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MT-Bench
          type: unknown
        metrics:
          - type: unknown
            value: 7.3
            name: score
        source:
          url: https://huggingface.co/spaces/lmsys/mt-bench

argilla/notus-7b-v1 - GGUF

This repo contains GGUF format model files for argilla/notus-7b-v1.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

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## Prompt template

<|system|>
{system_prompt}</s>
<|user|>
{prompt}</s>
<|assistant|>

Model file specification

Filename	Quant type	File Size	Description
notus-7b-v1-Q2_K.gguf	Q2_K	2.532 GB	smallest, significant quality loss - not recommended for most purposes
notus-7b-v1-Q3_K_S.gguf	Q3_K_S	2.947 GB	very small, high quality loss
notus-7b-v1-Q3_K_M.gguf	Q3_K_M	3.277 GB	very small, high quality loss
notus-7b-v1-Q3_K_L.gguf	Q3_K_L	3.560 GB	small, substantial quality loss
notus-7b-v1-Q4_0.gguf	Q4_0	3.827 GB	legacy; small, very high quality loss - prefer using Q3_K_M
notus-7b-v1-Q4_K_S.gguf	Q4_K_S	3.856 GB	small, greater quality loss
notus-7b-v1-Q4_K_M.gguf	Q4_K_M	4.068 GB	medium, balanced quality - recommended
notus-7b-v1-Q5_0.gguf	Q5_0	4.654 GB	legacy; medium, balanced quality - prefer using Q4_K_M
notus-7b-v1-Q5_K_S.gguf	Q5_K_S	4.654 GB	large, low quality loss - recommended
notus-7b-v1-Q5_K_M.gguf	Q5_K_M	4.779 GB	large, very low quality loss - recommended
notus-7b-v1-Q6_K.gguf	Q6_K	5.534 GB	very large, extremely low quality loss
notus-7b-v1-Q8_0.gguf	Q8_0	7.167 GB	very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/notus-7b-v1-GGUF --include "notus-7b-v1-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/notus-7b-v1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'