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

Libraries

How to use tensorblock/BioNER-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tensorblock/BioNER-GGUF", dtype="auto")

llama-cpp-python

How to use tensorblock/BioNER-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tensorblock/BioNER-GGUF",
	filename="BioNER-Q2_K.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use tensorblock/BioNER-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tensorblock/BioNER-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/BioNER-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tensorblock/BioNER-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/BioNER-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/BioNER-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf tensorblock/BioNER-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/BioNER-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tensorblock/BioNER-GGUF:Q2_K

Use Docker

docker model run hf.co/tensorblock/BioNER-GGUF:Q2_K

LM Studio
Jan
Ollama
How to use tensorblock/BioNER-GGUF with Ollama:
```
ollama run hf.co/tensorblock/BioNER-GGUF:Q2_K
```

Unsloth Studio new

How to use tensorblock/BioNER-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 tensorblock/BioNER-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 tensorblock/BioNER-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tensorblock/BioNER-GGUF to start chatting

Pi new

How to use tensorblock/BioNER-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf tensorblock/BioNER-GGUF:Q2_K

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "tensorblock/BioNER-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use tensorblock/BioNER-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf tensorblock/BioNER-GGUF:Q2_K

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 tensorblock/BioNER-GGUF:Q2_K

Run Hermes

hermes

Docker Model Runner
How to use tensorblock/BioNER-GGUF with Docker Model Runner:
```
docker model run hf.co/tensorblock/BioNER-GGUF:Q2_K
```

Lemonade

How to use tensorblock/BioNER-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tensorblock/BioNER-GGUF:Q2_K

Run and chat with the model

lemonade run user.BioNER-GGUF-Q2_K

List all available models

lemonade list

haydn-jones/BioNER - GGUF

This repo contains GGUF format model files for haydn-jones/BioNER.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.

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## Prompt template

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

Cutting Knowledge Date: December 2023
Today Date: 26 Jul 2024
{system_prompt}
Analyze the given paragraph to identify and categorize small molecules and macromolecules/biologics (or classes thereof), including their synonyms.

Output format:
{"categories":["cat1"],"molecules":[{"name":"name","alternatives":["alt1","alt2"],"is_class":false}],"biologics":[{"name":"name","alternatives":[],"is_class":true}]}

Instructions:
1. Identify all small molecules and biologics in the paragraph
2. Tag each entity, including all synonyms
3. Assign one or more of the following category tags to the paragraph if relevant information is present
   - Structure/Properties, Chemistry, Pharmacology, Synthesis/Formulation, Safety/Regulation, None<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Model file specification

Filename	Quant type	File Size	Description
BioNER-Q2_K.gguf	Q2_K	3.179 GB	smallest, significant quality loss - not recommended for most purposes
BioNER-Q3_K_S.gguf	Q3_K_S	3.665 GB	very small, high quality loss
BioNER-Q3_K_M.gguf	Q3_K_M	4.019 GB	very small, high quality loss
BioNER-Q3_K_L.gguf	Q3_K_L	4.322 GB	small, substantial quality loss
BioNER-Q4_0.gguf	Q4_0	4.661 GB	legacy; small, very high quality loss - prefer using Q3_K_M
BioNER-Q4_K_S.gguf	Q4_K_S	4.693 GB	small, greater quality loss
BioNER-Q4_K_M.gguf	Q4_K_M	4.921 GB	medium, balanced quality - recommended
BioNER-Q5_0.gguf	Q5_0	5.599 GB	legacy; medium, balanced quality - prefer using Q4_K_M
BioNER-Q5_K_S.gguf	Q5_K_S	5.599 GB	large, low quality loss - recommended
BioNER-Q5_K_M.gguf	Q5_K_M	5.733 GB	large, very low quality loss - recommended
BioNER-Q6_K.gguf	Q6_K	6.596 GB	very large, extremely low quality loss
BioNER-Q8_0.gguf	Q8_0	8.541 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/BioNER-GGUF --include "BioNER-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/BioNER-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'

Downloads last month: 37

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

Inference Providers NEW

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Model tree for tensorblock/BioNER-GGUF

Base model

haydn-jones/BioNER

Quantized

(1)

this model