Instructions to use Jaymerry/mistral-7b-itis-taxonomy-slm-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Jaymerry/mistral-7b-itis-taxonomy-slm-v3 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 Jaymerry/mistral-7b-itis-taxonomy-slm-v3
# Run inference directly in the terminal:
llama cli -hf Jaymerry/mistral-7b-itis-taxonomy-slm-v3

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Jaymerry/mistral-7b-itis-taxonomy-slm-v3
# Run inference directly in the terminal:
llama cli -hf Jaymerry/mistral-7b-itis-taxonomy-slm-v3

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 Jaymerry/mistral-7b-itis-taxonomy-slm-v3
# Run inference directly in the terminal:
./llama-cli -hf Jaymerry/mistral-7b-itis-taxonomy-slm-v3

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 Jaymerry/mistral-7b-itis-taxonomy-slm-v3
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Jaymerry/mistral-7b-itis-taxonomy-slm-v3

Use Docker

docker model run hf.co/Jaymerry/mistral-7b-itis-taxonomy-slm-v3

LM Studio
Jan
Ollama
How to use Jaymerry/mistral-7b-itis-taxonomy-slm-v3 with Ollama:
```
ollama run hf.co/Jaymerry/mistral-7b-itis-taxonomy-slm-v3
```

Unsloth Studio

How to use Jaymerry/mistral-7b-itis-taxonomy-slm-v3 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 Jaymerry/mistral-7b-itis-taxonomy-slm-v3 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 Jaymerry/mistral-7b-itis-taxonomy-slm-v3 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Jaymerry/mistral-7b-itis-taxonomy-slm-v3 to start chatting

How to use Jaymerry/mistral-7b-itis-taxonomy-slm-v3 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Jaymerry/mistral-7b-itis-taxonomy-slm-v3

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": "Jaymerry/mistral-7b-itis-taxonomy-slm-v3"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Jaymerry/mistral-7b-itis-taxonomy-slm-v3 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Jaymerry/mistral-7b-itis-taxonomy-slm-v3

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 Jaymerry/mistral-7b-itis-taxonomy-slm-v3

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Jaymerry/mistral-7b-itis-taxonomy-slm-v3 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Jaymerry/mistral-7b-itis-taxonomy-slm-v3

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 "Jaymerry/mistral-7b-itis-taxonomy-slm-v3" \
  --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"

Docker Model Runner
How to use Jaymerry/mistral-7b-itis-taxonomy-slm-v3 with Docker Model Runner:
```
docker model run hf.co/Jaymerry/mistral-7b-itis-taxonomy-slm-v3
```

Lemonade

How to use Jaymerry/mistral-7b-itis-taxonomy-slm-v3 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Jaymerry/mistral-7b-itis-taxonomy-slm-v3

Run and chat with the model

lemonade run user.mistral-7b-itis-taxonomy-slm-v3-{{QUANT_TAG}}

List all available models

lemonade list

ITIS Taxonomy SLM (Mistral 7B Fine-Tuned)

A lightweight Small Language Model (SLM) fine-tuned for taxonomy classification, based on Mistral-7B and optimized for local inference (GGUF). 👉 Use this model as:

an assistant
a formatter
a lightweight offline tool

👉 Do NOT use it as:

a replacement for ITIS
a source of scientific truth without verification

⚠️ Disclaimer

This model is not an authoritative source for full taxonomic lineage.

While it often produces well-structured outputs, individual lineage relationships may be incorrect.

Overview

This model is a domain-specific Small Language Model (SML) fine-tuned on structured data derived from the Integrated Taxonomic Information System (ITIS).

It explores how a relatively small model (~7B parameters) can perform on:

structured tasks
stable datasets
domain-specific reasoning

The goal is not to replace large models, but to demonstrate a lightweight, offline-capable alternative for constrained use cases.

Key Characteristics

Base model: Mistral 7B Instruct
Fine-tuning: QLoRA (4-bit)
Dataset: ~35K ITIS-derived samples
Quantization: GGUF (Q5_K_M)
Domain: Biological taxonomy
Designed for: offline / controlled environments

What This Model Does Well

This model performs best on short, structured outputs:

✅ Taxonomic rank identification
✅ Parent taxon identification
✅ Scientific ↔ common name mapping
✅ Basic validity checks
✅ Unknown taxon detection

👉 These tasks are highly reliable in practice

What Requires Caution

⚠️ Full Lineage Generation

Even when:

JSON is valid
structure looks correct

➡️ the taxonomy itself may be partially incorrect

Example risk:

correct genus
incorrect family

Benchmark (Strict Evaluation)

Task	Known Accuracy	Unknown Accuracy	Hallucination
Lineage	0.87	1.00	0.00
Parent	1.00	1.00	0.00
Rank	0.86	0.87	0.12
Validity	0.93	0.86	⚠️ Not measured

Key takeaway:

👉 Short tasks are reliable
👉 Long structured outputs require verification

Recommended Usage

High-confidence use cases

rank queries
parent queries
structured QA on known taxa
offline taxonomy assistants

How to Use

Option 1 — LM Studio

Download the .gguf file Load into LM Studio Use with prompt example below

Option 2 — llama.cpp

./main -m mistral-7b-itis-taxonomy-sml-v3.gguf -p "What is the lineage of Panthera leo?"

Option 3 — Python (recommended for tracking)

from huggingface_hub import hf_hub_download

model_path = hf_hub_download( repo_id="Jaymerry/mistral-7b-itis-taxonomy-slm-v3", filename="mistral-7b-itis-taxonomy-sml-v3.gguf" )

Example Prompt

➤ Taxonomic rank Input:

What taxonomic rank is Panthera leo?
Output:
- Species

Small Language Model (SML) Perspective

This project demonstrates that:

Small models can be effective when trained on structured, stable data.

Benefits:

low compute cost
offline usage
reproducibility
controlled knowledge scope

This is particularly relevant for:

enterprise use cases
scientific tooling
embedded / local AI

Dataset

Source:
Integrated Taxonomic Information System (ITIS)
https://www.itis.gov/

Properties:

public domain
structured
relatively stable over time

Legal Notice

ITIS data is public domain
this model is a statistical transformation, not a database
outputs may be incorrect or incomplete
verification is required for critical use

Limitations

domain-restricted
English-only
not general-purpose
lineage may contain factual errors
imperfect refusal on validity

Notes on Downloads

For accurate download tracking, prefer using: hf_hub_download(...) Downloads via direct links or external tools may not be counted.

Author

Jeremy Banchet (Jaymerry)

Downloads last month: 43

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

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Model tree for Jaymerry/mistral-7b-itis-taxonomy-slm-v3

Base model

mistralai/Mistral-7B-v0.3

Finetuned

mistralai/Mistral-7B-Instruct-v0.3

Quantized

(284)

this model

Collection including Jaymerry/mistral-7b-itis-taxonomy-slm-v3

Applied AI, Local Models & Data Quality

Collection

Personal experiments in domain fine-tuning, structured-data evaluation, local inference and reproducible data quality. • 3 items • Updated about 20 hours ago • 1