Instructions to use jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1 with MLX:

# Download the model from the Hub
pip install huggingface_hub[hf_xet]

huggingface-cli download --local-dir Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1 jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1

llama-cpp-python

How to use jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1",
	filename="Mistral-7B-Instruct-v0.3-q-Chemistry-v0.1.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 jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1
# Run inference directly in the terminal:
llama-cli -hf jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1
# Run inference directly in the terminal:
llama-cli -hf jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1

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 jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1
# Run inference directly in the terminal:
./llama-cli -hf jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1

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 jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1

Use Docker

docker model run hf.co/jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1

LM Studio
Jan
Ollama
How to use jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1 with Ollama:
```
ollama run hf.co/jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1
```

Unsloth Studio

How to use jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1 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 jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1 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 jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1 to start chatting

How to use jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1 with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1"

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 jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1 with Docker Model Runner:
```
docker model run hf.co/jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1
```

Lemonade

How to use jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1

Run and chat with the model

lemonade run user.Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1-{{QUANT_TAG}}

List all available models

lemonade list

Finetuned on SmolInstruct's property prediction instruction dataset and HoneyBee's instruction dataset.

[LoRA Config Parameters] train: true, fine_tune_type: lora, seed: 0, num_layers: 8, batch_size: 2, iters: 1000, val_batches: 25, learning_rate: 1e-5, steps_per_report: 10, steps_per_eval: 200, resume_adapter_file: null, adapter_path: "adapters", save_every: 100, test: false, test_batches: 100, max_seq_length: 2048, grad_checkpoint: false, lora_parameters: keys: ["self_attn.q_proj", "self_attn.v_proj"] rank: 8 alpha: 8 dropout: 0.0 scale: 20.0

Downloads last month: 27

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1

Base model

mistralai/Mistral-7B-v0.3

Finetuned

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

Quantized

(263)

this model

jarvisloh
/

Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1

Model tree for jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1

Dataset used to train jarvisloh/Mistral-7B-Instruct-v0.3-q-Chemistry-gguf-v0.1