Instructions to use Miki-T/JARVIS-Mistral-Phase1b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Miki-T/JARVIS-Mistral-Phase1b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Miki-T/JARVIS-Mistral-Phase1b-GGUF",
	filename="mistral-7b-jarvis-phase1b.Q4_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Miki-T/JARVIS-Mistral-Phase1b-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 Miki-T/JARVIS-Mistral-Phase1b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Miki-T/JARVIS-Mistral-Phase1b-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 Miki-T/JARVIS-Mistral-Phase1b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Miki-T/JARVIS-Mistral-Phase1b-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 Miki-T/JARVIS-Mistral-Phase1b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Miki-T/JARVIS-Mistral-Phase1b-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 Miki-T/JARVIS-Mistral-Phase1b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Miki-T/JARVIS-Mistral-Phase1b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Miki-T/JARVIS-Mistral-Phase1b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Miki-T/JARVIS-Mistral-Phase1b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Miki-T/JARVIS-Mistral-Phase1b-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Miki-T/JARVIS-Mistral-Phase1b-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Miki-T/JARVIS-Mistral-Phase1b-GGUF:Q4_K_M

Ollama
How to use Miki-T/JARVIS-Mistral-Phase1b-GGUF with Ollama:
```
ollama run hf.co/Miki-T/JARVIS-Mistral-Phase1b-GGUF:Q4_K_M
```

Unsloth Studio

How to use Miki-T/JARVIS-Mistral-Phase1b-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 Miki-T/JARVIS-Mistral-Phase1b-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 Miki-T/JARVIS-Mistral-Phase1b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Miki-T/JARVIS-Mistral-Phase1b-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use Miki-T/JARVIS-Mistral-Phase1b-GGUF with Docker Model Runner:
```
docker model run hf.co/Miki-T/JARVIS-Mistral-Phase1b-GGUF:Q4_K_M
```

Lemonade

How to use Miki-T/JARVIS-Mistral-Phase1b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Miki-T/JARVIS-Mistral-Phase1b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.JARVIS-Mistral-Phase1b-GGUF-Q4_K_M

List all available models

lemonade list

JARVIS-Mistral-Phase1b-GGUF

Model ID: Miki-T/JARVIS-Mistral-Phase1b-GGUF

GGUF-format versions of the JARVIS Mistral 7B Phase 1b model for use with llama.cpp and llama-cpp-python. This is the inference-ready deployment format of Miki-T/JARVIS-Mistral-Phase1b.

Files

File	Size	Description
`mistral-7b-jarvis-phase1b.Q4_K_M.gguf`	4.1GB	4-bit quantized — recommended for daily use
`mistral-7b-jarvis-phase1b.f16.gguf`	14.5GB	Full float16 — master file for requantization

Model Details

This model is the result of merging two LoRA adapters into base Mistral 7B:

Phase 1a adapter (Miki-T/JARVIS-Mistral-Phase1a) — Macedonian language foundation trained on 500k rows of Macedonian web text
Phase 1b adapter (Miki-T/JARVIS-Mistral-Phase1b) — Macedonian instruction following trained on 134k instruction-response pairs

The adapters were merged sequentially using merge_and_unload(), converted to GGUF via convert_hf_to_gguf.py, and quantized to Q4_K_M via llama-quantize.

Property	Value
Base model	mistralai/Mistral-7B-v0.1
Language	Macedonian (mk), English (en)
Format	GGUF
Quantization	Q4_K_M (recommended), f16 (master)
Context length	32768 tokens

Usage

With llama-cpp-python

from llama_cpp import Llama

model = Llama(
    model_path="mistral-7b-jarvis-phase1b.Q4_K_M.gguf",
    n_ctx=2048,
    n_gpu_layers=40,
    verbose=False,
)

prompt = "[INST] Кој е главен град на Македонија? [/INST]"
response = model(prompt, max_tokens=256, temperature=0.7)
print(response["choices"][0]["text"])
# Output: Скопје е главниот град на Македонија.

Prompt format

Uses Mistral instruct format: [INST] <> Your system prompt here <> User message [/INST] Assistant response

Hardware

Trained on: NVIDIA RTX 5070 (12GB VRAM)
Q4_K_M inference: ~4.1GB VRAM, runs on any GPU with 6GB+
f16 inference: ~14.5GB VRAM

Merge and Conversion

# Step 1 — Merge adapters
from transformers import AutoModelForCausalLM
from peft import PeftModel
import torch

model = AutoModelForCausalLM.from_pretrained(
    "mistralai/Mistral-7B-v0.1",
    torch_dtype=torch.float16,
    device_map="cpu",
)
model = PeftModel.from_pretrained(model, "path/to/phase1a/adapter")
model = model.merge_and_unload()
model = PeftModel.from_pretrained(model, "path/to/phase1b/adapter")
model = model.merge_and_unload()
model.save_pretrained("merged_model/")

# Step 2 — Convert to GGUF (requires llama.cpp)
# python convert_hf_to_gguf.py merged_model/ --outfile model.f16.gguf --outtype f16

# Step 3 — Quantize
# llama-quantize.exe model.f16.gguf model.Q4_K_M.gguf Q4_K_M

Related Repositories

Phase 1a adapter: Miki-T/JARVIS-Mistral-Phase1a
Phase 1b adapter: Miki-T/JARVIS-Mistral-Phase1b
Project: https://github.com/MikiTrajkovski/JARVIS

License

MIT License

Author: Miki Trajkovski | GitHub | HuggingFace

Last Updated: July 3, 2026

Downloads last month: 174

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

4-bit

16-bit

Model tree for Miki-T/JARVIS-Mistral-Phase1b-GGUF

Base model

mistralai/Mistral-7B-v0.1

Quantized

(193)

this model