Instructions to use jackcloudman/Leanstral-2603-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jackcloudman/Leanstral-2603-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="jackcloudman/Leanstral-2603-GGUF",
	filename="mistralai_Leanstral-128x3.9B-2603-Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use jackcloudman/Leanstral-2603-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf jackcloudman/Leanstral-2603-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf jackcloudman/Leanstral-2603-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf jackcloudman/Leanstral-2603-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf jackcloudman/Leanstral-2603-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 jackcloudman/Leanstral-2603-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf jackcloudman/Leanstral-2603-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 jackcloudman/Leanstral-2603-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jackcloudman/Leanstral-2603-GGUF:Q4_K_M

Use Docker

docker model run hf.co/jackcloudman/Leanstral-2603-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use jackcloudman/Leanstral-2603-GGUF with Ollama:
```
ollama run hf.co/jackcloudman/Leanstral-2603-GGUF:Q4_K_M
```

Unsloth Studio new

How to use jackcloudman/Leanstral-2603-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 jackcloudman/Leanstral-2603-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 jackcloudman/Leanstral-2603-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jackcloudman/Leanstral-2603-GGUF to start chatting

Docker Model Runner
How to use jackcloudman/Leanstral-2603-GGUF with Docker Model Runner:
```
docker model run hf.co/jackcloudman/Leanstral-2603-GGUF:Q4_K_M
```

Lemonade

How to use jackcloudman/Leanstral-2603-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jackcloudman/Leanstral-2603-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Leanstral-2603-GGUF-Q4_K_M

List all available models

lemonade list

Leanstral 119B A6B - GGUF

GGUF quantizations of mistralai/Leanstral-2603 for use with llama.cpp.

Leanstral is the first open-source code agent designed for Lean 4, a proof assistant for formal mathematics and software verification. Built as part of the Mistral Small 4 family, it combines multimodal capabilities with an efficient MoE + MLA architecture.

Available Quantizations

File	Quant	Size	Description
`mistralai_Leanstral-128x3.9B-2603-Q4_K_M.gguf`	Q4_K_M	68 GB	Best balance of quality and size. Runs on 2x RTX 4090 + RAM offload
`mistralai_Leanstral-128x3.9B-2603-Q8_0.gguf`	Q8_0	118 GB	Near-lossless. Good base for custom requantization

Architecture

Type: Mixture of Experts (MoE) + Multi-head Latent Attention (MLA)
GGUF arch: deepseek2 (Mistral 4 uses the same architecture as DeepSeek V3)
Total parameters: 119B (6.5B active per token)
Experts: 128 routed + 1 shared, 4 active per token
MLA: q_lora_rank=1024, kv_lora_rank=256, qk_rope_head_dim=64
Context: Up to 1M tokens (256k recommended)
RoPE: YaRN scaling (factor=128, original_ctx=8192)
Vocab: 131,072 tokens (Tekken tokenizer)

How to Run

llama-server (recommended)

./llama-server \
  -m mistralai_Leanstral-128x3.9B-2603-Q4_K_M.gguf \
  -fit on -fa on \
  --host 0.0.0.0 \
  --ctx-size 128000 \
  --jinja \
  --chat-template-file chat_template.jinja

Note: You need a chat template that supports [THINK] blocks for reasoning. Download the template from the original model repo.

Reasoning

The model supports reasoning_effort via the chat template:

"high" - Enables thinking (recommended for Lean 4 proofs and complex tasks)
"none" - Direct answers without reasoning

Pass reasoning_effort in your API request body, or modify the chat template default.

Performance

On 2x RTX 4090 (48GB VRAM) + 192GB RAM with Q4_K_M:

~34 tokens/s generation speed
Model splits between GPU and system RAM automatically with -fit on

Conversion Details

Source: FP8 (e4m3) consolidated weights from mistralai/Leanstral-2603
Pipeline: FP8 consolidated → dequant to BF16 → Q8_0 GGUF → Q4_K_M GGUF (with --allow-requantize)
Converter: convert_hf_to_gguf.py with --mistral-format flag (llama.cpp)
Tokenizer: Tekken v15 (requires mistral-common >= 1.10.0 for conversion)

License

Apache 2.0 - same as the original model.

Credits

Original model by Mistral AI
Quantized by jackcloudman

Downloads last month: 428

GGUF

Model size

119B params

Architecture

deepseek2

Hardware compatibility

4-bit

8-bit

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