Instructions to use Icodetools/Vulcan-1-coding-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Icodetools/Vulcan-1-coding-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 Icodetools/Vulcan-1-coding-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Icodetools/Vulcan-1-coding-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 Icodetools/Vulcan-1-coding-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Icodetools/Vulcan-1-coding-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf Icodetools/Vulcan-1-coding-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf Icodetools/Vulcan-1-coding-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Icodetools/Vulcan-1-coding-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use Icodetools/Vulcan-1-coding-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Icodetools/Vulcan-1-coding-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Icodetools/Vulcan-1-coding-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Icodetools/Vulcan-1-coding-GGUF:Q4_K_M

Ollama
How to use Icodetools/Vulcan-1-coding-GGUF with Ollama:
```
ollama run hf.co/Icodetools/Vulcan-1-coding-GGUF:Q4_K_M
```
Unsloth Desktop

How to use Icodetools/Vulcan-1-coding-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Icodetools/Vulcan-1-coding-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Icodetools/Vulcan-1-coding-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Icodetools/Vulcan-1-coding-GGUF with Docker Model Runner:
```
docker model run hf.co/Icodetools/Vulcan-1-coding-GGUF:Q4_K_M
```

Lemonade

How to use Icodetools/Vulcan-1-coding-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Icodetools/Vulcan-1-coding-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Vulcan-1-coding-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Icodetools/Vulcan-1-coding-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Icodetools/Vulcan-1-coding-GGUF:Q4_K_M

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 Icodetools/Vulcan-1-coding-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Icodetools/Vulcan-1-coding-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Icodetools/Vulcan-1-coding-GGUF:Q4_K_M

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 "Icodetools/Vulcan-1-coding-GGUF:Q4_K_M" \
  --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"

Vulcan-1-coding-GGUF / README.md

Icodetools

Update README.md

5d25760 verified about 9 hours ago

preview code

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---
license: apache-2.0
language:
  - en
library_name: gguf
pipeline_tag: text-generation
base_model:
  - DRUPEs/CherryPit-1.2-2B
base_model_relation: merge
tags:
  - code
  - coding
  - reasoning
  - math
  - aime
  - thinking
  - gguf
  - llama.cpp
  - lm-studio
  - ollama
  - merge
  - model-merge
  - small-model
  - 2b
  - local-llm
  - on-device
  - quantized
  - q4_k_m
  - cherrypit
  - minicpm
  - integer-space
  - vulcan
---
# Vulcan-1-Coder (GGUF)

![image](https://cdn-uploads.huggingface.co/production/uploads/6aa007cf4ae94de9b1aaa30c/uzEAciENqItRjhT0bwydS.png)
Results used no tools and a benchmarking harness. Also quantised to q8. 

A 2.5B coding and reasoning model built by using Integer Space Artificial Selection on **[CherryPit 1.2](https://huggingface.co/DRUPEs/CherryPit-1.2-2B)**, 
an already excellent model made by the team at DRUPEs. This model was generated and tested as a `.gguf`. Details
of the method will be published later and released for all. I'm working hard to put everything together. 

**Made in an afternoon.** Vulcan was produced in a single afternoon along with about 30 other models (12 in its
class), all generated and tested in integer space, on a laptop with a **6 GB VRAM** GPU, using only my own tools.
No training runs, no cluster.

Every run, lasting just a few hours, resulted in success. The techique can work for all types of models. It's a data-led technique where the training is
squeezed out rather than training it on more data. If the information exists in the model, we select for it. 

Named after Vulcan, the Roman god of fire and the forge, the smith who crafts tools for the gods.

**Safetensors version: coming soon.**

## Files

| file | quant | size | notes |
|---|---|---|---|
| `Vulcan-1-Coder-Q4_K_M-league.gguf` | Q4_K_M | 1.56 GB | the exact weights that won the selection league |

## Results

Notes: This model has not been fully tested or trained further after selection. It may have flaws or issues, for example one time it sent a bunch of symbols.
But generally it scores high. All benchmarks are with no tools AND quantised. 

### AIME 2025

Run 1:
21 / 30 = 70.0% with thinking on, up to 24K tokens, temperature 1.0, 1 sample, Q8_0. Benchmarking harness. No tools.
Per-problem results: Will add soon to a site I'm building.
![image](https://cdn-uploads.huggingface.co/production/uploads/6aa007cf4ae94de9b1aaa30c/IUzjwqtu0LM4C3ExVLocX.png)
### AIME 2026
Notes: Although at this level, it could be noise, I wonder if selecting for the model at Q4_K_M found a model that's best at Q4_K_M. I'll get access to a better GPU soon and test.
Run 1:
**23 / 30 = 76.7%** with thinking on, up to 24K tokens, temperature 1.0, 1 sample, Q4_K_M. Benchmarking harness. No tools.
Per-problem results:  Will add soon to a site I'm building.
![image](https://cdn-uploads.huggingface.co/production/uploads/6aa007cf4ae94de9b1aaa30c/jgLV83-gVlDHg_P9rO07w.png)
Run 2:
21 / 30 = 70.0% with thinking on, up to 24K tokens, temperature 1.0, 1 sample, Q8_0. Benchmarking harness. No tools.
Per-problem results: Will add soon to a site I'm building.

### Selection league

Scored on hard coding questions, plus some maths, agentic and reasoning tasks. No repeat questions and a large pool to avoid over-selecting for benchmarks. 
NO BENCHMARK QUESTIONS, all generated by Claude including some questions that are extremely hard. The league questions varied between models and the questions
were not enough to find true representative results. 

(0 / 0.5 / 1 per answer, Q4_K_M, temperature 1.0). Vulcan finished first. Plain CherryPit 1.2 is shown as the baseline. Note: While this is at the baseline,
there are many generated models that were below it. CherryPit was chosen for a reason, it's well above its class.

![league results, part 1](images/league-1.png)
![league results, part 2](images/league-2.png)

| model | score per question |
|---|---|
| **Vulcan-1-Coder** | **0.763** |
| CherryPit 1.2 (base) | 0.611 |

> **Note:** the league questions varied between models, so these scores are not fully representative of a
> head-to-head comparison. A like-for-like benchmark is coming.

## Usage

Recommended sampling: **temperature 1.0, top-p 0.95**, thinking on. Give it room to think (16K+ tokens for
hard problems): short limits cut off its answers.

Works in llama.cpp, LM Studio, Ollama and anything else that runs GGUF. Same architecture and size as CherryPit 1.2.

```
llama-server -m Vulcan-1-Coder-Q4_K_M-league.gguf -ngl 99 -c 32768 --jinja
```

For questions, contact ryan@filioai.com

## Thanks

- **The team at DRUPEs** for [CherryPit 1.2](https://huggingface.co/DRUPEs/CherryPit-1.2-2B), the base of Vulcan.
- **NANI-Nithin** for the [OpenCodeReasoning-2 LoRA adapter](https://huggingface.co/NANI-Nithin/MiniCPM5-2B-OpenCodeReasoning2-LoRA).
- **NVIDIA** for the [OpenCodeReasoning-2](https://huggingface.co/datasets/nvidia/OpenCodeReasoning-2) dataset that adapter was trained on ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)).

## Things to come - progress

#### Paper and files released - 50%

Hand writing the paper and hand-making the diagrams without AI so I'll release it as soon as I can as a pre-print. 
I will remove secrets from the folder and clean it up. Folder currently has thousands of files and I'd say about 25% are relevant. 

#### More benchmarks - ?

Whenever I feel up to it, adding some now. 

Edit: Results from AIME 2026: Vulcan-1-Coder (Q4_K_M) benchmark harness no tools.

Settings: thinking on, max 24,000 tokens, temperature 1.0, top-p 0.95, 1 sample per problem, llama.cpp (CUDA).

| problem | Vulcan's answer | correct answer | result | tokens used |
|---|---|---|---|---|
| A01 | 277 | 277 | ✓ | 2,020 |
| A02 | 62 | 62 | ✓ | 4,937 |
| A03 | 79 | 79 | ✓ | 3,468 |
| A04 | 68 | 70 | ✗ | 3,198 |
| A05 | 65 | 65 | ✓ | 2,848 |
| A06 | 441 | 441 | ✓ | 2,155 |
| A07 | 396 | 396 | ✓ | 4,241 |
| A08 | 244 | 244 | ✓ | 3,317 |
| A09 | 29 | 29 | ✓ | 17,086 |
| A10 | 156 | 156 | ✓ | 9,186 |
| A11 | 896 | 896 | ✓ | 11,527 |
| A12 | 161 | 161 | ✓ | 18,540 |
| A13 | 39 | 39 | ✓ | 23,518 |
| A14 | 681 | 681 | ✓ | 7,540 |
| A15 | 2 | 83 | ✗ | 7,237 |
| A16 | 178 | 178 | ✓ | 1,914 |
| A17 | - | 243 | ✗ (hit token limit) | 24,000 |
| A18 | 505 | 503 | ✗ | 8,668 |
| A19 | 279 | 279 | ✓ | 5,581 |
| A20 | 190 | 190 | ✓ | 7,950 |
| A21 | 50 | 50 | ✓ | 4,905 |
| A22 | 754 | 754 | ✓ | 5,987 |
| A23 | 245 | 245 | ✓ | 10,713 |
| A24 | 669 | 669 | ✓ | 5,061 |
| A25 | 850 | 850 | ✓ | 6,960 |
| A26 | 132 | 132 | ✓ | 8,593 |
| A27 | 223 | 223 | ✓ | 10,525 |
| A28 | 12 | 107 | ✗ | 17,496 |
| A29 | 3 | 157 | ✗ | 18,535 |
| A30 | 243 | 393 | ✗ | 15,340 |

**Score: 23/30 = 76.7%**

#### Quant perplexity-solver

Find the quants with the best perplexity

This unfortunately requires a runtime to go  with it.

#### Further processing -  30%

I've discovered another technique that works to post-train one of these models. I'm still perfecting it but I'm already getting good results. I think the models can be optimised further.

#### Qwen 27b - 20%

Started the analysis process.

#### STEM - 50%

Static Embedding. **Experimental native runtime** for the `.stem` model format and embedding engine. The current runtime remains intentionally narrow: 
**static embedding tables, token IDs, FP32 or INT8, mean pooling and optional L2**. It is not yet a general-purpose encoder/ONNX/GGUF runner.
It creates a .STEM format that is faster, smaller, and more accurate.

## Licence

Released under the [Apache License 2.0](LICENSE). Vulcan-1-Coder is a modified work that includes weights from:

- [DRUPEs/CherryPit-1.2-2B](https://huggingface.co/DRUPEs/CherryPit-1.2-2B): Apache-2.0
- [openbmb/MiniCPM5-2B](https://huggingface.co/openbmb/MiniCPM5-2B): Apache-2.0
- [NANI-Nithin/MiniCPM5-2B-OpenCodeReasoning2-LoRA](https://huggingface.co/NANI-Nithin/MiniCPM5-2B-OpenCodeReasoning2-LoRA): Apache-2.0
  (trained on NVIDIA OpenCodeReasoning-2, CC-BY-4.0)
- [zzzluvst/MiniCPM5-2B-SWE-smith-GRPO](https://huggingface.co/zzzluvst/MiniCPM5-2B-SWE-smith-GRPO): Apache-2.0
- [Caldalis/MiniCPM5-2B-Math](https://huggingface.co/Caldalis/MiniCPM5-2B-Math): Apache-2.0

	---
	license: apache-2.0
	language:
	- en
	library_name: gguf
	pipeline_tag: text-generation
	base_model:
	- DRUPEs/CherryPit-1.2-2B
	base_model_relation: merge
	tags:
	- code
	- coding
	- reasoning
	- math
	- aime
	- thinking
	- gguf
	- llama.cpp
	- lm-studio
	- ollama
	- merge
	- model-merge
	- small-model
	- 2b
	- local-llm
	- on-device
	- quantized
	- q4_k_m
	- cherrypit
	- minicpm
	- integer-space
	- vulcan
	---

	# Vulcan-1-Coder (GGUF)

	![image](https://cdn-uploads.huggingface.co/production/uploads/6aa007cf4ae94de9b1aaa30c/uzEAciENqItRjhT0bwydS.png)
	Results used no tools and a benchmarking harness. Also quantised to q8.

	A 2.5B coding and reasoning model built by using Integer Space Artificial Selection on [CherryPit 1.2](https://huggingface.co/DRUPEs/CherryPit-1.2-2B),
	an already excellent model made by the team at DRUPEs. This model was generated and tested as a `.gguf`. Details
	of the method will be published later and released for all. I'm working hard to put everything together.

	Made in an afternoon. Vulcan was produced in a single afternoon along with about 30 other models (12 in its
	class), all generated and tested in integer space, on a laptop with a 6 GB VRAM GPU, using only my own tools.
	No training runs, no cluster.

	Every run, lasting just a few hours, resulted in success. The techique can work for all types of models. It's a data-led technique where the training is
	squeezed out rather than training it on more data. If the information exists in the model, we select for it.

	Named after Vulcan, the Roman god of fire and the forge, the smith who crafts tools for the gods.

	Safetensors version: coming soon.

	## Files

	\| file \| quant \| size \| notes \|
	\|---\|---\|---\|---\|
	\| `Vulcan-1-Coder-Q4_K_M-league.gguf` \| Q4_K_M \| 1.56 GB \| the exact weights that won the selection league \|

	## Results

	Notes: This model has not been fully tested or trained further after selection. It may have flaws or issues, for example one time it sent a bunch of symbols.
	But generally it scores high. All benchmarks are with no tools AND quantised.

	### AIME 2025

	Run 1:
	21 / 30 = 70.0% with thinking on, up to 24K tokens, temperature 1.0, 1 sample, Q8_0. Benchmarking harness. No tools.
	Per-problem results: Will add soon to a site I'm building.

	![image](https://cdn-uploads.huggingface.co/production/uploads/6aa007cf4ae94de9b1aaa30c/IUzjwqtu0LM4C3ExVLocX.png)

	### AIME 2026

	Notes: Although at this level, it could be noise, I wonder if selecting for the model at Q4_K_M found a model that's best at Q4_K_M. I'll get access to a better GPU soon and test.

	Run 1:
	23 / 30 = 76.7% with thinking on, up to 24K tokens, temperature 1.0, 1 sample, Q4_K_M. Benchmarking harness. No tools.
	Per-problem results: Will add soon to a site I'm building.

	![image](https://cdn-uploads.huggingface.co/production/uploads/6aa007cf4ae94de9b1aaa30c/jgLV83-gVlDHg_P9rO07w.png)

	Run 2:
	21 / 30 = 70.0% with thinking on, up to 24K tokens, temperature 1.0, 1 sample, Q8_0. Benchmarking harness. No tools.
	Per-problem results: Will add soon to a site I'm building.

	### Selection league

	Scored on hard coding questions, plus some maths, agentic and reasoning tasks. No repeat questions and a large pool to avoid over-selecting for benchmarks.
	NO BENCHMARK QUESTIONS, all generated by Claude including some questions that are extremely hard. The league questions varied between models and the questions
	were not enough to find true representative results.

	(0 / 0.5 / 1 per answer, Q4_K_M, temperature 1.0). Vulcan finished first. Plain CherryPit 1.2 is shown as the baseline. Note: While this is at the baseline,
	there are many generated models that were below it. CherryPit was chosen for a reason, it's well above its class.

	![league results, part 1](images/league-1.png)
	![league results, part 2](images/league-2.png)

	\| model \| score per question \|
	\|---\|---\|
	\| Vulcan-1-Coder \| 0.763 \|
	\| CherryPit 1.2 (base) \| 0.611 \|

	> Note: the league questions varied between models, so these scores are not fully representative of a
	> head-to-head comparison. A like-for-like benchmark is coming.

	## Usage

	Recommended sampling: temperature 1.0, top-p 0.95, thinking on. Give it room to think (16K+ tokens for
	hard problems): short limits cut off its answers.

	Works in llama.cpp, LM Studio, Ollama and anything else that runs GGUF. Same architecture and size as CherryPit 1.2.

	```
	llama-server -m Vulcan-1-Coder-Q4_K_M-league.gguf -ngl 99 -c 32768 --jinja
	```

	For questions, contact ryan@filioai.com

	## Thanks

	- The team at DRUPEs for [CherryPit 1.2](https://huggingface.co/DRUPEs/CherryPit-1.2-2B), the base of Vulcan.
	- NANI-Nithin for the [OpenCodeReasoning-2 LoRA adapter](https://huggingface.co/NANI-Nithin/MiniCPM5-2B-OpenCodeReasoning2-LoRA).
	- NVIDIA for the [OpenCodeReasoning-2](https://huggingface.co/datasets/nvidia/OpenCodeReasoning-2) dataset that adapter was trained on ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)).

	## Things to come - progress

	#### Paper and files released - 50%

	Hand writing the paper and hand-making the diagrams without AI so I'll release it as soon as I can as a pre-print.
	I will remove secrets from the folder and clean it up. Folder currently has thousands of files and I'd say about 25% are relevant.

	#### More benchmarks - ?

	Whenever I feel up to it, adding some now.

	Edit: Results from AIME 2026: Vulcan-1-Coder (Q4_K_M) benchmark harness no tools.

	Settings: thinking on, max 24,000 tokens, temperature 1.0, top-p 0.95, 1 sample per problem, llama.cpp (CUDA).

	\| problem \| Vulcan's answer \| correct answer \| result \| tokens used \|
	\|---\|---\|---\|---\|---\|
	\| A01 \| 277 \| 277 \| ✓ \| 2,020 \|
	\| A02 \| 62 \| 62 \| ✓ \| 4,937 \|
	\| A03 \| 79 \| 79 \| ✓ \| 3,468 \|
	\| A04 \| 68 \| 70 \| ✗ \| 3,198 \|
	\| A05 \| 65 \| 65 \| ✓ \| 2,848 \|
	\| A06 \| 441 \| 441 \| ✓ \| 2,155 \|
	\| A07 \| 396 \| 396 \| ✓ \| 4,241 \|
	\| A08 \| 244 \| 244 \| ✓ \| 3,317 \|
	\| A09 \| 29 \| 29 \| ✓ \| 17,086 \|
	\| A10 \| 156 \| 156 \| ✓ \| 9,186 \|
	\| A11 \| 896 \| 896 \| ✓ \| 11,527 \|
	\| A12 \| 161 \| 161 \| ✓ \| 18,540 \|
	\| A13 \| 39 \| 39 \| ✓ \| 23,518 \|
	\| A14 \| 681 \| 681 \| ✓ \| 7,540 \|
	\| A15 \| 2 \| 83 \| ✗ \| 7,237 \|
	\| A16 \| 178 \| 178 \| ✓ \| 1,914 \|
	\| A17 \| - \| 243 \| ✗ (hit token limit) \| 24,000 \|
	\| A18 \| 505 \| 503 \| ✗ \| 8,668 \|
	\| A19 \| 279 \| 279 \| ✓ \| 5,581 \|
	\| A20 \| 190 \| 190 \| ✓ \| 7,950 \|
	\| A21 \| 50 \| 50 \| ✓ \| 4,905 \|
	\| A22 \| 754 \| 754 \| ✓ \| 5,987 \|
	\| A23 \| 245 \| 245 \| ✓ \| 10,713 \|
	\| A24 \| 669 \| 669 \| ✓ \| 5,061 \|
	\| A25 \| 850 \| 850 \| ✓ \| 6,960 \|
	\| A26 \| 132 \| 132 \| ✓ \| 8,593 \|
	\| A27 \| 223 \| 223 \| ✓ \| 10,525 \|
	\| A28 \| 12 \| 107 \| ✗ \| 17,496 \|
	\| A29 \| 3 \| 157 \| ✗ \| 18,535 \|
	\| A30 \| 243 \| 393 \| ✗ \| 15,340 \|

	Score: 23/30 = 76.7%

	#### Quant perplexity-solver

	Find the quants with the best perplexity

	This unfortunately requires a runtime to go with it.

	#### Further processing - 30%

	I've discovered another technique that works to post-train one of these models. I'm still perfecting it but I'm already getting good results. I think the models can be optimised further.

	#### Qwen 27b - 20%

	Started the analysis process.

	#### STEM - 50%

	Static Embedding. Experimental native runtime for the `.stem` model format and embedding engine. The current runtime remains intentionally narrow:
	static embedding tables, token IDs, FP32 or INT8, mean pooling and optional L2. It is not yet a general-purpose encoder/ONNX/GGUF runner.
	It creates a .STEM format that is faster, smaller, and more accurate.

	## Licence

	Released under the [Apache License 2.0](LICENSE). Vulcan-1-Coder is a modified work that includes weights from:

	- [DRUPEs/CherryPit-1.2-2B](https://huggingface.co/DRUPEs/CherryPit-1.2-2B): Apache-2.0
	- [openbmb/MiniCPM5-2B](https://huggingface.co/openbmb/MiniCPM5-2B): Apache-2.0
	- [NANI-Nithin/MiniCPM5-2B-OpenCodeReasoning2-LoRA](https://huggingface.co/NANI-Nithin/MiniCPM5-2B-OpenCodeReasoning2-LoRA): Apache-2.0
	(trained on NVIDIA OpenCodeReasoning-2, CC-BY-4.0)
	- [zzzluvst/MiniCPM5-2B-SWE-smith-GRPO](https://huggingface.co/zzzluvst/MiniCPM5-2B-SWE-smith-GRPO): Apache-2.0
	- [Caldalis/MiniCPM5-2B-Math](https://huggingface.co/Caldalis/MiniCPM5-2B-Math): Apache-2.0