Instructions to use Arki05/BLS-Mini-Code-1.0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Arki05/BLS-Mini-Code-1.0-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Arki05/BLS-Mini-Code-1.0-GGUF",
	filename="BLS-Mini-Code-1.0-BF16-00001-of-00002.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Arki05/BLS-Mini-Code-1.0-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Arki05/BLS-Mini-Code-1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Arki05/BLS-Mini-Code-1.0-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 Arki05/BLS-Mini-Code-1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Arki05/BLS-Mini-Code-1.0-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 Arki05/BLS-Mini-Code-1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Arki05/BLS-Mini-Code-1.0-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 Arki05/BLS-Mini-Code-1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Arki05/BLS-Mini-Code-1.0-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Arki05/BLS-Mini-Code-1.0-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Arki05/BLS-Mini-Code-1.0-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Arki05/BLS-Mini-Code-1.0-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": "Arki05/BLS-Mini-Code-1.0-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Arki05/BLS-Mini-Code-1.0-GGUF:Q4_K_M

Ollama
How to use Arki05/BLS-Mini-Code-1.0-GGUF with Ollama:
```
ollama run hf.co/Arki05/BLS-Mini-Code-1.0-GGUF:Q4_K_M
```

Unsloth Studio

How to use Arki05/BLS-Mini-Code-1.0-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 Arki05/BLS-Mini-Code-1.0-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 Arki05/BLS-Mini-Code-1.0-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Arki05/BLS-Mini-Code-1.0-GGUF to start chatting

How to use Arki05/BLS-Mini-Code-1.0-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Arki05/BLS-Mini-Code-1.0-GGUF:Q4_K_M

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": "Arki05/BLS-Mini-Code-1.0-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Arki05/BLS-Mini-Code-1.0-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Arki05/BLS-Mini-Code-1.0-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 Arki05/BLS-Mini-Code-1.0-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Arki05/BLS-Mini-Code-1.0-GGUF with Docker Model Runner:
```
docker model run hf.co/Arki05/BLS-Mini-Code-1.0-GGUF:Q4_K_M
```

Lemonade

How to use Arki05/BLS-Mini-Code-1.0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Arki05/BLS-Mini-Code-1.0-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.BLS-Mini-Code-1.0-GGUF-Q4_K_M

List all available models

lemonade list

BLS-Mini-Code-1.0 — GGUF

GGUF quantizations of CohereLabs/BLS-Mini-Code-1.0, a 30.5B-total / ~2.9B-active sparse MoE code model by Cohere (cohere2moe architecture: Command-R7B-style hybrid SWA/full attention with NoPE on global layers, parallel residual blocks, 128 fine-grained experts with sigmoid top-8 routing, reasoning-by-default chat format).

Status / requirements: needs llama.cpp with cohere2moe support — PR #24260 (not yet merged). Build that branch until it lands. The upstream model repo currently ships no license; these files inherit whatever terms Cohere attaches to the original weights.

Quants

All quality numbers are measured against the bf16 model as ground truth. The headline table uses wikitext-2 (test) — the only evaluation set that is fully held out from the imatrix calibration data — plus HumanEval/HumanEval+ (pass@1, greedy, thinking on, 6k token budget; remaining quants in progress).

file	size	PPL	mean KLD	top-1 %	HumanEval	HumanEval+
BF16 (2 shards)	61.0 GB	7.7126	—	—
Q8_0	32.4 GB	7.7356	0.007010	96.458	92.07	89.02
Q6_K	25.1 GB	7.7558	0.015611	94.602	93.29	88.41
Q5_K_M	21.7 GB	7.8333	0.020963	93.811	95.73	92.68
Q4_K_M	18.6 GB	7.9468	0.041855	91.342	93.29	90.24
IQ4_XS	16.4 GB	7.9794	0.049137	90.705	92.68	88.41
IQ3_M	13.6 GB	8.2776	0.112035	85.919	90.85	87.20
IQ2_M	10.3 GB	9.9756	0.283656	77.616	84.15	79.88
IQ2_XS	9.2 GB	11.0666	0.426120	73.339	79.88	77.44
IQ2_XXS	8.3 GB	12.6780	0.549859	69.743	59.15	59.15

HumanEval is pass@1 over 164 problems, so single-token greedy flips on a handful of problems move the score by a few points - read it as a sanity check, not a fine-grained ranking. The Q4-through-Q8 quants are statistically interchangeable on it (the spread is noise); mean KLD and top-1 % are the reliable quality ordering. The slope only becomes clear lower down: IQ3_M holds up, the IQ2 tier degrades visibly, and IQ2_XXS falls off a cliff (identical HumanEval/HumanEval+ is the giveaway - it produces enough malformed code that the extra tests prune almost nothing further).

Recommendations: Q5_K_M if you have the memory (effectively lossless), IQ4_XS for the best size/quality ratio (matches Q4_K_M at -2.2 GB), IQ3_M as the smallest quant still reasonable for code. The IQ2 tier exists for memory-constrained setups and degrades noticeably - use with expectations set accordingly. Embeddings are tied (also the output head) and kept at q6_K on Q4_K_M and below.

Per-domain breakdown

The three sets below are also part of the imatrix calibration corpus, so their numbers carry a mild in-distribution bias - read them as domain comparisons rather than held-out scores. All corpora are included in eval-corpora.tar.zst for reproduction.

General / multilingual (calibration_datav3)

bartowski's calibration_datav3: the de-facto community calibration mix - short English prose, multilingual snippets, code fragments, technical text and deliberate noise sections (~275 kB).

file	PPL	mean KLD	top-1 %
BF16	9.0079	—	—
Q8_0	9.0261	0.008424	96.788
Q6_K	9.0351	0.014500	95.286
Q5_K_M	9.0491	0.019470	94.506
Q4_K_M	9.1607	0.036786	92.031
IQ4_XS	9.1125	0.039540	91.882
IQ3_M	9.4710	0.087992	87.714
IQ2_M	10.2735	0.208782	80.580
IQ2_XS	11.1268	0.319906	76.376
IQ2_XXS	12.3083	0.427367	72.173

Code

A seeded random sample of real source files from the llama.cpp tree (MIT): C/C++ core and ggml, Python conversion tooling, shell scripts; capped at 25 kB per file, ~400 kB total. Note how confident the model is on code (PPL ~2.4) - and that top-1 agreement holds up better here than on prose at every quant level.

file	PPL	mean KLD	top-1 %
BF16	2.4043	—	—
Q8_0	2.4108	0.005231	98.512
Q6_K	2.4123	0.008321	97.731
Q5_K_M	2.4155	0.012198	97.145
Q4_K_M	2.4314	0.025947	95.898
IQ4_XS	2.4452	0.030205	95.472
IQ3_M	2.4996	0.072891	92.991
IQ2_M	2.7561	0.186894	88.646
IQ2_XS	3.0247	0.290555	85.260
IQ2_XXS	3.2342	0.368478	83.263

Chat (model-native format)

Hand-written for this release: 13 short programming conversations (Python/SQL/C/Rust/git topics, two in German), each with a thinking block, plus one complete tool-call round trip - rendered in the model's raw turn-token dialect (<|START_OF_TURN_TOKEN|>, <|START_THINKING|>, <|START_ACTION|>, ...). This exercises the control-token and expert-routing paths that real chat traffic hits and plain text never does. Small set (~7 chunks) - treat the numbers as indicative.

file	PPL	mean KLD	top-1 %
BF16	1.9660	—	—
Q8_0	1.9866	0.022651	98.431
Q6_K	1.9906	0.031189	98.170
Q5_K_M	1.9820	0.025972	97.778
Q4_K_M	1.9641	0.070232	96.993
IQ4_XS	1.9866	0.058722	96.601
IQ3_M	2.0809	0.081966	94.902
IQ2_M	2.1412	0.173477	92.288
IQ2_XS	2.1742	0.251918	89.412
IQ2_XXS	2.2247	0.297151	87.974

Reasoning / chat template

These GGUFs embed an additively normalized chat template (also in this repo as chat_template.jinja): the standard enable_thinking / reasoning_content conventions are mapped onto Cohere's native reasoning / reasoning_effort / thinking variables, so llama.cpp detects reasoning support automatically (thinking = 1), separates reasoning_content from content, and supports thinking toggles. All Cohere-native variables keep working; rendering is byte-identical for native invocations.

llama-server -m BLS-Mini-Code-1.0-Q5_K_M.gguf --jinja

thinking on (default): response arrives as reasoning_content + content
disable thinking per request: "chat_template_kwargs": {"enable_thinking": false} (or Cohere-native: {"reasoning_effort": "none"})
tool calling works through the OpenAI-compatible API (parallel calls included)

imatrix

BLS-Mini-Code-1.0.imatrix (included) was computed on the bf16 model over the v3 + code + chat mix described above (326x512-token chunks), reaching full coverage of all 128 experts in every layer.

Validation

f32 logit-level parity vs HF transformers on a truncated-expert variant of the checkpoint (full-vocab comparison at every position): top-1 agreement 26/27, mean |dlogprob| 0.012 - the only disagreement a 0.013 near-tie.
Tool calling, parallel calls, multi-turn with reasoning passback, and a live agentic tool-execution loop verified end to end via llama-server.
500k context advertised by the model; KV cache at long context stays small thanks to iSWA (only 13 of 49 layers are global; ~13.6 GB KV at 500k).

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GGUF

Model size

30B params

Architecture

cohere2moe

Hardware compatibility

2-bit

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5-bit

6-bit

8-bit

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Model tree for Arki05/BLS-Mini-Code-1.0-GGUF

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CohereLabs/BLS-Mini-Code-1.0

Quantized

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