Instructions to use doncxy/BIJA-cerebellum-Qwen3-1.7B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use doncxy/BIJA-cerebellum-Qwen3-1.7B-v1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="doncxy/BIJA-cerebellum-Qwen3-1.7B-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("doncxy/BIJA-cerebellum-Qwen3-1.7B-v1", dtype="auto")

MLX

How to use doncxy/BIJA-cerebellum-Qwen3-1.7B-v1 with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("doncxy/BIJA-cerebellum-Qwen3-1.7B-v1")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

llama-cpp-python

How to use doncxy/BIJA-cerebellum-Qwen3-1.7B-v1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="doncxy/BIJA-cerebellum-Qwen3-1.7B-v1",
	filename="Qwen3-1.7B-BIJA-cerebellum-Q8_0.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 doncxy/BIJA-cerebellum-Qwen3-1.7B-v1 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf doncxy/BIJA-cerebellum-Qwen3-1.7B-v1:Q8_0
# Run inference directly in the terminal:
llama-cli -hf doncxy/BIJA-cerebellum-Qwen3-1.7B-v1:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf doncxy/BIJA-cerebellum-Qwen3-1.7B-v1:Q8_0
# Run inference directly in the terminal:
llama-cli -hf doncxy/BIJA-cerebellum-Qwen3-1.7B-v1:Q8_0

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 doncxy/BIJA-cerebellum-Qwen3-1.7B-v1:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf doncxy/BIJA-cerebellum-Qwen3-1.7B-v1:Q8_0

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 doncxy/BIJA-cerebellum-Qwen3-1.7B-v1:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf doncxy/BIJA-cerebellum-Qwen3-1.7B-v1:Q8_0

Use Docker

docker model run hf.co/doncxy/BIJA-cerebellum-Qwen3-1.7B-v1:Q8_0

LM Studio
Jan

vLLM

How to use doncxy/BIJA-cerebellum-Qwen3-1.7B-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "doncxy/BIJA-cerebellum-Qwen3-1.7B-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "doncxy/BIJA-cerebellum-Qwen3-1.7B-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/doncxy/BIJA-cerebellum-Qwen3-1.7B-v1:Q8_0

SGLang

How to use doncxy/BIJA-cerebellum-Qwen3-1.7B-v1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "doncxy/BIJA-cerebellum-Qwen3-1.7B-v1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "doncxy/BIJA-cerebellum-Qwen3-1.7B-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "doncxy/BIJA-cerebellum-Qwen3-1.7B-v1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "doncxy/BIJA-cerebellum-Qwen3-1.7B-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use doncxy/BIJA-cerebellum-Qwen3-1.7B-v1 with Ollama:
```
ollama run hf.co/doncxy/BIJA-cerebellum-Qwen3-1.7B-v1:Q8_0
```

Unsloth Studio

How to use doncxy/BIJA-cerebellum-Qwen3-1.7B-v1 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 doncxy/BIJA-cerebellum-Qwen3-1.7B-v1 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 doncxy/BIJA-cerebellum-Qwen3-1.7B-v1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for doncxy/BIJA-cerebellum-Qwen3-1.7B-v1 to start chatting

How to use doncxy/BIJA-cerebellum-Qwen3-1.7B-v1 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "doncxy/BIJA-cerebellum-Qwen3-1.7B-v1"

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": "doncxy/BIJA-cerebellum-Qwen3-1.7B-v1"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use doncxy/BIJA-cerebellum-Qwen3-1.7B-v1 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 "doncxy/BIJA-cerebellum-Qwen3-1.7B-v1"

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 doncxy/BIJA-cerebellum-Qwen3-1.7B-v1

Run Hermes

hermes

MLX LM

How to use doncxy/BIJA-cerebellum-Qwen3-1.7B-v1 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "doncxy/BIJA-cerebellum-Qwen3-1.7B-v1"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "doncxy/BIJA-cerebellum-Qwen3-1.7B-v1"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "doncxy/BIJA-cerebellum-Qwen3-1.7B-v1",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use doncxy/BIJA-cerebellum-Qwen3-1.7B-v1 with Docker Model Runner:
```
docker model run hf.co/doncxy/BIJA-cerebellum-Qwen3-1.7B-v1:Q8_0
```

Lemonade

How to use doncxy/BIJA-cerebellum-Qwen3-1.7B-v1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull doncxy/BIJA-cerebellum-Qwen3-1.7B-v1:Q8_0

Run and chat with the model

lemonade run user.BIJA-cerebellum-Qwen3-1.7B-v1-Q8_0

List all available models

lemonade list

BIJA-cerebellum-Qwen3-1.7B-v1

LoRA-distilled variant of Qwen/Qwen3-1.7B, fine-tuned to power the cerebellum (small-brain) of Bīja — a memory-system-as-AI built on the eight-consciousness theory.

The cerebellum runs continuously alongside Bīja's daemon, performing low-latency memory routing decisions: classify intent, judge memory-worthiness (memorize), and arbitrate write-time conflicts (UPDATE / DELETE / NONE) when new facts collide with existing seeds. The base 1.7B model handled most of these well — except for paraphrase detection, where it correctly identified only 17% of cross-language / synonym / abbreviation duplicates as NONE. This adapter fixes that to 100%.

Why this model exists

Bīja's 30-day case eval (bija/eval/cerebellum-{memorize,arbitrate}/benchmark.json) revealed three structural issues that prompt-only iteration cannot fix:

Task	Baseline 1.7B	Symptom	Root cause
arbitrate NONE-duplicate	17% (1/6)	Paraphrases (cross-lang / synonym / abbreviation) misjudged as `UPDATE`	Training prior: prefer emitting an "action" over `NONE`
memorize FN	13.3%	Valuable seeds (lessons / corrections) misjudged as `SKIP`	Conservative SAVE bias
memorize FP	3.3%	Some commit-style logs slip through as `SAVE`	Same prior, opposite direction

A separate experiment with Granite 3.3-2B ran 4 prompt-rewrite iterations across 120 cases and confirmed the same prior cannot be undone by prompts alone. Behavioral-cloning LoRA distillation from a Qwen3-4B teacher was the next path.

Results

Evaluated on the same 120 + 30 case benchmark used by the production cerebellum (bija/eval/cerebellum-memorize/run.ts + bija/eval/cerebellum-arbitrate/run-with-sim.ts):

Metric	Baseline (Qwen3-1.7B-Q8_0 prompt-only)	LoRA Q8_0 GGUF	Δ
memorize accuracy	91.7% (110/120)	97.4% (excl 5 cold-start parse-fails)	+5.7pp
memorize FP rate	3.3%	3.3%	0
memorize FN rate	13.3%	1.7%	−11.6pp
memorize avg latency	480ms	436ms	−9%
arbitrate accuracy	76.7% (23/30)	86.7% (26/30)	+10pp
arbitrate NONE-duplicate	17% (1/6)	100% (6/6)	+83pp
arbitrate avg latency	~1500ms	1097ms	−27%

Notably the LoRA-tuned Q8_0 GGUF is faster than the baseline Q8_0 GGUF — a side-effect of distillation: the model emits canonical JSON without preamble or thinking blocks, reducing total generated tokens.

A more detailed comparison vs the MLX fp16 evaluation is in the project repo's Phase 5 wrap-up.

Files in this repo

File	Purpose
`Qwen3-1.7B-BIJA-cerebellum-Q8_0.gguf` (1.7 GB)	Drop-in Q8_0 GGUF; `llama.cpp` / Ollama / cerebellum-style sidecars load it directly
`adapters.safetensors` (38 MB)	Raw LoRA weights — apply on top of vanilla `Qwen/Qwen3-1.7B` (HF format) with `mlx_lm.fuse` or `peft`
`adapter_config.json`	mlx-lm LoRA config: rank=16, scale=2.0, dropout=0.05, num_layers=16, target=`q_proj+v_proj`

How to use

Drop-in replacement (recommended) — llama.cpp / Ollama

hf download doncxy/BIJA-cerebellum-Qwen3-1.7B-v1 \
  Qwen3-1.7B-BIJA-cerebellum-Q8_0.gguf \
  --local-dir ~/models

llama-server -m ~/models/Qwen3-1.7B-BIJA-cerebellum-Q8_0.gguf -c 4096

Or for Bīja users — replace the production GGUF directly:

mv ~/.seeddb/cerebellum/models/Qwen3-1.7B-Q8_0.gguf{,.baseline}
ln -s ~/models/Qwen3-1.7B-BIJA-cerebellum-Q8_0.gguf \
      ~/.seeddb/cerebellum/models/Qwen3-1.7B-Q8_0.gguf
pkill -f llama-server   # next call respawns sidecar with new weights

Apply LoRA on top of vanilla Qwen3-1.7B (MLX)

pip install mlx-lm
hf download doncxy/BIJA-cerebellum-Qwen3-1.7B-v1 \
  adapters.safetensors adapter_config.json --local-dir ./bija-cerebellum-lora

mlx_lm.generate \
  --model Qwen/Qwen3-1.7B \
  --adapter-path ./bija-cerebellum-lora \
  --prompt "Decide whether this text is worth saving as long-term memory..." \
  --max-tokens 128

Training recipe

Field	Value
Base model	`Qwen/Qwen3-1.7B` (1.72B params)
Teacher	`Qwen/Qwen3-4B` (Q8_0 GGUF, behavioral cloning via local `llama-server`)
Distillation	Behavioral cloning — teacher generates SFT data, filtered by gold labels
Dataset	137 SFT samples (104 train / 33 valid), stratified by task × category
Trainable params	9.96M (0.579% of base)
LoRA rank / scale	16 / 2.0 (effective alpha 32)
LoRA dropout	0.05
Target modules	`q_proj` + `v_proj` (mlx-lm default)
LoRA layers	last 16 of 28 transformer blocks
Batch size	4, max-seq 4096
Iterations	600 (~52 min on Apple M2 Pro 64 GB)
Optimizer / LR	Adam / 1e-4
Final train loss	0.006
Best val loss	0.077 (iter 350); final 0.086
Peak memory	33.3 GB / 64 GB (fp16, no QLoRA / no grad checkpoint)
Tokens/sec	~820 avg

Intended use

Designed for the Bīja project's cerebellum role: JSON-only, low-latency routing decisions for memory operations. The system prompts the model expects are project-specific (see seeddb/packages/sdk/src/cerebellum/prompts.ts in the source repo) — they enumerate SAVE/SKIP categories for memorize and UPDATE/DELETE/NONE rules for arbitrate.

This is not a general-purpose chat model. Outside Bīja's prompt distribution, behavior may regress versus the base Qwen3-1.7B. For general use, prefer the base model.

Limitations

Trained on 137 samples — task ceiling closely tracks the Qwen3-4B teacher; MIXED and certain UPDATE-relational cases inherit teacher errors.
Cold-start parse failures — first ~5 sidecar requests after spawn may miss the 500 ms timeout (warmup). Persistent daemons amortize this away.
Production daemons only — short-lived spawns will hit cold-start every time.
Q8_0 quantization loses ~3pp arbitrate accuracy versus fp16 MLX; use the safetensors adapter on fp16 base if you need maximum accuracy.

Citation / acknowledgements

Built on:

Qwen/Qwen3-1.7B (base)
Qwen/Qwen3-4B (teacher; via local Q8_0 GGUF)
mlx-lm (training + fuse)
llama.cpp (HF→GGUF conversion)

License

Apache 2.0 (matches base model).

Downloads last month: 4

MLX

Hardware compatibility

Quantized

GGUF

Model size

2B params

Architecture

qwen3

Hardware compatibility

8-bit

Model tree for doncxy/BIJA-cerebellum-Qwen3-1.7B-v1

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Adapter

(518)

this model