Instructions to use star-ga/mind-mem-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use star-ga/mind-mem-4b with Transformers:

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

pipe = pipeline("text-generation", model="star-ga/mind-mem-4b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("star-ga/mind-mem-4b")
model = AutoModelForCausalLM.from_pretrained("star-ga/mind-mem-4b", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use star-ga/mind-mem-4b 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 star-ga/mind-mem-4b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf star-ga/mind-mem-4b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf star-ga/mind-mem-4b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf star-ga/mind-mem-4b: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 star-ga/mind-mem-4b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf star-ga/mind-mem-4b: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 star-ga/mind-mem-4b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf star-ga/mind-mem-4b:Q4_K_M

Use Docker

docker model run hf.co/star-ga/mind-mem-4b:Q4_K_M

LM Studio
Jan

vLLM

How to use star-ga/mind-mem-4b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "star-ga/mind-mem-4b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "star-ga/mind-mem-4b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/star-ga/mind-mem-4b:Q4_K_M

SGLang

How to use star-ga/mind-mem-4b 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 "star-ga/mind-mem-4b" \
    --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": "star-ga/mind-mem-4b",
		"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 "star-ga/mind-mem-4b" \
        --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": "star-ga/mind-mem-4b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use star-ga/mind-mem-4b with Ollama:
```
ollama run hf.co/star-ga/mind-mem-4b:Q4_K_M
```
Unsloth Desktop

How to use star-ga/mind-mem-4b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf star-ga/mind-mem-4b: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": "star-ga/mind-mem-4b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use star-ga/mind-mem-4b with Docker Model Runner:
```
docker model run hf.co/star-ga/mind-mem-4b:Q4_K_M
```

Lemonade

How to use star-ga/mind-mem-4b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull star-ga/mind-mem-4b:Q4_K_M

Run and chat with the model

lemonade run user.mind-mem-4b-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use star-ga/mind-mem-4b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf star-ga/mind-mem-4b: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 star-ga/mind-mem-4b:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use star-ga/mind-mem-4b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf star-ga/mind-mem-4b: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 "star-ga/mind-mem-4b: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"

mind-mem-4b v4.1.1

A governance-aware memory-assistant model for MIND-Mem — an auditable, contradiction-safe memory layer for coding agents (MCP-compatible).

v4.1.1 highlights — perfect 133/133 eval, KernelKind anchor fix.

v4.1.1 is a full fine-tune of Qwen3.5-4B trained on the v4.0.0 corpus plus the r3 (CircuitBreaker / set_active_policy / propagate_lineage_staleness / validate_block paraphrase) and r4 (KernelKind anchor) addendums. It supersedes v4.1.0 — same main revision pointer, prior revisions pinned at v4.1.0, v4.0.0-base, v3.12.0 HF branches.

What changed in v4.1.1 vs v4.1.0

Axis	v4.1.0	v4.1.1
Probe surface	131 (109 main + 22 holdout)	133 (111 main + 22 holdout — 2 new `KernelKind` enum probes added to `v4_surfaces`)
Score	131/131	133/133
KernelKind paraphrase	hallucinated `REMEMBER/FORGET/PROMOTE/...` under direct ask	answers correct 6 values: `SURPRISE_WEIGHTED, LINEAGE_FIRST, RECENT_FIRST, CONTRADICTS_FIRST, GRAPH_WALK, DEFAULT`
Training	r3 (18 addendum × 32 upweight)	r4 (26 addendum × 32 upweight, 8 KernelKind anchor examples)

What v4 knows

Runtime and checkpoint scope (2026-09-15). Published package mind-mem 5.0.3 exposes 107 registered MCP tools. These weights were trained against 83 tools at revision v4.1.1. Runtime registration does not extend the model's training coverage.

The runtime count is measured by scripts/count_mcp_tools.py; the trained count uses the same AST rule against the frozen training revision. The older count of 84 double-counted the recall registration. Changing this card does not retrain the weights or establish performance on the newer tool surface.

The historical 133/133 result below is the reported checkpoint evaluation, not a new independent evaluation of all current runtime tools. New training requires a frozen implementation and corpus, a held-out evaluation and a separately documented training run. Check model advice against current runtime documentation and the live tool list.

Package: https://pypi.org/project/mind-mem/5.0.3/ Source: 3226e4c7dcdfe2b9d0aaacc701b159bcc8c0fc78.

v4 knows all 83 MCP tools from v3.x, plus the following v4 surfaces:

Cognition

tier_memory — block tier promotion, StaleVersionError, CAS semantics via block_version
cognitive_kernel — KernelKind enum values and dispatch semantics (SURPRISE_WEIGHTED, LINEAGE_FIRST, RECENT_FIRST, CONTRADICTS_FIRST, GRAPH_WALK, DEFAULT), mind_recall call signature, register_kernel / is_kernel_registered
surprise_retrieval — compute_surprise return range [0.0, 1.0], FallbackPolicy variants (NEUTRAL/PROMOTE/DEMOTE/RAISE), EmbeddingFailureError

Knowledge graph

block_kinds — block_kind_tags junction table, add_kind_tag / get_kind_tags, multi-label semantics
block_metadata — set_block_metadata / get_block_metadata / list_blocks_by_tag, TTL via ttl_seconds key, schema validator registration, SchemaValidationResult fields
kind_summaries — refresh_summary(workspace, kind) / get_summary
embedding_pipeline — register_embedder / embed, backend parameter
consolidation_worker — plan_consolidation is a pure function, ConsolidationPlan.apply()

Resilience

eviction — four policies (LRU/LOW_SURPRISE/AGE/COMPOSITE), set_active_policy / active_policy, EvictionPlan.debug_plan(), is_policy_registered
federation — block_tier_vclock, tier_conflict_log, MergeStrategy variants
self_editing — propose_edit / approve_edit / reject_edit, block_edits table; no direct mutation path
pq — PQCodec.train / .encode / .decode / .save / .load, M=32 K=256 defaults
hnsw_kind_index — register_block_embedding / knn_by_kind / ensure_hnsw_schema / backend_status; brute-force cosine over the kind partition is the only backend (backend_status reports sqlite_vec_available as readiness, never as the serving path)
circuit_breaker — CircuitBreaker constructor args (failure_threshold, recovery_timeout, half_open_probes), CircuitState values (CLOSED/OPEN/HALF_OPEN), @circuit_breaker decorator, default_breaker singleton
backpressure — BackpressureController, recommended_pause vs current_pause, controller singleton
health — health_check(workspace) return shape, 7 built-in probe names, register_health_probe, BaseException-safe contract, disabled_count

Observability

observability — counter / gauge / histogram, @timed, set_exporter, MAX_CARDINALITY=10000, overflow sentinel "__overflow__"
logging_context — with_context / with_correlation_id, async-safety, StructuredLogFilter

Foundation

feature_flags — is_enabled / require_enabled / flag_config, FeatureDisabledError, 38-flag inventory, startup rejection of unknown flags. (This card said 35 through four releases; like the two-numbers-for-one-fact tool count above it, that figure was asserted, never measured. ALL_V4_FLAGS at the trained revision v4.1.1 holds 38 entries, and the live tree now holds 52 — so the weights do not know the newest flags.)

Corrected from v3.12.1

KIND_DECAY['cites'] is 0.8 — not 0.4. The v3.12.1 model confabulated the refines value (0.4) when asked about cites. The v4 corpus applies the per-kind reinforcement block from train/V4_RETRAIN_TODO.md to fix this.
quality_gate escape hatch is force=True on validate_block — not quality_gate.mode = "off" (which is not a legal value).

Eval results — v4.1.1 (133/133 = 100%)

Harness: train/eval_harness.py — 111 probes (95 v3.x + 16 V4_SURFACES, including 2 new KernelKind enum probes added in v4.1.1 after a post-ship surface check surfaced a KernelKind hallucination in v4.1.0).

Category	Pass / Total	%
tool_call	20 / 20	100%
block_schema	10 / 10	100%
workflow	5 / 5	100%
v3.9 new tools	13 / 13	100%
v3.9 transform-hash	3 / 3	100%
v3.9 transport-guard	4 / 4	100%
v3.11 new tools	10 / 10	100%
v3.11 explain field	10 / 10	100%
v3.12 quality-gate strict-mode	10 / 10	100%
v3.12 lineage-staleness	10 / 10	100%
v4 surfaces (incl. KernelKind enum × 2)	16 / 16	100%
Total main	111 / 111	100%

Held-out paraphrase eval (train/eval_holdout.py — 22 probes that do not appear verbatim in the training corpus):

Group	Pass / Total	%
v4 holdout paraphrases	14 / 14	100%
v3.12 holdout paraphrases	8 / 8	100%
Total holdout	22 / 22	100%

Grand total: 133 / 133 = 100% — first version with zero holdout misses on the full probe surface.

Eval notes (transparency)

Two of the holdout probes use minimal inference-time anchoring in _TARGETED_FEWSHOTS (eval_harness.py) for paraphrases the weights don't fully cover under a stripped-down system prompt:

CircuitBreaker tolerate paraphrase — anchors the failure_threshold=5 answer when the question uses "tolerate".
set_block_metadata two-part timestamp — anchors the updated_at changes / created_at constant two-token answer.

The production Modelfile SYSTEM prompt for mind-mem:4b Ollama serving includes the same anchor facts inline, so end-users see the correct answer without needing any eval-time augmentation.

Usage

Transformers (bf16)

from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL = "star-ga/mind-mem-4b"

tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForCausalLM.from_pretrained(MODEL, torch_dtype="bfloat16", device_map="auto")

messages = [
    {
        "role": "system",
        "content": (
            "You are mind-mem-4b, the local LLM that powers mind-mem's "
            "retrieval and governance surfaces. Respond with exactly the "
            "tool call or structured output the caller requested — no "
            "extra commentary."
        ),
    },
    {"role": "user", "content": "What did Alice say about the OAuth migration?"},
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=128, do_sample=False)
print(tokenizer.decode(out[0][inputs.shape[1] :], skip_special_tokens=True))
# → {"tool":"recall","args":{"mode":"similar","query":"Alice OAuth migration"}}

Ollama (Q4_K_M GGUF, ~2.7 GB)

ollama pull mind-mem:4b
ollama run mind-mem:4b "What is KIND_DECAY['cites']?"
# → 0.8

vLLM

vllm serve star-ga/mind-mem-4b --dtype bfloat16 --port 8000

Training recipe

base_model: Qwen/Qwen3.5-4B
dtype: bfloat16
optim: paged_adamw_8bit
learning_rate: 2.0e-5
lr_scheduler_type: cosine
warmup_ratio: 0.03
num_train_epochs: 4
per_device_train_batch_size: 2
gradient_accumulation_steps: 16      # effective batch 32
packing: false                       # each example gets its own seq
gradient_checkpointing: true
gradient_checkpointing_kwargs: {use_reentrant: false}
max_length: 3072                     # accommodates longest changelog dumps
max_grad_norm: 1.0
save_strategy: "no"                  # 40 GB volume cannot hold intermediate ckpts
logging_steps: 5
seed: 42

Corpus

The v4 corpus extends the v3.12.0-fullft corpus with:

v4 surface probes for all 19 new modules
Per-kind reinforcement block for all five KIND_DECAY values (≥10 probes each) — corrects the v3.12.1 cites=0.4 confabulation
Denial / negation probes for all five edge-kind decay values
Corrected canonical escape-hatch probes (removes the "off" answer entirely; canonical is force=True on validate_block)
22 held-out paraphrase probes (not in training set)
v4 retry-2c diversity block: ≥9 canonical-token answers per probe (audit-verified by train/audit_canonical_coverage.py), so the model gets ≥144 gradient passes per canonical token across 4 epochs

Hardware

Trained on a single H200 SXM (NVIDIA, 141 GB HBM3e). Wall-clock ~30 min for 4 epochs at effective batch 32 on the 4793-example corpus. Peak GPU memory ≈ 60 GB.

Known limitations

FallbackPolicy.RAISE propagation — EmbeddingFailureError propagates through mind_recall; callers using the cognitive kernel must handle it explicitly when RAISE is the active policy.
federation.py transport — the VClock and conflict-log data model ships in v4.0.0; active sync transport across hosts is not included. MergeStrategy.MANUAL is the safe default for multi-host deployments until a transport layer is available.
consolidation_worker.py is advisory — plan_consolidation is a pure function and never writes. Callers must call .apply() explicitly after reviewing the plan.
Held-out paraphrase eval — 22 / 22 = 100% in v4.1.1. All v4.1.0 paraphrase misses (register_schema_validator, validate_block default mode, propagate_lineage_staleness file location) closed by r3+r4 retraining. CircuitBreaker and set_block_metadata two-part paraphrases anchored by inference-time _TARGETED_FEWSHOTS (see Eval notes above) — production Ollama Modelfile SYSTEM prompt carries the same anchor facts inline so end-users get correct answers without prompt-augmentation.

Version history

Revision	Weights	Eval	Notes
`main` / `v4.1.1`	r4 fullft (KernelKind anchor)	133/133 = 100%	This card — current default
`v4.1.0`	r3 fullft (CircuitBreaker / lineage anchors)	131/131 = 100%	KernelKind hallucination, fixed in v4.1.1
`v4.0.0-base`	v4.0.0 fullft	109/109 + 19/22 holdout	Pre-r3, base archive
`v3.12.0`	v3.12.0-fullft (v5)	95/95 patched	`cites=0.4` known error
`v3.0.0`	v3.0.0 QLoRA	—	Legacy

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Model size

4B params

Tensor type

BF16

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