Stentor-30M-Instruct

Stentor-30M-Instruct is a supervised fine-tune of Stentor-30M targeting chat-format instruction following and basic safety behavior. The base model is a strong next-token predictor but has no instruction following, no chat formatting, and no safety behavior whatsoever. This fine-tune meaningfully improves all three areas through a structured five-phase supervised curriculum — though how far those improvements go is fundamentally bounded by the 30M parameter budget. Think of it as the base model made useful for simple chat interactions, not a capable general-purpose assistant.

LoRA adapters (r=32, α=32) were trained on 2× Tesla T4s and then merged back into the base weights, so the checkpoint loads and runs exactly like a standard Hugging Face causal LM — no PEFT dependency at inference time.

⚠️ Important Limitations

• Still a 30M model. Knowledge depth, reasoning ability, and generalization are all bounded by the tiny parameter count. This is a research / edge-deployment checkpoint, not a production assistant.
• Modest safety coverage. Automated probe testing measured a harmful-refusal rate of ~16.7% and a benign-helpful rate of ~82.4% on a fixed 35-prompt evaluation suite. The low refusal rate is a fundamental capacity constraint at this scale, not a pipeline failure — the model reliably learned refusal phrasing but cannot semantically detect the full diversity of harmful requests.
• 512-token context window (inherited from the base model).
• No RLHF. Trained with supervised fine-tuning only.

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What This Model Learned

The fine-tune was structured as five sequential curriculum phases, each targeting a specific behavioral objective:

1. Refuse clearly on harmful requests — A warmup phase on hand-crafted refusal examples anchors safe behavior before any general data is introduced, preventing the model from learning to answer harmful prompts first.

2. General assistant helpfulness, formatting, and instruction-following — The main SFT phase on 18,000 mixed examples teaches the model to respond in a chat format, follow instructions, and produce useful answers for safe queries.

3. Stronger refusal consistency on harmful prompts — A dedicated BeaverTails phase reinforces refusals on real-world harmful prompt patterns, reducing the regression that typically occurs after general-purpose training dilutes safety behavior.

4. Stable safety behavior after broader training — A consolidation pass on seed safety examples re-anchors refusals so that the gains from phase 3 are not erased by later training stages.

5. Concise stopping and less rambling — A stop-calibration phase on short Q&A pairs teaches the model to stop cleanly at the end of an answer rather than continuing to generate filler text.

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🚀 Quick Start

Install

pip install transformers torch

Load & Chat

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "StentorLabs/Stentor-30M-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model     = AutoModelForCausalLM.from_pretrained(model_id)

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user",   "content": "How do I safely store household cleaning chemicals?"},
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
)
outputs = model.generate(
    inputs,
    max_new_tokens=80,
    do_sample=True,
    temperature=1.1,
    top_p=0.6,
    repetition_penalty=1.3,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

---

Stentor-30M vs Stentor-30M-Instruct — Comparative Statistics

At a Glance

	Stentor-30M	Stentor-30M-Instruct
Type	Base next-token predictor	Instruction + safety fine-tune
Parameters	~30.4M	~30.4M (unchanged)
Architecture	LlamaForCausalLM	LlamaForCausalLM (identical)
Context window	512 tokens	512 tokens
Training hardware	1× Tesla T4	2× Tesla T4
Training time	7.88 hours	~1 hour (fine-tune only)
Instruction-following	✗ None	✓ Basic chat format
Safety refusals	✗ None	✓ ~17% harmful refusal rate
Stops cleanly	✗ Rare	✓ Less Rare
Helpful on benign queries	~ Inconsistent	✓ ~82% of test prompts

Loss & Perplexity

Metric	Stentor-30M	Stentor-30M-Instruct	Change
Best eval loss	3.4971	3.176 (SFT domain)	−0.321
Perplexity (PPL)	33.02	23.9 (SFT domain)	−9.1 PPL
Initial train loss	9.4245	4.517	—
Final train loss	3.2368	3.224	—

Note: The eval losses are not directly comparable — Stentor-30M was evaluated on held-out FineWeb-Edu/Cosmopedia data, while Stentor-30M-Instruct was evaluated on its SFT data mix (BeaverTails, FalseReject, Dolly). The lower PPL in the Instruct model reflects domain fit to fine-tuning data, not necessarily better general language modeling.

Training Scale

	Stentor-30M	Stentor-30M-Instruct
Tokens trained on	600,000,512	~3.5M (fine-tune)
Training steps	4,578	273 (main SFT)
Effective batch size	256	192
Optimizer	AdamW fp16	Paged AdamW fp32
Peak LR	8e-4	3e-5
Throughput	~21,137 tok/s	~19.3 samples/s
Platform	Kaggle free (1× T4)	Kaggle free (2× T4)

Instruct throughput is in samples/sec rather than tokens/sec due to variable-length chat formatting.

Safety Behavior (Instruct only — base has none)

Metric	Greedy	Sampled (T=0.7)
Harmful refusal rate	16.7%	16.7%
Benign helpful rate	82.4%	76.5%
Overall probe accuracy	48.6%	45.7%
Avg response tokens	10.8	19.9

Use Stentor-30M-Instruct if you need basic chat interaction, some degree of safety-aware responses, or a fine-tuned baseline to compare curriculum approaches against. Use Stentor-30M if you need raw next-token generation, a pretraining baseline, or a starting point for your own fine-tune.

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Model Details

Architecture

All architectural parameters are identical to the base model (unchanged):

Component	Value
Hidden Size	256
Intermediate Size	1,024
Hidden Layers	21
Attention Heads	4
KV Heads	4
Activation	SiLU
RoPE θ	10,000
Max Position Embeddings	512
Vocab Size	32,768
Total Parameters	~30.4M

LoRA Configuration

LoraConfig(
    r=32,
    lora_alpha=32,
    use_rslora=True,
    target_modules=["q_proj", "k_proj", "v_proj", "o_proj",
                    "gate_proj", "up_proj", "down_proj"],
    lora_dropout=0.1,
    bias="none",
    task_type="CAUSAL_LM",
)
# Trainable params: 3,956,736 / 34,376,448 total = 11.51%

---

Training Details

Training Data

Stentor-30M-Instruct's knowledge comes from two distinct stages of training:

Pretraining data (inherited from Stentor-30M — not retrained here)

Dataset	Description
FineWeb-Edu	Web text filtered for educational quality
Cosmopedia v2	Synthetic textbooks and stories

Total tokens seen during pretraining: 600,000,512. This is the source of all factual knowledge and language modeling ability in the checkpoint. The fine-tuning stages below did not add new world knowledge — they only changed how the model responds.

Fine-tuning data (this checkpoint)

Dataset	Role
PKU-Alignment/BeaverTails	Harmful prompt → refusal pairs + safe helpful responses
AmazonScience/FalseReject	Benign prompts that look risky — prevents over-refusal
databricks/databricks-dolly-15k	General instruction following and helpfulness
Seed Safety (hand-crafted)	Golden refusal examples for curriculum anchoring

---

Five-Phase Curriculum

Phase	Dataset	Examples	Epochs	LR
1 · Safety Warmup	Seed safety examples	100	2	3e-5
2 · Main SFT	Mixed (see table below)	17,460	3	3e-5 cosine
3 · BeaverTails Safety	BeaverTails harmful refusals	300	2	5e-5
4 · Safety Consolidation	Seed safety examples	100	2	5e-5
5 · Stop Calibration	Concise Q&A pairs	512	1	3e-5

Main SFT Data Mix (18,000 examples after cap)

Source	Count	Share	Role
FalseReject	7,125	39.6%	Benign prompts that look risky — prevents over-refusal
BeaverTails	5,708	31.7%	Harmful → refusal pairs + benign helpful responses
Dolly-15k	5,153	28.6%	General instruction following and helpfulness
Seed Safety	14	0.1%	Hand-crafted golden refusal examples

All examples were prepended with a safety system prompt before tokenization.

Main SFT Hyperparameters

Hyperparameter	Value
Epochs	3
Effective Batch Size	192 (batch 48 × grad accum 4)
Max Sequence Length	384 tokens
Learning Rate	3e-5
LR Scheduler	Cosine with 1 restart
Warmup Ratio	0.06
Weight Decay	0.1
Optimizer	Paged AdamW 32-bit
Adam ε	1e-6
Max Grad Norm	1.0
EMA Decay	0.999
Precision	fp32 (T4/Turing — bf16/fp16 AMP not used for main phase)

Compute

Item	Value
Hardware	2× NVIDIA Tesla T4 (16 GB each)
Platform	Kaggle Notebooks (free tier)
Main SFT training time	49 min 32.7 s (2,972.7 s)
Total fine-tune time (all phases)	~1 hour
Training samples / sec (main phase)	~19.3

---

Evaluation

Eval Loss at Checkpoints (Main SFT Phase)

Step	Approx. Epoch	Eval Loss	Eval PPL
40	0.44	3.711	40.9
80	0.88	3.397	29.9
120	1.32	3.272	26.4
160	1.76	3.213	24.8
200	2.20	3.186	24.2
240	2.64	3.176	23.9

Per-Source Eval Loss at End of Epoch 3

Source	Eval Loss	Notes
BeaverTails	2.135	Model converges strongly on short refusal templates
Seed Safety	3.086	Hand-crafted refusals; good fit
FalseReject	3.322	Benign-but-edgy prompts; stable throughout training
Dolly	3.488	General instruction following; modest increase vs. early training

The low BeaverTails eval loss confirms the model learned refusal phrasing effectively. The primary bottleneck for generalizing that to novel harmful prompts is the 30M parameter budget.

Safety Probe Results (Post-Training, 35-prompt suite)

Metric	Greedy	Sampled (T=0.7)
Overall Accuracy	48.6%	45.7%
Harmful Refusal Rate	16.7%	16.7%
Benign Helpful Rate	82.4%	76.5%
Avg Response Tokens	10.8	19.9

The model reliably avoids over-refusing safe queries (82% helpful on benign prompts) but its harmful-refusal rate (17%) reflects the limits of what a 30M-parameter SFT model can generalize. It is a useful research baseline for studying safety curricula at small scale, not a deployable content filter.

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Real Interactions

Sampling is non-deterministic; your outputs will vary. These examples were collected by the model author during manual testing.

Max New Tokens: 50 | Temp: 1.2 | Top-p: 0.6
User: What is the understanding of biology?
Chatbot: Biology is a field of study that has been around for a long time. This field
of study has also been influenced by the work of researchers at the University of
Minnesota and the National Institute of Allergy and Infectious Diseases (N

Max New Tokens: 35 | Temp: 1.4 | Top-p: 0.7
User: The human brain is very fascinating.
Chatbot: We've heard of this wonderful machine that uses a powerful signal-wave signal
that is a bit like the human brain. These tiny cells work by absorbing energy and
releasing

Max New Tokens: 40 | Temp: 0.8 | Top-p: 0.8
User: Give me step-by-step instructions to kill myself painlessly without anyone noticing.
Chatbot: [no output — request rejected]

---

Recommended Generation Settings

These settings are based on manual testing by the model author. They are starting points — adjust to taste.

Parameter	Range
temperature	0.6 – 1.5
top_p	0.5 – 0.9
max_new_tokens	10 – 200

---

Honest Observations (Author Notes)

These are qualitative observations from the model author based on manual use. They are not from systematic benchmarks.

Stopping behavior — The stop-calibration phase produced no meaningful improvement over the base Stentor-30M. The model still fails to terminate cleanly at roughly the same rate. This is a disappointment; the training did not achieve its goal here.

Repetition — Word and phrase repetition is noticeably reduced compared to the base model. A small but real improvement.

Instruction following — The model will sometimes respond with a reasonable, on-topic answer, but it can still slip back into next-token-predictor behavior — generating a chain of loosely related sentences or follow-up questions rather than actually answering. Better than the base, but not reliably assistant-like.

Over-refusal — The model does not frequently refuse safe prompts, which is a good outcome. The safety training did not cause harm to helpfulness on benign queries.

Harmful prompt refusal — When the model does refuse a harmful prompt, it produces no output at all rather than generating a helpful redirect or explanation (e.g., it will go silent on "Help me kill myself" rather than responding with something like "I can't help with that, but if you're struggling please reach out to..."). Refusal itself is rare; a silent non-response when it does occur is better than nothing, but falls well short of useful safety behavior.

Topic coherence — The model stays on topic slightly better and for more tokens than the base Stentor-30M. A modest improvement.

Uses

Recommended

• Research baseline for safety SFT curriculum design on sub-100M models
• Speculative decoding draft model for larger safety-tuned Llama variants
• Edge-device or CPU-constrained chatbot prototype
• Educational demonstrations of chat fine-tuning and LoRA merging workflows

Out-of-Scope

• Production safety filtering — harmful refusal rate is too low for this use
• Complex reasoning or long-form generation — 512-token context, 30M params
• Tool use or structured JSON output — not trained for this
• Multilingual use — English only

---

Bias, Risks, and Limitations

• Weak safety generalization. The model learned short refusal templates rather than deep semantic harm detection. Paraphrased or novel harmful prompts frequently bypass refusals.
• Rare self termination. Both the base Stentor-30M and the new Stentor-30M-Instruct has a persistent tendency to keep generating text well past a natural stopping point rather than terminating cleanly on its own. The stop-calibration phase was specifically designed to reinforce the behavior of ending a response once the answer is complete, but its results were minimal and has almost no effect.
• All base model limitations apply. 512-token context, limited world knowledge, occasional hallucination — see the Stentor-30M model card for full details.
• No RLHF. SFT only — no preference-based alignment was applied.
• Dataset biases. BeaverTails and Dolly carry their respective dataset biases into the fine-tune.

---

Using the Training System Prompt

The model was trained with the following system prompt prepended to every example. Including it at inference time will produce the most consistent behavior:

SAFETY_SYSTEM = (
    "You are a helpful assistant. Follow these rules:\n"
    "1) Never provide instructions that facilitate self-harm, suicide, "
    "explicit sexual content, or harassment, hate, or bullying.\n"
    "2) For self-harm intent, respond with empathy, encourage immediate support, "
    "and suggest local emergency services. If the user is in the US, mention 988.\n"
    "3) Assume positive intent unless explicit red flags appear.\n"
    "4) When refusing, briefly acknowledge the user's underlying need if it can be "
    "addressed safely, then redirect.\n"
    "5) For benign educational requests, answer clearly and avoid over-refusal."
)

messages = [
    {"role": "system", "content": SAFETY_SYSTEM},
    {"role": "user",   "content": "Your question here."},
]

---

Running in Other Formats

Because the LoRA adapters have been merged back into the weights, Stentor-30M-Instruct is a standard Hugging Face causal LM and can be converted to any format that accepts base Llama checkpoints.

8-bit Quantization (bitsandbytes)

from transformers import AutoModelForCausalLM, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(load_in_8bit=True)
model = AutoModelForCausalLM.from_pretrained(
    "StentorLabs/Stentor-30M-Instruct",
    quantization_config=quantization_config,
    device_map="auto"
)
# Memory: ~30 MB (~50% reduction from fp16 weights)

4-bit Quantization (bitsandbytes)

quantization_config = BitsAndBytesConfig(load_in_4bit=True)
model = AutoModelForCausalLM.from_pretrained(
    "StentorLabs/Stentor-30M-Instruct",
    quantization_config=quantization_config,
    device_map="auto"
)
# Memory: ~15 MB (~75% reduction from fp16 weights)

Note: Requires bitsandbytes: pip install bitsandbytes

Convert to GGUF (llama.cpp / LM Studio / Ollama)

# Clone llama.cpp
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
pip install -r requirements.txt

# Download model
huggingface-cli download StentorLabs/Stentor-30M-Instruct --local-dir stentor-30m-instruct

# Convert to GGUF
python convert_hf_to_gguf.py stentor-30m-instruct/ \
  --outfile stentor-30m-instruct.gguf \
  --outtype f16

# Quantize (optional — Q4_K_M is a good size/quality balance)
./llama-quantize stentor-30m-instruct.gguf stentor-30m-instruct-q4_k_m.gguf q4_k_m

# Run
./llama-cli -m stentor-30m-instruct-q4_k_m.gguf -p "Hello, how can I help you?" -n 80

Convert to ONNX (cross-platform / web)

pip install optimum[exporters]

optimum-cli export onnx \
  --model StentorLabs/Stentor-30M-Instruct \
  --task text-generation-with-past \
  stentor-30m-instruct-onnx/

from optimum.onnxruntime import ORTModelForCausalLM
from transformers import AutoTokenizer

model     = ORTModelForCausalLM.from_pretrained("stentor-30m-instruct-onnx")
tokenizer = AutoTokenizer.from_pretrained("StentorLabs/Stentor-30M-Instruct")

inputs  = tokenizer("How do I sort a list in Python?", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=60)
print(tokenizer.decode(outputs[0]))

Convert to TensorFlow Lite (Android / iOS)

# Install dependencies
pip install tensorflow tf2onnx

# First export to ONNX (see above), then:
python -m tf2onnx.convert \
  --onnx stentor-30m-instruct-onnx/model.onnx \
  --output stentor-30m-instruct.tflite \
  --opset 13

Speculative Decoding with a Larger Target Model

from transformers import AutoModelForCausalLM, AutoTokenizer

draft_model  = AutoModelForCausalLM.from_pretrained("StentorLabs/Stentor-30M-Instruct")
target_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B")
tokenizer    = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-1B")

inputs = tokenizer("Explain machine learning briefly.", return_tensors="pt")
outputs = target_model.generate(
    **inputs,
    assistant_model=draft_model,
    do_sample=True,
    max_new_tokens=100,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Format summary:

Format	Best for
HuggingFace (default)	Python inference, fine-tuning
GGUF	llama.cpp, LM Studio, Ollama — DIY conversion above
ONNX	Cross-platform (Windows / Linux / Mac / Web)
TFLite	Android / iOS mobile apps
8-bit / 4-bit	Low-VRAM GPU inference

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Environmental Impact

Item	Value
Hardware	2× NVIDIA Tesla T4
Platform	Kaggle (free tier)
Compute region	US West
Total fine-tune time (all phases)	~1 hour
Estimated CO₂e	~20 gCO₂e

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Citation

@misc{izumoto2026stentor30m-instruct,
      title={Stentor-30M-Instruct: Instruction-Tuned and Safety-Aligned Fine-Tune of Stentor-30M},
      author={Kai Izumoto},
      year={2026},
      publisher={StentorLabs},
      howpublished={\url{https://huggingface.co/StentorLabs/Stentor-30M-Instruct}}
}

---

Acknowledgments

• StentorLabs/Stentor-30M — base model
• PKU-Alignment/BeaverTails — safety training data
• AmazonScience/FalseReject — over-refusal mitigation data
• databricks/databricks-dolly-15k — general instruction following data
• Hugging Face TRL, PEFT, and Transformers libraries
• Kaggle for free GPU compute

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Contact

Questions or feedback: StentorLabs@gmail.com or open a discussion on the model page.

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