CatQualia

gnarp-m2

Model Details

Table with columns: Property, Value
Property	Value
Base model	HuggingFaceTB/SmolLM2-360M-Instruct
Method	QLoRA (4-bit NF4, double quantization)
LoRA rank	16
LoRA alpha	32
LoRA dropout	0.05
Target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Trainable params	8,683,520 (2.34% of total)
Total params	370,504,640
Adapter size	34.8 MB (rank-16, alpha-32)
Merged model size	1.4 GB
Architecture	LlamaForCausalLM
Max sequence length	768 tokens

Training Data

Corpus: clean_corpus_v5.jsonl — 74,395 rows

Table with columns: Source, Rows, Description
Source	Rows	Description
isomorphism_sft.jsonl	53,403	Anime-to-software structural isomorphisms with failure_class from the 17,801-row moat
gpu_assay_verdicts.jsonl	11,989	GPU assay verification-labeled pairs
anime_metaphor_engine.jsonl	4,523	Cross-domain metaphor engine outputs
forge_bloom	2,117	Forge pipeline bloom outputs
capability/reasoning/seed.jsonl	1,232	Reasoning capability seed data
fleet_toolforge.jsonl

Positive + unlabeled rows only. Negative rows excluded from SFT targets.

Training Configuration

Table with columns: Parameter, Value
Parameter	Value
Epochs	1
Learning rate	1e-4 (cosine schedule, 5% warmup)
Batch size	1
Gradient accumulation	8
Effective batch size	8
Optimizer	AdamW (bf16)
Eval split	10% held out
Eval strategy	Every 500 steps
Best model selection	eval_loss (load_best_model_at_end)

Training Results

Table with columns: Metric, Value
Metric	Value
Final train loss	2.613
Final eval loss	2.509
Token accuracy	55.55%
Perplexity (train)	13.64

Evaluation: Cross-Domain Transfer Benchmark v2

36 cross-domain transfer tasks spanning anime, biology, physics, economics, fiction, geography, music, cooking, ecology, martial arts, psychology, logistics, chemistry, sports, agriculture, linguistics, city planning, finance, navigation, and architecture.

Scoring: Heuristic rubric (keyword + structural analysis). Trust DELTAS between models on the same tasks, not absolutes.

Table with columns: Model, Judge Mean, Delta vs Base, Avg Response (chars), Avg Latency (s)
Model	Judge Mean	Delta vs Base	Avg Response (chars)	Avg Latency (s)
gnarp-m2	0.7839	+14.1%	1,108	4.9
base (SmolLM2-360M-Instruct)	0.6871	—	1,489	6.9

Prior Model Lineage (heldout benchmark, qwen3:8b judge)

Table with columns: Model, Transfer Score, Heldout Loss, Perplexity, Refusal Rate, Training Data
Model	Transfer Score	Heldout Loss	Perplexity	Refusal Rate	Training Data
base	0.709	1.625	5.08	0.130	—
v1	0.218	1.850	6.36	0.385	~2,152 rows
v2	0.713

*m2 scored on transfer_benchmark_v2 (heuristic-only), not the qwen3:8b-judged heldout benchmark. Cross-benchmark comparisons should be treated with caution.

Limitations

360M parameters. Small model. Cannot match larger models on complex reasoning, long-form generation, or nuanced instruction following.
Single GPU, single epoch. Trained on consumer hardware (RTX 3080 8GB) for one epoch. More training could improve results but risks overfitting.
Heuristic eval. The transfer benchmark v2 uses keyword/structural heuristic scoring, not a strong LLM judge. The +14.1% delta is directionally meaningful but not precisely calibrated.
Cross-benchmark caveat. v1/v2/v3 were scored with a qwen3:8b judge; m2 was scored with heuristic-only. Direct numerical comparison across the two benchmarks is not valid.
Domain-specific training data. Over 71% of training data is isomorphism pairs. The model is optimized for cross-domain structural transfer and may underperform on general chat or coding tasks.
No safety fine-tuning beyond refusal data. The model includes 19 refusal pairs but is not extensively safety-tuned.

How to Use

With Ollama (recommended for local inference)

bash
# Create the Modelfile
cat > Modelfile << 'EOF'
FROM ./model/merged_gnarpm2
TEMPLATE """### Instruction: {{ .Prompt }} ### Response: """
PARAMETER num_ctx 4096
PARAMETER temperature 0.3
PARAMETER num_predict 512
SYSTEM You are gnarp-m2, a cross-domain transfer specialist.
EOF

ollama create gnarp-m2 -f Modelfile
ollama run gnarp-m2

With Transformers

python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "gnarp/gnarp-m2"  # or local path
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

prompt = "### Instruction:\nApply the concept of biological apoptosis to software deployment strategy.\n### Response:\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.3)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

With PEFT (adapter only)

python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM2-360M-Instruct")
model = PeftModel.from_pretrained(base, "path/to/adapter_gnarpm2")
model = model.merge_and_unload()

License

Model weights and code: MIT License
Training data (corpus): COPL (Community Open Public License) — derived from the WaveMotionExpansion isomorphism engine
Base model: Apache 2.0 (SmolLM2-360M-Instruct by HuggingFace)

Citation

bibtex
@model{gnarp-m2,
  title={gnarp-m2: Cross-Domain Transfer Fine-Tuned Language Model},
  author={WaveMotionExpansion},
  year={2026},
  base_model={HuggingFaceTB/SmolLM2-360M-Instruct},
  method={QLoRA},
  training_rows={74395},
  transfer_benchmark={0.7839}
}

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Model Details

Model Provider

CatQualia

Model Tree

Base

HuggingFaceTB/SmolLM2-360M-Instruct

Fine-tuned

this model

Input Modalities

Text

Output Modalities

Text

Supported Functionality

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Model Details

Table with columns: Property, Value
Property	Value
Base model	HuggingFaceTB/SmolLM2-360M-Instruct
Method	QLoRA (4-bit NF4, double quantization)
LoRA rank	16
LoRA alpha	32
LoRA dropout	0.05
Target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Trainable params	8,683,520 (2.34% of total)
Total params	370,504,640
Adapter size	34.8 MB (rank-16, alpha-32)
Merged model size	1.4 GB
Architecture	LlamaForCausalLM
Max sequence length	768 tokens

Training Data

Corpus: clean_corpus_v5.jsonl — 74,395 rows

Table with columns: Source, Rows, Description
Source	Rows	Description
isomorphism_sft.jsonl	53,403	Anime-to-software structural isomorphisms with failure_class from the 17,801-row moat
gpu_assay_verdicts.jsonl	11,989	GPU assay verification-labeled pairs
anime_metaphor_engine.jsonl	4,523	Cross-domain metaphor engine outputs
forge_bloom	2,117	Forge pipeline bloom outputs
capability/reasoning/seed.jsonl	1,232	Reasoning capability seed data
fleet_toolforge.jsonl

Positive + unlabeled rows only. Negative rows excluded from SFT targets.

Training Configuration

Table with columns: Parameter, Value
Parameter	Value
Epochs	1
Learning rate	1e-4 (cosine schedule, 5% warmup)
Batch size	1
Gradient accumulation	8
Effective batch size	8
Optimizer	AdamW (bf16)
Eval split	10% held out
Eval strategy	Every 500 steps
Best model selection	eval_loss (load_best_model_at_end)

Training Results

Table with columns: Metric, Value
Metric	Value
Final train loss	2.613
Final eval loss	2.509
Token accuracy	55.55%
Perplexity (train)	13.64

Evaluation: Cross-Domain Transfer Benchmark v2

Scoring: Heuristic rubric (keyword + structural analysis). Trust DELTAS between models on the same tasks, not absolutes.

Table with columns: Model, Judge Mean, Delta vs Base, Avg Response (chars), Avg Latency (s)
Model	Judge Mean	Delta vs Base	Avg Response (chars)	Avg Latency (s)
gnarp-m2	0.7839	+14.1%	1,108	4.9
base (SmolLM2-360M-Instruct)	0.6871	—	1,489	6.9

Prior Model Lineage (heldout benchmark, qwen3:8b judge)

Table with columns: Model, Transfer Score, Heldout Loss, Perplexity, Refusal Rate, Training Data
Model	Transfer Score	Heldout Loss	Perplexity	Refusal Rate	Training Data
base	0.709	1.625	5.08	0.130	—
v1	0.218	1.850	6.36	0.385	~2,152 rows
v2	0.713

*m2 scored on transfer_benchmark_v2 (heuristic-only), not the qwen3:8b-judged heldout benchmark. Cross-benchmark comparisons should be treated with caution.

Limitations

360M parameters. Small model. Cannot match larger models on complex reasoning, long-form generation, or nuanced instruction following.
Single GPU, single epoch. Trained on consumer hardware (RTX 3080 8GB) for one epoch. More training could improve results but risks overfitting.
Heuristic eval. The transfer benchmark v2 uses keyword/structural heuristic scoring, not a strong LLM judge. The +14.1% delta is directionally meaningful but not precisely calibrated.
Cross-benchmark caveat. v1/v2/v3 were scored with a qwen3:8b judge; m2 was scored with heuristic-only. Direct numerical comparison across the two benchmarks is not valid.
Domain-specific training data. Over 71% of training data is isomorphism pairs. The model is optimized for cross-domain structural transfer and may underperform on general chat or coding tasks.
No safety fine-tuning beyond refusal data. The model includes 19 refusal pairs but is not extensively safety-tuned.

How to Use

With Ollama (recommended for local inference)

bash
# Create the Modelfile
cat > Modelfile << 'EOF'
FROM ./model/merged_gnarpm2
TEMPLATE """### Instruction: {{ .Prompt }} ### Response: """
PARAMETER num_ctx 4096
PARAMETER temperature 0.3
PARAMETER num_predict 512
SYSTEM You are gnarp-m2, a cross-domain transfer specialist.
EOF

ollama create gnarp-m2 -f Modelfile
ollama run gnarp-m2

With Transformers

python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "gnarp/gnarp-m2"  # or local path
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

prompt = "### Instruction:\nApply the concept of biological apoptosis to software deployment strategy.\n### Response:\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.3)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

With PEFT (adapter only)

python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM2-360M-Instruct")
model = PeftModel.from_pretrained(base, "path/to/adapter_gnarpm2")
model = model.merge_and_unload()

License

Model weights and code: MIT License
Training data (corpus): COPL (Community Open Public License) — derived from the WaveMotionExpansion isomorphism engine
Base model: Apache 2.0 (SmolLM2-360M-Instruct by HuggingFace)

Citation

bibtex
@model{gnarp-m2,
  title={gnarp-m2: Cross-Domain Transfer Fine-Tuned Language Model},
  author={WaveMotionExpansion},
  year={2026},
  base_model={HuggingFaceTB/SmolLM2-360M-Instruct},
  method={QLoRA},
  training_rows={74395},
  transfer_benchmark={0.7839}
}

gnarp-m2

README

Model Details

Training Data

Training Configuration

Training Results

Evaluation: Cross-Domain Transfer Benchmark v2

Prior Model Lineage (heldout benchmark, qwen3:8b judge)

Limitations

How to Use

With Ollama (recommended for local inference)

With Transformers

With PEFT (adapter only)

License

Citation

Explore FriendliAI today

README

Model Details

Training Data

Training Configuration

Training Results

Evaluation: Cross-Domain Transfer Benchmark v2

Prior Model Lineage (heldout benchmark, qwen3:8b judge)

Limitations

How to Use

With Ollama (recommended for local inference)

With Transformers

With PEFT (adapter only)

License

Citation