Text Generation
Transformers
Safetensors
English
llama
qlora
smollm
360m
cross-domain-transfer
anime-isomorphism
fine-tuned
conversational
text-generation-inference
Instructions to use CatQualia/gnarp-m2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CatQualia/gnarp-m2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CatQualia/gnarp-m2") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("CatQualia/gnarp-m2") model = AutoModelForCausalLM.from_pretrained("CatQualia/gnarp-m2", 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
- vLLM
How to use CatQualia/gnarp-m2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CatQualia/gnarp-m2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CatQualia/gnarp-m2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/CatQualia/gnarp-m2
- SGLang
How to use CatQualia/gnarp-m2 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 "CatQualia/gnarp-m2" \ --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": "CatQualia/gnarp-m2", "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 "CatQualia/gnarp-m2" \ --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": "CatQualia/gnarp-m2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use CatQualia/gnarp-m2 with Docker Model Runner:
docker model run hf.co/CatQualia/gnarp-m2
Add results: comparison_table.md
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# gnarp-m2 Comparison Table
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## Cross-Domain Transfer Benchmark v2 (36 tasks, heuristic scoring)
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| Model | Judge Mean | vs Base | Avg Response (chars) | Avg Latency (s) |
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|-------|-----------|---------|---------------------|-----------------|
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| **gnarp-m2** | **0.7839** | **+14.1%** | 1,108 | 4.9 |
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| base (SmolLM2-360M-Instruct) | 0.6871 | — | 1,489 | 6.9 |
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## Prior gnarp-m1 Lineage (heldout benchmark, qwen3:8b judge, 36 tasks)
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| Model | Transfer | vs Base | Loss | Perplexity | Refusal | Train Data |
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|-------|---------|---------|------|-----------|---------|-----------|
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| base | 0.709 | — | 1.625 | 5.08 | 0.130 | — |
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| v1 | 0.218 | -69.2% | 1.850 | 6.36 | 0.385 | ~2,152 |
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| v2 | 0.713 | +0.6% | 1.800 | 6.05 | 0.340 | ~2,152 |
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| v3 | 0.561 | -20.9% | 1.790 | 5.99 | 0.400 | ~2,152 |
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| **m2** | **0.7839*** | **+14.1%*** | 2.509† | — | — | **74,395** |
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\* m2 scored on transfer_benchmark_v2 (heuristic-only), not the qwen3:8b-judged heldout benchmark.
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† m2 eval loss is from QLoRA training held-out split, not the same heldout eval as v1–v3.
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## Training Evolution
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| Version | Corpus Size | Epochs | Method | Key Change |
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|---------|-----------|--------|--------|-----------|
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| v1 | ~2,152 | 2 | QLoRA r=8 | First fine-tune, data loss incident |
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| v2 | ~2,152 | 2 | QLoRA r=8 | Fixed tag scaffolding poison |
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| v3 | ~2,152 | 2 | QLoRA r=8 | Positive-only targets |
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| **m2** | **74,395** | **1** | **QLoRA r=16** | **35x more data, single epoch** |
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## Key Takeaways
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1. **Data scale matters more than epochs.** m2's 35x data increase (74k vs 2k rows) with 1 epoch outperforms v3's 2 epochs on 2k rows.
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2. **Cross-domain transfer improved.** m2 shows the highest transfer score in the lineage on its benchmark.
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3. **m2 generates more concisely.** 1,108 chars avg vs base's 1,489 — the model learned to be more focused.
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4. **m2 is faster.** 4.9s avg latency vs base's 6.9s — shorter, more direct responses.
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5. **Cross-benchmark caveat.** v1–v3 were scored by qwen3:8b judge; m2 by heuristic. The two benchmarks are not directly comparable.
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