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Model: reaperdoesntknow/Gemma-3-270m-Opus-Distil Source: Original Platform
2026-08-06 06:27:16 +08:00
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+---
+library_name: transformers
+license: gemma
+datasets:
+- angrygiraffe/claude-opus-4.6-4.7-reasoning-8.7k
+language:
+- en
+base_model:
+- google/gemma-3-270m
+pipeline_tag: text-generation
+tags:
+- text-generation
+- causal-lm
+- gemma
+- gemma-3
+- gemma-3-270m
+- reasoning
+- instruction-tuning
+- sparse-finetuning
+- cixopt
+- transformers
+- bf16
+---
+
+# CIx-Gemma-3-270M Reasoning SFT
+
+## Model Summary
+
+This model is a fine-tuned derivative of google/gemma-3-270m, adapted using the Convergent Intelligence sparse fine-tuning setup originally tested on Liquid Foundation Models.
+
+The checkpoint was trained on reasoning-style English examples from angrygiraffe/claude-opus-4.6-4.7-reasoning-8.7k using a targeted adaptation strategy and the custom CIxOpt optimizer framework.
+
+The goal of this model is to test whether a compact Gemma 3 270M backbone can be shaped toward reasoning-style text generation through selective parameter participation rather than broad full-model modification.
+
+This is an experimental research checkpoint intended for evaluation, local testing, optimizer research, and continued fine-tuning.
+
+## Base Model
+
+- Base model: google/gemma-3-270m
+- Model family: Gemma 3
+- Approximate size: 270M parameters
+- Task: Causal language modeling / text generation
+- Language: English-focused fine-tuning
+- Library: Hugging Face Transformers
+- License: Gemma license
+
+If this checkpoint was instead trained from google/gemma-3-270m-it, update the base_model field accordingly.
+
+## Dataset
+
+Fine-tuning data:
+
+- angrygiraffe/claude-opus-4.6-4.7-reasoning-8.7k
+
+The dataset was processed into text-generation / chat-style training examples. Empty, malformed, or unusable samples were filtered before tokenization.
+
+Training used causal language modeling labels with padding masked using -100.
+
+## Training Method
+
+This model was trained using the same CIx sparse-adaptation setup used for LFM experiments.
+
+The training approach emphasized:
+
+text preserve the compact pretrained backbone adapt selected reasoning and response-shaping surfaces avoid unnecessary full-model disturbance use heterogeneous optimizer routing by parameter type 
+
+## CIxOpt Optimizer
+
+Training used CIxOpt, a custom heterogeneous optimizer designed for architecture-aware routing.
+
+CIxOpt supports:
+
+- AdamW-style adaptive updates
+- Lion-style sign momentum
+- AdaMax-compatible routing
+- Optional ASGD-style averaging
+- Optional low-rank projected momentum
+- Gradient centralization
+- Decoupled weight decay
+- Discrepancy-aware caution filtering for sign updates
+- fp32 optimizer state for bf16/fp16 safety
+- Parameter-name-aware routing
+
+The intended optimizer behavior is:
+
+text large projection matrices        -> Lion-style sign momentum normalization / sensitive params -> AdamW-style updates embedding / lm-head surfaces     -> conservative adaptive routing 
+
+This makes the checkpoint useful for testing whether small models can be efficiently adapted with custom optimizer routing rather than full uniform AdamW updates.
+
+## Sparse Fine-Tuning Strategy
+
+The setup used sparse parameter participation rather than unrestricted full-model training.
+
+The intended adaptation pattern was:
+
+text freeze or reduce movement in lower representational structure train selected higher-level adaptation surfaces preserve base language structure where possible shape reasoning and response behavior through targeted updates 
+
+This checkpoint should be treated as an experimental adaptation artifact, not a fully benchmarked general-purpose assistant.
+
+## Intended Use
+
+This model is intended for:
+
+- Research on compact Gemma fine-tuning
+- CIxOpt optimizer experiments
+- Small-model reasoning-style generation
+- Local text-generation experiments
+- Instruction-following and response-style studies
+- Efficient adaptation research
+- Continued fine-tuning and ablation testing
+- Comparison against the base google/gemma-3-270m
+
+Potential use cases:
+
+- Technical explanation
+- Lightweight reasoning experiments
+- Prompt-response generation
+- Local prototyping
+- Small agent backbone testing
+- Educational model behavior analysis
+
+## Out-of-Scope Use
+
+This model is not intended for high-stakes autonomous deployment.
+
+Do not use this model as the sole decision-maker for:
+
+- Medical diagnosis
+- Legal judgment
+- Financial decisions
+- Emergency response
+- Cyber offensive automation
+- Personnel screening
+- Surveillance or targeting decisions
+- Critical infrastructure decisions
+- Any setting requiring verified factual accuracy
+
+## Limitations
+
+This is an experimental fine-tuned checkpoint. Expected limitations include:
+
+- May hallucinate facts, dates, citations, or technical details
+- May inherit limitations from the Gemma 3 270M base model
+- May overproduce reasoning-style outputs
+- May be sensitive to prompt format
+- May repeat or drift during longer generations
+- Has not been fully evaluated for factuality, safety, math, coding, or instruction-following
+- Fine-tuning on reasoning-style data does not guarantee correct reasoning
+- Sparse adaptation may change some behaviors unevenly while leaving others close to the base model
+- Small model size limits world knowledge, reasoning depth, and robustness
+
+## Safety Notes
+
+Users should independently validate important outputs.
+
+Before deployment, additional evaluation is recommended:
+
+- Hallucination testing
+- Bias and toxicity evaluation
+- Refusal behavior testing
+- Prompt-injection sensitivity testing
+- Side-by-side comparison against the base model
+- Domain-specific factuality testing
+- Human review of outputs
+- Guardrails for public-facing applications
+
+## Example Usage
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "YOUR_USERNAME/YOUR_MODEL_REPO"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+prompt = "Explain why small language models are useful for edge reasoning experiments."
+
+inputs = tokenizer(
+    prompt,
+    return_tensors="pt",
+).to(model.device)
+
+with torch.inference_mode():
+    output = model.generate(
+        **inputs,
+        max_new_tokens=256,
+        do_sample=True,
+        temperature=0.7,
+        top_p=0.95,
+        repetition_penalty=1.05,
+        pad_token_id=tokenizer.eos_token_id,
+    )
+
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+## Chat-Style Usage
+
+If the tokenizer provides a chat template:
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "YOUR_USERNAME/YOUR_MODEL_REPO"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+messages = [
+    {
+        "role": "user",
+        "content": "Why is sparse fine-tuning useful for compact language models?"
+    }
+]
+
+inputs = tokenizer.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt",
+    return_dict=True,
+).to(model.device)
+
+with torch.inference_mode():
+    output = model.generate(
+        **inputs,
+        max_new_tokens=384,
+        do_sample=True,
+        temperature=0.7,
+        top_p=0.95,
+        repetition_penalty=1.05,
+        pad_token_id=tokenizer.eos_token_id,
+    )
+
+generated = output[0][inputs["input_ids"].shape[-1]:]
+print(tokenizer.decode(generated, skip_special_tokens=True))
+```
+## Suggested Generation Settings
+
+Balanced exploratory generation:
+
+```python
+generation_config = {
+    "max_new_tokens": 384,
+    "do_sample": True,
+    "temperature": 0.7,
+    "top_p": 0.95,
+    "repetition_penalty": 1.05,
+}
+```
+
+More deterministic generation:
+
+```python
+generation_config = {
+    "max_new_tokens": 384,
+    "do_sample": False,
+}
+```
+For smaller models, shorter outputs are often more stable:
+
+```python
+generation_config = {
+    "max_new_tokens": 128,
+    "do_sample": True,
+    "temperature": 0.6,
+    "top_p": 0.9,
+    "repetition_penalty": 1.1,
+}
+```
+## Training Configuration
+
+Approximate training configuration:
+```txt
+text base_model: google/gemma-3-270m
+dataset: angrygiraffe/claude-opus-4.6-4.7-reasoning-8.7k
+task: causal language modeling / reasoning-style SFT
+optimizer: CIxOpt state_dtype: fp32
+optimizer state model_dtype: bf16 where supported
+```
+
+## Evaluation
+
+Formal benchmark results have not yet been added.
+
+Recommended evaluations:
+
+- Held-out perplexity
+- Base model comparison against google/gemma-3-270m
+- Short-form reasoning checks
+- IFEval-style instruction-following tests
+- Repetition and degeneration testing
+- Human preference review
+- Truthfulness / hallucination checks
+- Prompt-format robustness testing
+- CIxOpt vs AdamW ablation
+
+## Responsible Use
+
+This model may generate plausible but incorrect text. It should be used with human oversight.
+
+Developers should follow the Gemma usage terms and apply appropriate safety review before deploying the model in user-facing or operational settings.
+
+## Citation
+
+Base model:
+```bib
+bibtex @misc{google_gemma_3_270m,
+title = {Gemma 3 270M},
+author = {Google DeepMind},
+publisher = {Hugging Face},
+year = {2025}
+} 
+```
+Fine-tuning dataset:
+```bib
+bibtex @misc{angrygiraffe_reasoning_dataset,
+title = {claude-opus-4.6-4.7-reasoning-8.7k},
+author = {angrygiraffe},
+publisher = {Hugging Face}
+} 
+```
+## Author / Maintainer
+
+Fine-tuning and optimizer experimentation by:
+
+Convergent Intelligence LLC
+
+Research focus: AI systems, intelligence analysis, mathematical frameworks, optimizer design, and efficient model adaptation.
+
+## Disclaimer
+
+This model is provided for research and experimentation. It should not be treated as a verified expert system. Outputs require human review, especially in factual, technical, legal, medical, financial, operational, or safety-critical contexts.
+<!-- cix-keeper-ts:2026-08-05T13:15:50Z -->