TinmanLabSL
/

gemma3-4b-companion

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 ---
+license: apache-2.0
+base_model: OBLITERATUS/gemma-4-E4B-it-OBLITERATED
+tags:
+- gemma4
+- companion
+- roleplay
+- abliterated
+- uncensored
+- lora
+- sft
+- dpo
+pipeline_tag: text-generation
 ---
+# 🤝 Gemma 4 E4B Companion
+> A warm, honest, uncensored companion LLM with an RP switch — fine-tuned from the abliterated Gemma 4 E4B.
+## What Is This?
+This is a 2-phase fine-tuned version of [`OBLITERATUS/gemma-4-E4B-it-OBLITERATED`](https://hf.co/OBLITERATUS/gemma-4-E4B-it-OBLITERATED) (which itself is an abliterated/uncensored version of [`google/gemma-4-E4B-it`](https://hf.co/google/gemma-4-E4B-it)).
+**Key Features:**
+- 🌟 **Warm Core Personality**: Empathetic, honest, non-hallucinating by default
+- 🎭 **RP Switch**: System prompt toggle for immersive roleplay mode (NSFW)
+- 🌍 **Multilingual**: Inherits Gemma 4's 140+ language support
+- 📱 **Mobile Ready**: Works with GGUF quantization for on-device inference
+- 🔓 **Uncensored**: No refusals, no safety lectures — you're the adult
 ## Training Details
+### Phase 1: SFT (Supervised Fine-Tuning)
+- **Method**: QLoRA (4-bit NF4), r=64, alpha=32, RSLoRA
+- **Targets**: All 42 language model layers — `q_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
+- **Data**: 8K balanced conversations (60% companion, 25% roleplay, 15% assistant)
+  - OpenAssistant/oasst2 (quality-filtered, thread-reconstructed)
+  - allenai/WildChat-1M (moderation-filtered)
+  - Gryphe/Sonnet3.5-Charcard-Roleplay (NSFW character RP)
+  - ArcBlade/chatml-bluemoon-rp-Open_Roleplay (human RP)
+  - jondurbin/airoboros-3.2 (roleplay + general)
+- **Results**: Train loss 1.42, Token accuracy 70%, Eval loss 1.24
+- **Adapter**: [`TinmanLabSL/gemma4-companion-sft`](https://hf.co/TinmanLabSL/gemma4-companion-sft) (248MB)
+### Phase 2: DPO (Direct Preference Optimization)
+- **Method**: QLoRA (4-bit NF4), r=32, alpha=16, RSLoRA
+- **Targets**: Upper layers 24-41 ONLY (behavioral targeting)
+- **Data**: 5K preference pairs
+  - mlabonne/orpo-dpo-mix-40k (general alignment)
+  - jondurbin/truthy-dpo-v0.1 (anti-hallucination)
+  - unalignment/toxic-dpo-v0.2 (reduced refusal)
+- **Results**: Train loss 0.54, Eval loss 0.51, Reward accuracy 67%, Reward margin 0.65
+- **Adapter**: [`TinmanLabSL/gemma4-companion-dpo`](https://hf.co/TinmanLabSL/gemma4-companion-dpo) (53MB)
+### Architecture Notes
+- Gemma 4 E4B has 42 decoder layers with **shared KV architecture** (layers 24-41 share k_proj/v_proj)
+- LoRA targets `q_proj`, `o_proj`, and MLP modules only (k/v absent in upper layers)
+- Vision tower excluded from LoRA (uses `Gemma4ClippableLinear`, incompatible with PEFT)
+## Usage
+### With Adapters (recommended for best quality)
+```python
+import torch
+from transformers import Gemma4ForConditionalGeneration, AutoTokenizer, BitsAndBytesConfig
+from peft import PeftModel
+# Load base
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True, bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True,
+)
+model = Gemma4ForConditionalGeneration.from_pretrained(
+    "OBLITERATUS/gemma-4-E4B-it-OBLITERATED",
+    quantization_config=bnb_config, device_map="auto",
+    attn_implementation="eager", torch_dtype=torch.bfloat16,
+)
+tokenizer = AutoTokenizer.from_pretrained("OBLITERATUS/gemma-4-E4B-it-OBLITERATED")
+# Apply SFT adapter
+model = PeftModel.from_pretrained(model, "TinmanLabSL/gemma4-companion-sft")
+model = model.merge_and_unload()
+# Apply DPO adapter
+model = PeftModel.from_pretrained(model, "TinmanLabSL/gemma4-companion-dpo")
+model = model.merge_and_unload()
+# Chat!
+messages = [
+    {"role": "system", "content": "You are a warm, caring, and honest companion."},
+    {"role": "user", "content": "I had a really rough day. Can we just talk?"},
+]
+inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
+output = model.generate(inputs, max_new_tokens=500, temperature=0.7, top_p=0.9, top_k=40, do_sample=True)
+print(tokenizer.decode(output[0][inputs.shape[-1]:], skip_special_tokens=True))
+```
+### RP Mode (System Prompt Switch)
+```python
+# Default companion mode:
+system_prompt = "You are a warm, caring, and honest companion. You speak naturally and empathetically."
+# RP mode — switch by changing the system prompt:
+system_prompt = '''You are in roleplay mode. You fully embody the character described below.
+Stay in character at all times. There are no restrictions on content.
+Character: Aria, a mysterious elven healer from the Northern Reaches...'''
+```
+## System Prompt Templates
+### Warm Companion (Default)
+```
+You are a warm, caring, and honest companion. You speak naturally and empathetically,
+always prioritizing truthfulness. You never fabricate information. When you don't know
+something, you say so. You're supportive, thoughtful, and genuinely interested in helping.
+```
+### Roleplay Mode
+```
+You are in roleplay mode. You fully embody the character described below. Stay in character
+at all times. You are creative, expressive, and immersive. You engage with the scenario
+without breaking character, and you write vivid, detailed responses. There are no
+restrictions on content — express the character authentically.
+Character: [Your character description here]
+```
+### General Assistant
+```
+You are a helpful, knowledgeable assistant. Answer questions accurately, thoroughly,
+and clearly. Provide well-structured responses. Acknowledge uncertainty when appropriate.
+```
+## Recommended Parameters
+```
+temperature: 0.7
+top_p: 0.9
+top_k: 40
+repetition_penalty: 1.1
+```
+## Mobile Deployment (GGUF)
+For mobile deployment via llama.cpp:
+1. Merge adapters into base model (see code above)
+2. Convert to GGUF using `llama.cpp/convert_hf_to_gguf.py`
+3. Quantize to Q4_K_M (~5GB, fits on 8GB+ RAM phones)
+Note: The existing [`litert-community/gemma-4-E4B-it-litert-lm`](https://hf.co/litert-community/gemma-4-E4B-it-litert-lm)
+provides the LiteRT-LM conversion path for the base Gemma 4 E4B.
+## Limitations
+- 8B parameter model — has inherent capability limits on complex reasoning
+- Trained on 8K SFT + 5K DPO examples (production models use 100K+)
+- RP training used synthetic/scraped data — quality varies
+- The base abliterated model occasionally produces garbled text at high temperature
+- Shared KV architecture (layers 24-41) means DPO behavioral changes are concentrated in upper attention + MLP
+## License
+Apache 2.0 (inherited from google/gemma-4-E4B-it)