Publish Prism Caption 1 Micro (MLX + GGUF)

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+logo.png filter=lfs diff=lfs merge=lfs -text
+prism_caption_1_micro_Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model: mlx-community/gemma-3-1b-it-4bit
+tags:
+- mlx
+- gguf
+- chat-titling
+- summarization
+- lora
+- distillation
+- conversational
+- text-generation
+pipeline_tag: text-generation
+---
+
+<div align="center">
+  <img src="logo.png" alt="Prism logo" width="220"/>
+
+  # Prism Caption 1 Micro
+
+  **A tiny, single-purpose model that titles chats -- distilled from Nemotron-3-Nano-Omni into Gemma 3 1B**
+</div>
+
+---
+
+## Overview
+
+Prism Caption 1 Micro does exactly one thing: given a user's first message in a new conversation, it produces a short, specific chat title -- the same "New chat" auto-naming behavior ChatGPT, Claude, and similar assistants provide. Nothing else. It's built to be small and fast enough to run this single narrow task cheaply and locally rather than routing it to a larger general-purpose model.
+
+Part of the **Prism** family of models.
+
+## Training
+
+- **Base model:** `mlx-community/gemma-3-1b-it-4bit`
+- **Teacher:** `nvidia/nemotron-3-nano-omni-30b-a3b-reasoning:free` (via OpenRouter's free tier)
+- **Dataset:** 339 examples of realistic first-messages across ~50 everyday topics (coding, travel, cooking, finance, health, and more), paired with teacher-generated titles
+- **Method:** LoRA fine-tuning (rank 8, 8 layers), 1,500 iterations
+- **Best validation loss:** 0.163 (reached at iteration 800; the run was essentially converged from iteration ~300 onward)
+
+## Evaluation
+
+A held-out base-vs-tuned comparison (20 fresh first-messages) shows the base Gemma-3-1B-it model is already fairly capable at this simple task -- the real gain from fine-tuning is **format reliability**:
+
+| Metric | Base Gemma-3-1B-it | Prism Caption 1 Micro |
+|---|---|---|
+| Bad-format outputs (too long / multiline / malformed) | 3/20 | **0/20** |
+| Avg word count | 4.0 | 4.0 |
+
+Example outputs (base -> tuned):
+- *"Explaining the difference between TCP and UDP"* -> "TCP vs UDP Explanation" -> "TCP vs UDP Overview"
+- *"How to start composting in a small apartment"* -> "Start Small Apartment Composting" -> "Small Apartment Composting Guide"
+- *"Debugging a React component that re-renders infinitely"* -> "React Component Re-renders" -> "React Component Re-render Issue"
+
+**Important inference note:** Gemma 3's tokenizer registers `<eos>` (id 1) as its only default stop token, but the chat template actually ends each turn with `<end_of_turn>` (id 106). If your inference code doesn't also treat `<end_of_turn>` as a stop condition, generation will run past the correct title into garbage/padding tokens. See usage examples below.
+
+## Formats available
+
+This repo includes both:
+
+| Format | File | Notes |
+|---|---|---|
+| **MLX** (4-bit) | `model.safetensors` + config | For Apple Silicon via `mlx-lm` |
+| **GGUF** (Q4_K_M) | `prism_caption_1_micro_Q4_K_M.gguf` | For `llama.cpp` and compatible runtimes (LM Studio, Ollama, etc.) |
+
+### Usage -- MLX
+
+```python
+from mlx_lm import load, generate
+
+model, tokenizer = load("VertexAGI/prism-caption-1-micro")
+tokenizer.eos_token_ids.add(106)  # <end_of_turn> -- see inference note above
+
+prompt = "How do I fix a leaking kitchen faucet?"
+messages = [{"role": "user", "content": prompt}]
+text = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
+
+title = generate(model, tokenizer, prompt=text, max_tokens=30)
+print(title)
+```
+
+### Usage -- GGUF (llama.cpp)
+
+llama.cpp's own chat template handling for Gemma 3 already stops correctly at `<end_of_turn>`, so no extra flags are needed:
+
+```bash
+llama-cli -hf VertexAGI/prism-caption-1-micro -m prism_caption_1_micro_Q4_K_M.gguf -st -p "How do I fix a leaking kitchen faucet?"
+```
+
+## System prompt
+
+For best results, use the system prompt the model was trained with:
+
+```
+You name chat conversations. Given the user's first message, reply with ONLY a short, specific chat title (3-6 words, title case, no quotes, no punctuation at the end, no preamble). Nothing else -- just the title.
+```
+
+## Limitations
+
+This is a 1B-parameter model fine-tuned via LoRA on a small (339-example) dataset for one narrow task -- it will not perform well on anything outside chat-titling. Titles for highly ambiguous or very short first messages may be generic. Not intended as a general-purpose chat model.
+
+## License
+
+Apache 2.0, inherited from the Gemma 3 base model.

added_tokens.json

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+{
+  "<image_soft_token>": 262144
+}

chat_template.jinja

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+{{ bos_token }}
+{%- if messages[0]['role'] == 'system' -%}
+    {%- if messages[0]['content'] is string -%}
+        {%- set first_user_prefix = messages[0]['content'] + '
+
+' -%}
+    {%- else -%}
+        {%- set first_user_prefix = messages[0]['content'][0]['text'] + '
+
+' -%}
+    {%- endif -%}
+    {%- set loop_messages = messages[1:] -%}
+{%- else -%}
+    {%- set first_user_prefix = "" -%}
+    {%- set loop_messages = messages -%}
+{%- endif -%}
+{%- for message in loop_messages -%}
+    {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
+        {{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
+    {%- endif -%}
+    {%- if (message['role'] == 'assistant') -%}
+        {%- set role = "model" -%}
+    {%- else -%}
+        {%- set role = message['role'] -%}
+    {%- endif -%}
+    {{ '<start_of_turn>' + role + '
+' + (first_user_prefix if loop.first else "") }}
+    {%- if message['content'] is string -%}
+        {{ message['content'] | trim }}
+    {%- elif message['content'] is iterable -%}
+        {%- for item in message['content'] -%}
+            {%- if item['type'] == 'image' -%}
+                {{ '<start_of_image>' }}
+            {%- elif item['type'] == 'text' -%}
+                {{ item['text'] | trim }}
+            {%- endif -%}
+        {%- endfor -%}
+    {%- else -%}
+        {{ raise_exception("Invalid content type") }}
+    {%- endif -%}
+    {{ '<end_of_turn>
+' }}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+    {{'<start_of_turn>model
+'}}
+{%- endif -%}

config.json

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+{
+    "architectures": [
+        "Gemma3ForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "attn_logit_softcapping": null,
+    "bos_token_id": 2,
+    "cache_implementation": "hybrid",
+    "eos_token_id": 1,
+    "final_logit_softcapping": null,
+    "head_dim": 256,
+    "hidden_activation": "gelu_pytorch_tanh",
+    "hidden_size": 1152,
+    "initializer_range": 0.02,
+    "intermediate_size": 6912,
+    "max_position_embeddings": 32768,
+    "model_type": "gemma3_text",
+    "num_attention_heads": 4,
+    "num_hidden_layers": 26,
+    "num_key_value_heads": 1,
+    "pad_token_id": 0,
+    "quantization": {
+        "group_size": 64,
+        "bits": 4
+    },
+    "quantization_config": {
+        "group_size": 64,
+        "bits": 4
+    },
+    "query_pre_attn_scalar": 256,
+    "rms_norm_eps": 1e-06,
+    "rope_local_base_freq": 10000,
+    "rope_scaling": null,
+    "rope_theta": 1000000,
+    "sliding_window": 512,
+    "sliding_window_pattern": 6,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.50.0.dev0",
+    "use_cache": true,
+    "vocab_size": 262144
+}

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