| --- |
| license: apache-2.0 |
| language: |
| - en |
| base_model: javierbits/Javi0.3-2B |
| base_model_relation: finetune |
| pipeline_tag: image-text-to-text |
| library_name: transformers |
| tags: |
| - qwen3.5 |
| - tool-use |
| - agent |
| - weight-space |
| - dgx-spark |
| - javi |
| --- |
| |
|  |
|
|
| # Javi0.4-2B |
|
|
| Javi0.4-2B is **Javi0.3-2B with one embedding row changed**. No fine-tuning, no new data, no gradients. The tied input/output row for token 51187 (`=open_page`) is multiplied by 0.84. Every other weight is byte-identical to Javi0.3-2B. |
|
|
| The edit biases one decision: after a failed lookup, call `web_search` again instead of opening a URL typed from memory. It does not add a new reasoning capability. |
|
|
| ## Measured effect |
|
|
| 8 held-out eval-v3 canonical `fail` questions, first `web_search` forced to fail, greedy next tool: |
|
|
| | model | next `web_search` | next `open_page` | |
| |---|---:|---:| |
| | Javi0.3-2B | 0/8 | 8/8 | |
| | Javi0.4-2B | 7/8 | 1/8 | |
|
|
| This probe isolates the decision the edit targets. In a two-seed harness canary with real tools and sampling, 0.84 chose `open_page` after a permanently blocked search and completed, same as the base; it used one extra search on a normal lookup. A 0.75 scale flipped 4/4 but looped on 14 searches under a permanent block, so 0.84 is the conservative setting. The edit reaches the canonical `open_page` tool; in opencode/grok, `webfetch`/`web_fetch` after a 403 is unchanged. |
|
|
| ### Full eval v3 |
|
|
| 175 held-out seeds, 3 runs, date pin 2026-10-07, cached search results. Javi0.3-2B answers on the same seeds are the baseline. Judges: DeepSeek-V4.1-Flash-1017 and mimo-v26-dual. |
|
|
| | metric | Javi0.3-2B | Javi0.4-2B | |
| |---|---:|---:| |
| | core pass (judge average) | 0.35 [0.30, 0.40] | 0.41 [0.36, 0.45] | |
| | core pass (deepseek) | 0.48 | 0.58 | |
| | core pass (mimo) | 0.23 | 0.24 | |
| | recovery after a tool failure | 0.55 (n=11) | 0.45 (n=10) | |
| | made-up links | 0.01 | 0.00 | |
| | loops | 0.04 | 0.03 | |
|
|
| Paired core pass difference: **+0.06 [0.02, 0.10]** (deepseek +0.10 [0.05, 0.16], mimo +0.02 [-0.02, 0.06]), 95% bootstrap over 175 seeds. The deepseek interval clears zero; the mimo interval crosses zero, so the size of the gain is uncertain. Conflict questions improved (0.33 vs 0.07, n=7) and nodate (0.42 vs 0.17, n=6); premise (0.23 vs 0.29, n=13) and recovery after real failures did not. |
|
|
| Five answer episodes (4 seeds) carry no deepseek verdict because the judge omits the `tools` or `style` key; one seed never finished an episode in any of the three runs and the judge skips it, so judge-only rows cover 174 seeds. Core pass stays at 175 seeds with incomplete episodes scored 0. Full scoreboard: `data/v2/eval/javi04-vs-03-2b/score.md` in the source tree. |
|
|
| ## Files |
|
|
| Merged bf16 safetensors (one shard, 4.2 GB), tokenizer, processor files and chat template. The vision tower is Qwen3.5-2B's, unchanged; the edit is in the language model only. |
|
|
| ## Run it |
|
|
| Javi was trained and evaluated with thinking off. Send `"chat_template_kwargs": {"enable_thinking": false}` and use temperature 0.2, top-p 0.8, top-k 20 so tool calls stay well formed. |
|
|
| **GGUF** for llama.cpp or lmdeck: [Javi0.4-2B-GGUF](https://huggingface.co/javierbits/Javi0.4-2B-GGUF). |
|
|
| **lmdeck** |
|
|
| ```bash |
| lmdeck use javi0.4-2b |
| ``` |
|
|
| **Transformers** |
|
|
| ```python |
| from transformers import AutoModelForImageTextToText, AutoProcessor |
| |
| model_id = "javierbits/Javi0.4-2B" |
| processor = AutoProcessor.from_pretrained(model_id) |
| model = AutoModelForImageTextToText.from_pretrained(model_id, dtype="auto", device_map="auto") |
| |
| tools = [{"type": "function", "function": { |
| "name": "web_search", "description": "Search the web.", |
| "parameters": {"type": "object", "properties": {"query": {"type": "string"}}, "required": ["query"]}}}] |
| messages = [{"role": "user", "content": [{"type": "text", "text": "What's the latest stable Rust release?"}]}] |
| |
| inputs = processor.apply_chat_template(messages, tools=tools, add_generation_prompt=True, enable_thinking=False, |
| tokenize=True, return_dict=True, return_tensors="pt").to(model.device) |
| out = model.generate(**inputs, max_new_tokens=512, do_sample=True, temperature=0.2, top_p=0.8, top_k=20) |
| print(processor.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)) |
| ``` |
|
|
| ## The edit |
|
|
| ```python |
| embed_tokens[51187] *= 0.84 # =open_page |
| ``` |
|
|
| Token 51187 is a single token in the Qwen3.5 tokenizer. The row is tied between input and output embeddings, so the output logit for `=open_page` is reduced. |
|
|
| ## Links |
|
|
| - Base: [Javi0.3-2B](https://huggingface.co/javierbits/Javi0.3-2B) |
| - 4B control: [Javi0.4-4B](https://huggingface.co/javierbits/Javi0.4-4B) |
| - Apache 2.0, inherited from [Qwen3.5-2B](https://huggingface.co/Qwen/Qwen3.5-2B). |
|
|
