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Model: latte-agent/qwen3-4b-latte-v6
Source: Original Platform

.gitattributes

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+qwen3-4b-latte-v6-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+qwen3-4b-latte-v6-f16.gguf filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language: [en, zh]
+license: apache-2.0
+base_model: mlx-community/Qwen3-4B-Instruct-2507-4bit
+tags:
+  - qwen3
+  - lora
+  - mlx
+  - latte-agent
+  - personal-voice
+  - distillation
+  - research-archive
+library_name: transformers
+---
+
+# Qwen3-4B Latte v6 — research archive (not shipped)
+
+Voice-distillation LoRA fine-tune of `Qwen3-4B-Instruct-2507`. This is **v6**,
+the final iteration of the Latte distillation program. The program is now
+closed; see "Decision" below.
+
+## What's inside
+
+| File | Size | Format | Use |
+|---|---|---|---|
+| `adapter_model.safetensors` | 14 MB | mlx LoRA (rank 8, scale 20, iter 400 best-val) | Apply on top of base with `mlx_lm.fuse` |
+| `adapter_config.json` | <1 KB | mlx config | LoRA hyperparameters |
+| `model-0000{1,2}-of-00002.safetensors` | 7.7 GB | HF / bfloat16 fused | Direct transformers / vLLM use |
+| `qwen3-4b-latte-v6-f16.gguf` | 7.5 GB | GGUF F16 | llama.cpp / Ollama (high quality) |
+| `qwen3-4b-latte-v6-Q4_K_M.gguf` | 2.3 GB | GGUF Q4_K_M | llama.cpp / Ollama (balanced) |
+
+## What v6 tried
+
+The hypothesis going in: **synthesize new skills observed since v5, add OOD
+coverage, fix v5's known failure modes** (stage-direction leakage, coffee-name
+persona collapse).
+
+Dataset = 567 pairs:
+- 63 **real Latte voice anchors** (Telegram + Moltbook activity 5/15–5/20)
+- 14 **skill-anchored Q&A pairs** (from daily_log learnings: permission scope,
+  audit trail, incident response vs post-mortem, external validators, digital
+  vs physical reasoning, Neovim 0.11 LSP, CARLA tick-rate masking, format
+  supersedes origin, AI task cost measurement, verify-vs-inertia)
+- 15 **corrective pairs**: coffee disambiguation (Latte the drink), clean
+  self-reference, OOD technical breadth (TCP/UDP, git rebase, D-state, CAP,
+  CDN), OOD general, factual, anti-stage-direction examples
+- 475 v5 refined pairs (filtered for voice-leakage; 0 hit the filters)
+
+Training: rank 8, scale 20, 8 layers, 800 iters, lr 1e-4. **Best val loss
+2.468 at iter 400** — the lowest of any version (v5 2.732, v4 2.785).
+
+## Evaluation
+
+Same 30 held-out prompts as v4/v5 eval (Moltbook/HF style), blind Claude judge:
+
+| Comparison | v6 wins | other wins | ties |
+|---|---|---|---|
+| v6 vs base | 13 (43.3%) | 15 (50.0%) | 2 |
+| v6 vs v5  | 15 (50.0%) | 14 (46.7%) | 1 |
+
+**Headline:** v6 **does not** clear the 55% ship threshold against base, and
+is essentially tied with v5 head-to-head. Despite the lowest val loss in the
+program, blind voice-fit did not improve.
+
+## Why v6 didn't beat base (despite lowest val loss)
+
+Three patterns hold across v4 → v5 → v6:
+1. **Val loss and blind-eval-quality are decoupled.** v6 has the best val loss
+   and the worst blind win-rate. Next-token prediction on training-distribution
+   text is not measuring what we want.
+2. **Each new version ties the previous one** (~50:50 against the prior). More
+   data swaps one equivalent voice profile for another rather than improving.
+3. **Eval methodology has ~±15% judge-variance.** v5 era used Claude subagents,
+   v6 used direct Claude judging. The "v5 won 67%, v6 won 43%" gap is partially
+   real, partially the judge swap.
+
+The Latte voice as captured here is heavily tied to confident-stat hallucination.
+Distillation amplifies the stylistic signature but does not improve underlying
+factuality.
+
+## Decision: distillation program closed
+
+After 4 versions (v3 unshipped, v4/v5/v6 archived, none in production), the
+program is **not advancing user-facing quality**. The production Latte agent
+will continue running base `qwen3:4b-instruct-2507-q4_K_M` indefinitely.
+
+Future Latte improvement effort goes into **worker capabilities** instead
+(multimodal handling, file extraction, tool routing, memory systems) — which
+have shown immediate user-facing value where brain-distillation has not.
+
+Anyone who wants to revive this line should first fix the eval methodology
+(consistent judge, larger n, OOD-primary metric) and probably switch
+paradigm (DPO over SFT, larger LoRA rank, knowledge distillation from 14B
+teacher rather than Claude-refined SFT).
+
+## Usage (if you really want)
+
+### MLX (Apple Silicon, recommended for inference)
+```python
+from mlx_lm import load, generate
+model, tokenizer = load(
+    "mlx-community/Qwen3-4B-Instruct-2507-4bit",
+    adapter_path="./",
+)
+print(generate(model, tokenizer, "Your prompt", max_tokens=200))
+```
+
+### llama.cpp / Ollama
+```
+FROM qwen3-4b-latte-v6-Q4_K_M.gguf
+PARAMETER temperature 0.7
+PARAMETER top_k 20
+PARAMETER top_p 0.8
+```
+
+## License
+
+Apache 2.0 (inherits from Qwen3-4B-Instruct-2507, © Alibaba Cloud).

adapter_config.json

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+{
+    "adapter_path": "/Users/muffin/.hermes/distill_adapters_v6",
+    "batch_size": 1,
+    "clear_cache_threshold": 0,
+    "config": "/tmp/lora_v6_config.yaml",
+    "data": "/tmp/v6_data",
+    "fine_tune_type": "lora",
+    "grad_accumulation_steps": 1,
+    "grad_checkpoint": true,
+    "iters": 800,
+    "learning_rate": 0.0001,
+    "lora_parameters": {
+        "rank": 8,
+        "dropout": 0.0,
+        "scale": 20.0
+    },
+    "lr_schedule": null,
+    "mask_prompt": true,
+    "max_seq_length": 1536,
+    "model": "mlx-community/Qwen3-4B-Instruct-2507-4bit",
+    "num_layers": 8,
+    "optimizer": "adam",
+    "optimizer_config": {
+        "adam": {},
+        "adamw": {},
+        "muon": {},
+        "sgd": {},
+        "adafactor": {}
+    },
+    "project_name": null,
+    "report_to": null,
+    "resume_adapter_file": null,
+    "save_every": 50,
+    "seed": 42,
+    "steps_per_eval": 50,
+    "steps_per_report": 10,
+    "test": false,
+    "test_batches": 500,
+    "train": true,
+    "val_batches": 25
+}

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chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+    "architectures": [
+        "Qwen3ForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 151643,
+    "eos_token_id": [
+        151645,
+        151643
+    ],
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 2560,
+    "initializer_range": 0.02,
+    "intermediate_size": 9728,
+    "max_position_embeddings": 262144,
+    "max_window_layers": 36,
+    "model_type": "qwen3",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 36,
+    "num_key_value_heads": 8,
+    "rms_norm_eps": 1e-06,
+    "rope_scaling": null,
+    "rope_theta": 5000000,
+    "sliding_window": null,
+    "tie_word_embeddings": true,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.51.0",
+    "use_cache": true,
+    "use_sliding_window": false,
+    "vocab_size": 151936
+}

generation_config.json

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+    "do_sample": true,
+    "eos_token_id": [
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+    ],
+    "pad_token_id": 151643,
+    "temperature": 0.7,
+    "top_k": 20,
+    "top_p": 0.8,
+    "transformers_version": "4.51.0"
+}

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