Card, banner, LoRA adapter (merged weights follow)

.gitattributes

@@ -33,3 +33,4 @@ 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
+adapter/tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,124 @@
+---
+license: apache-2.0
+base_model: Tesslate/OmniCoder-9B
+language:
+  - en
+  - zh
+pipeline_tag: text-generation
+tags:
+  - code
+  - reasoning
+  - compressed-reasoning
+  - chain-of-thought
+  - qwen3.5
+  - lora
+  - vllm
+model-index:
+  - name: Tessera-Preview-9B
+    results:
+      - task:
+          type: text-generation
+          name: Code generation
+        dataset:
+          name: LiveCodeBench release_v6 (full 1,055 problems, 16K budget)
+          type: livecodebench
+        metrics:
+          - type: pass@1
+            name: "pass@1, greedy (base OmniCoder-9B: 39.5)"
+            value: 34.9
+            verified: false
+          - type: pass@1
+            name: "pass@1, temperature 0.6 (base: 45.9)"
+            value: 33.7
+            verified: false
+      - task:
+          type: text-generation
+          name: Instruction following
+        dataset:
+          name: IFEval (full 541 prompts, greedy)
+          type: HuggingFaceH4/ifeval
+        metrics:
+          - type: accuracy
+            name: "prompt-strict (base: 59.9)"
+            value: 40.9
+            verified: false
+          - type: accuracy
+            name: "instruction-strict, macro over 834 instructions (base: 60.7)"
+            value: 45.7
+            verified: false
+---
+
+![Tessera-Preview-9B](banner.png)
+
+# Tessera-Preview-9B
+
+A 9B coding model fine-tuned to reason internally in a compressed CJK register while emitting ordinary code and well-formed tool calls. On the full LiveCodeBench set it spends a median **17.7x fewer output tokens per problem** than its own base at a cost of **4.6 points** of pass@1 at matched greedy budget. This is a proof of concept: one LoRA run, a 10,205-example corpus, about \$7.60 of training compute. The costs are real and are reported next to the savings.
+
+The reasoning channel is private by design. Users see normal code and normal tool calls; the compressed register never leaks into the output channel (0 of 46 completed problems in the measured run).
+
+**Paper:** [Tessera-Preview-9B: Compressed Reasoning at 18x Fewer Tokens, and What It Costs](https://zellige.ai/research/compressed-cjk-reasoning) covers the method, the pre-registered protocol, and every number below with confidence intervals.
+
+## What the compression looks like
+
+Private reasoning channel (real output, LiveCodeBench problem, solved):
+
+```text
+设 s 为 0-indexed 字符串。操作：选 i，c=s[i]，删除 i 左侧最近 c 和右侧最近 c（若存在）。
+观察：每次操作删除两个相同字符。关键：操作不改变字符种类集合，仅减少计数。
+故最终长度 = 不同字符数。算法：返回 len(set(s))。复杂度 O(n|1)。⊘：s 长度 1 → 返回 1。
+```
+
+Shipped answer (verbatim, complete):
+
+```python
+class Solution:
+    def minimizedStringLength(self, s: str) -> int:
+        return len(set(s))
+```
+
+## Results
+
+All numbers are paired measurements against the model's own re-measured base (Tesslate/OmniCoder-9B) on one pinned serving stack: vLLM 0.21.0, CUDA graphs, A100-80G, evalscope 1.9.0, temperature pinned per condition, 16,384-token generation budget, ceiling hits scored as failures.
+
+| Metric | Tessera | Base | Gap |
+| --- | --- | --- | --- |
+| LCB-1055 pass@1, greedy | **34.9%** [32.1, 37.8] | 39.5% [36.6, 42.5] | −4.6 (95% CI [−7.5, −1.8]) |
+| LCB-1055 pass@1, temp 0.6 | 33.7% | 45.9% | −12.2 |
+| IFEval-541 prompt-strict | 40.9% | 59.9% | −19.0 (95% CI [−24.5, −13.6]) |
+| Median output tokens per LCB problem | **639** | 16,384 (at ceiling) | 17.7x (median paired ratio) |
+| Budget deaths at greedy (LCB) | 21.6% | 58.8% | |
+
+The two models fail differently. The base almost never writes wrong code (95.9% of its completions pass) but thinks into the 16K ceiling on 58.8% of problems. Tessera completes 78% of problems at a median 639 tokens end to end and errs by writing wrong code. Forcing an empty think collapses accuracy from 66% to 4% on the archived 50-problem protocol: the compressed channel is load-bearing, not decoration.
+
+**Read the limitations before deploying.** The instruction-following gap is large: the training corpus is 100% code-agentic with zero IFEval-style prompts, and the model falls into 16K reasoning loops on 46.8% of such prompts. Sampling does not help this model (greedy is the intended operating point). Whether the gaps are a data-coverage artifact or intrinsic to the compression is the successor's question; the paper argues coverage is the likely major cause and says what would prove it.
+
+## Usage
+
+The repo ships vLLM-ready weights (LoRA merged, keys repacked, Qwen3.5-9B text config). Serve:
+
+```bash
+vllm serve ZelligeAI/tessera-preview-9b \
+  --served-model-name tessera-preview-9b \
+  --max-model-len 32768 --dtype bfloat16 \
+  --reasoning-parser qwen3 \
+  --enable-auto-tool-choice --tool-call-parser qwen3_coder
+```
+
+Pinned versions matter. All published numbers come from vLLM **0.21.0**; vLLM 0.24.0 degraded this model in our validation and we do not recommend it. Greedy point estimates for this model family are stack-sensitive (the paper documents a 12-point spread across serving stacks), so treat scores measured on other stacks accordingly.
+
+Two serving contracts to respect:
+
+- **Pin temperature 0.0 for code.** The model has no sampling headroom; the shipped `generation_config.json` is greedy by default.
+- **Do not disable thinking.** `enable_thinking: false` effectively disables the model (48 of 50 outputs empty in the ablation). There is no functional no-think mode.
+
+The reasoning arrives on the standard reasoning channel (`reasoning_content` with the qwen3 parser) and the answer on `content`. You can discard the reasoning; it is not written for reading.
+
+`adapter/` contains the original LoRA (r=16 on Q/K/V/O) plus tokenizer and chat template, for anyone who wants to re-merge against the base or continue training. The base ships behind a vision-language wrapper, so a bare adapter merge needs a key repack; the merge and repack recipe is in the paper's Appendix B.
+
+## Training
+
+LoRA SFT on 10,205 examples (7.62M target tokens, 2 epochs, one A100-80G, 6h58m). The corpus is 100% code-agentic: single-turn compressed-reasoning items, tool-call wrapped items, 1,500 execution-verified agentic trajectories compressed through [tessera-compressor](https://huggingface.co/ZelligeAI/tessera-compressor), and 400 multi-turn recall items. Training targets were rendered inference-faithfully (history turns carry empty thinks exactly as the serving template produces them). The run was gated by ten pre-registered behavioral probes frozen before any data existed.
+
+## License
+
+Apache-2.0, same as the base model. Trained on permissively licensed data (per-record licenses listed in the paper's Appendix B).

adapter/adapter_config.json

@@ -0,0 +1,45 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "Tesslate/OmniCoder-9B",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.19.1",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "v_proj",
+    "q_proj",
+    "o_proj",
+    "k_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter/adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d04305bfe76cf7496372799ac212aff112ebe1de360cdf66aab8f7261a2f76ce
+size 15738176

adapter/chat_template.jinja

@@ -0,0 +1,154 @@
+{%- set image_count = namespace(value=0) %}
+{%- set video_count = namespace(value=0) %}
+{%- macro render_content(content, do_vision_count, is_system_content=false) %}
+    {%- if content is string %}
+        {{- content }}
+    {%- elif content is iterable and content is not mapping %}
+        {%- for item in content %}
+            {%- if 'image' in item or 'image_url' in item or item.type == 'image' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain images.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set image_count.value = image_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Picture ' ~ image_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|image_pad|><|vision_end|>' }}
+            {%- elif 'video' in item or item.type == 'video' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain videos.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set video_count.value = video_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Video ' ~ video_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|video_pad|><|vision_end|>' }}
+            {%- elif 'text' in item %}
+                {{- item.text }}
+            {%- else %}
+                {{- raise_exception('Unexpected item type in content.') }}
+            {%- endif %}
+        {%- endfor %}
+    {%- elif content is none or content is undefined %}
+        {{- '' }}
+    {%- else %}
+        {{- raise_exception('Unexpected content type.') }}
+    {%- endif %}
+{%- endmacro %}
+{%- if not messages %}
+    {{- raise_exception('No messages provided.') }}
+{%- endif %}
+{%- if tools and tools is iterable and tools is not mapping %}
+    {{- '<|im_start|>system\n' }}
+    {{- "# Tools\n\nYou have access to the following functions:\n\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>" }}
+    {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {%- if content %}
+            {{- '\n\n' + content }}
+        {%- endif %}
+    {%- endif %}
+    {{- '<|im_end|>\n' }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {{- '<|im_start|>system\n' + 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" %}
+        {%- set content = render_content(message.content, false)|trim %}
+        {%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}
+            {%- set ns.multi_step_tool = false %}
+            {%- set ns.last_query_index = index %}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if ns.multi_step_tool %}
+    {{- raise_exception('No user query found in messages.') }}
+{%- endif %}
+{%- for message in messages %}
+    {%- set content = render_content(message.content, true)|trim %}
+    {%- if message.role == "system" %}
+        {%- if not loop.first %}
+            {{- raise_exception('System message must be at the beginning.') }}
+        {%- endif %}
+    {%- elif message.role == "user" %}
+        {{- '<|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 %}
+        {%- set reasoning_content = reasoning_content|trim %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content + '\n</think>\n\n' + content }}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if tool_call.function is defined %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {%- if loop.first %}
+                    {%- if content|trim %}
+                        {{- '\n\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- else %}
+                        {{- '<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- endif %}
+                {%- else %}
+                    {{- '\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                {%- endif %}
+                {%- if tool_call.arguments is defined %}
+                    {%- for args_name, args_value in tool_call.arguments|items %}
+                        {{- '<parameter=' + args_name + '>\n' }}
+                        {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %}
+                        {{- args_value }}
+                        {{- '\n</parameter>\n' }}
+                    {%- endfor %}
+                {%- endif %}
+                {{- '</function>\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.previtem and loop.previtem.role != "tool" %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if not loop.last and loop.nextitem.role != "tool" %}
+            {{- '<|im_end|>\n' }}
+        {%- elif loop.last %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- else %}
+        {{- raise_exception('Unexpected message role.') }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- else %}
+        {{- '<think>\n' }}
+    {%- endif %}
+{%- endif %}

adapter/tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:87a7830d63fcf43bf241c3c5242e96e62dd3fdc29224ca26fed8ea333db72de4
+size 19989343

adapter/tokenizer_config.json

@@ -0,0 +1,32 @@
+{
+  "add_prefix_space": false,
+  "audio_bos_token": "<|audio_start|>",
+  "audio_eos_token": "<|audio_end|>",
+  "audio_token": "<|audio_pad|>",
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "image_token": "<|image_pad|>",
+  "is_local": true,
+  "local_files_only": false,
+  "model_max_length": 262144,
+  "model_specific_special_tokens": {
+    "audio_bos_token": "<|audio_start|>",
+    "audio_eos_token": "<|audio_end|>",
+    "audio_token": "<|audio_pad|>",
+    "image_token": "<|image_pad|>",
+    "video_token": "<|video_pad|>",
+    "vision_bos_token": "<|vision_start|>",
+    "vision_eos_token": "<|vision_end|>"
+  },
+  "pad_token": "<|endoftext|>",
+  "pretokenize_regex": "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+|\\p{N}| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+  "split_special_tokens": false,
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": null,
+  "video_token": "<|video_pad|>",
+  "vision_bos_token": "<|vision_start|>",
+  "vision_eos_token": "<|vision_end|>"
+}