ersanbil/roka
1
0
Fork 0
Code Issues Pull Requests Actions Projects Releases Wiki Activity

初始化项目，由ModelHub XC社区提供模型

Browse Source 
Model: ersanbil/roka
Source: Original Platform
This commit is contained in:
ModelHub XC
2026-06-13 15:59:54 +08:00
commit 222526dca4
11 changed files with 251937 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

36
.gitattributes vendored Normal file
View File

@@ -0,0 +1,36 @@
*.7z filter=lfs diff=lfs merge=lfs -text
*.arrow filter=lfs diff=lfs merge=lfs -text
*.bin filter=lfs diff=lfs merge=lfs -text
*.bz2 filter=lfs diff=lfs merge=lfs -text
*.ckpt filter=lfs diff=lfs merge=lfs -text
*.ftz filter=lfs diff=lfs merge=lfs -text
*.gz filter=lfs diff=lfs merge=lfs -text
*.h5 filter=lfs diff=lfs merge=lfs -text
*.joblib filter=lfs diff=lfs merge=lfs -text
*.lfs.* filter=lfs diff=lfs merge=lfs -text
*.mlmodel filter=lfs diff=lfs merge=lfs -text
*.model filter=lfs diff=lfs merge=lfs -text
*.msgpack filter=lfs diff=lfs merge=lfs -text
*.npy filter=lfs diff=lfs merge=lfs -text
*.npz filter=lfs diff=lfs merge=lfs -text
*.onnx filter=lfs diff=lfs merge=lfs -text
*.ot filter=lfs diff=lfs merge=lfs -text
*.parquet filter=lfs diff=lfs merge=lfs -text
*.pb filter=lfs diff=lfs merge=lfs -text
*.pickle filter=lfs diff=lfs merge=lfs -text
*.pkl filter=lfs diff=lfs merge=lfs -text
*.pt filter=lfs diff=lfs merge=lfs -text
*.pth filter=lfs diff=lfs merge=lfs -text
*.rar filter=lfs diff=lfs merge=lfs -text
*.safetensors filter=lfs diff=lfs merge=lfs -text
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
*.tar.* filter=lfs diff=lfs merge=lfs -text
*.tar filter=lfs diff=lfs merge=lfs -text
*.tflite filter=lfs diff=lfs merge=lfs -text
*.tgz filter=lfs diff=lfs merge=lfs -text
*.wasm filter=lfs diff=lfs merge=lfs -text
*.xz 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
gguf/roka-v0.2-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text

202
LICENSE Normal file
View File

@@ -0,0 +1,202 @@
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright [yyyy] [name of copyright owner]
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

245
README.md Normal file
View File

@@ -0,0 +1,245 @@
---
language:
- tr
- en
license: apache-2.0
library_name: transformers
base_model: AlicanKiraz0/Kara-Kumru-v1.0-2B
pipeline_tag: text-generation
tags:
- turkish
- tool-calling
- function-calling
- hermes
- kara-kumru
- mistral
- gguf
---
# Roka — Turkish Tool-Calling Fine-Tune of Kara-Kumru 2B
Roka is a supervised fine-tune of `AlicanKiraz0/Kara-Kumru-v1.0-2B` that teaches a 2B-parameter Turkish language model to use five tools (web search, calculator, date/time, weather, URL reader) via a Hermes-style `<tool_call>…</tool_call>` output format.
This is a **v0.2 research preview**, released for reproducibility and community feedback. It is not a production-grade tool-calling agent and has known weaknesses (see *Limitations*).
The v0.2 training set is fully decontaminated against the evaluation set: no test-set query appears verbatim in train or validation.
## Model at a glance
| | |
|---|---|
| **Base model** | `AlicanKiraz0/Kara-Kumru-v1.0-2B` (Mistral architecture, Llama-3 chat template, Turkish-pretrained) |
| **Upstream base** | `vngrs-ai/Kumru-2B` |
| **Parameters** | ~2.15B |
| **Fine-tuning** | Full fine-tuning, 3 epochs, LR 5e-5 linear, bf16, TRL SFTTrainer |
| **Hardware** | Single NVIDIA A6000 (~65 min / epoch ~22 min) |
| **Languages** | Primarily Turkish; ~13% of the training mix is English (Glaive-sourced synthetic tool-calling examples) |
| **License** | Apache 2.0 (inherited from base chain) |
## Tool set
| Tool | Description |
|---|---|
| `web_search` | Internet search (DuckDuckGo) |
| `calculator` | Arithmetic expression evaluator |
| `datetime` | Date/time and calendar arithmetic (9 actions: `today`, `now`, `day_of_week`, `add_days`, `date_diff`, `days_until`, `day_of_year`, `end_of_month`, `days_until_weekday`) |
| `hava_durumu` | Weather query by city name |
| `sayfa_oku` | URL content reader |
The model is trained to emit tool calls as:
```
<tool_call>
{"name": "datetime", "arguments": {"action": "today"}}
</tool_call>
```
Tool results are fed back to the model wrapped in `<tool_response>…</tool_response>` inside a user turn, and the model synthesizes a final Turkish answer.
## Evaluation
The test set contains 260 Turkish prompts spread over six categories (simple tool calls, fullflow multi-step, parallel, multiple tools, irrelevance, adversarial). Scoring uses an alignment-aware harness (`scripts/rescore_aligned.py`) that normalizes equivalent datetime actions and accepts semantically equivalent arithmetic expressions.
### Overall results (Roka v0.2, April 2026)
| View | n | Full-Match | Tool-Call Acc. | Name Acc. | Arg Acc. |
|---|---|---|---|---|---|
| **All test (held-out)** | 260 | **73.5%** | 93.1% | 71.9% | 60.6% |
Every test query was verified to be absent from both `data/train.jsonl` and `data/val.jsonl`, so the 73.5% number above is a genuinely held-out measurement. See *Decontamination history* below for why this is lower than an earlier, un-decontaminated run.
### Per-subcategory results
| Subcategory | n | Full-Match |
|---|---|---|
| simple/web_search | 30 | **93.3%** |
| simple/weather | 20 | **100.0%** |
| simple/url_reader | 15 | **100.0%** |
| simple/calculator | 20 | 70.0% |
| simple/datetime | 15 | 46.7% |
| fullflow | 35 | **80.0%** |
| multiple | 45 | 64.4% |
| parallel | 15 | **0.0%** |
| adversarial/turkish_special | 10 | 90.0% |
| adversarial/edge_case | 5 | 40.0% |
| adversarial/ambiguous | 15 | 26.7% |
| irrelevance/greeting | 15 | **100.0%** |
| irrelevance/identity | 10 | **100.0%** |
| irrelevance/opinion | 10 | **100.0%** |
**Parallel tool calls score 0% because the training mix does not contain parallel-call examples.** This is a known gap, not a reproducibility failure.
### Decontamination history
During preparation for this release we audited the training set and found that **44 of the 260 test queries appeared verbatim in train/val** (8 in simple/datetime, 6 in simple/web_search, 7 in multiple, and the rest in irrelevance/identity and irrelevance/greeting). We removed all 76 matching train examples and 6 matching val examples, and retrained on the clean split. That retraining is the model reported above.
For transparency we also report the before-and-after numbers on the 216 test queries that were **not** affected by the decontamination (i.e., the genuinely held-out subset from the *pre-cleanup* model's perspective):
| Model | Training data | Clean-216 FM |
|---|---|---|
| v0.1 pre-clean | original (with 76 overlaps) | 78.2% |
| **v0.2** (released) | decontaminated | **73.6%** |
The ~4.6-point drop is informative: it is *not* contamination-inflation. The removed training examples were pattern-providing (datetime variants, fullflow web-search turns, distractor augmentations of the same base queries), and losing them cost about 4.6 points of generalization even on held-out queries. The cost of honest decontamination was larger than the narrow definition of "memorization gain" would predict. We report the post-decontamination number because it is the only one that is defensible as a held-out measurement. A future v0.3 will attempt to recover the gap by adding clean synthetic replacements for the removed examples.
## Development journey (brief)
Arriving at the final model required an honest amount of dead-ends.
1. **Baseline (Run 10)** — 62.7% aligned FM with an earlier pipeline, before any of the spec-005 data work.
2. **Phase A v1–v4 collapse** — four consecutive training runs where loss converged to near-zero but test-set Full-Match stayed at 0/260. All of them passed `loss` sanity checks, so the failure was invisible from inside the run.
3. **Root cause** — TRL issue [#3910](https://github.com/huggingface/trl/issues/3910): the `max_seq_length` argument was silently renamed to `max_length` (default 1024) in TRL 0.20+. Every assistant turn longer than 1024 tokens (≈75% of our fullflow examples) was being truncated before it contributed to the loss. The model trained to completion on fragments, not on full tool-calling traces. Fix: pass `max_length=4096` explicitly.
4. **Data iterations**
- Removed the `unit` argument from all `hava_durumu` training examples (the test set does not supply it). `simple/weather` Full-Match rose from 10% to 100%.
- Added 45 supplementary `datetime` examples covering `day_of_year`, `end_of_month`, and `days_until_weekday` — test actions that were absent from the R10 training data.
- Those supplementary examples caused a regression on `day_of_week` queries ("23 Nisan hangi güne denk geliyor?" was mis-routed to `day_of_year`). A targeted set of 30 `day_of_week` contrast examples fixed it.
5. **Final v0.1 model** — 4,778 training / 509 validation examples, 795 optimizer steps. Reported 76.9% all-test, 78.2% on the clean-216 subset.
6. **v0.2 — decontamination** — 76 train and 6 val examples whose first user turn matched a test query were removed, producing a 4,702 / 503 split. Retraining on this split gave the 73.5% number now reported above. The 4.6-point drop on the clean-216 subset between v0.1 and v0.2 is the cost of honest decontamination — see *Decontamination history*.
Total compute used across Phase A and v0.2: ~5 A6000-hours.
## Limitations
- **Multi-turn pattern lock-in.** The SFT mix contains very few multi-turn tool-calling sequences. If the user starts with a chit-chat turn ("selam"), the model tends to stay in plain-chat mode on subsequent turns and skip the tool call. The provided `scripts/serve_ui.py` works around this by feeding only the current user message (without prior turns) into the tool-decision loop.
- **Parallel tool calls: 0%.** Not trained.
- **`hava_durumu` has no temporal parameter.** Queries like "yarın İstanbul'da hava" still produce `{"city": "İstanbul"}` because that is what the schema allows. The fix is a schema change + data regeneration, not a prompt change.
- **Adversarial/ambiguous: 40%.** The model is easily nudged off-task by ambiguous phrasing.
- **Long-passage synthesis is brittle.** When `sayfa_oku` returns several paragraphs, the synthesized summary sometimes fragments quotes in an unnatural way.
- **Hermes parser coupling.** Native OpenAI-style `tool_calls` parsing via `llama-server` requires the provided `training/roka_tool_template.jinja` chat template and requires the client to pass the full list of 5 tools. Passing a subset confuses llama.cpp's Hermes detector.
- **Scoring discrepancy.** The in-training `training/eval.py` scorer disagrees slightly with the alignment-aware rescorer. Only the rescored numbers are reported above. Resolving the discrepancy is open work.
## Training data
- **4,778 train / 509 validation** examples, Hermes-format chat turns.
- **~72% Turkish, ~13% English, ~15% short/symbolic.** The English fraction is Glaive-sourced synthetic tool-calling data retained for multi-tool pattern coverage.
- **Deterministic generators** for `calculator`, `datetime`, `hava_durumu` (in `training/generators/`).
- **Real DuckDuckGo search results** cached in `data/ddg_cache.json` and used to construct `web_search` fullflow examples.
- **PII scan**: only two flagged matches in user-facing content, both false positives (embedded WSJ article IDs). No email addresses, Turkish ID numbers, credit cards, or IP addresses found.
## Contamination verification
The released v0.2 model is trained on a split where **no test query appears verbatim** in either train or validation. The decontamination script (`scripts/decontaminate.py`) normalizes whitespace and case before matching. The pre-decontamination overlap distribution (all removed in v0.2) was:
| Subcategory | Overlap (removed) |
|---|---|
| irrelevance/identity | 8 / 10 |
| irrelevance/greeting | 11 / 15 |
| simple/datetime | 8 / 15 |
| simple/web_search | 6 / 30 |
| multiple | 7 / 45 |
| adversarial/turkish_special | 1 / 10 |
| adversarial/opinion | 1 / 10 |
| simple/weather | 1 / 20 |
| fullflow | 1 / 35 |
Because augmentation variants of each base query (masked/distractor versions) shared the same user turn, removing 44 unique queries deleted 76 train examples and 6 val examples in total. The remaining 4,702 / 503 split is what v0.2 was trained on.
This decontamination is **exact-string**, not fuzzy. Near-duplicates (paraphrases that return the same tool call) are still present. Closing the paraphrase loophole requires a more elaborate embedding-based deduplication pass, which is left for v0.3.
## Repository layout
```
src/ Inference clients (transformers & llama-server)
training/
tools.py Tool schemas + training system prompt
train.py TRL SFTTrainer entry point
eval.py Test-set scorer (in-training)
roka_tool_template.jinja llama-server chat template with Hermes detection hook
generators/ Deterministic data generators per tool
scripts/
work_pipeline.py End-to-end pod orchestration
pod_run_and_dump.py On-pod training → prediction dump → HF upload
rescore_aligned.py Alignment-aware rescorer (authoritative numbers)
serve_ui.py FastAPI chat UI wrapping the agent
data/
train.jsonl, val.jsonl, test_set.json
specs/005-post-run10-75/ Spec, plan, and task list for this iteration
```
Github: (https://github.com/bilersan/roka)
## Reproducibility
1. Clone the repo and install requirements:
```bash
pip install -r requirements.txt
```
2. Regenerate the training set (deterministic):
```bash
python -m training.build_dataset
```
3. Train (RunPod-hosted, ~1 GPU-hour on an A6000):
```bash
python -m scripts.work_pipeline
```
4. Rescore predictions with the alignment-aware harness:
```bash
python -m scripts.rescore_aligned --predictions .work/artifacts/predictions/<run_id>.json
```
The training recipe is fully specified in `training/config.yaml`. The only hyperparameter that is unusually specific is `max_length: 4096` in `training/train.py` — removing it reproduces the Phase A v1–v4 collapse described above.
## Intended use and out-of-scope use
**Intended**: Turkish-language tool-calling agents for well-defined tools, research on small-model function calling, educational demonstrations of the SFT pipeline.
**Out of scope**:
- Safety-critical applications. The model has not been evaluated for harmful-content refusal beyond what Kara-Kumru inherits from its base.
- Parallel / agentic planning over large tool catalogs.
- Multi-turn conversational agents that need to preserve long prior context.
- Any application that requires the model to use tools not present in the training schema.
## License
This repository and the released weights are distributed under the **Apache License 2.0**, inherited from both `AlicanKiraz0/Kara-Kumru-v1.0-2B` and its upstream base `vngrs-ai/Kumru-2B`. See `LICENSE`.
## Citation
If you use Roka in research, please cite both the base model and this work:
```bibtex
@misc{roka_2026,
title = {Roka: Turkish Tool-Calling Fine-Tune of Kara-Kumru 2B},
author = {Bilik, Ersan},
year = {2026},
url = {https://huggingface.co/ersanbil/roka}
}
@misc{karakumru_2025,
title = {Kara-Kumru-v1.0-2B},
author = {Kiraz, Alican},
year = {2025},
url = {https://huggingface.co/AlicanKiraz0/Kara-Kumru-v1.0-2B}
}
```
## Acknowledgements
- **vngrs-ai** for the open Turkish base model `Kumru-2B`.
- **Alican Kiraz** for the Turkish-conversational fine-tune `Kara-Kumru-v1.0-2B`.
- **Hugging Face TRL / Unsloth** for the training stack.
- **Glaive-AI function-calling dataset** for the English portion of the multi-tool synthetic mix.
## Contact and feedback
Issues and pull requests are welcome on the GitHub mirror. This is a research preview — please file bugs for any behavior that contradicts the documented limitations above; those are the interesting cases.

5
chat_template.jinja Normal file
View File

@@ -0,0 +1,5 @@
{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>
'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>
' }}{% endif %}

30
config.json Normal file
View File

@@ -0,0 +1,30 @@
{
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 2,
"dtype": "bfloat16",
"eos_token_id": 3,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 3072,
"initializer_range": 0.02,
"intermediate_size": 10752,
"max_position_embeddings": 8192,
"model_type": "mistral",
"num_attention_heads": 16,
"num_hidden_layers": 18,
"num_key_value_heads": 4,
"pad_token_id": 0,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 500000,
"rope_type": "default"
},
"sliding_window": null,
"tie_word_embeddings": false,
"transformers_version": "5.5.4",
"use_cache": false,
"vocab_size": 50180
}

9
generation_config.json Normal file
View File

@@ -0,0 +1,9 @@
{
"_from_model_config": true,
"bos_token_id": 2,
"eos_token_id": [
3
],
"pad_token_id": 0,
"transformers_version": "5.5.4"
}

3
gguf/roka-v0.2-Q4_K_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:e565e14a0cc4c5b0de9376fd7838143bdc09321e5cbf0845a6cf14c4790b7671
size 1458575232

3
model.safetensors Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:8bf89c2de19eaef8351c7c9d34d6dc6d696fa2d79898d187420192d57cd7519c
size 4750344848

73
roka_tool_template.jinja Normal file
View File

@@ -0,0 +1,73 @@
{#-
Roka tool-calling chat template — Llama-3 header format + Hermes-style <tool_call> parsing.
Compatible with llama.cpp's Hermes-2-Pro output parser (detected by the literal
"<tool_call>" string in this template source). Uses Kara-Kumru's Llama-3 special
tokens: <|start_header_id|>, <|end_header_id|>, <|eot_id|>.
Design notes:
- The model was fine-tuned with an exact Turkish system prompt from
`training/tools.py::get_system_prompt()`. That prompt already contains the
tool schemas and calling conventions. We therefore DO NOT synthesize a
generic Hermes-English tool block from `tools=[...]` — doing so made the
model go meta / mis-route, because it never saw that format in training.
- Callers should pass the training system prompt via `messages[0]` (role=system).
Any `tools=[...]` parameter is accepted but ignored when building the prompt;
its only job is to let llama.cpp expose the parsed result via OpenAI's
`tool_calls` field.
- The literal string "<tool_call>" below (inside a comment hint) is what
llama.cpp uses to detect Hermes-2-Pro output format: do not remove it.
Format hint for llama.cpp detector: the assistant wraps tool calls as
<tool_call>{"name": "...", "arguments": {...}}</tool_call>.
-#}
{%- macro llama3_header(role) -%}
<|start_header_id|>{{ role }}<|end_header_id|>
{% endmacro -%}
{{- bos_token -}}
{%- for message in messages -%}
{%- if message.role == 'system' -%}
{{- llama3_header('system') -}}
{{- message.content | trim -}}
<|eot_id|>
{%- elif message.role == 'user' -%}
{{- llama3_header('user') -}}
{{- message.content | trim -}}
<|eot_id|>
{%- elif message.role == 'assistant' -%}
{{- llama3_header('assistant') -}}
{%- if message.content -%}
{{- message.content | trim -}}
{%- endif -%}
{%- if message.tool_calls is defined and message.tool_calls -%}
{%- for tc in message.tool_calls -%}
{%- if tc.function is defined -%}
{%- set call = tc.function -%}
{%- else -%}
{%- set call = tc -%}
{%- endif %}
<tool_call>
{%- if call.arguments is string %}
{"name": "{{ call.name }}", "arguments": {{ call.arguments }}}
{%- else %}
{{ {'name': call.name, 'arguments': call.arguments} | tojson }}
{%- endif %}
</tool_call>
{%- endfor -%}
{%- endif -%}
<|eot_id|>
{%- elif message.role == 'tool' -%}
{{- llama3_header('user') -}}
<tool_response>
{{ message.content }}
</tool_response>
<|eot_id|>
{%- endif -%}
{%- endfor -%}
{%- if add_generation_prompt -%}
{{- llama3_header('assistant') -}}
{%- endif -%}

251314
tokenizer.json Normal file
View File

File diff suppressed because it is too large Load Diff

17
tokenizer_config.json Normal file
View File

@@ -0,0 +1,17 @@
{
"backend": "tokenizers",
"bos_token": "<BOS>",
"clean_up_tokenization_spaces": true,
"eos_token": "<EOS>",
"extra_special_tokens": [
"<tool_call>",
"</tool_call>",
"<tool_response>",
"</tool_response>"
],
"is_local": false,
"model_max_length": 1000000000000000019884624838656,
"pad_token": "<PAD>",
"tokenizer_class": "TokenizersBackend",
"unk_token": "<UNK>"
}