Fine-tuned Qwen/Qwen3-Embedding-4B for code search

.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
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "word_embedding_dimension": 2560,
+    "pooling_mode_cls_token": false,
+    "pooling_mode_mean_tokens": false,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": true,
+    "include_prompt": true
+}

README.md

@@ -0,0 +1,439 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:800
+- loss:ContrastiveLoss
+base_model: Qwen/Qwen3-Embedding-4B
+widget:
+- source_sentence: "Collects the tracing data from the given parameters.\n       \
+    \ :param request: The Flask request.\n        :param response: The flask response.\n\
+    \        :param error: The error occurred if any.\n        :param latency: The\
+    \ time elapsed to process the request.\n        :return: The tracing data."
+  sentences:
+  - "def enum_to_yaml(cls: Type[T_EnumToYAML], representer: Representer, data: T_EnumToYAML)\
+    \ -> ruamel.yaml.nodes.ScalarNode:\n    \n    return representer.represent_scalar(\n\
+    \        f\"!{cls.__name__}\",\n        f\"{str(data)}\"\n    )"
+  - "def subclasses(self, inherited=False):\n        \n        data = clips.data.DataObject(self._env)\n\
+    \n        lib.EnvClassSubclasses(self._env, self._cls, data.byref, int(inherited))\n\
+    \n        for klass in classes(self._env, data.value):\n            yield klass"
+  - "def identify(self, req, resp, resource, uri_kwargs):\n        \n        header\
+    \ = req.get_header(, False)\n        auth = header.split() if header else None\n\
+    \n        if auth is None or auth[0].lower() != :\n            return None\n\n\
+    \        if len(auth) != 2:\n            raise HTTPBadRequest(\n             \
+    \   \"Invalid Authorization header\",\n                \"The Authorization header\
+    \ for Token auth should be in form:\\n\"\n                \"Authorization: Token\
+    \ <token_value>\"\n            )\n\n        return auth[1]"
+- source_sentence: "A wrapper for `os.walk` that skips hidden files and directories.\n\
+    \n    This function does not have the parameter `topdown` from\n    `os.walk`:\
+    \ the directories must always be recursed top-down when\n    using this function.\n\
+    \n    See also\n    --------\n    os.walk : For a description of the parameters"
+  sentences:
+  - "def expand_all(self):\n        \n\n        def aux(item):\n            self.item(item,\
+    \ open=True)\n            children = self.get_children(item)\n            for\
+    \ c in children:\n                aux(c)\n\n        children = self.get_children(\"\
+    \")\n        for c in children:\n            aux(c)"
+  - "def create_extended_model(model, db_penalty=None, ex_penalty=None,\n        \
+    \                  tp_penalty=None, penalties=None):\n    \n\n    \n    model_extended\
+    \ = model.create_metabolic_model()\n    extra_compartment = model.extracellular_compartment\n\
+    \n    compartment_ids = set(c.id for c in model.compartments)\n\n    \n    if\
+    \ len(compartment_ids) > 0:\n        logger.info(\n            .format(\n    \
+    \            .join(.format(c) for c in compartment_ids)))\n        db_added =\
+    \ add_all_database_reactions(model_extended, compartment_ids)\n    else:\n   \
+    \     logger.warning(\n            \n            \n            )\n        db_added\
+    \ = set()\n\n    \n    logger.info(\n        .format(\n            extra_compartment))\n\
+    \    ex_added = add_all_exchange_reactions(\n        model_extended, extra_compartment,\
+    \ allow_duplicates=True)\n\n    \n    boundaries = model.compartment_boundaries\n\
+    \    if len(boundaries) > 0:\n        logger.info(\n            \n           \
+    \ .format(\n                .join(.format(c1, c2) for c1, c2 in boundaries)))\n\
+    \        tp_added = add_all_transport_reactions(\n            model_extended,\
+    \ boundaries, allow_duplicates=True)\n    else:\n        logger.warning(\n   \
+    \         \n            )\n        tp_added = set()\n\n    \n    weights = {}\n\
+    \    if db_penalty is not None:\n        weights.update((rxnid, db_penalty) for\
+    \ rxnid in db_added)\n    if tp_penalty is not None:\n        weights.update((rxnid,\
+    \ tp_penalty) for rxnid in tp_added)\n    if ex_penalty is not None:\n       \
+    \ weights.update((rxnid, ex_penalty) for rxnid in ex_added)\n\n    if penalties\
+    \ is not None:\n        for rxnid, penalty in iteritems(penalties):\n        \
+    \    weights[rxnid] = penalty\n    return model_extended, weights"
+  - "def walk_skip_hidden(top, onerror=None, followlinks=False):\n    \n\n    for\
+    \ root, dirs, files in os.walk(\n            top, topdown=True, onerror=onerror,\n\
+    \            followlinks=followlinks):\n        \n        \n        dirs[:] =\
+    \ [d for d in dirs if not is_path_hidden(d)]\n        files[:] = [f for f in files\
+    \ if not is_path_hidden(f)]\n        yield root, dirs, files"
+- source_sentence: Show stack frames for a task
+  sentences:
+  - "def do_where(self, taskid: int) -> None:\n        \n        task = task_by_id(taskid,\
+    \ self._loop)\n        if task:\n            self._sout.write(_format_stack(task))\n\
+    \            self._sout.write()\n        else:\n            self._sout.write(\
+    \ % taskid)"
+  - "def apt_add_repository_from_apt_string(apt_string, apt_file):\n    \n\n    apt_file_path\
+    \ =  % apt_file\n\n    if not file_contains(apt_file_path, apt_string.lower(),\
+    \ use_sudo=True):\n        file_append(apt_file_path, apt_string.lower(), use_sudo=True)\n\
+    \n        with hide(, ):\n            sudo(\"DEBIAN_FRONTEND=noninteractive apt-get\
+    \ update\")"
+  - "def _kl_laplace_laplace(a, b, name=None):\n  \n  with tf.name_scope(name or \"\
+    kl_laplace_laplace\"):\n    \n    \n    distance = tf.abs(a.loc - b.loc)\n   \
+    \ ratio = a.scale / b.scale\n\n    return (-tf.math.log(ratio) - 1 + distance\
+    \ / b.scale +\n            ratio * tf.exp(-distance / a.scale))"
+- source_sentence: Read the ical file
+  sentences:
+  - "def read_ical(self, ical_file_location):  \n        \n        with open(ical_file_location,\
+    \ ) as ical_file:\n            data = ical_file.read()\n        self.cal = Calendar.from_ical(data)\n\
+    \        return self.cal"
+  - "def _size_from_header(cls, header):\n        \n\n        \n        result = []\n\
+    \n        for data in header:\n            \n\n            \n            result.append(header[data])\n\
+    \n        \n        return result"
+  - "def dockermachine_ip() -> Optional[str]:\n    \n    if not check_dockermachine():\n\
+    \        return None\n\n    \n    try:\n        out = subprocess.check_output([,\
+    \ ])\n        return out.decode(\"utf-8\").strip()\n    except Exception:\n  \
+    \      logger.debug(f\"docker machine not present\")\n        return None"
+- source_sentence: 'list[VolumeExtent]: sections.'
+  sentences:
+  - "def cast_to_a1_notation(method):\n    \n    @wraps(method)\n    def wrapper(self,\
+    \ *args, **kwargs):\n        try:\n            if len(args):\n               \
+    \ int(args[0])\n\n            \n            range_start = rowcol_to_a1(*args[:2])\n\
+    \            range_end = rowcol_to_a1(*args[-2:])\n            range_name = .join((range_start,\
+    \ range_end))\n\n            args = (range_name,) + args[4:]\n        except ValueError:\n\
+    \            pass\n\n        return method(self, *args, **kwargs)\n\n    return\
+    \ wrapper"
+  - "def readfmt(self, fmt):\n        \n        size = struct.calcsize(fmt)\n    \
+    \    blob = self.read(size)\n        obj, = struct.unpack(fmt, blob)\n       \
+    \ return obj"
+  - "def equals(self, other):\n        \n        self._run(unittest_case.assertEqual,\
+    \ (self._subject, other))\n        return ChainInspector(self._subject)"
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on Qwen/Qwen3-Embedding-4B
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Qwen/Qwen3-Embedding-4B](https://huggingface.co/Qwen/Qwen3-Embedding-4B). It maps sentences & paragraphs to a 2560-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [Qwen/Qwen3-Embedding-4B](https://huggingface.co/Qwen/Qwen3-Embedding-4B) <!-- at revision 5cf2132abc99cad020ac570b19d031efec650f2b -->
+- **Maximum Sequence Length:** 40960 tokens
+- **Output Dimensionality:** 2560 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 40960, 'do_lower_case': False, 'architecture': 'Qwen3Model'})
+  (1): Pooling({'word_embedding_dimension': 2560, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': True, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("TakalaWang/qwen3-embedding-4B-code-search")
+# Run inference
+queries = [
+    "list[VolumeExtent]: sections.",
+]
+documents = [
+    'def equals(self, other):\n        \n        self._run(unittest_case.assertEqual, (self._subject, other))\n        return ChainInspector(self._subject)',
+    'def cast_to_a1_notation(method):\n    \n    @wraps(method)\n    def wrapper(self, *args, **kwargs):\n        try:\n            if len(args):\n                int(args[0])\n\n            \n            range_start = rowcol_to_a1(*args[:2])\n            range_end = rowcol_to_a1(*args[-2:])\n            range_name = .join((range_start, range_end))\n\n            args = (range_name,) + args[4:]\n        except ValueError:\n            pass\n\n        return method(self, *args, **kwargs)\n\n    return wrapper',
+    'def readfmt(self, fmt):\n        \n        size = struct.calcsize(fmt)\n        blob = self.read(size)\n        obj, = struct.unpack(fmt, blob)\n        return obj',
+]
+query_embeddings = model.encode_query(queries)
+document_embeddings = model.encode_document(documents)
+print(query_embeddings.shape, document_embeddings.shape)
+# [1, 2560] [3, 2560]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(query_embeddings, document_embeddings)
+print(similarities)
+# tensor([[1., 1., 1.]], dtype=torch.float16)
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+* Size: 800 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 800 samples:
+  |         | sentence_0                                                                          | sentence_1                                                                            | label                                                         |
+  |:--------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------|
+  | type    | string                                                                              | string                                                                                | float                                                         |
+  | details | <ul><li>min: 2 tokens</li><li>mean: 65.92 tokens</li><li>max: 3156 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 132.37 tokens</li><li>max: 1236 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence_0                                                                                                     | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | label            |
+  |:---------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
+  | <code>Go through a stream and print out anything not in observed set</code>                                    | <code>def t_VAR(self, t):<br>        r<br>        t.type = self.reserved.get(t.value.lower(), )<br>        return t</code>                                                                                                                                                                                                                                                                                                                                                      | <code>0.0</code> |
+  | <code>Move a page to before some other page of the document. Specify 'to = -1' to move after last page.</code> | <code>def movePage(self, pno, to = -1):<br>        <br>        pl = list(range(len(self)))<br>        if pno < 0 or pno > pl[-1]:<br>            raise ValueError(" page number out of range")<br>        if to < -1 or to > pl[-1]:<br>            raise ValueError(" page number out of range")<br>        pl.remove(pno)<br>        if to == -1:<br>            pl.append(pno)<br>        else:<br>            pl.insert(to-1, pno)<br>        return self.select(pl)</code> | <code>1.0</code> |
+  | <code>Create an empty dataset in the current repo.</code>                                                      | <code>def libvlc_media_player_set_agl(p_mi, drawable):<br>    <br>    f = _Cfunctions.get(, None) or \<br>        _Cfunction(, ((1,), (1,),), None,<br>                    None, MediaPlayer, ctypes.c_uint32)<br>    return f(p_mi, drawable)</code>                                                                                                                                                                                                                           | <code>0.0</code> |
+* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
+  ```json
+  {
+      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
+      "margin": 0.5,
+      "size_average": true
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 1
+- `per_device_eval_batch_size`: 1
+- `num_train_epochs`: 2
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 1
+- `per_device_eval_batch_size`: 1
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 2
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `parallelism_config`: None
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `hub_revision`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch | Step | Training Loss |
+|:-----:|:----:|:-------------:|
+| 0.625 | 500  | 0.0583        |
+| 1.25  | 1000 | 0.0635        |
+| 1.875 | 1500 | 0.0638        |
+
+
+### Framework Versions
+- Python: 3.13.7
+- Sentence Transformers: 5.1.1
+- Transformers: 4.56.2
+- PyTorch: 2.8.0+cu128
+- Accelerate: 1.10.1
+- Datasets: 4.1.1
+- Tokenizers: 0.22.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### ContrastiveLoss
+```bibtex
+@inproceedings{hadsell2006dimensionality,
+    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
+    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
+    title={Dimensionality Reduction by Learning an Invariant Mapping},
+    year={2006},
+    volume={2},
+    number={},
+    pages={1735-1742},
+    doi={10.1109/CVPR.2006.100}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

added_tokens.json

@@ -0,0 +1,24 @@
+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

@@ -0,0 +1,54 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# 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' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.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": [
+    "Qwen3Model"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "float16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "layer_types": [
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+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 40960,
+  "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": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.56.2",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151665
+}

config_sentence_transformers.json

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+{
+  "prompts": {
+    "query": "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery:",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine",
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.1.1",
+    "transformers": "4.56.2",
+    "pytorch": "2.8.0+cu128"
+  }
+}

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+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b7e24abbf8db66065c500459b3f6b876165878c4d45486d8a54466da3b8e0f81
+size 11423215

tokenizer_config.json

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+{
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+  "tokenizer_class": "Qwen2Tokenizer",
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+}