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Model: mrshu/qwen3-14b-sk-alpaca-slovak-it Source: Original Platform
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README.md
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---
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library_name: transformers
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license: apache-2.0
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language:
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- sk
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- en
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base_model:
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- slovak-nlp/Qwen3-14B-sk
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datasets:
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- saillab/alpaca-slovak-cleaned
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tags:
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- slovak
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- instruction-tuned
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- qwen3
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- lora
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- merged-lora
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- text-generation
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pipeline_tag: text-generation
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---
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# Qwen3-14B-sk Alpaca Slovak IT
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Qwen3-14B-sk Alpaca Slovak IT is an instruction-tuned Slovak assistant model
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derived from `slovak-nlp/Qwen3-14B-sk`. It was trained with a LoRA supervised
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fine-tuning recipe on Slovak Alpaca-style instruction data and then merged back
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into the base model, so the released artifact can be loaded directly with
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`transformers`.
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The model is designed to answer in Slovak by default while still attempting to
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follow explicit user requests for another language or a specific output format.
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## Intended Use
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This model is intended for Slovak instruction following, Slovak question
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answering, drafting, rewriting, summarization-style prompts, and general
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assistant workflows where Slovak is the primary language.
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It can also respond to English prompts and translation-style requests, but
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language control is not guaranteed. Use additional application-level checks for
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strict formatting, policy compliance, or high-reliability translation.
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Do not use this model as the sole source for medical, legal, financial, safety,
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or other high-stakes decisions. It has not been safety aligned, red-teamed, or
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moderated for production deployment.
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## Model Details
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| Field | Value |
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| --- | --- |
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| Base model | `slovak-nlp/Qwen3-14B-sk` |
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| Base revision | `61bc40572b2fa4720e75a7a9de457151aefce396` |
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| Release repository | [`mrshu/qwen3-14b-sk-alpaca-slovak-it`](https://huggingface.co/mrshu/qwen3-14b-sk-alpaca-slovak-it) |
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| Architecture | Qwen3 causal language model |
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| Tuned artifact | Merged full-weight checkpoint |
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| Adapter used during training | LoRA |
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| Weight dtype | bfloat16 |
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| License | Apache-2.0 |
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## Training Data
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The model was instruction-tuned on
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[`saillab/alpaca-slovak-cleaned`](https://huggingface.co/datasets/saillab/alpaca-slovak-cleaned),
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a Slovak Alpaca-style instruction dataset.
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Dataset preparation converted each example into a chat-style conversation with
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system, user, and assistant messages. Empty instruction/output examples were
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excluded, and duplicate instruction/input/output triples were removed across
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the prepared splits.
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| Split | Rows |
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| --- | ---: |
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| Train | 41,601 |
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| Held-out | 10,401 |
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The data preparation used dataset revision
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`058172466eb1d6a28b161f29c74350911d154161`.
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Each training conversation used this system prompt:
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```text
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Si užitočný asistent. Riaď sa jazykom a požadovaným formátom používateľa. Ak používateľ nežiada iný jazyk, odpovedaj po slovensky.
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```
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## Training Recipe
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The model was trained with supervised fine-tuning using PEFT LoRA. Only the
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assistant turns were included in the training loss.
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| Setting | Value |
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| --- | --- |
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| LoRA rank / alpha / dropout | `32` / `64` / `0.05` |
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| Target modules | linear projection modules |
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| Sequence length | `4096` |
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| Sample packing | enabled |
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| Epochs | `1` |
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| Effective batch size | `16` |
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| Micro batch size | `1` |
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| Gradient accumulation | `16` |
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| Learning rate | `1e-4` |
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| Scheduler | cosine |
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| Warmup ratio | `0.06` |
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| Optimizer | fused AdamW |
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| Precision | bfloat16 |
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| Gradient checkpointing | enabled |
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| Seed | `42` |
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After training, the LoRA adapter was merged into the base model with PEFT and
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saved as a standalone safetensors checkpoint.
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## Usage
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "mrshu/qwen3-14b-sk-alpaca-slovak-it"
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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trust_remote_code=True,
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)
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messages = [
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{
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"role": "system",
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"content": (
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"Si užitočný asistent. Riaď sa jazykom a požadovaným formátom "
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"používateľa. Ak používateľ nežiada iný jazyk, odpovedaj po slovensky."
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),
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},
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{"role": "user", "content": "Stručne vysvetli, čo je LoRA."},
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]
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inputs = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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return_tensors="pt",
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).to(model.device)
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outputs = model.generate(
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inputs,
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max_new_tokens=512,
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do_sample=True,
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temperature=0.6,
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top_p=0.95,
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)
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print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))
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```
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## Limitations
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- The supervised fine-tuning data is translated instruction data, so the model
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may inherit translation artifacts, unnatural phrasing, or source-dataset
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biases.
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- The model is biased toward Slovak responses. Explicit requests for another
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language may work, but they are not guaranteed.
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- Strict JSON, exact labels, citations, and other constrained formats should be
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validated outside the model.
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- The model may hallucinate facts, produce unsafe content, or follow malicious
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instructions without additional safeguards.
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89
chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# 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>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\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" }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for message in messages[::-1] %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- 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>')) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
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{%- endfor %}
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{%- for message in messages %}
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{%- if message.content is string %}
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{%- set content = message.content %}
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{%- else %}
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{%- set content = '' %}
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{%- endif %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is string %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in content %}
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{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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{%- set content = content.split('</think>')[-1].lstrip('\n') %}
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{%- endif %}
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{%- endif %}
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{%- if loop.index0 > ns.last_query_index %}
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{%- if loop.last or (not loop.last and reasoning_content) %}
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{{- '\n' }}
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{%- endif %}
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{%- if tool_call.arguments is string %}
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{{- tool_call.arguments }}
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{%- else %}
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{{- tool_call.arguments | tojson }}
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{%- endif %}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- if enable_thinking is defined and enable_thinking is false %}
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{{- '<think>\n\n</think>\n\n' }}
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{%- endif %}
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{%- endif %}
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config.json
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
|
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"bos_token_id": 151643,
|
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"dtype": "bfloat16",
|
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"eos_token_id": 151645,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 5120,
|
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"initializer_range": 0.02,
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"intermediate_size": 17408,
|
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"layer_types": [
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
||||
"full_attention",
|
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"full_attention",
|
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"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
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"full_attention",
|
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"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
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"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
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"full_attention",
|
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"full_attention",
|
||||
"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 40960,
|
||||
"max_window_layers": 40,
|
||||
"model_type": "qwen3",
|
||||
"num_attention_heads": 40,
|
||||
"num_hidden_layers": 40,
|
||||
"num_key_value_heads": 8,
|
||||
"pad_token_id": null,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_parameters": {
|
||||
"rope_theta": 1000000,
|
||||
"rope_type": "default"
|
||||
},
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": false,
|
||||
"transformers_version": "5.5.0",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
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"vocab_size": 151936
|
||||
}
|
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generation_config.json
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generation_config.json
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{
|
||||
"bos_token_id": 151643,
|
||||
"do_sample": true,
|
||||
"eos_token_id": [
|
||||
151645,
|
||||
151643
|
||||
],
|
||||
"pad_token_id": 151643,
|
||||
"temperature": 0.6,
|
||||
"top_k": 20,
|
||||
"top_p": 0.95,
|
||||
"transformers_version": "5.5.0"
|
||||
}
|
||||
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3
tokenizer.json
Normal file
3
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Normal file
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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18
tokenizer_config.json
Normal file
18
tokenizer_config.json
Normal file
@@ -0,0 +1,18 @@
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{
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}
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||||
Reference in New Issue
Block a user