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Model: sengtha/Qwen3-0.6B-khm-ft3 Source: Original Platform
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README.md
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---
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license: apache-2.0
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language:
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- km
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- en
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base_model:
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- Qwen/Qwen3-0.6B
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- khmer
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- qwen3
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- rag
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- on-device
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- small-language-model
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---
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# Qwen3-0.6B-khm-ft3 — a small Khmer model for on-device RAG
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A **0.6B** Khmer-focused language model fine-tuned from **Qwen3-0.6B**, built to
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run **on a phone, offline** — it's the answering model behind
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[iAny](https://github.com/sengtha/iAny) and runs on a 2019 Galaxy S10.
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The key trick: the base Qwen3 vocabulary (~150k tokens) is **trimmed to ~32k
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Khmer-focused tokens**, which shrinks the output logits buffer enough to fit
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weak devices while keeping Khmer coverage. It is then continued-pretrained and
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instruction-tuned on Khmer.
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## Lineage
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| Stage | What | Data |
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| --- | --- | --- |
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| Base | Qwen3-0.6B | — |
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| Vocab trim | ~32k Khmer-focused vocabulary (smaller logits → fits low-RAM phones) | via [alphaedge-ai](https://huggingface.co/alphaedge-ai) |
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| CPT | Continued pre-training for Khmer fluency | FineWeb-2 (Khmer) + ParaCrawl |
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| SFT (ft3) | Instruction / Q&A tuning for correct, fuller answers | [`sengtha/khmer-qa`](https://huggingface.co/datasets/sengtha/khmer-qa) |
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`ft3` is the current release — retrained on a richer `khmer-qa` for fuller
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answers than the earlier `ft`/`ft2` checkpoints.
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## Intended use
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Grounded **Khmer question-answering / RAG** on-device: the app retrieves
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context from the user's own documents and this model writes the answer in
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Khmer. Small and Khmer-first — not a general-purpose assistant, and it can be
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wrong or hallucinate, especially without retrieved context.
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## Usage (transformers)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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m = "sengtha/Qwen3-0.6B-khm-ft3"
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tok = AutoTokenizer.from_pretrained(m)
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model = AutoModelForCausalLM.from_pretrained(m)
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msgs = [{"role": "user", "content": "តើភ្នំពេញជាអ្វី?"}]
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inputs = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt")
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out = model.generate(inputs, max_new_tokens=256)
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print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))
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```
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Uses the standard **Qwen3 chat template** (ChatML). Append `/no_think` to a
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message to skip the reasoning block for faster, direct answers.
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## On-device (GGUF)
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Quantized **GGUF** builds (Q4_K_M + Q8_0) for `llama.cpp` / `llama.rn` live in
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**[`sengtha/Qwen3-0.6B-khm-ft3-Q8_0-GGUF`](https://huggingface.co/sengtha/Qwen3-0.6B-khm-ft3-Q8_0-GGUF)**.
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Q4 (~0.4 GB) fits weak phones; Q8 is higher quality.
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## License & attribution
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**Apache-2.0**, inherited from the base **[Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)**
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(© Alibaba, Apache-2.0). 32k-vocab base via **alphaedge-ai**. Training corpora:
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**FineWeb-2**, **ParaCrawl**, and **`sengtha/khmer-qa`**. Please keep this
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attribution in derivatives.
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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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63
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": null,
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"dtype": "bfloat16",
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"eos_token_id": 32744,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 1024,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_types": [
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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",
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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",
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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",
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"full_attention"
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],
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"max_position_embeddings": 40960,
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"max_window_layers": 28,
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"model_type": "qwen3",
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"num_attention_heads": 16,
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"num_hidden_layers": 28,
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"num_key_value_heads": 8,
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"pad_token_id": 32742,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000000,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "5.13.1",
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size": 32768
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}
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generation_config.json
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{
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"do_sample": true,
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"eos_token_id": [
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32744
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],
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"pad_token_id": 32742,
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"temperature": 0.6,
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"top_k": 20,
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"top_p": 0.95,
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"transformers_version": "5.13.1"
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}
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model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:9889f24ef9211ae378831b1b3a8bf40f44bedab9c7f88ee4ba921072ba9fe388
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size 948078864
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162975
tokenizer.json
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tokenizer.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": null,
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"eos_token_id": 32744,
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"errors": "replace",
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"extra_special_tokens": [
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"<|endoftext|>",
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"<|im_start|>",
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"<|im_end|>",
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"<|object_ref_start|>",
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"<|object_ref_end|>",
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"<|box_start|>",
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"<|box_end|>",
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"<|quad_start|>",
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"<|quad_end|>",
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"<|vision_start|>",
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"<|vision_end|>",
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"<|vision_pad|>",
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"<|image_pad|>",
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"<|video_pad|>",
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"<tool_call>",
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"</tool_call>",
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"<|fim_prefix|>",
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"<|fim_middle|>",
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"<|fim_suffix|>",
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"<|fim_pad|>",
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"<|repo_name|>",
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"<|file_sep|>",
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"<tool_response>",
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"</tool_response>",
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"<think>",
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"</think>"
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],
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"is_local": false,
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"local_files_only": false,
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"model_max_length": 131072,
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"pad_token": "<|endoftext|>",
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"pad_token_id": 32742,
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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