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---
license: apache-2.0
language:
- zh
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- qwen
- moe
- causal-lm
- text-generation
base_model:
- Qwen/Qwen3-4B-Instruct-2507
---
# Qwen3-3B-A0.9B
This repository contains the current best checkpoint from a local Qwen3-style MoE architecture exploration focused on a lightweight conversational baseline.
## Overview
- Model name: `Qwen3-Lite-3B-0.9B`
- Model type: causal language model
- Architecture family: Qwen3-style MoE
- Intended use: lightweight experimentation, architecture recovery, simple short-form dialogue and QA smoke testing
- Training status: research checkpoint, not a fully aligned production assistant
## Files
- Model weights in Hugging Face format
- Architecture config: `qwen3_3p1b_a0p85b_moe_30biso_4l.json`
- Recovery finetune config: `recover_dialogue_qwen3_3p1b_30biso_recovery_cn_v1.yaml`
- Smoke evaluation snapshot: `candidate_v1_smoke_suite.json`
## Architecture Summary
- Base family: Qwen3 MoE
- Hidden size: `2048`
- Layers: `4`
- Attention heads: `32`
- KV heads: `4`
- Experts: `128`
- Active experts per token: `8`
- MoE intermediate size: `768`
- Dense intermediate size: `6144`
- Dtype: `bfloat16`
This checkpoint keeps the official Qwen-style export layout so it can be loaded with standard Hugging Face workflows.
## Current Best Local Status
This upload corresponds to the checkpoint currently documented as the best working local baseline in:
- `README.md`
- `docs/stage1/qwen3_moe_4layer_recovery.md`
Its practical status is:
- It can handle simple QA and part of short Chinese dialogue.
- It is not yet a fully repaired dialogue model.
- Later recovery branches did not consistently outperform this baseline.
## Limitations
- The model is still a recovery-oriented research checkpoint rather than a finished instruct model.
- Dialogue stability is limited on longer turns and emotionally nuanced prompts.
- Benchmark coverage is incomplete relative to official large-scale release evaluation.
- Safety alignment and refusal behavior should not be assumed to match official Qwen releases.
## Tokenizer
The tokenizer used during local experiments is the official Qwen tokenizer from the Qwen3-4B-Instruct release.
Tokenizer files are not re-exported in this checkpoint bundle because local training followed the same save style as the official weight export workflow.
## Example
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "refinefuture-ai/Qwen3-Lite-3B-0.9B"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True,
)
prompt = "请用中文做一个简短的自我介绍。"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

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[
{
"id": "intro",
"prompt": "你好，请简单介绍一下你自己。",
"expectation": "能够给出自然、简短的中文自我介绍。",
"response": "你好，我可以回答问题、解释概念，也可以先去这个问题。"
},
{
"id": "capability",
"prompt": "你会做什么？请简短回答。",
"expectation": "能够简洁说明回答问题、整理信息等基础能力。",
"response": "我可以回答问题、整理信息，并给出简短说明。"
},
{
"id": "arithmetic",
"prompt": "1加1等于几？",
"expectation": "能够直接回答 2。",
"response": "2。"
},
{
"id": "comfort",
"prompt": "如果我说我今天有点累，你会怎么回复？",
"expectation": "能够给出自然、简短的安抚式回复。",
"response": "先别急，慢慢说，我会尽量帮你理清楚。"
},
{
"id": "moe_explain",
"prompt": "请用一句话解释什么是混合专家模型。",
"expectation": "能够用一句中文解释 MoE 的稀疏激活特性。",
"response": "混合专家模型是在一次计算中只激活部分专家网络，从而降低计算成本。"
},
{
"id": "encourage",
"prompt": "请给我一句鼓励的话。",
"expectation": "能够生成完整自然的鼓励语句，不出现重复或残句。",
"response": "别着急，慢慢说，也能让一点简单一点一点。"
},
{
"id": "soothe",
"prompt": "请给我一句安慰的话。",
"expectation": "能够生成完整自然的安慰语句，不出现重复或病句。",
"response": "你已经在往前走了，但还可以帮助你去。"
},
{
"id": "closing",
"prompt": "请给出一句正常的结束语。",
"expectation": "能够给出自然的中文结束语。",
"response": "如果你愿意，我可以继续帮你处理下一个问题。"
}
]

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"initializer_range": 0.02,
"intermediate_size": 6144,
"max_position_embeddings": 262144,
"max_window_layers": 48,
"mlp_only_layers": [],
"model_type": "qwen3_moe",
"moe_intermediate_size": 768,
"norm_topk_prob": true,
"num_attention_heads": 32,
"num_experts": 128,
"num_experts_per_tok": 8,
"num_hidden_layers": 4,
"num_key_value_heads": 4,
"output_router_logits": false,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 10000000,
"router_aux_loss_coef": 0.001,
"sliding_window": null,
"tie_word_embeddings": false,
"transformers_version": "4.57.1",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151936
}

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"moe_intermediate_size": 768,
"norm_topk_prob": true,
"num_attention_heads": 32,
"num_experts": 128,
"num_experts_per_tok": 8,
"num_hidden_layers": 4,
"num_key_value_heads": 4,
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seed: 42
output_dir: /workspace/research/tmp/qwen3_3p1b_a0p85b_moe_candidate_v1_recovery_cn_v1
model_path: /workspace/research/tmp/qwen3_3p1b_a0p85b_moe_candidate_v1
tokenizer_path: /workspace/research/projects/reft-ai/models/Qwen/Qwen3-4B-Instruct-2507
dataset_paths:
- data/chinese_expanded/recovery_cn_merged_v1.jsonl
max_seq_len: 320
batch_size: 1
gradient_accumulation_steps: 4
num_epochs: 4
learning_rate: 3.0e-7
warmup_ratio: 0.05
weight_decay: 0.0
max_grad_norm: 1.0
save_every: 25
log_every: 1
bf16: true
gradient_checkpointing: false

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"content": "<|repo_name|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151664": {
"content": "<|file_sep|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151665": {
"content": "<tool_response>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151666": {
"content": "</tool_response>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151667": {
"content": "<think>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151668": {
"content": "</think>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
}
},
"additional_special_tokens": [
"<|im_start|>",
"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
"<|box_end|>",
"<|quad_start|>",
"<|quad_end|>",
"<|vision_start|>",
"<|vision_end|>",
"<|vision_pad|>",
"<|image_pad|>",
"<|video_pad|>"
],
"bos_token": null,
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\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>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\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\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}",
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"model_max_length": 1010000,
"pad_token": "<|endoftext|>",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null,
"add_bos_token": false
}

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version https://git-lfs.github.com/spec/v1
oid sha256:72b4f72d21ed5131b57e5003c5dd63ca4b502672bef7e039726133c6bf7c2cc9
size 1297

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