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Model: refinefuture-ai/Qwen3-Lite-3B-0.9B
Source: Original Platform

README.md

@@ -0,0 +1,95 @@
+---
+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))
+```