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Model: sengtha/Qwen3-0.6B-khm-ft3
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+  - km
+  - en
+base_model:
+  - Qwen/Qwen3-0.6B
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+  - khmer
+  - qwen3
+  - rag
+  - on-device
+  - small-language-model
+---
+
+# Qwen3-0.6B-khm-ft3 — a small Khmer model for on-device RAG
+
+A **0.6B** Khmer-focused language model fine-tuned from **Qwen3-0.6B**, built to
+run **on a phone, offline** — it's the answering model behind
+[iAny](https://github.com/sengtha/iAny) and runs on a 2019 Galaxy S10.
+
+The key trick: the base Qwen3 vocabulary (~150k tokens) is **trimmed to ~32k
+Khmer-focused tokens**, which shrinks the output logits buffer enough to fit
+weak devices while keeping Khmer coverage. It is then continued-pretrained and
+instruction-tuned on Khmer.
+
+## Lineage
+
+| Stage | What | Data |
+| --- | --- | --- |
+| Base | Qwen3-0.6B | — |
+| Vocab trim | ~32k Khmer-focused vocabulary (smaller logits → fits low-RAM phones) | via [alphaedge-ai](https://huggingface.co/alphaedge-ai) |
+| CPT | Continued pre-training for Khmer fluency | FineWeb-2 (Khmer) + ParaCrawl |
+| SFT (ft3) | Instruction / Q&A tuning for correct, fuller answers | [`sengtha/khmer-qa`](https://huggingface.co/datasets/sengtha/khmer-qa) |
+
+`ft3` is the current release — retrained on a richer `khmer-qa` for fuller
+answers than the earlier `ft`/`ft2` checkpoints.
+
+## Intended use
+
+Grounded **Khmer question-answering / RAG** on-device: the app retrieves
+context from the user's own documents and this model writes the answer in
+Khmer. Small and Khmer-first — not a general-purpose assistant, and it can be
+wrong or hallucinate, especially without retrieved context.
+
+## Usage (transformers)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+m = "sengtha/Qwen3-0.6B-khm-ft3"
+tok = AutoTokenizer.from_pretrained(m)
+model = AutoModelForCausalLM.from_pretrained(m)
+
+msgs = [{"role": "user", "content": "តើភ្នំពេញជាអ្វី?"}]
+inputs = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt")
+out = model.generate(inputs, max_new_tokens=256)
+print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))
+```
+
+Uses the standard **Qwen3 chat template** (ChatML). Append `/no_think` to a
+message to skip the reasoning block for faster, direct answers.
+
+## On-device (GGUF)
+
+Quantized **GGUF** builds (Q4_K_M + Q8_0) for `llama.cpp` / `llama.rn` live in
+**[`sengtha/Qwen3-0.6B-khm-ft3-Q8_0-GGUF`](https://huggingface.co/sengtha/Qwen3-0.6B-khm-ft3-Q8_0-GGUF)**.
+Q4 (~0.4 GB) fits weak phones; Q8 is higher quality.
+
+## License & attribution
+
+**Apache-2.0**, inherited from the base **[Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)**
+(© Alibaba, Apache-2.0). 32k-vocab base via **alphaedge-ai**. Training corpora:
+**FineWeb-2**, **ParaCrawl**, and **`sengtha/khmer-qa`**. Please keep this
+attribution in derivatives.