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sengtha
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Model Details
Model Provider
sengtha
Model Tree
Base
Qwen/Qwen3-0.6B
Fine-tuned
this model
Input Modalities
Text
Output Modalities
Text
Supported Functionality
Dedicated Endpoints
Container
| Stage | What | Data |
|---|---|---|
| Base | Qwen3-0.6B | — |
| Vocab trim | ~32k Khmer-focused vocabulary (smaller logits → fits low-RAM phones) | via 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 |
ft3 is the current release — retrained on a richer khmer-qa for fuller
answers than the earlier ft/ft2 checkpoints.
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.
python
from transformers import AutoModelForCausalLM, AutoTokenizerm = "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.
Quantized GGUF builds (Q4_K_M + Q8_0) for llama.cpp / llama.rn live in
sengtha/Qwen3-0.6B-khm-ft3-Q8_0-GGUF.
Q4 (~0.4 GB) fits weak phones; Q8 is higher quality.
Apache-2.0, inherited from the base 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.