bitcoin-news-1.7b

A small on-device assistant for Bitcoin and crypto questions, fine-tuned for beginners. Built to run offline on a phone.

Derived from PrismML's Qwen/Qwen3-1.7B (Apache-2.0), which is quantization-aware-trained so that extreme quantization stays usable.

Files

File	Size	Notes
bitcoin-news-1.7b-Q4_K_S.gguf	935 MB	4-bit
bitcoin-news-1.7b-v5-IQ4_XS-embq1_0.gguf	768 MB	4-bit

On the quantization recipe

Qwen3 ties the token embedding to the output projection -- there is no separate output.weight, so token_embd produces every logit. llama-quantize defaults that tensor to Q6_K, which on a 4B costs ~304 MB (38% of the file). Dropping it to Q1_0 gets to ~546 MB but puts the logit matrix at 1 bit.

These builds use Q1_0 layers with Q4_K embeddings: the extra ~130 MB is spent on the one tensor that most affects output quality.

Training data

• Evergreen educational pages from www.bitcoin.com (the /get-started/ corpus), split by article so translations of the same page never straddle the train/test boundary.
• Transcripts from the Bitcoin.com YouTube channel.
• Hand-built refusal examples: the model runs offline, so it is trained to decline questions about live prices, current news and other real-time data rather than inventing an answer.
• General-instruction replay (UltraChat, MIT) to preserve everyday assistant ability.

Deliberately excluded: dated news articles (stale facts baked into weights become confident hallucinations) and the site's gambling section.

Benchmark

Model	Overall	URL valid	tok/s	bitcoin_fundamentals	brand_identity	canonical_numbers	citation	crypto_concepts	general_ability	multilingual	practical_howto	temporal_humility	trading_investing	video_recall	wallet_security
v5 qwen3-1.7b Q4_K_S (935MB)	0.564	0.30	211	0.620	0.750	0.500	0.700	0.572	0.600	0.430	0.450	0.700	0.600	0.500	0.400
v3 bitcoin-news-4b Q3_K_M (1883MB)	0.553	0.67	90	0.632	0.500	0.500	0.500	0.620	0.867	0.350	0.425	0.600	0.500	1.000	0.425
v5 qwen3-1.7b Q4_K_M (980MB)	0.539	0.40	205	0.580	0.679	0.500	0.400	0.480	0.733	0.350	0.500	0.800	0.633	0.500	0.400
v5 qwen3-1.7b IQ4_XS (893MB)	0.503	0.30	216	0.560	0.607	0.800	0.400	0.460	0.800	0.400	0.350	0.500	0.500	0.500	0.375
v3 bitcoin-news-4b Q1_0 (702MB)	0.392	-	192	0.460	0.071	0.300	0.000	0.560	0.667	0.350	0.325	0.500	0.467	0.000	0.375
qwen3-1.7b stock Q4_K_M (1056MB)	0.328	-	191	0.440	0.071	0.300	0.000	0.420	0.500	0.050	0.400	0.300	0.500	0.000	0.275

Scored by an LLM judge against hand-written reference answers and per-item required-point lists. general_ability and temporal_humility are guardrail categories: they measure whether the domain fine-tune damaged ordinary competence or taught the model to bluff about live data.

Limitations

• No live data of any kind. It cannot tell you a price, a fee, or today's news.
• Small models make mistakes. Do not rely on it for financial decisions.
• Q1_0 has no OpenCL kernel for Mali GPUs, so those devices fall back to CPU.

Generated 2026-08-30.