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feat(models): add the Qwen3.8 family and fix Qwen generation parsing (#893)
Pin all four official Qwen3.8 repos in the curated catalog:
Qwen/Qwen3.8-27B 27.78B dense, 64 layers (16 full / 48 linear)
Qwen/Qwen3.8-27B-FP8 same weights, FP8 checkpoint
Qwen/Qwen3.8-2.4T-A95B 2.446T total / 95B active, 512 experts (10 active),
92 layers (23 full / 69 linear)
Qwen/Qwen3.8-2.4T-A95B-FP8 same, FP8 checkpoint
The automated weekly refresh in #892 picked up three of these via trending
discovery, but none were in TARGET_MODELS, so they could drop out of any
later scrape; the non-FP8 2.4T-A95B was missing entirely. Catalog entries
were merged in surgically (four entries) rather than by a full re-scrape so
this diff stays reviewable alongside #892.
infer_attention_layout_from_name now matches qwen3.8- and handles the new
-a95b 92-layer shape, and OLLAMA_MAPPINGS gains qwen3.8-27b -> qwen3.8:27b
(Ollama publishes only the 27B; the 2.4T has no library entry).
Also fixes a generation-scoring bug this surfaced. parse_generation
consulted the architecture string before the model name, but Qwen3.6 and
Qwen3.8 both ship as model_type "qwen3_5" -- so every Qwen3.6 model in the
catalog scored as generation 3.5, and the name-based 3.6 branch added in
5397a8a was dead code for anything with an architecture set. The existing
test passed None for architecture, so it never caught this.
qwen_minor_generation_from_name is now consulted first inside the Qwen arch
branch. It matches only explicit minor versions and returns None for a bare
"Qwen3", so qwen3_next still resolves from the architecture string instead
of regressing to 3.0. scripts/validate_generation_scoring.py, which
documents itself as a mirror of the Rust logic, is updated to match.
Quality bonus is (generation - 1) * 3 clamped to 9, so Qwen3.6 models gain
+0.3 and Qwen3.8 lands at +8.4.
This commit is contained in:
Alex Jones
GitHub
parent
a9ac7ed91c
commit
6e024b2e51
6 changed files with 283 additions and 18 deletions
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@ -1,6 +1,6 @@
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# Supported Models
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llmfit ships with a curated database of 106 LLM models from HuggingFace. All memory estimates assume Q4_K_M quantization (0.5 bytes per parameter) unless noted otherwise.
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llmfit ships with a curated database of 108 LLM models from HuggingFace. All memory estimates assume Q4_K_M quantization (0.5 bytes per parameter) unless noted otherwise.
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### 01.ai
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@ -34,6 +34,7 @@ llmfit ships with a curated database of 106 LLM models from HuggingFace. All mem
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| [Qwen/Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B) | 14.8B | Q4_K_M | 128k | General purpose text generation |
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| [Qwen/Qwen2.5-Coder-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct) | 14.8B | Q4_K_M | 32k | Code generation and completion |
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| [Qwen/Qwen3.5-27B](https://huggingface.co/Qwen/Qwen3.5-27B) | 27.8B | Q4_K_M | 256k | Multimodal, vision and text |
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| [Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B) | 27.8B | Q4_K_M | 256k | Multimodal, vision and text |
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| [Qwen/Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) | 30.5B (MoE) | Q4_K_M | 40k | Efficient MoE, general purpose |
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| [Qwen/Qwen3.5-35B-A3B](https://huggingface.co/Qwen/Qwen3.5-35B-A3B) | 36.0B (MoE) | Q4_K_M | 256k | Multimodal, vision and text |
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| [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) | 32.5B | Q4_K_M | 128k | Instruction following, chat |
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@ -44,6 +45,7 @@ llmfit ships with a curated database of 106 LLM models from HuggingFace. All mem
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| [Qwen/Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B) | 235B (MoE) | Q4_K_M | 40k | State-of-the-art, MoE architecture |
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| [Qwen/Qwen3.5-397B-A17B](https://huggingface.co/Qwen/Qwen3.5-397B-A17B) | 403.4B (MoE) | Q4_K_M | 256k | Multimodal, vision and text |
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| [Qwen/Qwen3-Coder-480B-A35B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct) | 480B (MoE) | Q4_K_M | 256k | Code generation and completion |
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| [Qwen/Qwen3.8-2.4T-A95B](https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B) | 2446.2B (MoE) | Q4_K_M | 256k | General purpose text generation |
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### Allen Institute
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@ -274430,6 +274430,88 @@
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}
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]
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},
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{
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"name": "Qwen/Qwen3.8-27B",
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"provider": "Alibaba",
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"parameter_count": "27.8B",
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"parameters_raw": 27781427952,
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"min_ram_gb": 15.5,
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"recommended_ram_gb": 25.9,
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"min_vram_gb": 14.2,
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"quantization": "Q4_K_M",
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"format": "gguf",
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"context_length": 262144,
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"use_case": "Multimodal, vision and text",
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"capabilities": [
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"vision",
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"tool_use"
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],
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"languages": [],
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"pipeline_tag": "image-text-to-text",
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"architecture": "qwen3_5",
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"hf_downloads": 267725,
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"hf_likes": 10385,
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"release_date": "2026-08-05",
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"num_hidden_layers": 64,
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"num_attention_heads": 24,
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"num_key_value_heads": 4,
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"head_dim": 256,
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"hidden_size": 5120,
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"vocab_size": 248320,
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"moe_intermediate_size": 17408,
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"shared_expert_intermediate_size": null,
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"license": "apache-2.0",
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"gguf_sources": [
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{
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"repo": "unsloth/Qwen3.8-27B-GGUF",
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"provider": "unsloth"
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},
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{
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"repo": "bartowski/Qwen3.8-27B-GGUF",
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"provider": "bartowski"
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},
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{
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"repo": "ggml-org/Qwen3.8-27B-GGUF",
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"provider": "ggml-org"
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},
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{
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"repo": "mradermacher/Qwen3.8-27B-GGUF",
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"provider": "mradermacher"
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}
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]
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},
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{
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"name": "Qwen/Qwen3.8-27B-FP8",
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"provider": "Alibaba",
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"parameter_count": "27.8B",
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"parameters_raw": 27781427952,
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"min_ram_gb": 15.5,
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"recommended_ram_gb": 25.9,
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"min_vram_gb": 14.2,
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"quantization": "Q4_K_M",
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"format": "gguf",
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"context_length": 262144,
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"use_case": "Multimodal, vision and text",
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"capabilities": [
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"vision",
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"tool_use"
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],
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"languages": [],
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"pipeline_tag": "image-text-to-text",
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"architecture": "qwen3_5",
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"hf_downloads": 352971,
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"hf_likes": 494,
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"release_date": "2026-08-13",
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"num_hidden_layers": 64,
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"num_attention_heads": 24,
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"num_key_value_heads": 4,
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"head_dim": 256,
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"hidden_size": 5120,
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"vocab_size": 248320,
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"moe_intermediate_size": 17408,
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"shared_expert_intermediate_size": null,
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"license": "apache-2.0"
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},
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{
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"name": "cbert33/Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-FP8-KV-Calibrated",
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"provider": "cbert33",
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@ -345304,6 +345386,82 @@
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"_discovered": true,
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"license": "kimi-k3"
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},
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{
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"name": "Qwen/Qwen3.8-2.4T-A95B",
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"provider": "Alibaba",
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"parameter_count": "2446.2B",
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"parameters_raw": 2446182725504,
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"min_ram_gb": 1366.9,
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"recommended_ram_gb": 2278.2,
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"min_vram_gb": 1253.0,
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"quantization": "Q4_K_M",
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"format": "gguf",
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"context_length": 262144,
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"use_case": "General purpose text generation",
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"capabilities": [
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"tool_use"
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],
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"languages": [],
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"pipeline_tag": "text-generation",
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"architecture": "qwen3_5_moe_text",
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"hf_downloads": 7932,
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"hf_likes": 1016,
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"release_date": "2026-08-08",
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"num_hidden_layers": 92,
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"num_attention_heads": 64,
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"num_key_value_heads": 4,
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"head_dim": 256,
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"hidden_size": 8192,
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"vocab_size": 248320,
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"moe_intermediate_size": 2048,
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"shared_expert_intermediate_size": 2048,
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"license": "qwen3.8-max",
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"is_moe": true,
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"num_experts": 512,
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"active_experts": 10,
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"active_parameters": 95000000000,
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"gguf_sources": [
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{
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"repo": "unsloth/Qwen3.8-2.4T-A95B-GGUF",
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"provider": "unsloth"
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}
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]
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},
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{
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"name": "Qwen/Qwen3.8-2.4T-A95B-FP8",
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"provider": "Alibaba",
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"parameter_count": "2446.2B",
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"parameters_raw": 2446182725504,
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"min_ram_gb": 1366.9,
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"recommended_ram_gb": 2278.2,
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"min_vram_gb": 1253.0,
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"quantization": "Q4_K_M",
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"format": "gguf",
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"context_length": 262144,
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"use_case": "General purpose text generation",
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"capabilities": [
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"tool_use"
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],
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"languages": [],
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"pipeline_tag": "text-generation",
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"architecture": "qwen3_5_moe_text",
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"hf_downloads": 11311,
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"hf_likes": 210,
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"release_date": "2026-08-08",
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"num_hidden_layers": 92,
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"num_attention_heads": 64,
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"num_key_value_heads": 4,
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"head_dim": 256,
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"hidden_size": 8192,
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"vocab_size": 248320,
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"moe_intermediate_size": 2048,
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"shared_expert_intermediate_size": 2048,
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"license": "qwen3.8-max",
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"is_moe": true,
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"num_experts": 512,
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"active_experts": 10,
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"active_parameters": 95000000000
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},
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{
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"name": "moonshotai/Kimi-K3",
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"provider": "moonshotai",
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@ -114,6 +114,26 @@ pub fn quant_quality_penalty(quant: &str) -> f64 {
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}
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}
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/// Explicit Qwen minor version spelled out in a repo name (`Qwen3.8-27B` → 3.8).
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///
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/// Only matches names that carry the minor version. A bare `Qwen3` returns
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/// `None` so callers can fall back to the architecture string, which is what
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/// distinguishes variants like `qwen3_next`.
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fn qwen_minor_generation_from_name(name_lower: &str) -> Option<f64> {
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const VERSIONS: &[(&str, &str, f64)] = &[
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("qwen3.8", "qwen3_8", 3.8),
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("qwen3.6", "qwen3_6", 3.6),
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("qwen3.5", "qwen3_5", 3.5),
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("qwen2.5", "qwen2_5", 2.5),
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];
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VERSIONS
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.iter()
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.find(|(dotted, underscored, _)| {
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name_lower.contains(dotted) || name_lower.contains(underscored)
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})
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.map(|(_, _, generation)| *generation)
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}
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/// Parse model generation from architecture string and model name.
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///
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/// Returns a generation number (e.g. 2.0 for "qwen2", 3.5 for "qwen3_5_moe",
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}
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// Qwen: qwen2, qwen3, qwen3_moe, qwen3_5, qwen3_5_moe, qwen3_next
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if let Some(suffix) = arch_lower.strip_prefix("qwen") {
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// Qwen3.6 and Qwen3.8 ship under the `qwen3_5` architecture string,
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// so an explicit minor version in the repo name wins over the arch.
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if let Some(generation) = qwen_minor_generation_from_name(&name.to_lowercase()) {
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return Some(generation);
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}
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if suffix.starts_with("3_5") || suffix.starts_with("3.5") {
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return Some(3.5);
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}
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// Fallback: parse generation from model name
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let name_lower = name.to_lowercase();
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// Qwen3.6, Qwen3.5, Qwen3, Qwen2.5, Qwen2
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if name_lower.contains("qwen3.6") || name_lower.contains("qwen3_6") {
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return Some(3.6);
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}
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if name_lower.contains("qwen3.5") || name_lower.contains("qwen3_5") {
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return Some(3.5);
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// Qwen3.8, Qwen3.6, Qwen3.5, Qwen3, Qwen2.5, Qwen2
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if let Some(generation) = qwen_minor_generation_from_name(&name_lower) {
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return Some(generation);
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}
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if name_lower.contains("qwen3") {
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return Some(3.0);
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}
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if name_lower.contains("qwen2.5") || name_lower.contains("qwen2_5") {
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return Some(2.5);
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}
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if name_lower.contains("qwen2") {
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return Some(2.0);
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}
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@ -1572,10 +1591,18 @@ pub fn infer_attention_layout_from_name(name: &str) -> Option<AttentionLayout> {
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});
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}
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// Qwen3.5 / Qwen3.6 hybrid models use 1 full attention per 4 layers.
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// Qwen3.5 / Qwen3.6 / Qwen3.8 hybrid models use 1 full attention per 4
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// layers (`full_attention_interval: 4` in their configs).
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// The dense 27B variants have 64 layers → 16 full + 48 linear.
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// The MoE A3B variants have 40 layers → 10 full + 30 linear.
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if lower.contains("qwen3.5-") || lower.contains("qwen3.6-") {
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// Qwen3.8-2.4T-A95B has 92 layers → 23 full + 69 linear.
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if lower.contains("qwen3.5-") || lower.contains("qwen3.6-") || lower.contains("qwen3.8-") {
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if lower.contains("-a95b") {
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return Some(AttentionLayout {
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full: 23,
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linear: 69,
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});
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}
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if lower.contains("-a3b") || lower.contains("-a10b") || lower.contains("-a17b") {
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return Some(AttentionLayout {
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full: 10,
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@ -3231,6 +3258,19 @@ mod tests {
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assert!(layout.compressible_fraction() < 0.5);
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}
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/// Layer counts here come from the published `config.json` for each repo:
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/// Qwen3.8-27B is 64 layers (16 full), Qwen3.8-2.4T-A95B is 92 (23 full).
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#[test]
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fn test_infer_attention_layout_qwen3_8() {
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let dense = infer_attention_layout_from_name("Qwen/Qwen3.8-27B").unwrap();
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assert_eq!(dense.full, 16);
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assert_eq!(dense.linear, 48);
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let moe = infer_attention_layout_from_name("Qwen/Qwen3.8-2.4T-A95B").unwrap();
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assert_eq!(moe.full, 23);
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assert_eq!(moe.linear, 69);
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}
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#[test]
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fn test_infer_attention_layout_dense_returns_none() {
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assert!(infer_attention_layout_from_name("meta-llama/Llama-3.1-8B").is_none());
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@ -3311,6 +3351,7 @@ mod tests {
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// Name-only (no architecture)
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assert_eq!(parse_generation(None, "Qwen/Qwen3.6-35B-A3B"), Some(3.6));
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assert_eq!(parse_generation(None, "Qwen/Qwen3.8-27B"), Some(3.8));
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assert_eq!(parse_generation(None, "Qwen/Qwen2.5-72B"), Some(2.5));
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assert_eq!(
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parse_generation(None, "deepseek-ai/DeepSeek-V4-Flash"),
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@ -3319,6 +3360,33 @@ mod tests {
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assert_eq!(parse_generation(None, "google/gemma-3-12b-it"), Some(3.0));
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}
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/// Qwen3.6 and Qwen3.8 both ship under the `qwen3_5` architecture string,
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/// so the minor version in the repo name has to win over the arch.
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#[test]
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fn test_parse_generation_name_beats_qwen_architecture() {
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assert_eq!(
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parse_generation(Some("qwen3_5"), "Qwen/Qwen3.8-27B"),
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Some(3.8)
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);
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assert_eq!(
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parse_generation(Some("qwen3_5_moe"), "Qwen/Qwen3.8-2.4T-A95B"),
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Some(3.8)
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);
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assert_eq!(
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parse_generation(Some("qwen3_5"), "Qwen/Qwen3.6-27B"),
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Some(3.6)
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);
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// A bare Qwen3 name must not override the more specific arch string.
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assert_eq!(
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parse_generation(Some("qwen3_next"), "Qwen/Qwen3-Next-80B-A3B"),
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Some(3.8)
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);
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assert_eq!(
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parse_generation(Some("qwen3_5"), "Qwen/Qwen3.5-27B"),
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Some(3.5)
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);
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}
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#[test]
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fn test_generation_quality_bonus_values() {
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// Gen 1.0: bonus = 0
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@ -3810,6 +3810,9 @@ const OLLAMA_MAPPINGS: &[(&str, &str)] = &[
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|||
("qwen3.5-27b", "qwen3.5"),
|
||||
("qwen3.5-35b-a3b", "qwen3.5:35b"),
|
||||
("qwen3.5-122b-a10b", "qwen3.5:122b"),
|
||||
// Qwen 3.8 — 27B is the only size Ollama publishes; the 2.4T-A95B MoE
|
||||
// has no library entry.
|
||||
("qwen3.8-27b", "qwen3.8:27b"),
|
||||
// Qwen3-Coder-Next
|
||||
("qwen3-coder-next", "qwen3-coder-next"),
|
||||
// DeepSeek
|
||||
|
|
@ -4516,6 +4519,14 @@ mod tests {
|
|||
assert!(candidates.contains(&"qwen2.5:14b".to_string()));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_qwen3_8_resolves_to_its_ollama_tag() {
|
||||
assert_eq!(
|
||||
hf_name_to_ollama_candidates("Qwen/Qwen3.8-27B"),
|
||||
vec!["qwen3.8:27b".to_string()]
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_llama_mapping() {
|
||||
let candidates = hf_name_to_ollama_candidates("meta-llama/Llama-3.1-8B-Instruct");
|
||||
|
|
|
|||
|
|
@ -109,6 +109,11 @@ TARGET_MODELS = [
|
|||
"Qwen/Qwen3.6-27B",
|
||||
"Qwen/Qwen3.6-35B-A3B",
|
||||
"huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated",
|
||||
# Qwen 3.8 (hybrid attention, Aug 2026)
|
||||
"Qwen/Qwen3.8-27B",
|
||||
"Qwen/Qwen3.8-27B-FP8",
|
||||
"Qwen/Qwen3.8-2.4T-A95B",
|
||||
"Qwen/Qwen3.8-2.4T-A95B-FP8",
|
||||
# Microsoft Phi
|
||||
"microsoft/phi-3-mini-4k-instruct",
|
||||
"microsoft/Phi-3-medium-14b-instruct",
|
||||
|
|
@ -346,6 +351,8 @@ MOE_ACTIVE_PARAMS = {
|
|||
"Qwen/Qwen3.5-397B-A17B": 17_000_000_000,
|
||||
"Qwen/Qwen3.6-35B-A3B": 3_000_000_000,
|
||||
"huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated": 3_000_000_000, # Qwen3.6-35B-A3B finetune
|
||||
"Qwen/Qwen3.8-2.4T-A95B": 95_000_000_000,
|
||||
"Qwen/Qwen3.8-2.4T-A95B-FP8": 95_000_000_000,
|
||||
"meta-llama/Llama-4-Scout-17B-16E-Instruct": 17_000_000_000,
|
||||
"meta-llama/Llama-4-Maverick-17B-128E-Instruct": 17_000_000_000,
|
||||
"xai-org/grok-1": 86_000_000_000,
|
||||
|
|
|
|||
|
|
@ -12,6 +12,23 @@ import sys
|
|||
from collections import defaultdict
|
||||
|
||||
|
||||
def qwen_minor_generation_from_name(name_lower: str) -> float | None:
|
||||
"""Mirror of models::qwen_minor_generation_from_name in Rust.
|
||||
|
||||
Only matches names carrying an explicit minor version; a bare ``Qwen3``
|
||||
returns ``None`` so callers can fall back to the architecture string.
|
||||
"""
|
||||
for dotted, underscored, generation in (
|
||||
("qwen3.8", "qwen3_8", 3.8),
|
||||
("qwen3.6", "qwen3_6", 3.6),
|
||||
("qwen3.5", "qwen3_5", 3.5),
|
||||
("qwen2.5", "qwen2_5", 2.5),
|
||||
):
|
||||
if dotted in name_lower or underscored in name_lower:
|
||||
return generation
|
||||
return None
|
||||
|
||||
|
||||
def parse_generation(architecture: str | None, name: str) -> float | None:
|
||||
"""Mirror of models::parse_generation in Rust."""
|
||||
if architecture:
|
||||
|
|
@ -30,6 +47,11 @@ def parse_generation(architecture: str | None, name: str) -> float | None:
|
|||
# Qwen
|
||||
if arch.startswith("qwen"):
|
||||
suffix = arch[len("qwen"):]
|
||||
# Qwen3.6 and Qwen3.8 ship under the `qwen3_5` architecture string,
|
||||
# so an explicit minor version in the repo name wins over the arch.
|
||||
named = qwen_minor_generation_from_name(name.lower())
|
||||
if named is not None:
|
||||
return named
|
||||
if suffix.startswith("3_5") or suffix.startswith("3.5"):
|
||||
return 3.5
|
||||
if suffix.startswith("3_next") or suffix.startswith("3next"):
|
||||
|
|
@ -99,14 +121,11 @@ def parse_generation(architecture: str | None, name: str) -> float | None:
|
|||
# Fallback: name-based
|
||||
name_lower = name.lower()
|
||||
|
||||
if "qwen3.6" in name_lower or "qwen3_6" in name_lower:
|
||||
return 3.6
|
||||
if "qwen3.5" in name_lower or "qwen3_5" in name_lower:
|
||||
return 3.5
|
||||
named = qwen_minor_generation_from_name(name_lower)
|
||||
if named is not None:
|
||||
return named
|
||||
if "qwen3" in name_lower:
|
||||
return 3.0
|
||||
if "qwen2.5" in name_lower or "qwen2_5" in name_lower:
|
||||
return 2.5
|
||||
if "qwen2" in name_lower:
|
||||
return 2.0
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue