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llm, large refactor on the choices, add a lot of Mistral family models too.

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2026-10-14 07:25:12 -07:00
parent b7738a1b78
commit 852b904dd4
1 changed files with 42 additions and 16 deletions
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tasks/workstation/linux/software/llm.yml
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@@ -101,24 +101,39 @@
ansible.builtin.set_fact: ansible.builtin.set_fact:
llm_installs: [] llm_installs: []
llm_removals: llm_removals:
- { "purpose": "chat", "model": "qwen3:4b", "provider": "qwen_30_0040", "size_mb": 4000, "context": 123456 } ##### QWEN #####
- { "purpose": "chat", "model": "qwen3.6:27b", "provider": "qwen_36_0270", "size_mb": 27000, "context": 123456 } - { "purpose": "chat", "model": "qwen3:4b", "provider": "qwen_30_0040", "size_mb": 4000, "context": 123456 } # succeeded
- { "purpose": "chat", "model": "qwen3.5:27b", "provider": "qwen_35_0270", "size_mb": "{{ llm_size_coef * 17000 }}", "context": 123456 } - { "purpose": ["chat", "code"], "model": "qwen3.6:35b", "provider": "qwen_36_0350", "size_mb": "{{ llm_size_coef * 23000 }}", "context": 262144 } # succeeded, not dense so not recommended
- { "purpose": ["chat", "code"], "model": "qwen3.5:27b", "provider": "qwen_35_0270", "size_mb": "{{ llm_size_coef * 17000 }}", "context": 123456 } # succeeded
##### Mistral #####
- { "purpose": "code", "model": "devstral:24b", "provider": "mistral_dev10_0240", "size_mb": "{{ llm_size_coef * 14000 }}", "context": 128000 } # succeeded
# https://ollama.com/library/mistral-small : Last Updated 2025-01-30, succeeded
- { "purpose": "chat", "model": "mistral-small:24b", "provider": "mistral_small30_0240", "size_mb": "{{ llm_size_coef * 14000 }}", "context": 32000 }
- { "purpose": "chat", "model": "mistral-small:22b", "provider": "mistral_small30_0220", "size_mb": "{{ llm_size_coef * 13000 }}", "context": 128000 } # Yes, the smaller one has larger context.
# https://ollama.com/library/mistral-small3.1 : Last Update 2025-04-07, succeeded
- { "purpose": "chat", "model": "mistral-small3.1:24b", "provider": "mistral_small31_0240", "size_mb": "{{ llm_size_coef * 15000 }}", "context": 128000 }
llm_choices: llm_choices:
##### QWEN #####
### QWEN >3.5 - Need to try! ### QWEN >3.5 - Need to try!
- { "purpose": ["chat", code], "model": "qwen3.8:27b", "provider": "qwen_38_0270", "size_mb": 27000, "context": 123456 } # Supposedly great for coding over any previous version.
- { "purpose": ["chat", code], "model": "qwen3.6:35b", "provider": "qwen_36_0350", "size_mb": 35000, "context": 123456 } # Prefer 27b (dense) over anything higher which likely uses MoE (essentially only uses 3b inside of a 35b, dumber but faster and potentially has access to more info).
# https://ollama.com/library/qwen3.8 : Last Updated 2026-09-25
# Known to overthink, suggested to prefer 3.6:27b unless setting temp to low/medium.
- { "purpose": ["code", "chat", "lang"], "model": "qwen3.8:27b", "provider": "qwen_38_0270", "size_mb": "{{ llm_size_coef * 18000 }}", "context": 262144 }
# https://ollama.com/library/qwen3.6 : Last Updated 2026-10-13 (yesterday as of this commit!!)
# Has been succeeded but dense and does not overthink so still worthy
- { "purpose": ["code", "chat", "lang"], "model": "qwen3.6:27b", "provider": "qwen_36_0270", "size_mb": "{{ llm_size_coef * 18000 }}", "context": 262144 }
### QWEN 3.5 - Good for generic usage and a little coding. ### QWEN 3.5 - Good for generic usage and a little coding.
- { "purpose": "chat", "model": "qwen3.5:0.8b", "provider": "qwen_35_0008", "size_mb": "{{ llm_size_coef * 1200 }}", "context": 256000 } - { "purpose": "chat", "model": "qwen3.5:0.8b", "provider": "qwen_35_0008", "size_mb": "{{ llm_size_coef * 1200 }}", "context": 256000 }
- { "purpose": "chat", "model": "qwen3.5:2b", "provider": "qwen_35_0020", "size_mb": "{{ llm_size_coef * 2700 }}", "context": 256000 } - { "purpose": "chat", "model": "qwen3.5:2b", "provider": "qwen_35_0020", "size_mb": "{{ llm_size_coef * 2700 }}", "context": 256000 }
- { "purpose": "chat", "model": "qwen3.5:4b", "provider": "qwen_35_0040", "size_mb": "{{ llm_size_coef * 3300 }}", "context": 256000 } - { "purpose": "chat", "model": "qwen3.5:4b", "provider": "qwen_35_0040", "size_mb": "{{ llm_size_coef * 3300 }}", "context": 256000 }
- { "purpose": "chat", "model": "qwen3.5:9b", "provider": "qwen_35_0090", "size_mb": "{{ llm_size_coef * 6600 }}", "context": 256000 } - { "purpose": "chat", "model": "qwen3.5:9b", "provider": "qwen_35_0090", "size_mb": "{{ llm_size_coef * 6600 }}", "context": 256000 }
# skip 27b and use 35b # skip 27b and use 35b if going this high.
- { "purpose": ["chat", code], "model": "qwen3.5:35b", "provider": "qwen_35_0350", "size_mb": "{{ llm_size_coef * 22000 }}", "context": 256000 } - { "purpose": ["chat", "code"], "model": "qwen3.5:35b", "provider": "qwen_35_0350", "size_mb": "{{ llm_size_coef * 22000 }}", "context": 256000 }
- { "purpose": ["chat", code], "model": "qwen3.5:122b", "provider": "qwen_35_1220", "size_mb": "{{ llm_size_coef * 81000 }}", "context": 256000 } - { "purpose": ["chat", "code"], "model": "qwen3.5:122b", "provider": "qwen_35_1220", "size_mb": "{{ llm_size_coef * 81000 }}", "context": 256000 }
### QWEN 3.0 Coder - Supposed to be better than 2.5 for machines that can handle it. ### QWEN 3.0 Coder - Supposed to be better than 2.5 for machines that can handle it.
- { "purpose": ["code", chat], "model": "qwen3-coder:30b", "provider": "qwen_30_coder_0300", "size_mb": "{{ llm_size_coef * 19000 }}", "context": 123456 } - { "purpose": ["code", "chat"], "model": "qwen3-coder:30b", "provider": "qwen_30_coder_0300", "size_mb": "{{ llm_size_coef * 19000 }}", "context": 123456 }
- { "purpose": ["code", chat], "model": "qwen3-coder:420b", "provider": "qwen_30_coder_4200", "size_mb": "{{ llm_size_coef * 290000 }}", "context": 123456 } - { "purpose": ["code", "chat"], "model": "qwen3-coder:420b", "provider": "qwen_30_coder_4200", "size_mb": "{{ llm_size_coef * 290000 }}", "context": 123456 }
### QWEN 2.5 Coder - Ol'reliable! ### QWEN 2.5 Coder - Ol'reliable!
- { "purpose": "code", "model": "qwen2.5-coder:0.5b", "provider": "qwen_25_coder_0005", "size_mb": "{{ llm_size_coef * 398 }}", "context": 32000 } - { "purpose": "code", "model": "qwen2.5-coder:0.5b", "provider": "qwen_25_coder_0005", "size_mb": "{{ llm_size_coef * 398 }}", "context": 32000 }
- { "purpose": "code", "model": "qwen2.5-coder:1.5b", "provider": "qwen_25_coder_0015", "size_mb": "{{ llm_size_coef * 986 }}", "context": 32000 } - { "purpose": "code", "model": "qwen2.5-coder:1.5b", "provider": "qwen_25_coder_0015", "size_mb": "{{ llm_size_coef * 986 }}", "context": 32000 }
@@ -126,12 +141,23 @@
- { "purpose": "code", "model": "qwen2.5-coder:7b", "provider": "qwen_25_coder_0070", "size_mb": "{{ llm_size_coef * 4700 }}", "context": 32000 } - { "purpose": "code", "model": "qwen2.5-coder:7b", "provider": "qwen_25_coder_0070", "size_mb": "{{ llm_size_coef * 4700 }}", "context": 32000 }
- { "purpose": "code", "model": "qwen2.5-coder:14b", "provider": "qwen_25_coder_0140", "size_mb": "{{ llm_size_coef * 9000 }}", "context": 32000 } - { "purpose": "code", "model": "qwen2.5-coder:14b", "provider": "qwen_25_coder_0140", "size_mb": "{{ llm_size_coef * 9000 }}", "context": 32000 }
- { "purpose": "code", "model": "qwen2.5-coder:32b", "provider": "qwen_25_coder_0320", "size_mb": "{{ llm_size_coef * 20000 }}", "context": 32000 } - { "purpose": "code", "model": "qwen2.5-coder:32b", "provider": "qwen_25_coder_0320", "size_mb": "{{ llm_size_coef * 20000 }}", "context": 32000 }
### Mistral, Ministral, and Devstral - By a French company? ##### Mistral, Ministral, and Devstral - By a French company? #####
- { "purpose": "code", "model": "devstral-small-2:24b", "provider": "devstral_20_0240_small", "size_mb": 24000, "context": 123456 } # https://ollama.com/library/devstral-small-2 : Last Updated 2025-12-13
#- mistral:7b # Most recommended in Mistral family for agentic coding.
#- ministral-3:3b - { "purpose": "code", "model": "devstral-small-2:24b", "provider": "mistral_dev20_0240_small", "size_mb": "{{ llm_size_coef * 15000 }}", "context": 384000 }
#- ministral-3:8b # https://ollama.com/library/codestral : Last Updated 2024-09-03
#- ministral-3:14b # Mistral's first ever coder agent, recommended for autocomplete (FIM).
- { "purpose": "code", "model": "codestral:22b", "provider": "mistral_code_0220", "size_mb": "{{ llm_size_coef * 12000 }}", "context": 32000 }
# https://ollama.com/library/mistral : Last Updated 2025-07-13
- { "purpose": "chat", "model": "mistral:7b", "provider": "mistral_gen_0070", "size_mb": "{{ llm_size_coef * 4400 }}", "context": 32000 }
# https://ollama.com/library/mistral-small3.2 : Last Updated 2025-06-20
- { "purpose": ["chat", "lang"], "model": "mistral-small3.2:24b", "provider": "mistral_small32_0240", "size_mb": "{{ llm_size_coef * 15000 }}", "context": 128000 } # rated best for Croatian
# https://ollama.com/library/ministral-3 : Last Updated 2025-12-13
- { "purpose": ["chat", "lang"], "model": "ministral-3:3b", "provider": "mistral_mini30_0030", "size_mb": "{{ llm_size_coef * 3000 }}", "context": 256000 }
- { "purpose": ["chat", "lang"], "model": "ministral-3:8b", "provider": "mistral_mini30_0080", "size_mb": "{{ llm_size_coef * 6000 }}", "context": 256000 }
- { "purpose": ["chat", "lang"], "model": "ministral-3:14b", "provider": "mistral_mini30_0140", "size_mb": "{{ llm_size_coef * 9100 }}", "context": 256000 } # also rated super high for Croatian, gives more head room.
# https://ollama.com/library/mistral-nemo : Last Updated 2025-07-23
- { "purpose": ["chat", "lang"], "model": "mistral-nemo:12b", "provider": "mistral_nemo_0120", "size_mb": "{{ llm_size_coef * 7100 }}", "context": 128000 } # says 1000K in table but 128k in desc, going with desc.
##### TBD ##### ##### TBD #####
### QWEN 2.5 - Older, but does not have "thinking", tempting! ### QWEN 2.5 - Older, but does not have "thinking", tempting!
#- qwen2.5:0.5b #- qwen2.5:0.5b