Model reference · open weights
Hermes-3-Llama-3.2 is an open-weight language model from NousResearch. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Maker | NousResearch |
|---|---|
| Type | Language models |
| Task | Text gen |
| Parameters (lead) | 3.2B |
| Context | 128k tokens |
| Runs with | transformers |
| Based on | meta-llama/Meta-Llama-3.2-3B |
| Released | 2024-12-03 |
| Popularity | 5k downloads / month |
| Licence | Open, with conditions |
About
Hermes 3 3B is a small but mighty new addition to the Hermes series of LLMs by Nous Research, and is Nous's first fine-tune in this parameter class.
For details on Hermes 3, please see the Hermes 3 Technical Report.
Hermes 3 is a generalist language model with many improvements over Hermes 2, including advanced agentic capabilities, much better roleplaying, reasoning, multi-turn conversation, long context coherence, and improvements across the board.
Hermes 3 3B is a full parameter fine-tune of the Llama-3.2 3B foundation model, focused on aligning LLMs to the user, with powerful steering capabilities and control given to the end user.
The Hermes 3 series builds and expands on the Hermes 2 set of capabilities, including more powerful and reliable function calling and structured output capabilities, generalist assistant capabilities, and improved code generation skills.
Hermes 3 3B was trained on H100s on LambdaLabs GPU Cloud. Check out LambdaLabs' cloud offerings here.
Hermes 3 is competitive, if not superior, to Llama-3.1 Instruct models at general capabilities, with varying strengths and weaknesses attributable between the two.
| Tasks |Version|Filter|n-shot| Metric | |Value | |Stderr|
|-------------|------:|------|-----:|--------|---|-----:|---|-----:|
|arc_challenge| 1|none | 0|acc |↑ |0.4411|± |0.0145|
| | |none | 0|acc_norm|↑ |0.4377|± |0.0145|
|arc_easy | 1|none | 0|acc |↑ |0.7399|± |0.0090|
| | |none | 0|acc_norm|↑ |0.6566|± |0.0097|
|boolq | 2|none | 0|acc |↑ |0.8327|± |0.0065|
|hellaswag | 1|none | 0|acc |↑ |0.5453|± |0.0050|
| | |none | 0|acc_norm|↑ |0.7047|± |0.0046|
|openbookqa | 1|none | 0|acc |↑ |0.3480|± |0.0213|
| | |none | 0|acc_norm|↑ |0.4280|± |0.0221|
|piqa | 1|none | 0|acc |↑ |0.7639|± |0.0099|
| | |none | 0|acc_norm|↑ |0.7584|± |0.0100|
|winogrande | 1|none | 0|acc |↑ |0.6590|± |0.0133|
Average: 64.00
| Tasks |Version|Filter|n-shot| Metric | |Value | |Stderr|
|------------------------------|------:|------|-----:|--------|---|-----:|---|-----:|
|agieval_aqua_rat | 1|none | 0|acc |↑ |0.2283|± |0.0264|
| | |none | 0|acc_norm|↑ |0.2441|± |0.0270|
|agieval_logiqa_en | 1|none | 0|acc |↑ |0.3057|± |0.0181|
| | |none | 0|acc_norm|↑ |0.3272|± |0.0184|
|agieval_lsat_ar | 1|none | 0|acc |↑ |0.2304|± |0.0278|
| | |none | 0|acc_norm|↑ |0.1957|± |0.0262|
|agieval_lsat_lr | 1|none | 0|acc |↑ |0.3784|± |0.0215|
| | |none | 0|acc_norm|↑ |0.3588|± |0.0213|
|agieval_lsat_rc | 1|none | 0|acc |↑ |0.4610|± |0.0304|
| | |none | 0|acc_norm|↑ |0.4275|± |0.0302|
|agieval_sat_en | 1|none | 0|acc |↑ |0.6019|± |0.0342|
| | |none | 0|acc_norm|↑ |0.5340|± |0.0348|
|agieval_sat_en_without_passage| 1|none | 0|acc |↑ |0.3981|± |0.0342|
| | |none | 0|acc_norm|↑ |0.3981|± |0.0342|
|agieval_sat_math | 1|none | 0|acc |↑ |0.2500|± |0.0293|
| | |none | 0|acc_norm|↑ |0.2636|± |0.0298|
Average: 34.36
| Tasks |Version|Filter|n-shot| Metric | |Value | |Stderr|
|-------------------------------------------------------|------:|------|-----:|--------|---|-----:|---|-----:|
|leaderboard_bbh_boolean_expressions | 1|none | 3|acc_norm|↑ |0.7560|± |0.0272|
|leaderboard_bbh_causal_judgement | 1|none | 3|acc_norm|↑ |0.6043|± |0.0359|
|leaderboard_bbh_date_understanding | 1|none | 3|acc_norm|↑ |0.3280|± |0.0298|
|leaderboard_bbh_disambiguation_qa | 1|none | 3|acc_norm|↑ |0.5880|± |0.0312|
|leaderboard_bbh_formal_fallacies | 1|none | 3|acc_norm|↑ |0.5280|± |0.0316|
|leaderboard_bbh_geometric_shapes | 1|none | 3|acc_norm|↑ |0.3560|± |0.0303|
|leaderboard_bbh_hyperbaton | 1|none | 3|acc_norm|↑ |0.6280|± |0.0306|
|leaderboard_bbh_logical_deduction_five_objects | 1|none | 3|acc_norm|↑ |0.3400|± |0.0300|
|leaderboard_bbh_logical_deduction_seven_objects | 1|none | 3|acc_norm|↑ |0.2880|± |0.0287|
|leaderboard_bbh_logical_deduction_three_objects | 1|none | 3|acc_norm|↑ |0.4160|± |0.0312|
|leaderboard_bbh_movie_recommendation | 1|none | 3|acc_norm|↑ |0.6760|± |0.0297|
|leaderboard_bbh_navigate | 1|none | 3|acc_norm|↑ |0.5800|± |0.0313|
|leaderboard_bbh_object_counting | 1|none | 3|acc_norm|↑ |0.3640|± |0.0305|
|leaderboard_bbh_penguins_in_a_table | 1|none | 3|acc_norm|↑ |0.3836|± |0.0404|
|leaderboard_bbh_reasoning_about_colored_objects | 1|none | 3|acc_norm|↑ |0.3560|± |0.0303|
|leaderboard_bbh_ruin_names | 1|none | 3|acc_norm|↑ |0.4160|± |0.0312|
|leaderboard_bbh_salient_translation_error_detection | 1|none | 3|acc_norm|↑ |0.3080|± |0.0293|
|leadFrom the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys hermes-3-llama-3-2 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (hermes-3-llama-3-2 below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"hermes-3-llama-3-2","messages":[{"role":"user","content":"Hello"}]}'
Create an account — your API key is available in the console. 5M tokens/month currently included with every new account at launch.