Model reference · open weights
CollectiveCognition-Mistral is an open-weight language model from teknium. 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 | teknium |
|---|---|
| Type | Language models |
| Task | Text gen |
| Context | 32k tokens |
| Runs with | transformers |
| Based on | mistralai/Mistral-7B-v0.1 |
| Released | 2023-10-04 |
| Popularity | 31 downloads / month |
| Licence | Open weights |
About
Collective Cognition v1.1 - Mistral 7B
Collective Cognition v1.1 is a state-of-the-art model fine-tuned using the Mistral approach. This model is particularly notable for its performance, outperforming many 70B models on the TruthfulQA benchmark. This benchmark assesses models for common misconceptions, potentially indicating hallucination rates.
Special thanks to @a16z and all contributors to the Collective Cognition dataset for making the development of this model possible.
The model was trained using data from the Collective Cognition website. The efficacy of this dataset is demonstrated by the model's stellar performance, suggesting that further expansion of this dataset could yield even more promising results. The data is reminiscent of that collected from platforms like ShareGPT.
You can contribute to the growth of the dataset by sharing your own ChatGPT chats here.
You can download the datasets created by Collective Cognition here: https://huggingface.co/CollectiveCognition
Prompt Format:
USER:
ASSISTANT:
OR
USER:
ASSISTANT:
Collective Cognition v1.0 TruthfulQA:
| Task |Version|Metric|Value | |Stderr|
|-------------|------:|------|-----:|---|-----:|
|truthfulqa_mc| 1|mc1 |0.4051|± |0.0172|
| | |mc2 |0.5738|± |0.0157|
Collective Cognition v1.1 GPT4All:
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.5085|± |0.0146|
| | |acc_norm|0.5384|± |0.0146|
|arc_easy | 0|acc |0.7963|± |0.0083|
| | |acc_norm|0.7668|± |0.0087|
|boolq | 1|acc |0.8495|± |0.0063|
|hellaswag | 0|acc |0.6399|± |0.0048|
| | |acc_norm|0.8247|± |0.0038|
|openbookqa | 0|acc |0.3240|± |0.0210|
| | |acc_norm|0.4540|± |0.0223|
|piqa | 0|acc |0.7992|± |0.0093|
| | |acc_norm|0.8107|± |0.0091|
|winogrande | 0|acc |0.7348|± |0.0124|
Average: 71.13
AGIEval:
| Task |Version| Metric |Value | |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat | 0|acc |0.1929|± |0.0248|
| | |acc_norm|0.2008|± |0.0252|
|agieval_logiqa_en | 0|acc |0.3134|± |0.0182|
| | |acc_norm|0.3333|± |0.0185|
|agieval_lsat_ar | 0|acc |0.2217|± |0.0275|
| | |acc_norm|0.2043|± |0.0266|
|agieval_lsat_lr | 0|acc |0.3412|± |0.0210|
| | |acc_norm|0.3216|± |0.0207|
|agieval_lsat_rc | 0|acc |0.4721|± |0.0305|
| | |acc_norm|0.4201|± |0.0301|
|agieval_sat_en | 0|acc |0.6068|± |0.0341|
| | |acc_norm|0.5777|± |0.0345|
|agieval_sat_en_without_passage| 0|acc |0.3932|± |0.0341|
| | |acc_norm|0.3641|± |0.0336|
|agieval_sat_math | 0|acc |0.2864|± |0.0305|
| | |acc_norm|0.2636|± |0.0298|
Average: 33.57
Training run on wandb here: https://wandb.ai/teknium1/collectivecognition-mistral-7b/runs/collectivecognition-mistral-8/workspace
Apache 2.0
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys collectivecognition-mistral for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (collectivecognition-mistral 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":"collectivecognition-mistral","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.