Model reference · open weights
llama is an open-weight language model from JackFram. 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
| Released by | JackFram |
|---|---|
| Type | Language models |
| Task | Text gen |
| Context | 2k tokens |
| Runs with | transformers |
| Released | 2023-07-19 |
| Popularity | 109k downloads / month |
| Licence | Open weights |
About
This is a LLaMA-like model with only 68M parameters trained on Wikipedia and part of the C4-en and C4-realnewslike datasets.
No evaluation has been conducted yet, so use it with care.
The model is mainly developed as a base Small Speculative Model in the SpecInfer paper.
| Category | Benchmark | Metric | Score / Value | Status |
|---|---|---|---|---|
| Linguistics & Grammar | BLiMP | Accuracy | 70.57% | Success |
| Commonsense & Reasoning | PIQA | Normalized Accuracy | 59.25% | Success |
| BoolQ | Accuracy | 57.71% | Success | |
| COPA | Accuracy | 53.00% | Success | |
| WinoGrande | Accuracy | 50.59% | Success | |
| HellaSwag | Normalized Accuracy | 29.04% | Success | |
| RACE | Accuracy | 25.36% | Success | |
| CommonsenseQA | Accuracy | 19.82% | Success | |
| Academic & Knowledge | SciQ | Normalized Accuracy | 57.80% | Success |
| ARC-Easy | Normalized Accuracy | 35.98% | Success | |
| OpenBookQA | Normalized Accuracy | 25.60% | Success | |
| MMLU | Accuracy | 22.96% | Success | |
| ARC-Challenge | Normalized Accuracy | 22.87% | Success | |
| Language Modeling | TriviaQA | Accuracy | TriviaQA Standard | Success |
| LAMBADA | Accuracy | 13.24% | Success | |
| C4-Perplexity | Word Perplexity | 205.79 | Success | |
| WikiText-2 | Word Perplexity | 306.79 | Success |
Notes on Failed Tasks: The
ArithmeticandSocialIQAbenchmarks failed during execution due to runtime pipeline incompatibilities, yielding no score. Total evaluation runtime was 44.74 minutes.
To cite the model, please use
@misc{miao2023specinfer,
title={SpecInfer: Accelerating Generative LLM Serving with Speculative Inference and Token Tree Verification},
author={Xupeng Miao and Gabriele Oliaro and Zhihao Zhang and Xinhao Cheng and Zeyu Wang and Rae Ying Yee Wong and Zhuoming Chen and Daiyaan Arfeen and Reyna Abhyankar and Zhihao Jia},
year={2023},
eprint={2305.09781},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys jackfram-llama for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (jackfram-llama 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":"jackfram-llama","messages":[{"role":"user","content":"Hello"}]}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.