p-e-w/gpt-oss-20b-heretic-ara-v3
Text Generation • 2B • Updated
• 111 • 17
Coenraad PRO
dagelf
·
AI & ML interests
Meta epistemology
Recent Activity
new activity
about 9 hours ago
unsloth/README:https://huggingface.co/YuanLabAI A model to watch liked
a model about 10 hours ago
p-e-w/gpt-oss-20b-heretic-ara-v3 liked
a model 4 days ago
allenai/SERA-32B Organizations
https://huggingface.co/YuanLabAI A model to watch
1
#23 opened 3 days ago
by
VaLtEc-BoY
liked a model about 10 hours ago
liked a model 4 days ago
Randomly does reasoning in LMStudio, but reasoning not indicated in model.json
1
#4 opened 21 days ago
by
dagelf
Random reasoning
1
#8 opened 21 days ago
by
dagelf
manifestai/powercoder-3b
#7041 opened 3 months ago
by
dagelf
Qwen/Qwen3-VL-32B-Thinking-FP8
#7040 opened 3 months ago
by
dagelf
commented on Introducing any-llm: A unified API to access any LLM provider 3 months ago
xkcd 927
I have not found a single library or proxy like this that doesn't fail regularly, this one included eg. as of today Gemini Pro 3 doesn't work...
Openrouter doesn't require the user to set up a proxy, this does! I'm not following, would someone care to explain? But a hosted proxy seems best because it can have integrated automated tests and update, without me having to update my code...
Adding `safetensors` variant of this model
3
#23 opened about 2 years ago
by
fseiler275
Why is this exactly the same size as the 8-bit one?
2
#1 opened almost 2 years ago
by
dagelf
Question
2
#2 opened almost 2 years ago
by
mrfakename
Some error when running
2
#1 opened almost 2 years ago
by
Williams07
reacted to philschmid's post with 🔥 almost 2 years ago
Post
8511
New state-of-the-art open LLM! 🚀 Databricks just released DBRX, a 132B MoE trained on 12T tokens. Claiming to surpass OpenAI GPT-3.5 and is competitive with Google Gemini 1.0 Pro. 🤯
TL;DR
🧮 132B MoE with 16 experts with 4 active in generation
🪟 32 000 context window
📈 Outperforms open LLMs on common benchmarks, including MMLU
🚀 Up to 2x faster inference than Llama 2 70B
💻 Trained on 12T tokens
🔡 Uses the GPT-4 tokenizer
📜 Custom License, commercially useable
Collection: databricks/dbrx-6601c0852a0cdd3c59f71962
Demo: https://huggingface.co/spaces/databricks/dbrx-instruct
Kudos to the Team at Databricks and MosaicML for this strong release in the open community! 🤗
TL;DR
🧮 132B MoE with 16 experts with 4 active in generation
🪟 32 000 context window
📈 Outperforms open LLMs on common benchmarks, including MMLU
🚀 Up to 2x faster inference than Llama 2 70B
💻 Trained on 12T tokens
🔡 Uses the GPT-4 tokenizer
📜 Custom License, commercially useable
Collection: databricks/dbrx-6601c0852a0cdd3c59f71962
Demo: https://huggingface.co/spaces/databricks/dbrx-instruct
Kudos to the Team at Databricks and MosaicML for this strong release in the open community! 🤗
- 4 replies
reacted to akhaliq's post with ❤️ about 2 years ago
Post
Chain-of-Thought Reasoning Without Prompting
paper page: Chain-of-Thought Reasoning Without Prompting (2402.10200)
In enhancing the reasoning capabilities of large language models (LLMs), prior research primarily focuses on specific prompting techniques such as few-shot or zero-shot chain-of-thought (CoT) prompting. These methods, while effective, often involve manually intensive prompt engineering. Our study takes a novel approach by asking: Can LLMs reason effectively without prompting? Our findings reveal that, intriguingly, CoT reasoning paths can be elicited from pre-trained LLMs by simply altering the decoding process. Rather than conventional greedy decoding, we investigate the top-k alternative tokens, uncovering that CoT paths are frequently inherent in these sequences. This approach not only bypasses the confounders of prompting but also allows us to assess the LLMs' intrinsic reasoning abilities. Moreover, we observe that the presence of a CoT in the decoding path correlates with a higher confidence in the model's decoded answer. This confidence metric effectively differentiates between CoT and non-CoT paths. Extensive empirical studies on various reasoning benchmarks show that the proposed CoT-decoding substantially outperforms the standard greedy decoding.
paper page: Chain-of-Thought Reasoning Without Prompting (2402.10200)
In enhancing the reasoning capabilities of large language models (LLMs), prior research primarily focuses on specific prompting techniques such as few-shot or zero-shot chain-of-thought (CoT) prompting. These methods, while effective, often involve manually intensive prompt engineering. Our study takes a novel approach by asking: Can LLMs reason effectively without prompting? Our findings reveal that, intriguingly, CoT reasoning paths can be elicited from pre-trained LLMs by simply altering the decoding process. Rather than conventional greedy decoding, we investigate the top-k alternative tokens, uncovering that CoT paths are frequently inherent in these sequences. This approach not only bypasses the confounders of prompting but also allows us to assess the LLMs' intrinsic reasoning abilities. Moreover, we observe that the presence of a CoT in the decoding path correlates with a higher confidence in the model's decoded answer. This confidence metric effectively differentiates between CoT and non-CoT paths. Extensive empirical studies on various reasoning benchmarks show that the proposed CoT-decoding substantially outperforms the standard greedy decoding.
liked a Space about 2 years ago
Does this model support multi-turn conversations?
2
#18 opened about 2 years ago
by
apepkuss79