sarvamai/shuka-1
Audio-Text-to-Text โข 9B โข Updated โข 605 โข 92
SA
Qagent
ยท
AI & ML interests
RL, PAC learning, NLP
Recent Activity
liked a model almost 2 years ago
sarvamai/shuka-1 liked a model almost 2 years ago
sarvamai/sarvam-1-v0.5Organizations
liked 2
models almost 2 years ago
reacted to tomaarsen's post with ๐ about 2 years ago
Post
2524
NuMind has just released 3 new state-of-the-art GLiNER models for Named Entity Recognition/Information Extraction. These GLiNER models allow you to specify any label that you want, and it'll find spans in the text corresponding to your label. It's been shown to work quite well on unusual domains, e.g. celestial entities in my picture.
There are 3 models released:
- numind/NuNER_Zero:
The primary model, SOTA & can detect really long entities.
- numind/NuNER_Zero-span:
Slightly better performance than NuNER Zero, but can't detect entities longer than 12 tokens.
- numind/NuNER_Zero-4k:
Slightly worse than NuNER Zero, but has a context length of 4k tokens.
Some more details about these models in general:
- They are *really* small, orders of magnitude smaller than LLMs, which don't reach this level of performance.
- Because they're small - they're fast: <1s per sentence on free GPUs.
- They have an MIT license: free commercial usage.
Try out the demo here: https://huggingface.co/spaces/numind/NuZero
Or check out all of the models here: numind/nunerzero-zero-shot-ner-662b59803b9b438ff56e49e2
If there's ever a need for me to extract some information from any text: I'll be using these. Great work @Serega6678 !
There are 3 models released:
- numind/NuNER_Zero:
The primary model, SOTA & can detect really long entities.
- numind/NuNER_Zero-span:
Slightly better performance than NuNER Zero, but can't detect entities longer than 12 tokens.
- numind/NuNER_Zero-4k:
Slightly worse than NuNER Zero, but has a context length of 4k tokens.
Some more details about these models in general:
- They are *really* small, orders of magnitude smaller than LLMs, which don't reach this level of performance.
- Because they're small - they're fast: <1s per sentence on free GPUs.
- They have an MIT license: free commercial usage.
Try out the demo here: https://huggingface.co/spaces/numind/NuZero
Or check out all of the models here: numind/nunerzero-zero-shot-ner-662b59803b9b438ff56e49e2
If there's ever a need for me to extract some information from any text: I'll be using these. Great work @Serega6678 !
- 3 replies
reacted to Yoben's post with ๐ฅ about 2 years ago
Post
1210
IBM has launched its latest #Granite code models.
๐๐ฟ๐ฎ๐ป๐ถ๐๐ฒ ๐ผ๐ป ๐๐๐ด๐ด๐ถ๐ป๐ด ๐๐ฎ๐ฐ๐ฒ - https://lnkd.in/g2KaHWxC
The figure illustrates how#Granite-8B-Code-Base outperforms #Mistral-7B, #LLama-3-8B, and other open-source models in coding tasks.
Models available:
- ibm-granite/granite-3b-code-base
- ibm-granite/granite-3b-code-instruct
- ibm-granite/granite-8b-code-base
- ibm-granite/granite-8b-code-instruct
- ibm-granite/granite-20b-code-base
- ibm-granite/granite-20b-code-instruct
- ibm-granite/granite-34b-code-base
- ibm-granite/granite-34b-code-instruct
๐๐ฟ๐ฎ๐ป๐ถ๐๐ฒ ๐ผ๐ป ๐๐๐ด๐ด๐ถ๐ป๐ด ๐๐ฎ๐ฐ๐ฒ - https://lnkd.in/g2KaHWxC
The figure illustrates how#Granite-8B-Code-Base outperforms #Mistral-7B, #LLama-3-8B, and other open-source models in coding tasks.
Models available:
- ibm-granite/granite-3b-code-base
- ibm-granite/granite-3b-code-instruct
- ibm-granite/granite-8b-code-base
- ibm-granite/granite-8b-code-instruct
- ibm-granite/granite-20b-code-base
- ibm-granite/granite-20b-code-instruct
- ibm-granite/granite-34b-code-base
- ibm-granite/granite-34b-code-instruct
reacted to QagentS's post with โค๏ธ๐๐ฅ about 2 years ago
Post
993
Hi folks ,
colab[https://colab.research.google.com/drive/10av3SxFf0Psx_IkmZbcUhiVznStV5pVS?usp=sharing]
#OpenSourcing
pip-code-bandit
-- a model to act as intelligence unit in agentic workflows.
pipflow
-- a library to manage and run goal oriented agentic system.
pip-code-bandit attributes-
-- number of params ~ 1.3b [2.9 Gb GPU memory footprint]
-- sequence length ~ 16.3k [Can go higher but will show performance degradation]
-- license - apache 2.0
-- instruction following , RL tuned.
-- tasks:
complex planning(plan) of sequential function calls | a list of callables and goal
corrected plan | feedback instructions with error
function calling | doc or code and goal
code generation | plan and goal
code generation | goal
doc generation | code
code generation | doc
file parsed to json | any raw data
sql generation | schema, question, instructions and examples
#Strategy
We used a simulator to simulate environments where the model could play games to achieve goals, given a set of actions available to it. All the model could do was find the right action and config to incur a positive reward. The reward policy is around the concept of a model going to a stable state of zero net sum reward for both good and bad behaviour. In this setup, the model, which was pre-trained on code, function documentation, and similar OS datasets, was RL-tuned for reliability and instruction-following.
Do try it out and let me know how its working for you.
Thank you :)
colab[https://colab.research.google.com/drive/10av3SxFf0Psx_IkmZbcUhiVznStV5pVS?usp=sharing]
#OpenSourcing
pip-code-bandit
-- a model to act as intelligence unit in agentic workflows.
pipflow
-- a library to manage and run goal oriented agentic system.
pip-code-bandit attributes-
-- number of params ~ 1.3b [2.9 Gb GPU memory footprint]
-- sequence length ~ 16.3k [Can go higher but will show performance degradation]
-- license - apache 2.0
-- instruction following , RL tuned.
-- tasks:
complex planning(plan) of sequential function calls | a list of callables and goal
corrected plan | feedback instructions with error
function calling | doc or code and goal
code generation | plan and goal
code generation | goal
doc generation | code
code generation | doc
file parsed to json | any raw data
sql generation | schema, question, instructions and examples
#Strategy
We used a simulator to simulate environments where the model could play games to achieve goals, given a set of actions available to it. All the model could do was find the right action and config to incur a positive reward. The reward policy is around the concept of a model going to a stable state of zero net sum reward for both good and bad behaviour. In this setup, the model, which was pre-trained on code, function documentation, and similar OS datasets, was RL-tuned for reliability and instruction-following.
Do try it out and let me know how its working for you.
Thank you :)
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