Michal Mucha MichaMucha

## HOWTO.md

      
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                WolframRavenwolf
                / HOWTO.md
            
            
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              September 20, 2025 15:50
            
              
                HOWTO: Use Qwen3-Coder (or any other LLM) with Claude Code (via LiteLLM)
              
          
Wolfram Ravenwolf on X: "I'm now using Qwen3-Coder in Claude Code. Works with any model actually, but this is surely the best one currently. There are a bunch of proxies on GitHub that make this possible, but none worked well enough for me, so I implemented this myself using LiteLLM. Guide in comments: https://t.co/Wqbv75nxlp" / X
HOWTO: Use Qwen3-Coder (or any other LLM) with Claude Code (via LiteLLM) : r/LocalLLaMA

Here's a simple way for Claude Code users to switch from the costly Claude models to the newly released SOTA open-source/weights coding model, Qwen3-Coder, via OpenRouter using LiteLLM on your local machine.
This process is quite universal and can be easily adapted to suit your needs. Feel free to explore other models (including local ones) as well as different providers and coding agents.
I'm sharing what works for me. This gu

  
## torch_jacobian.py
def get_jacobian(net, x, noutputs):
    x = x.squeeze()
    n = x.size()[0]
    x = x.repeat(noutputs, 1)
    x.requires_grad_(True)
    y = net(x)
    y.backward(torch.eye(noutputs))
    return x.grad.data

## top-k-top-p.py
def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')):
    """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
        Args:
            logits: logits distribution shape (vocabulary size)
            top_k >0: keep only top k tokens with highest probability (top-k filtering).
            top_p >0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
                Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
    """
    assert logits.dim() == 1  # batch size 1 for now - could be updated for more but the code would be less clear
    top_k = min(top_k, logits.size(-1))  # Safety check

## __init2__.py
"""
Version retrieving.

This is free and unencumbered software released into the public domain.

Anyone is free to copy, modify, publish, use, compile, sell, or
distribute this software, either in source code form or as a compiled
binary, for any purpose, commercial or non-commercial, and by any
means.

## iterm2-solarized.md

      
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                kevin-smets
                / iterm2-solarized.md
            
            
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              October 16, 2025 10:42
            
              
                iTerm2 + Oh My Zsh + Solarized color scheme + Source Code Pro Powerline + Font Awesome + [Powerlevel10k] - (macOS)
              
          
    Default


Powerlevel10k
	def get_jacobian(net, x, noutputs):
	x = x.squeeze()
	n = x.size()[0]
	x = x.repeat(noutputs, 1)
	x.requires_grad_(True)
	y = net(x)
	y.backward(torch.eye(noutputs))
	return x.grad.data
	def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')):
	""" Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
	Args:
	logits: logits distribution shape (vocabulary size)
	top_k >0: keep only top k tokens with highest probability (top-k filtering).
	top_p >0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
	Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
	"""
	assert logits.dim() == 1 # batch size 1 for now - could be updated for more but the code would be less clear
	top_k = min(top_k, logits.size(-1)) # Safety check
	"""
	Version retrieving.

	This is free and unencumbered software released into the public domain.

	Anyone is free to copy, modify, publish, use, compile, sell, or
	distribute this software, either in source code form or as a compiled
	binary, for any purpose, commercial or non-commercial, and by any
	means.