deepseek-ai/DeepSeek-V4.1-Flash-1017
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Rattanarit Prasomsab
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Welcome Gemma 4: Frontier multimodal intelligence on device
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merve, pcuenq, sergiopaniego, burtenshaw, Steveeeeeeen, alvarobartt, SaylorTwift
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We've built over 10K containerized reproductions of papers from arXiv!
Instead of spending all day trying to build an environment to test that new idea, just pull the Docker container from the Remyx registry.
And with Remyx, you can start experimenting faster by generating a test PR in your codebase based on the ideas found in your paper of choice.
Hub: https://hub.docker.com/u/remyxai
Remyx docs: https://docs.remyx.ai/resources/ideate
Coming soon, explore reproduced papers with AG2 + Remyx: https://github.com/ag2ai/ag2/pull/2141
Instead of spending all day trying to build an environment to test that new idea, just pull the Docker container from the Remyx registry.
And with Remyx, you can start experimenting faster by generating a test PR in your codebase based on the ideas found in your paper of choice.
Hub: https://hub.docker.com/u/remyxai
Remyx docs: https://docs.remyx.ai/resources/ideate
Coming soon, explore reproduced papers with AG2 + Remyx: https://github.com/ag2ai/ag2/pull/2141
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I recently worked on a LoRA that improves tool use in LLM. Thought the approach might interest folks here.
The issue I have had when trying to use some of the local LLMs with coding agents is this:
Me: "Find all API endpoints with authentication in this codebase"
LLM: "You should look for @app .route decorators and check if they have auth middleware..."
But I often want it to search the files and show me but the LLM doesn't trigger a tool use call.
To fine-tune it for tool use I combined two data sources:
1. Magpie scenarios - 5000+ diverse tasks (bug hunting, refactoring, security audits)
2. Real execution - Ran these on actual repos (FastAPI, Django, React) to get authentic tool responses
This ensures the model learns both breadth (many scenarios) and depth (real tool behavior).
Tools We Taught:
-
-
-
-
-
-
Improvements:
- Tool calling accuracy: 12% → 80%
- Correct parameters: 8% → 87%
- Multi-step tasks: 3% → 78%
- End-to-end completion: 5% → 80%
- Tools per task: 0.2 → 3.8
The LoRA really improves on intential tool call as an example consider the query: "Find ValueError in payment module"
The response proceeds as follows:
1. Calls
2. Gets 4 matches across 3 files
3. Calls
4. Analyzes context
5. Reports: "Found 3 ValueError instances: payment/processor.py:47 for invalid amount, payment/validator.py:23 for unsupported currency..."
Resources:
- Colab notebook https://colab.research.google.com/github/codelion/ellora/blob/main/Ellora_Recipe_3_Enhanced_Tool_Calling_and_Code_Understanding.ipynb
- Model - codelion/Llama-3.2-1B-Instruct-tool-calling-lora
- GitHub - https://github.com/codelion/ellora
The issue I have had when trying to use some of the local LLMs with coding agents is this:
Me: "Find all API endpoints with authentication in this codebase"
LLM: "You should look for @app .route decorators and check if they have auth middleware..."
But I often want it to search the files and show me but the LLM doesn't trigger a tool use call.
To fine-tune it for tool use I combined two data sources:
1. Magpie scenarios - 5000+ diverse tasks (bug hunting, refactoring, security audits)
2. Real execution - Ran these on actual repos (FastAPI, Django, React) to get authentic tool responses
This ensures the model learns both breadth (many scenarios) and depth (real tool behavior).
Tools We Taught:
-
read_file - Actually read file contents-
search_files - Regex/pattern search across codebases-
find_definition - Locate classes/functions-
analyze_imports - Dependency tracking-
list_directory - Explore structure-
run_tests - Execute test suitesImprovements:
- Tool calling accuracy: 12% → 80%
- Correct parameters: 8% → 87%
- Multi-step tasks: 3% → 78%
- End-to-end completion: 5% → 80%
- Tools per task: 0.2 → 3.8
The LoRA really improves on intential tool call as an example consider the query: "Find ValueError in payment module"
The response proceeds as follows:
1. Calls
search_files with pattern "ValueError"2. Gets 4 matches across 3 files
3. Calls
read_file on each match4. Analyzes context
5. Reports: "Found 3 ValueError instances: payment/processor.py:47 for invalid amount, payment/validator.py:23 for unsupported currency..."
Resources:
- Colab notebook https://colab.research.google.com/github/codelion/ellora/blob/main/Ellora_Recipe_3_Enhanced_Tool_Calling_and_Code_Understanding.ipynb
- Model - codelion/Llama-3.2-1B-Instruct-tool-calling-lora
- GitHub - https://github.com/codelion/ellora
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