Text Generation
PEFT
Safetensors
English
qwen2.5
qwen2.5-coder
code-generation
browser-automation
web-agent
tool-calling
function-calling
agent
conversational
lora
adapter
sft
trl
sakthai
house-of-sak
Eval Results (legacy)
Eval Results
Instructions to use Nanthasit/sakthai-coder-browser-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Nanthasit/sakthai-coder-browser-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-1.5B-Instruct") model = PeftModel.from_pretrained(base_model, "Nanthasit/sakthai-coder-browser-lora") - Notebooks
- Google Colab
- Kaggle
cron: metadata eval update 2026-08-01T09:29:00Z
Browse files
.eval_results/eval-cron-sakthai-coder-browser-lora-20260801T092900Z.yaml
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eval:
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model: Nanthasit/sakthai-coder-browser-lora
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timestamp: '2026-08-01T09:29:00Z'
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type: metadata_cron
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metadata:
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downloads: 55
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likes: 0
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pipeline_tag: text-generation
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tags:
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- peft
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- lora
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- qwen2.5
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- qwen2.5-coder
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- code-generation
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- browser-automation
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- web-agent
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- tool-calling
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- function-calling
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- agent
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- conversational
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- sft
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- trl
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- sakthai
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- house-of-sak
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- safetensors
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base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
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license: apache-2.0
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language:
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- en
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datasets:
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- Nanthasit/sakthai-combined-v8
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- Nanthasit/sakthai-combined-v11
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- Nanthasit/sakthai-irrelevance-supplement
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- Nanthasit/cycle-bench
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inference:
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parameters:
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temperature: 0.3
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max_new_tokens: 256
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top_p: 0.9
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metrics:
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tool_calling_accuracy: 1.0
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tool_calling_verified: false
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