142 lines
4.3 KiB
Markdown
142 lines
4.3 KiB
Markdown
---
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tags:
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- unsloth
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- lua
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base_model:
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- nwdxlgzs/XL-LuaCopilot-1.7B-FFT-checkpoint-46000
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license: gpl-3.0
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library_name: transformers
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pipeline_tag: text-generation
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---
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# XL-LuaCopilot-1.7B-FFT
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XL-LuaCopilot-1.7B-FFT is a large language model (LLM) based on the Qwen architecture(Qwen3-1.7B), specifically designed for code generation tasks in Lua programming language. It has been full fine-tuned (FFT) to improve its performance and efficiency when generating Lua code.
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I sugggest you use `"chat_template_kwargs": {"enable_thinking": false}` because my train data with none thinking. I also found low `temperature` ususually works well for code generation tasks.
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quantize=["Q4_0", "Q4_1", "Q5_0", "Q5_1", "IQ3_XXS", "IQ3_S", "IQ3_M", "Q3_K", "IQ3_XS", "Q3_K_S", "Q3_K_M", "Q3_K_L", "IQ4_NL", "IQ4_XS", "Q4_K", "Q4_K_S", "Q4_K_M", "Q5_K", "Q5_K_S", "Q5_K_M", "Q6_K", "Q8_0", "F16", "BF16"]
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> `checkpoint-37000` is the checkpoint where the model had just entered the plateau phase with a lower loss, and it might be better than `checkpoint-46000`. However, the GGUF files I provide will still be released based on the final checkpoint at the end of training.
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## Train Samples
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1472000 (steps=46000)x(per_device_train_batch_size=8)x(gradient_accumulation_steps=4)x(device=1)
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datasets: 1464339 (luafiles=488113)x(split=3)
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epochs = 1.005
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## How To Use
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> With OpenAI Compatible API (llama.cpp:llama-server)
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```json
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-> REQUEST ->
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{
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"model": "XL-LuaCopilot-1.7B-FFT",
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"messages": [
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{"role": "system","content": "prefix"},
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{"role": "user","content": "do\n--打印：你好世界\n local tex"},
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{"role": "system","content": "suffix"},
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{"role": "user","content": "nd"},
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{"role": "system","content": "middle"}
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],
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"stream": false,
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"cache_prompt": false,
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"samplers": "edkypmxt",
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"temperature": 0.2,
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"dynatemp_range": 0.1,
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"dynatemp_exponent": 1,
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"top_k": 20,
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"top_p": 0.9,
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"min_p": 0.05,
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"typical_p": 1,
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"xtc_probability": 0,
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"xtc_threshold": 0.1,
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"repeat_last_n": 32,
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"repeat_penalty": 1.1,
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"presence_penalty": 0,
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"frequency_penalty": 0.5,
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"dry_multiplier": 0,
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"dry_base": 1.75,
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"dry_allowed_length": 2,
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"dry_penalty_last_n": -1,
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"max_tokens": -1,
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"timings_per_token": true,
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"chat_template_kwargs": {"enable_thinking": false}
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}
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-> RESPONSE ->
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{
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"choices": [
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{
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"finish_reason": "stop",
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"index": 0,
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"message": {
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"role": "assistant",
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"content": "<think>\n\n</think>\n\nt = \"你好世界\"\n print(text)\ne"
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}
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}
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],
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...
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}
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```
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> I know Qwen has `<|fim_prefix|>` / `<|fim_suffix|>` / `<|fim_middle|>` tokens, but I'm not sure Qwen3 trains these tokens (I just know Qwen2.5-Coder does). To use code generation easily, I use chatml format.
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> If you just want to chat with it, you can use some tricks like this:
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```
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<|im_end|>
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<|im_start|>system
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prefix<|im_end|>
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<|im_start|>user
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do
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--打印：你好世界
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local tex<|im_end|>
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<|im_start|>system
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suffix<|im_end|>
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<|im_start|>user
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nd<|im_end|>
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<|im_start|>system
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middle
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```
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It dosen't work very well, but it's a good way let you fast try. It will convert to this prompt text:
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```
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<|im_start|>user
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<|im_end|>
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<|im_start|>system
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prefix<|im_end|>
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<|im_start|>user
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do
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--打印：你好世界
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local tex<|im_end|>
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<|im_start|>system
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suffix<|im_end|>
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<|im_start|>user
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nd<|im_end|>
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<|im_start|>system
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middle<|im_end|>
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```
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Hope model skip first `<|im_start|>user\n<|im_end|>` part.
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# Train Device
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> Online GPU is Expensive !
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| 类别 | 配置详情 |
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|----------------|---------------------------------------------------|
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| **镜像** | Ubuntu 22.04 |
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| **PyTorch** | 2.5.1 |
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| **Python** | 3.12 |
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| **CUDA** | 12.4 |
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| **GPU** | RTX 4090 (24GB) * 1 |
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| **CPU** | 25 vCPU Intel(R) Xeon(R) Platinum 8481C |
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| **内存** | 90GB |
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| **硬盘** | 30 GB + 50 GB |
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| **时长** | 3 Day |
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