119 lines
4.7 KiB
Markdown
119 lines
4.7 KiB
Markdown
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---
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license: other
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license_name: "inherits-base-model-and-dataset-terms"
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base_model: "reallexi/lexi-coder-v4.3"
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library_name: transformers
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pipeline_tag: "text-generation"
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tags:
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- "ai-model-builder"
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- "fine-tuned"
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- lora
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- reallexi
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- "text-generation"
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---
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# lexi-coder-v5.1
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**lexi-coder-v5.1** by Reallexi LLC AI Model Builder — [llm.reallexi.io](https://llm.reallexi.io)
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Copyright (c) 2026 Reallexi LLC. All rights reserved.
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A standalone model of 3.86B parameters, derived from [`reallexi/lexi-coder-v4.3`](https://huggingface.co/reallexi/lexi-coder-v4.3).
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The adapter has been merged into the base weights, so no PEFT adapter is needed at runtime.
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## Size and requirements
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| Parameters | 3,859,090,432 (3.86B) |
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| Weights on disk | 7.15 GB |
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| Trained context length | 1,024 tokens |
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| Base model | `reallexi/lexi-coder-v4.3` |
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Approximate memory to hold the weights. Add context and runtime overhead on top.
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| Precision | Weights |
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|---|---|
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| FP16 / BF16 | 7.19 GB |
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| 8-bit (Q8_0) | 3.59 GB |
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| 4-bit (Q4_K_M) | 1.98 GB |
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## Training
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| Strategy | lora |
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| Adapter | Auto LoRA |
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| LoRA rank / alpha | 16 / 32 |
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| Dataset | `reallexi/lexi-coder-v3-datasest` |
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| Samples learned | 110,000 (through phase 28 of 258) |
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| Training steps | 15,000 |
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| Epochs | 3 |
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## Before / after samples
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The same prompts, drawn from the training data, run through the base model before training and the finished model after. This shows what the run changed on representative prompts -- it is not a benchmark. Full outputs are in `SAMPLES.md` and `samples.json` alongside this file.
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**Prompt:** def partition(p, r): i = p for j in range(p, r): if A[r][1] >= A[j][1]: A[i], A[j] = A[j], A[i]
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- Before: i += 1 return partition(p, r) if partition(p, r) == partition(p, r): return partition(p, r) else: return partition(p, r)
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n = int(input())
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A = [tuple(map(int, input().split())) for _ in range(n)]
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print(*partition(0, n)[1:]) for i in range(n) if partition(0,
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- After: i += 1 return i
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def quicksort(p, r): if p < r: i = partition(p, r) quicksort(p, i-1) quicksort(i, r)
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def mergeSort(p, r): if p < r: q = partition(p, r) mergeSort(p, q) mergeSort(q, r)
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def printList(): print(" ", end =
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**Prompt:** {i : Node(None, None, None) for i in range(n)} # 情報を入れるdictをNodeクラスで作成 for _ in range(n): # 変数名を「 _ 」にすることによって、「その変数を使っていない」ことを表現している（Pythonの習慣） tmp
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- Before: = input().split() # 変数名と値を入れる tmp[0] = tmp[0].strip() # 変数名を入れる tmp[1] = tmp[1].strip() # 値を入れる tmp[2] = tmp[2].strip() # 値を入れる tmp[3] = tmp[3].strip() #
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- After: = input().split() # 入れられた値を変数に入れる tmp[0] = int(tmp[0]) # 変数の値をintに変換 tmp[1] = int(tmp[1]) # 変数の値をintに変換 tmp[2] = int(tmp[2]) # 変数の値をintに変
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**Prompt:** rmat(node_id), end = '') _pre_walk(self.nodes[node_id].left_child) _pre_walk(self.nodes[node_id].right_child) _pre_walk(self.root_id) print('') def inorder_walk…
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- Before: _in_walk(self.nodes[node_id].left_child) print(str(self.nodes[node_id].value)) _in_walk(self.nodes[node_id].right_child) def _post_walk(self, node_id, end = ''): if node_id != -1: _post_walk(self.nodes[node_id].left_child) print(str(self.nodes[node_id].value)) _post_walk(self.nodes[node_id].right
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- After: print(' ', end = '') print(node_id, end = '') _in_walk(self.nodes[node_id].left_child) _in_walk(self.nodes[node_id].right_child) _in_walk(self.nodes[node_id].parent) def post_walk(self): print('Post') root_node = self.nodes[self.root_id] def _post_walk(node_id): if node_id != -1: _post_walk
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## Training curve
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("lexi-coder-v5.1")
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tokenizer = AutoTokenizer.from_pretrained("lexi-coder-v5.1")
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```
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## License and attribution
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The effective terms are inherited from the base model and the training data, which are not necessarily the same as this project's own license. Review both before redistributing.
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- Base model: [`reallexi/lexi-coder-v4.3`](https://huggingface.co/reallexi/lexi-coder-v4.3)
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- Training data: `reallexi/lexi-coder-v3-datasest`
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Copyright (c) 2026 Reallexi LLC. All rights reserved.
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Produced by Reallexi LLC AI Model Builder from training job #161.
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Core: https://llm.reallexi.io
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## Who, where, and what platform trained this?
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Produced by Reallexi LLC on [Reallexi AI Model Builder](https://llm.reallexi.io), a local-first training platform (https://llm.reallexi.io). Hugging Face repository: [reallexi/lexi-coder-v5.1](https://huggingface.co/reallexi/lexi-coder-v5.1).
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Copyright (c) 2026 Reallexi LLC. All rights reserved.
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