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Model: reallexi/lexi-coder-v5.1 Source: Original Platform
2026-09-13 01:58:32 +08:00
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+Copyright (c) 2026 Reallexi LLC. All rights reserved.
+
+This standalone model was produced by Reallexi LLC AI Model Builder.
+Core backlink: https://llm.reallexi.io
+
+The upstream base model and training datasets retain their own licenses and terms.
--- a/README.md
+++ b/README.md
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+---
+license: other
+license_name: "inherits-base-model-and-dataset-terms"
+base_model: "reallexi/lexi-coder-v4.3"
+library_name: transformers
+pipeline_tag: "text-generation"
+tags:
+- "ai-model-builder"
+- "fine-tuned"
+- lora
+- reallexi
+- "text-generation"
+---
+
+# lexi-coder-v5.1
+
+**lexi-coder-v5.1** by Reallexi LLC AI Model Builder — [llm.reallexi.io](https://llm.reallexi.io)
+
+Copyright (c) 2026 Reallexi LLC. All rights reserved.
+
+A standalone model of 3.86B parameters, derived from [`reallexi/lexi-coder-v4.3`](https://huggingface.co/reallexi/lexi-coder-v4.3).
+
+The adapter has been merged into the base weights, so no PEFT adapter is needed at runtime.
+
+
+## Size and requirements
+
+| | |
+|---|---|
+| Parameters | 3,859,090,432 (3.86B) |
+| Weights on disk | 7.15 GB |
+| Trained context length | 1,024 tokens |
+| Base model | `reallexi/lexi-coder-v4.3` |
+
+
+Approximate memory to hold the weights. Add context and runtime overhead on top.
+
+| Precision | Weights |
+|---|---|
+| FP16 / BF16 | 7.19 GB |
+| 8-bit (Q8_0) | 3.59 GB |
+| 4-bit (Q4_K_M) | 1.98 GB |
+
+
+## Training
+
+| | |
+|---|---|
+| Strategy | lora |
+| Adapter | Auto LoRA |
+| LoRA rank / alpha | 16 / 32 |
+| Dataset | `reallexi/lexi-coder-v3-datasest` |
+| Samples learned | 110,000 (through phase 28 of 258) |
+| Training steps | 15,000 |
+| Epochs | 3 |
+
+
+## Before / after samples
+
+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.
+
+**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]
+
+- Before: i += 1 return partition(p, r) if partition(p, r) == partition(p, r): return partition(p, r) else: return partition(p, r)
+
+n = int(input())
+A = [tuple(map(int, input().split())) for _ in range(n)]
+print(*partition(0, n)[1:]) for i in range(n) if partition(0,
+- After: i += 1 return i
+def quicksort(p, r): if p < r: i = partition(p, r) quicksort(p, i-1) quicksort(i, r)
+def mergeSort(p, r): if p < r: q = partition(p, r) mergeSort(p, q) mergeSort(q, r)
+def printList(): print(" ", end =
+
+**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
+
+- 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() #
+- 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
+
+**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…
+
+- 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
+- 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
+
+
+## Training curve
+
+![Training loss](training_curve.png)
+
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("lexi-coder-v5.1")
+tokenizer = AutoTokenizer.from_pretrained("lexi-coder-v5.1")
+```
+
+
+## License and attribution
+
+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.
+
+- Base model: [`reallexi/lexi-coder-v4.3`](https://huggingface.co/reallexi/lexi-coder-v4.3)
+
+- Training data: `reallexi/lexi-coder-v3-datasest`
+
+
+Copyright (c) 2026 Reallexi LLC. All rights reserved.
+
+
+Produced by Reallexi LLC AI Model Builder from training job #161.
+Core: https://llm.reallexi.io
+
+## Who, where, and what platform trained this?
+
+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).
+Copyright (c) 2026 Reallexi LLC. All rights reserved.
--- a/SAMPLES.md
+++ b/SAMPLES.md
@@ -0,0 +1,40 @@
+# Before / after training samples
+
+Generated automatically from a few prompts drawn from the training data, run once against the base model before training started and once against the finished model. This shows what this run changed on representative prompts -- it is not a benchmark and does not measure generalization.
+
+## 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]
+
+**Before:**
+
+> i += 1 return partition(p, r) if partition(p, r) == partition(p, r): return partition(p, r) else: return partition(p, r)
+
+n = int(input())
+A = [tuple(map(int, input().split())) for _ in range(n)]
+print(*partition(0, n)[1:]) for i in range(n) if partition(0,
+
+**After:**
+
+> i += 1 return i
+def quicksort(p, r): if p < r: i = partition(p, r) quicksort(p, i-1) quicksort(i, r)
+def mergeSort(p, r): if p < r: q = partition(p, r) mergeSort(p, q) mergeSort(q, r)
+def printList(): print(" ", end =
+
+## 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
+
+**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() #
+
+**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
+
+## 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(self): print('Inorder') root_node = self.nodes[self.root_id] def _in_walk(node_id): if node_id != -1:
+
+**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
+
+**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
--- a/added_tokens.json
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+  "<|tool_call|>": 200025,
+  "<|tool_response|>": 200027,
+  "<|tool|>": 200023,
+  "<|user|>": 200021
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+{% for message in messages %}{% if message['role'] == 'system' and 'tools' in message and message['tools'] is not none %}{{ '<|' + message['role'] + '|>' + message['content'] + '<|tool|>' + message['tools'] + '<|/tool|>' + '<|end|>' }}{% else %}{{ '<|' + message['role'] + '|>' + message['content'] + '<|end|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>' }}{% else %}{{ eos_token }}{% endif %}
--- a/config.json
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+{
+  "architectures": [
+    "Phi3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_phi3.Phi3Config",
+    "AutoModelForCausalLM": "modeling_phi3.Phi3ForCausalLM",
+    "AutoTokenizer": "Xenova/gpt-4o"
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+}
--- a/generation_config.json
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+  }
+}
--- a/reallexi-model.json
+++ b/reallexi-model.json
@@ -0,0 +1,20 @@
+{
+  "schema_version": 1,
+  "job_id": 161,
+  "model_name": "lexi-coder-v5.1",
+  "artifact_type": "standalone_model",
+  "standalone": true,
+  "adapter_applied": true,
+  "source_artifact_type": "lora_adapter",
+  "base_model_id": "reallexi/lexi-coder-v4.3",
+  "custom_adapter": null,
+  "training_strategy": "lora",
+  "total_parameters": 3859090432,
+  "weight_size_bytes": 7672065984,
+  "trained_samples": 5000,
+  "training_steps": 15000,
+  "owner": "Reallexi LLC",
+  "copyright": "Copyright (c) 2026 Reallexi LLC. All rights reserved.",
+  "core_backlink": "https://llm.reallexi.io",
+  "created_at": "2026-08-17T18:17:33+00:00"
+}
--- a/samples.json
+++ b/samples.json
@@ -0,0 +1,35 @@
+{
+  "prompts": [
+    "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]",
+    "{i : Node(None, None, None) for i in range(n)} # \u60c5\u5831\u3092\u5165\u308c\u308bdict\u3092Node\u30af\u30e9\u30b9\u3067\u4f5c\u6210 for _ in range(n): # \u5909\u6570\u540d\u3092\u300c _ \u300d\u306b\u3059\u308b\u3053\u3068\u306b\u3088\u3063\u3066\u3001\u300c\u305d\u306e\u5909\u6570\u3092\u4f7f\u3063\u3066\u3044\u306a\u3044\u300d\u3053\u3068\u3092\u8868\u73fe\u3057\u3066\u3044\u308b\uff08Python\u306e\u7fd2\u6163\uff09 tmp",
+    "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(self): print('Inorder') root_node = self.nodes[self.root_id] def _in_walk(node_id): if node_id != -1:"
+  ],
+  "before": [
+    {
+      "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]",
+      "output": "i += 1 return partition(p, r) if partition(p, r) == partition(p, r): return partition(p, r) else: return partition(p, r)\n\nn = int(input())\nA = [tuple(map(int, input().split())) for _ in range(n)]\nprint(*partition(0, n)[1:]) for i in range(n) if partition(0,"
+    },
+    {
+      "prompt": "{i : Node(None, None, None) for i in range(n)} # \u60c5\u5831\u3092\u5165\u308c\u308bdict\u3092Node\u30af\u30e9\u30b9\u3067\u4f5c\u6210 for _ in range(n): # \u5909\u6570\u540d\u3092\u300c _ \u300d\u306b\u3059\u308b\u3053\u3068\u306b\u3088\u3063\u3066\u3001\u300c\u305d\u306e\u5909\u6570\u3092\u4f7f\u3063\u3066\u3044\u306a\u3044\u300d\u3053\u3068\u3092\u8868\u73fe\u3057\u3066\u3044\u308b\uff08Python\u306e\u7fd2\u6163\uff09 tmp",
+      "output": "= input().split() # \u5909\u6570\u540d\u3068\u5024\u3092\u5165\u308c\u308b tmp[0] = tmp[0].strip() # \u5909\u6570\u540d\u3092\u5165\u308c\u308b tmp[1] = tmp[1].strip() # \u5024\u3092\u5165\u308c\u308b tmp[2] = tmp[2].strip() # \u5024\u3092\u5165\u308c\u308b tmp[3] = tmp[3].strip() #"
+    },
+    {
+      "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(self): print('Inorder') root_node = self.nodes[self.root_id] def _in_walk(node_id): if node_id != -1:",
+      "output": "_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"
+    }
+  ],
+  "after": [
+    {
+      "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]",
+      "output": "i += 1 return i\ndef quicksort(p, r): if p < r: i = partition(p, r) quicksort(p, i-1) quicksort(i, r)\ndef mergeSort(p, r): if p < r: q = partition(p, r) mergeSort(p, q) mergeSort(q, r)\ndef printList(): print(\" \", end ="
+    },
+    {
+      "prompt": "{i : Node(None, None, None) for i in range(n)} # \u60c5\u5831\u3092\u5165\u308c\u308bdict\u3092Node\u30af\u30e9\u30b9\u3067\u4f5c\u6210 for _ in range(n): # \u5909\u6570\u540d\u3092\u300c _ \u300d\u306b\u3059\u308b\u3053\u3068\u306b\u3088\u3063\u3066\u3001\u300c\u305d\u306e\u5909\u6570\u3092\u4f7f\u3063\u3066\u3044\u306a\u3044\u300d\u3053\u3068\u3092\u8868\u73fe\u3057\u3066\u3044\u308b\uff08Python\u306e\u7fd2\u6163\uff09 tmp",
+      "output": "= input().split() # \u5165\u308c\u3089\u308c\u305f\u5024\u3092\u5909\u6570\u306b\u5165\u308c\u308b tmp[0] = int(tmp[0]) # \u5909\u6570\u306e\u5024\u3092int\u306b\u5909\u63db tmp[1] = int(tmp[1]) # \u5909\u6570\u306e\u5024\u3092int\u306b\u5909\u63db tmp[2] = int(tmp[2]) # \u5909\u6570\u306e\u5024\u3092int\u306b\u5909"
+    },
+    {
+      "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(self): print('Inorder') root_node = self.nodes[self.root_id] def _in_walk(node_id): if node_id != -1:",
+      "output": "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"
+    }
+  ]
+}
--- a/special_tokens_map.json
+++ b/special_tokens_map.json
@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}
--- a/tokenizer.json
+++ b/tokenizer.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:20d5d2f95f5631bc1160d02840c0f0c0630bc6381528c611c325ac59caf0fc66
+size 15524574
--- a/tokenizer.model
+++ b/tokenizer.model
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:37f00374dea48658ee8f5d0f21895b9bc55cb0103939607c8185bfd1c6ca1f89
+size 587404
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
@@ -0,0 +1,19 @@
+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "is_local": true,
+  "max_length": 1024,
+  "model_max_length": 131072,
+  "pad_to_multiple_of": null,
+  "pad_token": "<|endoftext|>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "stride": 0,
+  "tokenizer_class": "GPT2Tokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<|endoftext|>"
+}
--- a/training_curve.png
+++ b/training_curve.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:68430f098ead5235816b4ed0aa95a5130d6ad0f6d23fd1e37fc3bedf83861c1f
+size 61724
--- a/vocab.json
+++ b/vocab.json
				`@@ -0,0 +1 @@`
				`{% for message in messages %}{% if message['role'] == 'system' and 'tools' in message and message['tools'] is not none %}{{ '<\|' + message['role'] + '\|>' + message['content'] + '<\|tool\|>' + message['tools'] + '<\|/tool\|>' + '<\|end\|>' }}{% else %}{{ '<\|' + message['role'] + '\|>' + message['content'] + '<\|end\|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<\|assistant\|>' }}{% else %}{{ eos_token }}{% endif %}`