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Model: tkeskin/qwen2.5-coder-1.5b-code-translation
Source: Original Platform

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

Modelfile

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+# ollama modelfile auto-generated by llamafactory
+
+FROM .
+
+TEMPLATE """{{ if .System }}<|im_start|>system
+{{ .System }}<|im_end|>
+{{ end }}{{ range .Messages }}{{ if eq .Role "user" }}<|im_start|>user
+{{ .Content }}<|im_end|>
+<|im_start|>assistant
+{{ else if eq .Role "assistant" }}{{ .Content }}<|im_end|>
+{{ end }}{{ end }}"""
+
+SYSTEM """You are Qwen, created by Alibaba Cloud. You are a helpful assistant."""
+
+PARAMETER stop "<|im_end|>"
+PARAMETER num_ctx 4096

README.md

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+---
+base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
+license: apache-2.0
+datasets:
+  - tkeskin/leetcode-solutions
+language:
+  - en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+  - lora
+  - llama-factory
+  - code
+  - code-translation
+  - qwen
+  - qwen2
+  - code
+---
+
+# qwen2.5-coder-1.5b-code-translation
+
+A fine-tuned version of [Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct) for translating code between **C++**, **Java**, and **Python**.
+
+## Training
+
+- **Base model:** Qwen/Qwen2.5-Coder-1.5B-Instruct
+- **Method:** LoRA (Low-Rank Adaptation) via [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)
+- **Dataset:** [tkeskin/leetcode-solutions](https://huggingface.co/datasets/tkeskin/leetcode-solutions) (`instruct` config) — directed C++/Java/Python translation pairs derived from LeetCode solutions
+- **Hardware:** AMD MI210 (ROCm) / NVIDIA CUDA, `flash_attn: sdpa`
+- **LoRA target:** all linear layers (`lora_target: all`)
+- **Precision:** bf16
+
+## Evaluation
+
+Evaluated with an **execution-based** translation benchmark: each held-out `evaluation`-config payload from [tkeskin/leetcode-solutions](https://huggingface.co/datasets/tkeskin/leetcode-solutions) is a directed source→target translation whose output is compiled and run against the problem's input/output pairs. The eval split is held out from training (no leakage). Metric is **pass@1** (all test cases pass), n-weighted over 3,336 payloads.
+
+| | Base (Qwen2.5-Coder-1.5B-Instruct) | This model | Δ |
+|---|---|---|---|
+| **pass@1** | 29.3% | **61.9%** | **+32.6** |
+| **compile rate** | 59.6% | **84.5%** | **+24.9** |
+
+pass@1 by language pair × difficulty (%):
+
+| source | target | difficulty | base | this model |
+|---|---|---|---|---|
+| cpp | java | Easy | 41.4 | 81.4 |
+| cpp | java | Hard | 12.7 | 47.5 |
+| cpp | java | Medium | 27.9 | 69.4 |
+| cpp | python | Easy | 40.7 | 76.7 |
+| cpp | python | Hard | 29.8 | 45.0 |
+| cpp | python | Medium | 38.6 | 66.6 |
+| java | cpp | Easy | 39.5 | 85.0 |
+| java | cpp | Hard | 32.8 | 47.1 |
+| java | cpp | Medium | 40.0 | 68.5 |
+| java | python | Easy | 18.6 | 78.5 |
+| java | python | Hard | 15.3 | 45.8 |
+| java | python | Medium | 22.7 | 66.6 |
+| python | cpp | Easy | 25.9 | 72.1 |
+| python | cpp | Hard | 14.3 | 22.7 |
+| python | cpp | Medium | 25.9 | 57.8 |
+| python | java | Easy | 44.1 | 62.8 |
+| python | java | Hard | 10.2 | 24.6 |
+| python | java | Medium | 28.7 | 54.7 |
+
+The base model also redefined the harness-provided `ListNode`/`TreeNode` helper types on ~6% of problems (a compile error); this fine-tune does so on **none**, having learned the dataset's convention. Full methodology is in the [llm-fine-tune](https://github.com/tkeskin/llm-fine-tune) repo (Stage 5).
+
+### Standard benchmarks and the specialization trade-off
+
+Translation specialization has a measurable cost on standard benchmarks (base → this model):
+
+- **Held-out perplexity** on the translation test set drops **1.29 → 1.07** — training fit the target distribution.
+- **General ability is preserved** — MMLU is flat (**0.512 → 0.514**); lm-eval reasoning tasks unchanged within noise.
+- **Code generation _from a natural-language spec_ regresses** — HumanEval pass@1 (Python) **75% → 53%**.
+
+That regression is mostly **output-format specialization, not lost ability**. Trained on LeetCode solutions — which wrap every answer in `class Solution { ... }` with camelCase methods — the model now answers HumanEval-style prompts in that same idiom, e.g. emitting `class Solution { bool hasCloseElements(...) }` instead of the requested free `has_close_elements(...)`. The logic is frequently correct but mismatches the benchmark's free-function contract (C++ class-wrapping rises from **0% in the base to 78%** here). The model improved at its trained format and regressed on unfamiliar ones, while keeping general knowledge intact.
+
+## Intended use
+
+Given source code in one of C++, Java, or Python, the model generates a translation into the target language, following the same logic and structure. The Qwen2.5-Coder base model includes code-specific pre-training across C, C++, Java, Python, and many other languages, giving it a stronger prior for code structure.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "tkeskin/qwen2.5-coder-1.5b-code-translation"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+messages = [
+    {
+        "role": "user",
+        "content": "Translate the following C++ code to Python:\n\nint add(int a, int b) { return a + b; }"
+    }
+]
+inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
+outputs = model.generate(inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))
+```

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "initializer_range": 0.02,
+  "intermediate_size": 8960,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.6.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.1,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.6.0"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:64c9bc629d82111783bde5fc07a635fcb5326b2c29e9a3dc8d6bb777935ffd47
+size 3087467144

tokenizer.json

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+oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
+size 11421892

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "is_local": false,
+  "local_files_only": false,
+  "model_max_length": 32768,
+  "pad_token": "<|endoftext|>",
+  "padding_side": "left",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}