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Model: harshpreet931/Qwen3-4B-C-Coder-SFT-v1
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-4B-Instruct-2507
+pipeline_tag: text-generation
+library_name: transformers
+language:
+  - en
+tags:
+  - code
+  - c
+  - qlora
+  - unsloth
+  - text-generation-inference
+---
+
+# Qwen3-4B-C-Coder-SFT-v1
+
+A C-language coding specialist built on **Qwen3-4B-Instruct-2507**, fine-tuned with QLoRA
+(rank 32, all linear layers) on ~23k curated C instruction pairs. This is **SFT v1** — the
+first stage of a larger pipeline (synthetic execution-filtered data and GRPO with a
+compiler/sanitizer reward are planned follow-ups).
+
+The headline improvement is **instruction compliance for C**: the model follows exact
+function signatures and emits compilable code far more reliably than its base.
+
+## Results
+
+All benchmarks are execution-based: generated C is compiled with gcc and run against hidden
+tests in a sandboxed container. `safe-pass@1` additionally requires zero ASan/UBSan reports.
+
+![CEval-priv: base vs SFT v1](images/ceval_priv.png)
+
+| Benchmark | Metric | Base Qwen3-4B | **This model** | Δ |
+|---|---|---|---|---|
+| CEval-priv (161 tasks) | pass@1 | 36.6% | **59.0%** | **+22.4** |
+| CEval-priv | compile rate | 41% | **80%** | +39 |
+| McEval-C (50 tasks) | pass@1 | 52.0% | 48.0% | −4 (within ±14 pt CI) |
+| McEval-C | compile rate | 72% | **90%** | +18 |
+
+- **CEval-priv** is a private, contamination-proof eval set: 161 tasks machine-translated from
+  HumanEval+/MBPP+ to C with deterministic type mapping, kept only if the reference solution
+  compiles, passes its own tests, and runs sanitizer-clean. It was never trained on and all
+  training data was decontaminated against it (10-gram overlap).
+- **McEval-C** is the public McEval C-generation split. The pass@1 delta is within noise at
+  n=50, single-sample; the compile-rate gain is the real signal.
+- Sanitizer-clean pass rates equalled pass@1 for both models on both benchmarks.
+
+**Interpretation:** one epoch of public C data (StackOverflow Q&A, curated instruction sets)
+teaches *behavior* — signature compliance, compilable output — not new algorithmic ability.
+Exactly what you'd expect, and what the later pipeline stages are for.
+
+## Training
+
+| | |
+|---|---|
+| Base | Qwen/Qwen3-4B-Instruct-2507 (non-thinking) |
+| Method | QLoRA via Unsloth: r=32, α=64, dropout 0.05, all linear layers (66M trainable, 1.62%) |
+| Data | 22,913 train / 467 valid; max seq 2048; loss on assistant tokens only |
+| Schedule | 2,400 steps (≈0.84 epoch), effective batch 8, cosine LR 1e-4, warmup 100 |
+| Hardware | Single Kaggle T4 (fp16), 8h33m, total compute cost $0 |
+| Dynamics | train loss 1.78 → 0.97; eval loss 1.094 → 1.067, improving monotonically (no overfit) |
+
+### Data
+
+~34.7k cleaned pairs, sampled to 22.9k train after mixing:
+
+| Slice | Count | Source / license |
+|---|---|---|
+| StackOverflow C Q&A | ~15k (capped) | [Mxode/StackOverflow-QA-C-Language-40k](https://huggingface.co/datasets/Mxode/StackOverflow-QA-C-Language-40k) — **CC BY-SA**; content © original Stack Overflow contributors, attribution per SO terms |
+| Glaive code assistant v3 (C, syntax-verified) | ~3.9k | [glaiveai/glaive-code-assistant-v3](https://huggingface.co/datasets/glaiveai/glaive-code-assistant-v3) (Apache 2.0) — kept only samples whose code parses as C |
+| McEval-Instruct (C) | ~1k | [Multilingual-Multimodal-NLP/McEval-Instruct](https://huggingface.co/datasets/Multilingual-Multimodal-NLP/McEval-Instruct) |
+| General replay (anti-forgetting) | 15% | [allenai/tulu-3-sft-mixture](https://huggingface.co/datasets/allenai/tulu-3-sft-mixture) (ODC-BY) |
+
+All slices were exact-deduplicated and decontaminated (word-level 10-gram overlap) against
+McEval, MdEval, HumanEval(+), MBPP(+), and the private eval set.
+
+## Usage
+
+Non-thinking model — use Qwen's recommended sampling: `temperature=0.7, top_p=0.8, top_k=20`.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "harshpreet931/Qwen3-4B-C-Coder-SFT-v1"
+tok = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
+
+messages = [{"role": "user", "content":
+    "Write a C function `int popcount32(unsigned int x)` that counts set bits."}]
+inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
+out = model.generate(inputs, max_new_tokens=512, temperature=0.7, top_p=0.8, top_k=20)
+print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))
+```
+
+Apple Silicon (4-bit MLX): see
+[harshpreet931/Qwen3-4B-C-Coder-SFT-v1-mlx-4bit](https://huggingface.co/harshpreet931/Qwen3-4B-C-Coder-SFT-v1-mlx-4bit).
+LoRA adapters only: [harshpreet931/Qwen3-4B-C-Coder-SFT-v1-LoRA](https://huggingface.co/harshpreet931/Qwen3-4B-C-Coder-SFT-v1-LoRA).
+
+## Limitations
+
+- **Algorithmic ability is unchanged from the base model.** This stage improved format/signature
+  compliance and compile rates, not problem-solving. Don't expect gains on hard competitive tasks.
+- English-only instruction data; C17/glibc-flavored; not tuned for embedded/kernel dialects.
+- Inherits base-model limitations and possible biases; generated code should be reviewed and
+  tested — compile-and-run verification (ideally with `-fsanitize=address,undefined`) is cheap, use it.
+
+## Provenance
+
+Built as part of an open, $0-compute project (MacBook M4 Pro + Kaggle free T4s): six-agent
+research sweep → sandboxed compile/run/sanitizer harness → data pipeline → this SFT run.
+Fun fact surfaced by the harness: one of McEval-C's own canonical solutions fails
+LeakSanitizer.

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# 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' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- 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": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": null,
+  "torch_dtype": "float16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "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",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 262144,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151669,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 5000000,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "unsloth_fixed": true,
+  "unsloth_version": "2026.6.9",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936,
+  "rope_scaling": null,
+  "rope_theta": 5000000,
+  "transformers_version": "4.51.0"
+}

generation_config.json

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+{
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "max_length": 262144,
+  "pad_token_id": 151669,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.0.0"
+}

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tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d7430e9138b76e93fb6f93462394d236b411111aef53cb421ba97d2691040cca
+size 11423114

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|>",
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+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "is_local": false,
+  "model_max_length": 262144,
+  "model_specific_special_tokens": {},
+  "pad_token": "<|PAD_TOKEN|>",
+  "padding_side": "left",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
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+      "normalized": false,
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+      "special": false
+    },
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+      "single_word": false,
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+    "151668": {
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+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {{- messages[0].content + '\\n\\n' }}\n    {%- endif %}\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>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\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\" }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n        {%- set ns.multi_step_tool = false %}\n        {%- set ns.last_query_index = index %}\n    {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n    {%- if message.content is string %}\n        {%- set content = message.content %}\n    {%- else %}\n        {%- set content = '' %}\n    {%- endif %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n        {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {%- set reasoning_content = '' %}\n        {%- if message.reasoning_content is string %}\n            {%- set reasoning_content = message.reasoning_content %}\n        {%- else %}\n            {%- if '</think>' in content %}\n                {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n                {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n            {%- endif %}\n        {%- endif %}\n        {%- if loop.index0 > ns.last_query_index %}\n            {%- if loop.last or (not loop.last and reasoning_content) %}\n                {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n            {%- else %}\n                {{- '<|im_start|>' + message.role + '\\n' + content }}\n            {%- endif %}\n        {%- else %}\n            {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- endif %}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}"
+}