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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.

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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 %}

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{
"architectures": [
"Qwen3ForCausalLM"
],
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"attention_dropout": 0.0,
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"max_position_embeddings": 262144,
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"model_type": "qwen3",
"num_attention_heads": 32,
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"rope_type": "default"
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"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"
}

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{
"do_sample": true,
"eos_token_id": [
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151643
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"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 Normal file
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tokenizer_config.json Normal file
View File

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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|>",
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"<|vision_start|>",
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"<|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,
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"added_tokens_decoder": {
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"content": "<|endoftext|>",
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},
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},
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},
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},
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},
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"special": true
},
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},
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},
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},
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"content": "<tool_call>",
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"special": false
},
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},
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"content": "<|repo_name|>",
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"special": true
}
},
"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 %}"
}