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Model: Harsha901/qwen2.5-coder-3b-distilled-from-14b-merged Source: Original Platform
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167
README.md
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README.md
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---
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language:
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- en
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license: apache-2.0
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base_model: Qwen/Qwen2.5-Coder-3B-Instruct
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tags:
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- qwen2.5
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- code
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- knowledge-distillation
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- gkd
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- qlora
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- python
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- causal-lm
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datasets:
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- iamtarun/python_code_instructions_18k_alpaca
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pipeline_tag: text-generation
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---
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# Qwen2.5-Coder-3B — GKD Distilled from 14B
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A **merged** (LoRA-free) version of `Qwen2.5-Coder-3B-Instruct` whose weights have been updated via **Generalized Knowledge Distillation (GKD)** from `Qwen2.5-Coder-14B-Instruct` as the teacher.
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The LoRA adapter was trained with TRL's `DistillationTrainer` on a single NVIDIA A100 80 GB (Google Colab) for ~5 hours, then merged back into the base weights so the model loads exactly like the original 3B — no adapter plumbing required.
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---
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## Evaluation — HumanEval pass@1
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Greedy decoding, 164 Python programming tasks.
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| Model | Passed | Total | pass@1 |
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|---|---:|---:|---:|
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| Base `Qwen2.5-Coder-3B-Instruct` | 133 | 164 | 81.1% |
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| **This model (distilled, 300 steps)** | **137** | **164** | **83.5%** |
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| Δ | +4 | — | **+2.44 pp** |
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**Task-level breakdown:**
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| Outcome | Count |
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|---|---:|
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| Both pass | 126 |
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| Only distilled passes (gained) | 11 |
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| Only base passes (lost) | 7 |
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| Neither passes | 20 |
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| **Net change** | **+4** |
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**Gained tasks (11):** HumanEval/26, /64, /75, /89, /93, /95, /110, /123, /124, /125, /135 — reasoning-heavy string manipulation, cipher encoding, and date parsing problems.
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**Lost tasks (7):** HumanEval/10, /46, /99, /103, /141, /154, /159 — precise numeric/sequence edge cases (bankers rounding, fib4 base cases, cyclic rotation).
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> Note: HumanEval pass@1 with greedy decoding has ~±2pp noise. The +2.44pp delta is at the edge of statistical significance for a 300-step run; longer training is expected to widen the gap.
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---
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## Training Details
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### Setup
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| Component | Detail |
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|---|---|
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| **Teacher** | `Qwen/Qwen2.5-Coder-14B-Instruct` — frozen, loaded in 4-bit NF4 |
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| **Student** | `Qwen/Qwen2.5-Coder-3B-Instruct` + LoRA (r=16, α=32) |
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| **Distillation** | TRL `DistillationTrainer`, GKD with λ=0.25, β=0.5 |
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| **Dataset** | `iamtarun/python_code_instructions_18k_alpaca` — 17,681 train / 931 eval |
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| **Hardware** | Google Colab A100 80 GB |
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| **Wall time** | ~5 hours |
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### GKD Loss
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```
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L = λ · L_on_policy + (1−λ) · L_teacher_forced
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where L = β · KL(student ∥ teacher) + (1−β) · KL(teacher ∥ student)
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```
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- **λ=0.25** — 25% on-policy (student-generated) sequences, 75% teacher-forced; keeps training stable at low step counts while reducing exposure bias.
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- **β=0.5** — symmetric Jensen–Shannon divergence between teacher and student logits.
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### Hyperparameters
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| Hyperparameter | Value |
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|---|---|
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| LoRA rank (r) | 16 |
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| LoRA alpha | 32 |
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| LoRA dropout | 0.05 |
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| Target modules | q/k/v/o_proj, gate/up/down_proj |
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| Optimizer | paged_adamw_8bit |
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| Learning rate | 1.5e-4 |
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| LR schedule | Cosine with 30-step warmup |
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| Max steps | 300 |
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| Per-device batch size | 16 |
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| Gradient accumulation | 2 (effective batch = 32) |
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| Max sequence length | 1024 |
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| Precision | bf16 |
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| Quantization (both models) | 4-bit NF4, double quant, bf16 compute |
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---
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## Usage
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This is a standard causal-LM checkpoint — load it exactly like the base model.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "Harsha901/qwen2.5-coder-3b-distilled-from-14b-merged"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [
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{"role": "user", "content": "Write a Python function that checks if a number is prime."}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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)
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inputs = tokenizer([text], return_tensors="pt").to(model.device)
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with torch.no_grad():
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output = model.generate(
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**inputs,
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max_new_tokens=512,
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do_sample=False,
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)
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print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
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```
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### Memory requirements
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| Precision | Approx. VRAM |
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|---|---|
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| bf16 (this checkpoint) | ~6.5 GB |
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| 4-bit NF4 (bitsandbytes) | ~2.5 GB |
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The model runs comfortably on a 24 GB consumer GPU (RTX 3090/4090) in full bf16 precision.
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---
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## Limitations and Future Work
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- **Only 300 steps trained** — the model has seen ~9,600 examples (~0.54 epochs of the training set). Longer training (1,000–2,000 steps or 3 full epochs) is expected to improve further.
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- **Regressions on arithmetic edge cases** — the 7 lost tasks suggest slight distribution shift away from precise numeric corner cases; increasing LoRA rank or adding targeted examples may help.
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- **Training dataset is Python-only** — generalisation to other languages is untested.
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- **Distillation dataset differs from eval benchmark** — HumanEval is held-out; the training set is `python_code_instructions_18k_alpaca`, which covers general Python instruction-following rather than competitive algorithmic problems.
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Planned next steps:
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1. Train for 1,000+ steps and track HumanEval every 50 steps.
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2. Tune λ toward 0.5–0.75 for more on-policy exposure.
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3. Benchmark inference latency and VRAM vs. the 14B teacher to quantify serving cost reduction.
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---
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## Related Resources
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- **LoRA adapter (pre-merge):** [Harsha901/qwen2.5-coder-3b-distilled-from-14b](https://huggingface.co/Harsha901/qwen2.5-coder-3b-distilled-from-14b)
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- **Base student model:** [Qwen/Qwen2.5-Coder-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct)
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- **Teacher model:** [Qwen/Qwen2.5-Coder-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct)
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- **Training dataset:** [iamtarun/python_code_instructions_18k_alpaca](https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca)
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- **TRL DistillationTrainer docs:** [huggingface.co/docs/trl](https://huggingface.co/docs/trl)
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54
chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\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>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\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" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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config.json
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"layer_types": [
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"full_attention",
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"full_attention",
|
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 32768,
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"max_window_layers": 36,
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"model_type": "qwen2",
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"num_attention_heads": 16,
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"num_hidden_layers": 36,
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"num_key_value_heads": 2,
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"pad_token_id": null,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000000.0,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "5.0.0",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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14
generation_config.json
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14
generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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|
151645,
|
||||||
|
151643
|
||||||
|
],
|
||||||
|
"pad_token_id": 151643,
|
||||||
|
"repetition_penalty": 1.05,
|
||||||
|
"temperature": 0.7,
|
||||||
|
"top_k": 20,
|
||||||
|
"top_p": 0.8,
|
||||||
|
"transformers_version": "5.0.0"
|
||||||
|
}
|
||||||
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:cd941c776f4840e8ea421e58cf1e088f495a35d4fce826857f109eea14343f5d
|
||||||
|
size 6171927112
|
||||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
|
||||||
|
size 11421892
|
||||||
29
tokenizer_config.json
Normal file
29
tokenizer_config.json
Normal file
@@ -0,0 +1,29 @@
|
|||||||
|
{
|
||||||
|
"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,
|
||||||
|
"model_max_length": 32768,
|
||||||
|
"pad_token": "<|endoftext|>",
|
||||||
|
"split_special_tokens": false,
|
||||||
|
"tokenizer_class": "Qwen2Tokenizer",
|
||||||
|
"unk_token": null
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user