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Model: Harsha901/qwen2.5-coder-3b-distilled-from-14b-merged Source: Original Platform
2026-08-28 20:54:19 +08:00
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--- a/README.md
+++ b/README.md
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+---
+language:
+- en
+license: apache-2.0
+base_model: Qwen/Qwen2.5-Coder-3B-Instruct
+tags:
+- qwen2.5
+- code
+- knowledge-distillation
+- gkd
+- qlora
+- python
+- causal-lm
+datasets:
+- iamtarun/python_code_instructions_18k_alpaca
+pipeline_tag: text-generation
+---
+
+# Qwen2.5-Coder-3B — GKD Distilled from 14B
+
+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.
+
+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.
+
+---
+
+## Evaluation — HumanEval pass@1
+
+Greedy decoding, 164 Python programming tasks.
+
+| Model | Passed | Total | pass@1 |
+|---|---:|---:|---:|
+| Base `Qwen2.5-Coder-3B-Instruct` | 133 | 164 | 81.1% |
+| **This model (distilled, 300 steps)** | **137** | **164** | **83.5%** |
+| Δ | +4 | — | **+2.44 pp** |
+
+**Task-level breakdown:**
+
+| Outcome | Count |
+|---|---:|
+| Both pass | 126 |
+| Only distilled passes (gained) | 11 |
+| Only base passes (lost) | 7 |
+| Neither passes | 20 |
+| **Net change** | **+4** |
+
+**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.
+
+**Lost tasks (7):** HumanEval/10, /46, /99, /103, /141, /154, /159 — precise numeric/sequence edge cases (bankers rounding, fib4 base cases, cyclic rotation).
+
+> 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.
+
+---
+
+## Training Details
+
+### Setup
+
+| Component | Detail |
+|---|---|
+| **Teacher** | `Qwen/Qwen2.5-Coder-14B-Instruct` — frozen, loaded in 4-bit NF4 |
+| **Student** | `Qwen/Qwen2.5-Coder-3B-Instruct` + LoRA (r=16, α=32) |
+| **Distillation** | TRL `DistillationTrainer`, GKD with λ=0.25, β=0.5 |
+| **Dataset** | `iamtarun/python_code_instructions_18k_alpaca` — 17,681 train / 931 eval |
+| **Hardware** | Google Colab A100 80 GB |
+| **Wall time** | ~5 hours |
+
+### GKD Loss
+
+```
+L = λ · L_on_policy + (1−λ) · L_teacher_forced
+    where L = β · KL(student ∥ teacher) + (1−β) · KL(teacher ∥ student)
+```
+
+- **λ=0.25** — 25% on-policy (student-generated) sequences, 75% teacher-forced; keeps training stable at low step counts while reducing exposure bias.
+- **β=0.5** — symmetric Jensen–Shannon divergence between teacher and student logits.
+
+### Hyperparameters
+
+| Hyperparameter | Value |
+|---|---|
+| LoRA rank (r) | 16 |
+| LoRA alpha | 32 |
+| LoRA dropout | 0.05 |
+| Target modules | q/k/v/o_proj, gate/up/down_proj |
+| Optimizer | paged_adamw_8bit |
+| Learning rate | 1.5e-4 |
+| LR schedule | Cosine with 30-step warmup |
+| Max steps | 300 |
+| Per-device batch size | 16 |
+| Gradient accumulation | 2 (effective batch = 32) |
+| Max sequence length | 1024 |
+| Precision | bf16 |
+| Quantization (both models) | 4-bit NF4, double quant, bf16 compute |
+
+---
+
+## Usage
+
+This is a standard causal-LM checkpoint — load it exactly like the base model.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "Harsha901/qwen2.5-coder-3b-distilled-from-14b-merged"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+messages = [
+    {"role": "user", "content": "Write a Python function that checks if a number is prime."}
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    output = model.generate(
+        **inputs,
+        max_new_tokens=512,
+        do_sample=False,
+    )
+
+print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
+```
+
+### Memory requirements
+
+| Precision | Approx. VRAM |
+|---|---|
+| bf16 (this checkpoint) | ~6.5 GB |
+| 4-bit NF4 (bitsandbytes) | ~2.5 GB |
+
+The model runs comfortably on a 24 GB consumer GPU (RTX 3090/4090) in full bf16 precision.
+
+---
+
+## Limitations and Future Work
+
+- **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.
+- **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.
+- **Training dataset is Python-only** — generalisation to other languages is untested.
+- **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.
+
+Planned next steps:
+1. Train for 1,000+ steps and track HumanEval every 50 steps.
+2. Tune λ toward 0.5–0.75 for more on-policy exposure.
+3. Benchmark inference latency and VRAM vs. the 14B teacher to quantify serving cost reduction.
+
+---
+
+## Related Resources
+
+- **LoRA adapter (pre-merge):** [Harsha901/qwen2.5-coder-3b-distilled-from-14b](https://huggingface.co/Harsha901/qwen2.5-coder-3b-distilled-from-14b)
+- **Base student model:** [Qwen/Qwen2.5-Coder-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct)
+- **Teacher model:** [Qwen/Qwen2.5-Coder-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct)
+- **Training dataset:** [iamtarun/python_code_instructions_18k_alpaca](https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca)
+- **TRL DistillationTrainer docs:** [huggingface.co/docs/trl](https://huggingface.co/docs/trl)
--- a/chat_template.jinja
+++ b/chat_template.jinja
@@ -0,0 +1,54 @@
+{%- 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 %}
--- a/config.json
+++ b/config.json
@@ -0,0 +1,69 @@
+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "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": 32768,
+  "max_window_layers": 36,
+  "model_type": "qwen2",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 36,
+  "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": "5.0.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}
--- a/generation_config.json
+++ b/generation_config.json
@@ -0,0 +1,14 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    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"
+}
--- a/model.safetensors
+++ b/model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cd941c776f4840e8ea421e58cf1e088f495a35d4fce826857f109eea14343f5d
+size 6171927112
--- a/tokenizer.json
+++ b/tokenizer.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
+size 11421892
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
@@ -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
+}