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Model: pymlex/qwen3-4b-gsm8k Source: Original Platform
2026-06-16 06:59:17 +08:00
commit a0ce9f2c95
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--- a/.eval_results/gsm8k.yaml
+++ b/.eval_results/gsm8k.yaml
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+- dataset:
+    id: openai/gsm8k
+    task_id: gsm8k
+    config: main
+    split: test
+  value: 0.095527
+  date: "2026-05-09"
+  notes: "greedy, no-tools, local eval"
--- a/.gitattributes
+++ b/.gitattributes
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--- a/README.md
+++ b/README.md
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+---
+library_name: transformers
+license: gpl-3.0
+datasets:
+- openai/gsm8k
+language:
+- en
+metrics:
+- exact_match
+base_model:
+- MegaScience/Qwen3-4B-MegaScience
+pipeline_tag: text-generation
+---
+
+# Qwen3-4B-MegaScience GSM8K fine-tune
+
+## Overview
+
+`MegaScience/Qwen3-4B-MegaScience` is a 4B Qwen3 checkpoint. We fine-tuned it on GSM8K, a grade-school math dataset with calculation annotations. The model learns to keep the reasoning trace in `<think>` and the final result in `<answer>`. A sample training target looks like this:
+
+```text
+<think>
+She sells 16 - 3 - 4 = 9 eggs each day.
+She makes 9 * 2 = 18.
+</think>
+<answer>
+18
+</answer>
+````
+
+## Dataset
+
+The training data comes from the official `openai/gsm8k` `main` split. Each example contains a question and a worked solution. The final answer is taken from the `####` line and moved into the `<answer>` block during formatting. The split is `95/5` from the official train split:
+
+* Train: `7,099` samples
+* Validation: `374` samples
+* Test: `1,319` samples
+
+The maximum sequence length is `768`. The token-length distribution:
+
+![output_10_0](https://cdn-uploads.huggingface.co/production/uploads/6957bafe54c6b170be4df9cb/8Npjy6HLV7DdqV15X7PJ8.png)
+
+
+## Training
+
+Fine-tuning was performed with LoRA and supervised fine-tuning on a single RTX 5090.
+
+Training settings:
+
+* GPU: NVIDIA GeForce RTX 5090
+* VRAM: 31.36 GB
+* CPU: Ryzen 9 9950X
+* RAM: 62 GB
+
+Training configuration:
+
+* max sequence length: `768`
+* batch size: `4`
+* gradient accumulation: `8`
+* epochs: `1`
+* learning rate: `2e-4`
+* warmup steps: `20`
+* scheduler: cosine
+* optimiser: `adamw_torch`
+* LoRA rank: `16`
+* LoRA alpha: `32`
+* LoRA dropout: `0.05`
+
+## Loss and validation curves
+
+Training loss and validation loss move down during the run and then settle near a stable plateau:
+
+![download](https://cdn-uploads.huggingface.co/production/uploads/6957bafe54c6b170be4df9cb/IJ4BnJrXsDT-BAarODB8S.png)
+
+A logarithmic view:
+
+![download](https://cdn-uploads.huggingface.co/production/uploads/6957bafe54c6b170be4df9cb/w3n_k-CcHPvsfTqD1SeZR.png)
+
+Training doesn’t show any improvement in the accuracy metric, which demonstrates the inefficiency of training on task-solution pairs while trying to give the model common sense.
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/6957bafe54c6b170be4df9cb/XNDNiO7rxygvJg8KqA5UM.png)
+
+## Evaluation
+
+The final test evaluation is run with greedy decoding on the full GSM8K test split. Results:
+
+| | Loss | Perplexity |
+|---|---|---|
+| Validation | 0.3690 | 1.4463 |
+| Test | 0.3441 | 1.4107 |
+
+Metrics:
+
+* Validation exact match on 100 examples: `0.2100`
+* Test exact match: `0.0955`
+
+## Inference
+
+Use these two cells for inference.
+
+```python
+import re
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+base_model_id = "MegaScience/Qwen3-4B-MegaScience"
+adapter_id = "pymlex/qwen3-4b-gsm8k"
+
+tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+tokenizer.padding_side = "left"
+
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    device_map="auto",
+    torch_dtype=torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float16,
+    trust_remote_code=True,
+)
+
+model = PeftModel.from_pretrained(base_model, adapter_id)
+model.eval()
+```
+
+```python
+SYSTEM_PROMPT = (
+    "You solve grade-school math problems. "
+    "Put the reasoning in <think>...</think>. "
+    "Put only the final result as a single number in <answer>...</answer>."
+)
+
+def build_prompt(question):
+    messages = [
+        {"role": "system", "content": SYSTEM_PROMPT},
+        {"role": "user", "content": question.strip()},
+    ]
+    return tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True,
+    )
+
+def solve_question(model, tokenizer, question, max_new_tokens=512):
+    prompt = build_prompt(question)
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    prompt_len = inputs["input_ids"].shape[-1]
+
+    end_answer_id = tokenizer.convert_tokens_to_ids("</answer>")
+    eos_id = tokenizer.eos_token_id
+
+    with torch.inference_mode():
+        output = model.generate(
+            **inputs,
+            max_new_tokens=max_new_tokens,
+            do_sample=False,
+            eos_token_id=[eos_id, end_answer_id],
+            pad_token_id=tokenizer.pad_token_id,
+        )
+
+    new_tokens = output[0][prompt_len:]
+    text = tokenizer.decode(new_tokens, skip_special_tokens=False)
+
+    if "</answer>" in text:
+        text = text.split("</answer>")[0] + "</answer>"
+
+    return text.strip()
+
+sample_question = (
+    "Janet’s ducks lay 16 eggs per day. She eats three for breakfast every morning and "
+    "bakes muffins for her friends every day with four. She sells the remainder at the "
+    "farmers' market daily for $2 per fresh duck egg. How much in dollars does she make "
+    "every day at the farmers' market?"
+)
+
+print(solve_question(model, tokenizer, sample_question))
+```
--- a/chat_template.jinja
+++ b/chat_template.jinja
@@ -0,0 +1,85 @@
+{%- 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 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.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set content = message.content %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in message.content %}
+                {%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
+                {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').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' }}
+        {{- 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' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}
--- a/config.json
+++ b/config.json
@@ -0,0 +1,71 @@
+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": null,
+  "dtype": "bfloat16",
+  "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": 32768,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.8.0",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151671
+}
--- a/generation_config.json
+++ b/generation_config.json
@@ -0,0 +1,9 @@
+{
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "max_new_tokens": 2048,
+  "pad_token_id": 151643,
+  "transformers_version": "5.8.0"
+}
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--- a/tokenizer_config.json
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