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Model: Divij/Qwen2.5-3B-Instruct-sft-without-thoughts Source: Original Platform
2026-06-12 18:18:16 +08:00
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--- a/README.md
+++ b/README.md
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+---
+base_model: Qwen/Qwen2.5-3B-Instruct
+library_name: transformers
+license: other
+pipeline_tag: text-generation
+tags:
+- sft
+- scientific-reasoning
+- instruction-tuning
+- open-instruct
+---
+
+# Divij/Qwen2.5-3B-Instruct-sft-without-thoughts
+
+Supervised fine-tune of [`Qwen/Qwen2.5-3B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) on a scientific-methodology
+instruction dataset, where each assistant response is a plain step-by-step research methodology.
+
+The project goal is to compare whether including explicit `<Thought_i>` reasoning
+traces alongside each `<Step_i>` action during SFT produces stronger scientific-methodology
+generators than training on step-only plans.
+
+## Variant
+
+This checkpoint is the **without-thoughts** variant:
+The assistant target is only `<Step_1>...</Step_1>` ... `<Step_n>...</Step_n>` — the reasoning traces are excluded. Trained with `max_seq_length=2048`.
+
+## Training data
+
+- Source: `sft_without_thoughts.jsonl` from the `verl_scientific_discovery`
+  repeated-sampling pipeline.
+- 4,990 `messages`-format examples (`system` + `user` + `assistant`).
+- Each assistant response is a step-by-step research methodology for a given
+  `Research Goal` + `Constraints` prompt.
+
+## Training setup
+
+- **Framework:** [open-instruct](https://github.com/allenai/open-instruct) `finetune.py` (accelerate + FSDP2).
+- **Hardware:** 2× NVIDIA H100 NVL (96 GB).
+- **Precision:** bf16 mixed precision.
+- **Attention:** FlashAttention-2.
+- **Memory:** gradient checkpointing enabled.
+
+### Hyperparameters
+
+| | |
+|---|---|
+| `max_seq_length` | **2048** |
+| `num_train_epochs` | 3 |
+| `per_device_train_batch_size` | 1 |
+| `gradient_accumulation_steps` | 8 |
+| Effective batch size | 16 (1 × 2 GPU × 8 accum) |
+| `learning_rate` | 2e-5 |
+| `lr_scheduler_type` | linear |
+| `warmup_ratio` | 0.03 |
+| `weight_decay` | 0.0 |
+| `seed` | 42 |
+| Optimizer | fused AdamW |
+| Total optimization steps | 936 |
+| **Final training loss** | **2.054** |
+
+The chat template is inherited from the base model
+(`Qwen/Qwen2.5-3B-Instruct`). Labels are masked on the `system` and
+`user` turns so only the assistant response contributes to the loss
+(open-instruct's `sft_tulu_tokenize_and_truncate_v1` transform).
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+repo = "Divij/Qwen2.5-3B-Instruct-sft-without-thoughts"
+tokenizer = AutoTokenizer.from_pretrained(repo)
+model = AutoModelForCausalLM.from_pretrained(
+    repo,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+messages = [
+    {"role": "system", "content": "Given a research goal and constraints, provide a step-by-step methodology.\n\nFormat:\n<Step_1>...</Step_1>\n<Step_2>...</Step_2>"},
+    {"role": "user", "content": (
+        "You are given a scientific research problem.\n\n"
+        "Research Goal:\n<your research goal here>\n\n"
+        "Constraints:\n1) <constraint 1>\n2) <constraint 2>"
+    )},
+]
+
+inputs = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt",
+).to(model.device)
+
+output = model.generate(
+    inputs,
+    max_new_tokens=1024,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9,
+)
+print(tokenizer.decode(output[0][inputs.shape[-1]:], skip_special_tokens=True))
+```
+
+## Notes
+
+- **Context length.** Use `max_seq_length` ≥ **2048** at inference time to match
+  the training regime; generations longer than this were not seen during training.
+- **Intended use.** Research artifact for generating structured scientific research
+  plans. Not aligned for general-purpose chat or safety-critical use.
+- **Compared to sibling.** A matching **with-thoughts** checkpoint at
+  [`Divij/Qwen2.5-3B-Instruct-sft-with-thoughts`](https://huggingface.co/Divij/Qwen2.5-3B-Instruct-sft-with-thoughts) is trained on
+  the same data but with the opposite treatment of reasoning traces.
--- a/chat_template.jinja
+++ b/chat_template.jinja
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+{%- 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
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+{
+  "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": 70,
+  "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": "5.5.3",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}
--- a/generation_config.json
+++ b/generation_config.json
@@ -0,0 +1,4 @@
+{
+  "eos_token_id": 151645,
+  "transformers_version": "5.5.3"
+}
--- a/model.safetensors
+++ b/model.safetensors
@@ -0,0 +1,3 @@
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--- a/tokenizer.json
+++ b/tokenizer.json
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+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": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}