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Model: Divij/Llama-3.2-3B-Instruct-sft-with-thoughts
Source: Original Platform

.gitattributes

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README.md

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+---
+base_model: meta-llama/Llama-3.2-3B-Instruct
+library_name: transformers
+license: llama3.2
+pipeline_tag: text-generation
+tags:
+- sft
+- scientific-reasoning
+- instruction-tuning
+- open-instruct
+---
+
+# Divij/Llama-3.2-3B-Instruct-sft-with-thoughts
+
+Supervised fine-tune of [`meta-llama/Llama-3.2-3B-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on a scientific-methodology
+instruction dataset, where each assistant response interleaves `<Thought_i>` reasoning with `<Step_i>` actions.
+
+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 **with-thoughts** variant:
+The assistant target alternates `<Thought_i>` / `<Step_i>` pairs, so the model learns to produce explicit reasoning before each action. Trained with `max_seq_length=6144` to fit the longer sequences.
+
+## Training data
+
+- Source: `sft_with_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` | **6144** |
+| `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** | **0.839** |
+
+The chat template is inherited from the base model
+(`meta-llama/Llama-3.2-3B-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/Llama-3.2-3B-Instruct-sft-with-thoughts"
+tokenizer = AutoTokenizer.from_pretrained(repo)
+model = AutoModelForCausalLM.from_pretrained(
+    repo,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+messages = [
+    {"role": "system", "content": "You are an expert research scientist. Produce reasoning/action pairs: <Thought_i> followed by <Step_i>."},
+    {"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` ≥ **6144** 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 **without-thoughts** checkpoint at
+  [`Divij/Llama-3.2-3B-Instruct-sft-without-thoughts`](https://huggingface.co/Divij/Llama-3.2-3B-Instruct-sft-without-thoughts) is trained on
+  the same data but with the opposite treatment of reasoning traces.

chat_template.jinja

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+{{- bos_token }}
+{%- if custom_tools is defined %}
+    {%- set tools = custom_tools %}
+{%- endif %}
+{%- if not tools_in_user_message is defined %}
+    {%- set tools_in_user_message = true %}
+{%- endif %}
+{%- if not date_string is defined %}
+    {%- if strftime_now is defined %}
+        {%- set date_string = strftime_now("%d %b %Y") %}
+    {%- else %}
+        {%- set date_string = "26 Jul 2024" %}
+    {%- endif %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+
+{#- This block extracts the system message, so we can slot it into the right place. #}
+{%- if messages[0]['role'] == 'system' %}
+    {%- set system_message = messages[0]['content']|trim %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+{%- endif %}
+
+{#- System message #}
+{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
+{%- if tools is not none %}
+    {{- "Environment: ipython\n" }}
+{%- endif %}
+{{- "Cutting Knowledge Date: December 2023\n" }}
+{{- "Today Date: " + date_string + "\n\n" }}
+{%- if tools is not none and not tools_in_user_message %}
+    {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+{%- endif %}
+{{- system_message }}
+{{- "<|eot_id|>" }}
+
+{#- Custom tools are passed in a user message with some extra guidance #}
+{%- if tools_in_user_message and not tools is none %}
+    {#- Extract the first user message so we can plug it in here #}
+    {%- if messages | length != 0 %}
+        {%- set first_user_message = messages[0]['content']|trim %}
+        {%- set messages = messages[1:] %}
+    {%- else %}
+        {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
+{%- endif %}
+    {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
+    {{- "Given the following functions, please respond with a JSON for a function call " }}
+    {{- "with its proper arguments that best answers the given prompt.\n\n" }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+    {{- first_user_message + "<|eot_id|>"}}
+{%- endif %}
+
+{%- for message in messages %}
+    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
+        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
+    {%- elif 'tool_calls' in message %}
+        {%- if not message.tool_calls|length == 1 %}
+            {{- raise_exception("This model only supports single tool-calls at once!") }}
+        {%- endif %}
+        {%- set tool_call = message.tool_calls[0].function %}
+        {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+        {{- '{"name": "' + tool_call.name + '", ' }}
+        {{- '"parameters": ' }}
+        {{- tool_call.arguments | tojson }}
+        {{- "}" }}
+        {{- "<|eot_id|>" }}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+        {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
+        {%- if message.content is mapping or message.content is iterable %}
+            {{- message.content | tojson }}
+        {%- else %}
+            {{- message.content }}
+        {%- endif %}
+        {{- "<|eot_id|>" }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "bfloat16",
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 24,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pad_token_id": null,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_theta": 500000.0,
+    "rope_type": "llama3"
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.5.3",
+  "use_cache": true,
+  "vocab_size": 128264
+}

generation_config.json

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+{
+  "bos_token_id": 128000,
+  "eos_token_id": 128009,
+  "transformers_version": "5.5.3"
+}

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+size 7213632392

tokenizer.json

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tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "bos_token": "<|begin_of_text|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "is_local": false,
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "pad_token": "<pad>",
+  "tokenizer_class": "TokenizersBackend"
+}