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Model: orbit-ai/orbit-4b-ablation-training-mix-124-v0.1
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-4B
+datasets:
+- PeterJinGo/nq_hotpotqa_train
+- orbit-ai/orbit-20k
+language:
+- en
+tags:
+- qwen3
+- coversational
+- verl
+- verl-tool
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+> [!NOTE]
+> For full information, go check out the ORBIT paper [here](https://arxiv.org/abs/2604.01195).
+> This is an ablation model: ORBIT-4B with a training mix of 1:2:4 of NQ:HotpotQA:ORBIT.
+> We advise the reader to use [orbit-ai/orbit-4b-v0.1](https://huggingface.co/orbit-ai/orbit-4b-v0.1) for their own usecases.
+
+<img src="https://huggingface.co/orbit-ai/orbit-4b-v0.1/resolve/main/orbit-with-name-logo.png" alt="Figure 1" width="500"/>
+
+## Orbit-4B (Abltation of Data Mixing)
+
+This is the RL-trained ablation model checkpoint of Orbit-4B, a small-sized (4B parameters) expert open search agent using the [Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) base model, fine-tuned with GRPO to use web search as a tool for multi-turn question answering. It was trained using the [verl-tool](https://github.com/TIGER-AI-Lab/verl-tool) framework with a live DDGS-based retriever.
+
+Training was conducted for **165 GRPO steps** on a mixed dataset of Natural Questions (NQ), HotpotQA, and our ORBIT dataset (orbit-ai/orbit) in a 1:2:4 ratio.
+
+---
+
+## Model Details
+
+| Property | Value |
+|---|---|
+| **Architecture** | Qwen3-4B |
+| **Base checkpoint** | `Qwen/Qwen3-4B` |
+| **Training algorithm** | GRPO |
+| **Training steps** | 165 |
+| **Training framework** | [verl-tool](https://github.com/TIGER-AI-Lab/verl-tool) |
+| **Hardware** | 4 × H100 SXM5 (80 GB HBM3), NVLink |
+| **Parallelism** | FSDP (param + optimizer offload) |
+| **Rollout mode** | Async (vLLM v1) |
+
+---
+
+## Training Dataset
+
+The model was trained on a mixed retrieval QA dataset with 1:2:4 sampling across three tasks:
+
+| Task | Source | Type |
+|---|---|---|
+| Natural Questions (NQ) | Open-domain QA | Single-hop factoid |
+| HotpotQA | Multi-hop QA | 2-hop reasoning |
+| ORBIT | Multi-hop QA | Difficult and multi-hop reasoning queries |
+
+**Dataset name:** `PeterJinGo/nq_hotpotqa_train`, `orbit-ai/orbit-20k`  
+**Train batch size:** 256 samples per step (n=8 rollouts per sample → 2048 trajectories/step)  
+
+---
+
+## Training Hyperparameters
+
+| Hyperparameter | Value |
+|---|---|
+| Batch size    | 256 |
+| Rollouts      | 8   |
+| Learning rate | `1e-6` |
+| LR warmup steps | 10 |
+| LR warmup ratio | 0.285 |
+| Optimizer | AdamW |
+| GRPO rollouts per sample (n) | 8 |
+| PPO mini-batch size | 32 |
+| PPO micro-batch size per GPU | 1 |
+| Temperature | 1.0 |
+| Top-p | 1.0 |
+| Top-k | disabled (−1) |
+| KL loss coefficient | 0.0 |
+| KL loss type | `low_var_kl` |
+| Entropy coefficient | 0.0 |
+| Max prompt length | 2048 tokens |
+| Max response length | 8192 tokens |
+| Max action length | 2048 tokens |
+| Max observation length | 1024 tokens |
+| Max turns | 5 |
+| Max concurrent trajectories | 32 |
+| GPU memory utilization (vLLM) | 0.6 |
+| vLLM max model length | 8192 |
+| Sequence parallelism | 1 (disabled) |
+| FSDP size | −1 (full sharding) |
+
+---
+
+## Tool Configuration
+
+The model was trained with live web search via a DDGS-based retrieval server:
+
+| Setting | Value |
+|---|---|
+| Retriever | DDGS (Dux Distributed Global Search) |
+| Search backends | `google`, `brave`, `bing`, `wikipedia`, `grokipedia` |
+| Top-k documents per query | 5 |
+| Backend strategy | **Parallel fan-out** — all backends queried simultaneously, results merged and deduplicated |
+| Per-backend HTTP timeout | 10 s |
+| Tool server workers | 4 |
+| Action stop tokens | `</search>`, `</answer>` |
+| Observations masked in loss | Yes (`mask_observations=True`) |
+
+The retriever server runs as a FastAPI service. At each agent turn the model issues a `<search> query </search>` action; the tool server retrieves results and returns them as `<information>…</information>` observations. The trajectory ends when the model emits `<answer> … </answer>` or the turn budget is exhausted.
+
+---
+
+## Training Infrastructure
+
+| Setting | Value |
+|---|---|
+| Nodes | 1 × H100 node (g-series) |
+| GPUs per node | 4 × H100 SXM5 80 GB |
+| CPUs per node | 48 (allocated) |
+| System memory | 256 GB |
+| Local scratch (SSD) | 200 GB (`$TMPDIR`, used for Triton/Ray caches) |
+| NCCL | NVLink P2P enabled (no `NCCL_P2P_DISABLE`) |
+| vLLM version | v1 (`VLLM_USE_V1=1`) |
+| Checkpoint frequency | Every 5 steps |
+
+---
+
+## Reward Model
+
+**Reward manager:** `search_r1_qa_em`  
+Reward is computed as exact-match (EM) between the model's `<answer>` and the reference target string. Multi-turn rollouts are scored at the final answer only; intermediate search turns receive no intermediate reward. Observations are masked from the KL and policy gradient loss.
+
+---
+
+## Usage
+
+This model is designed to be used with a running tool server that handles `<search>` actions. Inference without a live retriever will fall back to the model's parametric knowledge.
+
+### With verl-tool (recommended)
+
+```bash
+git clone https://github.com/TIGER-AI-Lab/verl-tool
+cd verl-tool
+uv sync
+source .venv/bin/activate
+
+# Start the DDGS retriever
+python ddgs_retrieval_optimized.py --port 8280 --topk 5 \
+    --backend "google,brave,bing,wikipedia,grokipedia"
+
+# Start the tool server
+python -m verl_tool.servers.serve \
+    --host 0.0.0.0 --port 30500 \
+    --tool_type search_retrieval \
+    --workers_per_tool 4
+```
+
+### Direct inference (parametric knowledge only)
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "orbit-ai/orbit-4b-ablation-training-mix-124-v0.1"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
+
+prompt = (
+    "Answer the given question. Please break down the question, using it to plan "
+    "a potential solution trajectory. You must conduct reasoning inside <think> and "
+    "</think> first, then you may use tools to gather information. "
+    "For search, use <search> query </search>. "
+    "Provide your final answer with <answer> answer </answer>.\n\n"
+    "Question: What percentage of blood is made up of plasma?"
+)
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=512, temperature=1.0, do_sample=True)
+print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
+```
+
+---
+
+## Intended Use & Limitations
+
+- **Intended use:** Research into multi-turn retrieval-augmented reasoning and RL-based tool-use training.
+- **Language:** English only.
+- **Search dependency:** Peak performance requires a live web search backend. Without search, the model still reasons using parametric knowledge but accuracy degrades on fine-grained entity questions (particularly InfoSeek).
+- **Not intended for production deployment** without additional safety filtering.
+
+---
+
+## Citation
+
+If you use this model or the training methodology, please cite:
+
+```
+@misc{thakur2026orbit,
+      title={ORBIT: Scalable and Verifiable Data Generation for Search Agents on a Tight Budget}, 
+      author={Nandan Thakur and Zijian Chen and Xueguang Ma and Jimmy Lin},
+      year={2026},
+      eprint={2604.01195},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2604.01195}, 
+}
+```
+
+---

added_tokens.json

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+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
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+  "<|fim_pad|>": 151662,
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+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

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+{%- 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 message.content is string 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.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.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' }}
+        {{- 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 %}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "max_position_embeddings": 40960,
+  "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_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.52.4",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.52.4"
+}

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