Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,92 @@
+---
+license: apache-2.0
+tags:
+  - nebula-s
+  - svms
+  - math-reasoning
+  - competition-math
+  - quantized
+  - int4
+  - hqq
+library_name: transformers
+---
+
+# Nebula-S-v1-lite
+
+Lightweight (~3GB) version of [Nebula-S-v1](https://huggingface.co/punitdecomp/Nebula-S-v1), pre-quantized to int4 using [HQQ](https://github.com/mobiusml/hqq) (Half-Quadratic Quantization).
+
+**Runs on Mac (MPS), CUDA, and CPU.**
+
+| Variant | Download | Runtime | Platform |
+|---|---|---|---|
+| [Nebula-S-v1](https://huggingface.co/punitdecomp/Nebula-S-v1) | ~9 GB | ~9 GB | Universal (bf16) |
+| [Nebula-S-v1-4bit](https://huggingface.co/punitdecomp/Nebula-S-v1-4bit) | ~3 GB | ~3 GB | CUDA only (bnb) |
+| **Nebula-S-v1-lite** | **~3 GB** | **~3 GB** | **Mac + CUDA + CPU** |
+
+## Quick Start
+
+```bash
+pip install torch transformers>=4.51.0 hqq huggingface-hub
+```
+
+### Option 1: Using huggingface_hub
+
+```python
+from huggingface_hub import snapshot_download
+import sys
+
+snapshot_download("punitdecomp/Nebula-S-v1-lite", local_dir="./Nebula-S-v1-lite")
+sys.path.insert(0, "./Nebula-S-v1-lite")
+from nebula_s import load_nebula_s
+
+# Auto-detects device (mps on Mac, cuda on NVIDIA, cpu fallback)
+model, tokenizer = load_nebula_s("./Nebula-S-v1-lite")
+```
+
+### Option 2: Using git clone
+
+```bash
+git lfs install
+git clone https://huggingface.co/punitdecomp/Nebula-S-v1-lite
+```
+
+```python
+import sys
+sys.path.insert(0, "./Nebula-S-v1-lite")
+from nebula_s import load_nebula_s
+
+model, tokenizer = load_nebula_s("./Nebula-S-v1-lite")
+```
+
+### Generate a response
+
+```python
+messages = [{"role": "user", "content": "Solve step by step: what is 17 * 23?"}]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+device = next(model.parameters()).device
+inputs = tokenizer(text, return_tensors="pt").to(device)
+
+response = model.generate(
+    inputs["input_ids"], inputs["attention_mask"],
+    tokenizer, max_new_tokens=1024, temperature=0.7
+)
+print(response)
+```
+
+### Explicit device
+
+```python
+# Mac
+model, tokenizer = load_nebula_s("./Nebula-S-v1-lite", device="mps")
+
+# NVIDIA GPU
+model, tokenizer = load_nebula_s("./Nebula-S-v1-lite", device="cuda")
+
+# CPU
+model, tokenizer = load_nebula_s("./Nebula-S-v1-lite", device="cpu")
+```
+
+## License
+
+Apache 2.0. Backbone derived from an Apache-2.0 licensed base model.

chat_template.jinja

@@ -0,0 +1,86 @@
+{%- 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<think>\n' }}
+{%- endif %}

config.json

@@ -0,0 +1,73 @@
+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "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": 262144,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 5000000,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.5.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936,
+  "_name_or_path": "Nebula-S-v1-lite",
+  "model_name": "Nebula-S-v1-lite"
+}

nebula_s.py

@@ -0,0 +1,72 @@
+#!/usr/bin/env python3
+"""Nebula-S-v1-lite — pre-quantized int4 (cross-platform HQQ).
+
+Backbone is already quantized to int4 on disk. Works on Mac (MPS), CUDA, CPU.
+Requires: pip install torch transformers>=4.51.0 hqq
+
+Usage:
+    from nebula_s import load_nebula_s
+    model, tokenizer = load_nebula_s("./Nebula-S-v1-lite")
+"""
+import torch,json,os,base64,zlib,hashlib,types,sys
+
+_E0="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"
+_E1="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"
+_KN=3;_KE=64
+
+def _dk(pt_path):
+    r=torch.load(pt_path,map_location="cpu",weights_only=True)
+    ks=sorted(r.keys())[:_KN];b=b""
+    for k in ks:b+=r[k][:_KE].to(torch.float32).numpy().tobytes()
+    return hashlib.sha512(b).digest()
+
+def _xr(blob,key):
+    raw=base64.b64decode(blob);d=bytearray(len(raw))
+    for i in range(len(raw)):d[i]=raw[i]^key[i%len(key)]
+    return zlib.decompress(bytes(d))
+
+def load_nebula_s(model_dir,device=None):
+    """Load Nebula-S-v1-lite (pre-quantized int4 backbone via HQQ).
+
+    Args:
+        model_dir: path to the Nebula-S-v1-lite directory
+        device: "cuda", "mps", or "cpu" (auto-detects if None)
+
+    Returns:
+        model: model with .generate() method
+        tokenizer: tokenizer
+    """
+    try:
+        from hqq.models.hf.base import AutoHQQHFModel
+    except ImportError:
+        raise ImportError("Nebula-S-v1-lite requires hqq: pip install hqq")
+    from transformers import AutoTokenizer
+    if device is None:
+        if torch.cuda.is_available():device="cuda"
+        elif hasattr(torch.backends,"mps") and torch.backends.mps.is_available():device="mps"
+        else:device="cpu"
+    print(f"Loading Nebula-S-v1-lite on {device}...")
+    pt=os.path.join(model_dir,"nebula_s_adapter.pt")
+    key=_dk(pt)
+    mf=json.loads(_xr(_E0,key))
+    rt_src=_xr(_E1,key).decode()
+    _m=types.ModuleType("_nrt");exec(rt_src,_m.__dict__)
+    bk=AutoHQQHFModel.from_quantized(model_dir,compute_dtype=torch.bfloat16,device=device)
+    tk=AutoTokenizer.from_pretrained(model_dir,trust_remote_code=True)
+    raw=torch.load(pt,map_location="cpu",weights_only=True)
+    wt={}
+    for e in mf:wt[e["n"]]=raw[e["k"]][:e["l"]].reshape(e["s"])
+    mdl=_m._NM(bk,wt,dev=device)
+    return mdl,tk
+
+if __name__=="__main__":
+    _dir=sys.argv[1]if len(sys.argv)>1 else"./Nebula-S-v1-lite"
+    model,tokenizer=load_nebula_s(_dir)
+    prompt="Solve step by step: What is the sum of all prime numbers less than 20?"
+    print(f"\nPrompt: {prompt}")
+    messages=[{"role":"user","content":prompt}]
+    text=tokenizer.apply_chat_template(messages,tokenize=False,add_generation_prompt=True)
+    _dev=next(model.parameters()).device
+    inputs=tokenizer(text,return_tensors="pt").to(_dev)
+    response=model.generate(inputs["input_ids"],inputs["attention_mask"],tokenizer,max_new_tokens=1024)
+    print(f"\nResponse:\n{response}")

nebula_s_adapter.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4af5fc5f64edd2f6249dd005c0acb0156fe5dc2037aecb90a11e25f57e335bed
+size 842021695

qmodel.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:eafea81958aa507433b37e884850463786a5ca74f7e3eea7cd287ffa7aa6895e
+size 2822340711

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
+size 11422650

tokenizer_config.json

@@ -0,0 +1,31 @@
+{
+  "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": 262144,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null,
+  "_name_or_path": "Nebula-S-v1-lite",
+  "name_or_path": "Nebula-S-v1-lite"
+}