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@@ -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 +1,288 @@
+---
+license: apache-2.0
+tags:
+- base-model
+- causal-lm
+- qwen3
+- transformer
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# Qwen3-1.7B (from-scratch, 41B-token pretrain)
+
+A 1.7B-parameter decoder-only transformer (Qwen3 family) pre-trained **from scratch** on ~**41B tokens** of multi-domain text with **BF16 mixed precision** and a **4,096-token** context. Checkpoints are provided in standard Hugging Face format for easy inference and fine-tuning.
+
+---
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** Qvac by Tether
+- **Model type:** Decoder-only Transformer (causal LM)
+- **Language(s) (NLP):** Primarily English 
+- **License:** Apache-2.0
+- **Finetuned from model:** **None (trained from scratch)**
+- **Intended stage:** **Base pre-trained model** (no SFT / RLHF alignment)
+
+### Model Sources
+
+- **Repository:** https://huggingface.co/qvac/genesisI-model
+- **Paper / Blog :** Coming Soon
+
+---
+
+## Uses
+
+### Direct Use
+
+- General language modeling: next-token prediction, continuation, summarization, drafting.
+- Research baseline for scaling, data ablations, or tokenizer studies.
+
+### Downstream Use (recommended)
+
+- **SFT** for assistants, domain experts, or task-specific models.
+- **Preference optimization / RLHF** for safer, more helpful behavior.
+- **Adapters/LoRA** for efficient domain specialization.
+
+### Out-of-Scope Use
+
+- High-stakes decision-making (medical/financial/legal).
+- Safety-critical or autonomous control systems.
+- Unfiltered end-user chat deployment without alignment / safety layers.
+- Any use that violates applicable laws or platform policies.
+
+---
+
+## Bias, Risks, and Limitations
+
+- **Bias & toxicity:** May reflect or amplify biases present in web text.
+- **Hallucinations:** Can produce confident but incorrect statements or citations.
+- **Security / privacy:** May emit continous random strings.
+- **Context limit:** 4,096 tokens; longer inputs require chunking.
+
+### Recommendations
+
+- Disclose limitations to downstream users.
+
+---
+
+## How to Get Started
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "qvac/genesisI-model"
+
+tok = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,   # trained with BF16 mixed precision
+    device_map="auto"
+)
+
+prompt = "Explain precision vs. recall in one paragraph."
+inputs = tok(prompt, return_tensors="pt").to(model.device)
+out = model.generate(
+    **inputs,
+    max_new_tokens=256,
+    do_sample=True,
+    top_p=0.9,
+    temperature=0.7
+)
+print(tok.decode(out[0], skip_special_tokens=True))
+````
+
+*Tip: On consumer GPUs, consider loading in `float16` or using 4/8-bit quantization (e.g., bitsandbytes/AutoGPTQ).*
+
+---
+
+## Training Details
+
+### Training Data
+
+* **Size:** ~**41B tokens**, single epoch.
+* **Domains:** Mixed general + STEM/technical sources (expository text, problem sets, references).
+* **Format:** Hugging Face Datasets (Arrow).
+* **Tokenizer:** **Qwen3** tokenizer.
+* **Processing:** Normalization, filtering of extremes, document chunking to fit **4096** context, sequence packing where applicable.
+* **Dataset Card:** *Coming Soon*
+
+### Training Procedure
+
+#### Preprocessing
+
+* Unicode normalization, whitespace cleanup, control-char stripping.
+* Length filtering; chunking to 4096; optional packing to improve throughput.
+
+#### Training Hyperparameters
+
+* **Optimizer:** AdamW (β₁=0.9, β₂=0.95), **weight decay 0.01**
+* **Learning rate:** **2e-4** (linear warmup)
+* **Warmup:** **600** steps (~10% of max steps)
+* **Precision:** **BF16 mixed precision**
+* **Gradient clipping:** **1.0**
+* **Seed:** **42**
+* **Logging:** Every **50** steps
+* **Eval:** Every **500** steps (20 iters)
+* **Checkpointing:** Every **1000** steps (sharded; full optimizer/state resume)
+
+#### Speeds, Sizes, Times
+
+* **Per-GPU micro-batch:** 4
+* **Grad accumulation:** 8
+* **World size:** 480 GPUs
+* **Effective global batch:** `4 × 8 × 480 = 15,360` samples/step
+* **Step time (indicative):** ~**1.5 s/step** (cluster/I-O dependent)
+
+#### Stability & Performance
+
+* Activation checkpointing.
+* Fused kernels where available (fused attention/optimizer).
+* **FlashAttention-2** on H100.
+* `torch.compile` (safe mode) after warmup stability.
+* Dynamic loss scaling to mitigate BF16 overflow.
+* Fragmentation mitigations (e.g., `max_split_size_mb=512`, expandable segments, GC threshold ~0.8).
+
+---
+
+## Multi-Node GPU Setup
+
+* **Cluster:** ~**60 nodes**, each **8× NVIDIA H100 80GB** (total **480 GPUs**), ~800 GB RAM/node.
+* **Scheduler:** Slurm (priority partition, exclusive allocation, 72-hour limit).
+* **Launch:** `srun` + PyTorch DDP (world size 480; ranks bound via Slurm env).
+* **Storage:** Sharded checkpoints; periodic saves for robust resume.
+* **Networking:** NCCL over InfiniBand with UCX
+
+  * `NCCL_IB_DISABLE=0`, `NCCL_IB_HCA="mlx5*"`, `NCCL_SOCKET_IFNAME=<ib0/enoX>`, `NCCL_BLOCKING_WAIT=1`
+  * Watchdog ~**720s** for fail-fast on fabric issues
+* **I/O:** Async dataset prefetching; pinned FS threads.
+* **Observability:** W&B + structured logs (throughput, TFLOPs/GPU, mem, step time).
+* **Reproducibility:** Fixed seeds; exact launch scripts/env logged; effective tokens/step reported.
+
+> Final checkpoint converted to **Hugging Face format** for plug-and-play inference.
+
+---
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+* **Testing data:** Standard academic suites (e.g., EleutherAI LM Evaluation Harness).
+* **Factors:** Domain/topic (STEM vs. general), task type (multi-choice vs. open-ended).
+* **Metrics:** Accuracy (MCQ), EM/F1 (QA), plus task-native metrics.
+
+**Suggested suite (edit as applicable):**
+
+* General knowledge & reasoning: **MMLU (STEM subsets)**, **ARC-E/ARC-C**, **HellaSwag**, **PIQA**, **Winogrande**
+* Math/coding (optional): **GSM8K**, **HumanEval**
+* Reading comprehension (optional): **BoolQ**, **RACE**
+
+### Results
+
+* *To be released with an evaluated checkpoint and harness version pin.*
+  Include tables with exact versions, seeds, and commit hashes.
+
+#### Summary
+
+* Base LM targets broad generalization at 41B tokens.
+* Expect material gains after SFT + preference optimization for target tasks.
+
+---
+
+## Technical Specifications
+
+### Model Architecture and Objective
+
+* **Architecture:** Qwen3-style decoder-only Transformer
+* **Parameters:** ~**1.7B**
+* **Context length:** **4,096** tokens
+* **Positional encoding:** *Rotary / relative (specify)*
+* **Attention:** Multi-head scaled dot-product; FlashAttention-2 enabled on H100
+* **Activation:** *GELU / SiLU (specify)*
+* **Norms:** *RMSNorm / LayerNorm (specify)*
+* **Objective:** **Causal LM** (next-token prediction)
+
+### Compute Infrastructure
+
+**Hardware**
+
+* 60 nodes × 8× H100 80GB, ~800 GB RAM/node, InfiniBand fabric.
+
+**Software**
+
+* PyTorch ≥ 2.1 (CUDA 12.x), FlashAttention-2, UCX/NCCL
+* Slurm for orchestration; W&B for logging
+* (Optional) DeepSpeed/Zero-3 for training; HF conversion post-train
+
+---
+
+## Reproducibility (Launch Sketch)
+
+```bash
+# Slurm (illustrative)
+srun -N 60 -n 480 --ntasks-per-node=8 --gpus-per-task=1 \
+  --cpus-per-task=8 --mem=0 \
+  bash -lc '
+  export NCCL_IB_DISABLE=0
+  export NCCL_IB_HCA="mlx5*"
+  export NCCL_SOCKET_IFNAME=ib0
+  export NCCL_BLOCKING_WAIT=1
+  export TORCH_DISTRIBUTED_DEBUG=DETAIL
+
+  python train.py \
+    --model qwen3_1p7b_from_scratch \
+    --tokenizer qwen3 \
+    --data_path /path/to/arrow \
+    --context_length 4096 \
+    --optimizer adamw --weight_decay 0.01 \
+    --lr 2e-4 --warmup_steps 600 \
+    --precision bf16-mixed \
+    --micro_batch_size 4 \
+    --grad_accum_steps 8 \
+    --eval_every 500 --log_every 50 \
+    --ckpt_every 1000 \
+    --activation_checkpointing \
+    --flash_attn 2 \
+    --compile safe \
+    --seed 42
+'
+```
+
+---
+
+## Conversion & Inference
+
+* Checkpoints are **HF-compatible**: load with `AutoModelForCausalLM`.
+* For memory-limited environments, prefer half-precision or 4/8-bit loading.
+* Distribute as `safetensors` for integrity.
+
+---
+
+## Citation
+
+If you use this model, please cite:
+
+**BibTeX**
+
+Xxxx
+
+**APA**
+
+xxxxxx
+
+---
+
+
+## Model Card Authors
+
+XXXYYYYZZZ
+
+---
+
+## Changelog
+
+* **v0.1 (YYYY-MM-DD):** Initial public release — 41B-token 1-epoch pretrain; HF conversion.

added_tokens.json

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config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "head_dim": 128,
+  "hidden_act": "silu",
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+  "initializer_range": 0.02,
+  "intermediate_size": 6144,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen3",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.3",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+  "eos_token_id": 151643,
+  "max_new_tokens": 2048,
+  "transformers_version": "4.51.3"
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model.safetensors

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model_info.json

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+{
+  "model_type": "Qwen3",
+  "base_model": "Qwen/Qwen3-1.7B-Base",
+  "parameters": 1720574976,
+  "conversion_date": "2025-11-14 14:08:51.702307",
+  "architecture": "Qwen3ForCausalLM"
+}

special_tokens_map.json

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tokenizer.json

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+  ],
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {{- messages[0].content + '\\n\\n' }}\n    {%- endif %}\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>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\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\" }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- if ns.multi_step_tool and message.role == \"user\" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n        {%- set ns.multi_step_tool = false %}\n        {%- set ns.last_query_index = index %}\n    {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {%- set content = message.content %}\n        {%- set reasoning_content = '' %}\n        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n            {%- set reasoning_content = message.reasoning_content %}\n        {%- else %}\n            {%- if '</think>' in message.content %}\n                {%- set content = message.content.split('</think>')[-1].lstrip('\\n') %}\n                {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n            {%- endif %}\n        {%- endif %}\n        {%- if loop.index0 > ns.last_query_index %}\n            {%- if loop.last or (not loop.last and reasoning_content) %}\n                {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n            {%- else %}\n                {{- '<|im_start|>' + message.role + '\\n' + content }}\n            {%- endif %}\n        {%- else %}\n            {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- endif %}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n    {%- if enable_thinking is defined and enable_thinking is false %}\n        {{- '<think>\\n\\n</think>\\n\\n' }}\n    {%- endif %}\n{%- endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 4096,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}