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Model: mkd-hossain/keural-14.8b-stage2-vllm Source: Original Platform
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README.md
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---
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language:
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- ko
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- en
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license: apache-2.0
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tags:
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- mixtral
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- moe
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- korean
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- english
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- bilingual
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- pretrained
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base_model: mkd-hossain/keural-14.8b-stage2-vllm
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library_name: transformers
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pipeline_tag: text-generation
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---
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# Keural 14.8B — Stage 2 (vLLM Ready)
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Keural is a **14.83B parameter bilingual Korean-English Mixture-of-Experts language model** trained from scratch on Korean and English text. This repository contains the **Stage 2 annealing checkpoint** (120,000 steps total) in HuggingFace Mixtral-compatible safetensors format, ready to serve with vLLM or Transformers.
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> **Note:** This is a **base pretrained model**, not an instruction-following or chat model. SFT (Supervised Fine-Tuning) training is planned as the next stage.
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---
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## Model Architecture
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| Property | Value |
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|---|---|
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| Architecture | Mixtral MoE (MixtralForCausalLM) |
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| Parameters | ~14.83B total (~2.9B active per token) |
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| Layers | 24 |
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| Hidden size | 4096 |
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| Attention heads | 32 (GQA: 8 KV heads) |
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| Experts | 8 total, top-2 active per token |
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| FFN intermediate size | 5632 |
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| Context length | 4096 tokens |
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| Vocabulary | 131,072 |
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| RoPE theta | 500,000 |
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| Sliding window | 512 |
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| Activation | SiLU |
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| dtype | bfloat16 |
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---
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## Tokenizer
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Custom SentencePiece tokenizer trained on Korean and English text.
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| Token | String | ID |
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|---|---|---|
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| BOS | `<bos>` | 1 |
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| EOS | `<eos>` | 2 |
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| PAD | `<pad>` | 0 |
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| UNK | `<unk>` | 3 |
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---
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## Training Details
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### Stage 1 — Pretraining
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- **Steps:** 100,000
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- **Tokens:** ~43 billion
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- **Data:** Korean and English web text (FineWeb, WanJuan, HPLT, etc.)
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- **Batch size:** Large-scale FSDP distributed training
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- **Hardware:** 2× NVIDIA H200 (150GB each)
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- **Learning rate:** Cosine decay from 3e-4 to 3e-5
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### Stage 2 — Annealing (Clean Data)
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- **Steps:** 20,000 (steps 100K → 120K)
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- **Tokens:** ~5.16 billion (clean subset)
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- **Data:** High-quality filtered Korean and English text only
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- FineWeb-Edu (English)
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- FineWeb2 Korean
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- HPLT Korean
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- WanJuan Korean
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- **Learning rate:** Cosine continued ~4.8e-5 → 3e-5
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- **Purpose:** Improve output quality by annealing on clean data
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---
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## Usage
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### vLLM (Recommended)
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Install vLLM:
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```bash
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pip install vllm==0.9.2 --no-build-isolation
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pip install "transformers==4.57.0"
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```
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Serve as OpenAI-compatible API:
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```bash
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vllm serve mkd-hossain/keural-14.8b-stage2-vllm \
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--dtype bfloat16 \
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--max-model-len 4096
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```
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Query the API:
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```python
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from openai import OpenAI
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client = OpenAI(base_url="http://localhost:8000/v1", api_key="token")
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response = client.completions.create(
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model="mkd-hossain/keural-14.8b-stage2-vllm",
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prompt="인공지능이란 무엇인가?",
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max_tokens=256,
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temperature=0.7,
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)
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print(response.choices[0].text)
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```
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### Transformers
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "mkd-hossain/keural-14.8b-stage2-vllm"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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prompt = "인공지능이란 무엇인가?"
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=256,
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temperature=0.7,
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top_p=0.9,
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top_k=50,
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repetition_penalty=1.1,
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do_sample=True,
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)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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### Korean Example
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```python
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prompt = "한국의 역사에 대해 설명해 주세요."
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```
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### English Example
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```python
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prompt = "Explain the history of artificial intelligence."
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```
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---
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## Limitations
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- This is a **base pretrained model** — it continues text, it does not follow instructions or answer questions in a chat format.
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- Context length is limited to **4096 tokens**.
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- Outputs may be repetitive or incoherent for complex reasoning tasks — SFT and RLHF training will improve this significantly.
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- Not aligned or safety-filtered. Use responsibly.
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---
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## Roadmap
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- [x] Stage 1 Pretraining — 100K steps, ~43B tokens
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- [x] Stage 2 Annealing — 20K steps, ~5.16B clean tokens
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- [ ] SFT (Supervised Fine-Tuning) — instruction following
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- [ ] RLHF / DPO alignment
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- [ ] Keural Chat model release
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---
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## Citation
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```bibtex
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@misc{keural2026,
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title = {Keural: A Bilingual Korean-English MoE Language Model},
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author = {MKD Hossain},
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year = {2026},
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url = {https://huggingface.co/mkd-hossain/keural-14.8b-stage2-vllm}
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}
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```
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---
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*Trained from scratch on KT Cloud NIPA2-H200 infrastructure using FSDP distributed training.*
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config.json
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{
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"architectures": [
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"MixtralForCausalLM"
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],
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"model_type": "mixtral",
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"vocab_size": 131072,
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"hidden_size": 4096,
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"intermediate_size": 5632,
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"num_hidden_layers": 24,
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"num_attention_heads": 32,
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"num_key_value_heads": 8,
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"head_dim": 128,
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"num_local_experts": 8,
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"num_experts_per_tok": 2,
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"max_position_embeddings": 4096,
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"rope_theta": 500000.0,
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"rms_norm_eps": 1e-05,
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"sliding_window": 512,
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"hidden_act": "silu",
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"initializer_range": 0.02,
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"use_cache": true,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.40.0",
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"output_router_logits": false,
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"router_aux_loss_coef": 0.001,
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"keural_step": 120000
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}
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generation_config.json
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"bos_token_id": 1,
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"eos_token_id": 2,
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"pad_token_id": 0,
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"max_new_tokens": 512,
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"temperature": 0.7,
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"top_p": 0.9,
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"top_k": 50,
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"repetition_penalty": 1.1,
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"do_sample": true,
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"transformers_version": "4.40.0"
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}
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"model.layers.23.block_sparse_moe.experts.0.w1.weight": "model-00006-of-00007.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.0.w3.weight": "model-00006-of-00007.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.0.w2.weight": "model-00006-of-00007.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.1.w1.weight": "model-00006-of-00007.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.1.w3.weight": "model-00006-of-00007.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.1.w2.weight": "model-00006-of-00007.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.2.w1.weight": "model-00006-of-00007.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.2.w3.weight": "model-00007-of-00007.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.2.w2.weight": "model-00007-of-00007.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.3.w1.weight": "model-00007-of-00007.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.3.w3.weight": "model-00007-of-00007.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.3.w2.weight": "model-00007-of-00007.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.4.w1.weight": "model-00007-of-00007.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.4.w3.weight": "model-00007-of-00007.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.4.w2.weight": "model-00007-of-00007.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.5.w1.weight": "model-00007-of-00007.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.5.w3.weight": "model-00007-of-00007.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.5.w2.weight": "model-00007-of-00007.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.6.w1.weight": "model-00007-of-00007.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.6.w3.weight": "model-00007-of-00007.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.6.w2.weight": "model-00007-of-00007.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.7.w1.weight": "model-00007-of-00007.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.7.w3.weight": "model-00007-of-00007.safetensors",
|
||||
"model.layers.23.block_sparse_moe.experts.7.w2.weight": "model-00007-of-00007.safetensors",
|
||||
"model.norm.weight": "model-00007-of-00007.safetensors",
|
||||
"lm_head.weight": "model-00007-of-00007.safetensors"
|
||||
}
|
||||
}
|
||||
30
special_tokens_map.json
Normal file
30
special_tokens_map.json
Normal file
@@ -0,0 +1,30 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<bos>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<eos>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<pad>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:b982818ea2f2057ba791e2006d17683799f1d8ceb9c91322018a638c4ec4b170
|
||||
size 2657284
|
||||
11
tokenizer_config.json
Normal file
11
tokenizer_config.json
Normal file
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"bos_token": "<bos>",
|
||||
"eos_token": "<eos>",
|
||||
"unk_token": "<unk>",
|
||||
"pad_token": "<pad>",
|
||||
"model_max_length": 4096,
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"sp_model_kwargs": {},
|
||||
"legacy": false,
|
||||
"chat_template": "{% for message in messages %}{% if message['role'] == 'system' %}{{ bos_token + '[INST] <<SYS>>\\n' + message['content'] + '\\n<</SYS>>\\n\\n' }}{% elif message['role'] == 'user' %}{{ message['content'] + ' [/INST] ' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token }}{% endif %}{% endfor %}"
|
||||
}
|
||||
Reference in New Issue
Block a user